Global drivers of cryptocurrency infrastructure adoption

Abstract

A vast digital ecosystem of entrepreneurship and exchange has sprung up with Bitcoin’s digital infrastructure at its core. We explore the worldwide spread of infrastructure necessary to maintain and grow Bitcoin as a system (Bitcoin nodes) and infrastructure enabling the use of bitcoins for everyday economic transactions (Bitcoin merchants). Specifically, we investigate the role of legal, criminal, financial, and social determinants of the adoption of Bitcoin infrastructure. We offer some support for the view that the adoption of cryptocurrency infrastructure is driven by perceived failings of traditional financial systems, in that the spread of Bitcoin infrastructure is associated with low trust in banks and the financial system among inhabitants of a region, and with the occurrence of country-level inflation crises. On the other hand, our findings also suggest that active support for Bitcoin is higher in locations with well-developed banking services. Finally, we find support for the view that bitcoin adoption is also partly driven by cryptocurrencies’ usefulness in engaging in illicit trade.

Full text

Global drivers of cryptocurrency infrastructure adoption
Ed Saiedi &Anders Broström &Felipe Ruiz
Accepted: 10 December 2019
#The Author(s) 2020
Abstract A vast digital ecosystem of entrepreneurship
and exchange has sprung up with Bitcoin’s digital infra-
structure at its core. We explore the worldwide spread of
infrastructure necessary to maintain and grow Bitcoin as
a system (Bitcoin nodes) and infrastructure enabling the
use of bitcoins for everyday economic transactions
(Bitcoin merchants). Specifically, we investigate the role
of legal, criminal, financial, and social determinants of
the adoption of Bitcoin infrastructure. We offer some
support for the view that the adoption of cryptocurrency
infrastructure is driven by perceived failings of traditional
financial systems, in that the spread of Bitcoin infrastruc-
ture is associated with low trust in banks and the financial
system among inhabitants of a region, and with the
occurrence of country-level inflation crises. On the other
hand, our findings also suggest that active support for
Bitcoin is higher in locations with well-developed bank-
ing services. Finally, we find support for the view that
bitcoin adoption is also partly driven by cryptocurrencies’
usefulness in engaging in illicit trade.
Keywords Bitcoin network .Digital currencies .
Cryptocurrencies .Financial technology (Fintech) .
Bitcoin nodes .Bitcoin merchants
JEL classifications O3 .P40 .O57 .L86 .L17 .D84 .
L26
1 Introduction
Cryptocurrencies are proliferating. A decade on since their
dawn with the invention of Bitcoin, the value of all
cryptocurrencies reached $0.25 trillion. To put that in
perspective, there is $1.7 trillion USD and $1.4 trillion
Euros in circulation today (European Central Bank 2019;
U.S. Federal Reserve Board 2019). As of November 2019,
bitcoin is the world’s sixth largest currency in circulation.
1
The average daily trading of cryptocurrencies has
surpassed 1% of trading in foreign exchange markets, the
world’s largest market by trading volume.
2
Bitcoin
https://doi.org/10.1007/s11187-019-00309-8
1
https://howmuch.net/articles/how-much-currencies-are-worth
2
In October2019, daily trading in all cryptocurrencies varied between
$41.7B and $15 6.3B (source: https://coinmarketcap.com/charts/),
whereas daily trading in global foreign exchange was approximately
$6.6 T in April 2019 (Bank of International Settlements 2019).
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s11187-019-00309-8)contains
supplementary material, which is available to authorized users.
E. Saiedi (*):A. Broström
Department of Industrial Economics and Management, KTH
Royal Institute of Technology, Lindstedtsvägen 30, 114
28 Stockholm, Sweden
e-mail: ed.saiedi@indek.kth.se
A. Broström
e-mail: anders.brostrom@indek.kth.se
E. Saiedi :F. Ruiz
Department of Business Administration and Statistics, School of
Industrial Engineering (ETSII), Universidad Politécnica de
Madrid, 28006 Madrid, Spain
F. Ru iz
e-mail: felipe.ruiz@upm.es
/ Published online: 2 March 2020
Small Bus Econ (2021) 57:353–406
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transactions and unique accounts alone have grown at
nearly 60% per annum over the past 5 years. In short,
cryptocurrencies are being adopted rapidly and broadly.
While theoretical papers are emerging discussing why
cryptocurrencies, or digital currencies in general, may be
adopted by individuals or businesses (e.g., Cohen 2017;
Dierksmeier and Seele 2018; Dodgson et al. 2015), there is
a scarcity of global empirical studies on drivers of their
adoption.
The emergence of cryptocurrencies has often been
viewed as driven by the opportunity for radical innova-
tion and entrepreneurship in financial solutions as cre-
ated through the spread of new Internet-based technol-
ogy (Iyidogan 2018;Teo2015). However, recent studies
have emphasized that in order to understand the histor-
ical growth and future prospect of fintech innovations,
we must also understand the nature of the needs ad-
dressed by such innovations (Cohen 2017; Huang et al.
2019; Saiedi et al. 2017). The development and spread
of technology is in this sense a prerequisite—but not a
sufficient factor—for the spread of cryptocurrencies. In
this paper, we focus on socio-economic and institutional
factors related to potential drivers for interest in
cryptocurrency development. Thereby, we seek to offer
a novel and broader account for the growth of this type
of financial technology.
Digital entrepreneurial ecosystems have two founda-
tional pillars of digital infrastructure and users (Sussan
and Acs 2017) and infrastructure facilitating connectivity,
e.g., digital infrastructure, are found to particularly enhance
startupactivity(Audretschetal.2015). Digital infrastruc-
tures enable innovation (Henfridsson and Bygstad 2013;
Sussan and Acs 2017), anchor open entrepreneurship
(Ingram Bogusz and Morisse 2018), allow for fintech
platforms to grow (Yermack 2018), and create
decentralized work organizations (Tilson et al. 2010).
While recent studies have shed light on determinants of
fintech startup activity or fundraising using
cryptocurrencies (Fisch 2019; Haddad and Hornuf 2019;
Huang et al. 2019), there are, as of yet, no empirical studies
exploring determinants of adoption of the infrastructure
supporting cryptocurrencies. This study fills this gap.
We explore drivers behind the globaluptake of digital
infrastructure enabling the use of the most prominent
digital currency to date; Bitcoin (spelled with capital B
when referring to the system, and with lower-case b
when referring to the unit of account, in keeping with
convention in the computer science literature.) We con-
sider infrastructure necessary for Bitcoin’s blockchain
information dissemination and transaction verification,
Bitnodes, as well as infrastructure necessary for the
adoption of bitcoin as a means of payment in regular
retail. We believe these types of Bitcoin infrastructure
3
to
provide an informative lens and context for exploring the
drivers behind the recent growth of the cryptocurrency.
In particular, we believe that that by studying patterns of
adoption of these two different types of infrastructure,
we obtain complimentary perspectives of the drivers
behind the growth of Bitcoin as a system.
We chose to focus on Bitcoin since it is presently the
most well-known and widespread cryptocurrency.
4
Most cryptocurrencies are recorded as clones or as var-
iations of the Bitcoin technology (e.g., Litecoin), build
their blockchain as a fork of the Bitcoin blockchain
(e.g., Bitcoin cash) or on nearly identical transaction
ledgers (Gandal and Halaburda 2014;Wangetal.
2019). Bitcoin has the highest cryptocurrency trading
volume, constitutes more than half of cryptocurrencies’
market capitalization, provides much-needed liquidity
to other cryptocurrencies, and enables a secondary mar-
ket for the cryptocurrency ecosystem to thrive on.
Bitcoin’s decade-long dominating position is demon-
strated by it being the only cryptocurrency whose price
has causality effects on alternative cryptocurrencies or
altcoins
5
(Svetlana et al. 2017).
Bitcoin has also been shown to play an important role
for the emergence of new digital entrepreneurial ecosys-
tems, consisting of Initial Coin Offering (ICO) issuers,
payment processors, exchanges, wallets, financial ser-
vices, mining hardware, and developers (Sameeh 2018).
Bitcoin, or emulations of Bitcoin, play a central role in
this development (The Economist 2008). For example,
the first ICO was carried out in July 2013 by
3
A review of these two types of technical infrastructure and their role
for bitcoin as a currency is provided in the Appendix.
4
See https://www.blockchain.com/en/charts/n-transactions-
total?timespan=all for bitcoin transactions and https://www.quandl.
com/data/BCHAIN/NADDU-Bitcoin-Number-of-Unique-Bitcoin-
Addresses-Used for unique accounts.
5
Bitcoin constitutes ~ 20–35% of all cryptocurrency trading volumes,
and litecoin or bitcoin cash, which utilize Bitcoin’s blockchain, are
respectively the 4th and 7th largest cryptocurrency by volume as of
January 3, 2019 (coinmarketcap.com/charts). Wei (2018) calculates
Amihud-illiquidity ratios for 456 cryptocurrencies, listing Bitcoin as
the most liquid. Hu et al. (2018) document average daily and monthly
bitcoin price cross-correlation of respectively 0.174 and 0.21 with all
altcoins with market values exceeding $1 M. Bitcoin’spricecross-
correlation with ether, litecoin, and monero was 0.88 (Fisch 2019),
0.43 and 0.43 (Hu, Parlour, and Rajan 2018) respectively. ICO-bitcoin
correlations have increased since January 2018’s cryptocurrency price
peaks (Fatás and Weder 2019).
354 E. Saiedi et al.
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Mastercoin, a cryptocurrency built on the Bitcoin
blockchain (Shin 2017). The central role of bitcoin is
illustrated by the findings of Masiak et al. (2019), who
find that shocks in bitcoin prices positively influence
ether—the cryptocurrency underlying most ICOs’
blockchain platforms—but not vice versa. In this light,
the development of Bitcoin infrastructure might be ex-
pected to play a role in enabling the expansion of digital
financing and entrepreneurship rapidly emerging
through cryptocurrencies.
Our results provide some support for the view that
bitcoin adoption is driven by perceived failings of tradi-
tional financial systems (see, e.g., Cohen 2017;Shiller
2019; Vigna and Casey 2015). In particular, we find
more adoption where distrust in banks and the financial
system are greater, as well as in countries experiencing
inflation crises. Meanwhile, the spread of Bitcoin infra-
structure seems to be complimentary to existing finan-
cial systems, as we observe less adoption where bank
rents and share of the unbanked are highest. In line with
expectations that interest in bitcoin as a speculative
investment is a partial driver of adoption of bitcoin
(e.g., Baur et al. 2018b), we find more Bitcoin infra-
structure where the willingness to take risks are higher.
We furthermore show that bitcoin adoption is greatest
where the risk of narcotics-related money laundering is
greatest, as well as where perceptions of the rule of law
is strongest. This latter finding may hint at a shift to
pseudonymous online cryptocurrency crime where
offline law enforcement is strong.
While a number of studies make strides in
researching the adoption of bitcoins—qualitatively by
surveying experts (Ermakova et al. 2017)orusers
(Henry et al. 2018; Schuh and Shy 2016), anonymous
online marketplaces (Böhme et al. 2015), archival data
(Sadhya and Sadhya 2018), or quantitatively using the
small de-anonymized fraction of an online forum
(Athey et al. 2016) or within one continent solely
(Yermack 2018), to the best of our knowledge, we
provide the first global empirical study on the Bitcoin
phenomenon. Our study is also the first to empirically
analyze the growth of Bitcoin infrastructure. We thereby
contribute to a nascent body of research that delves into
exploring the adoption of financial technologies (Xue
et al. 2011,Rau2017, Haddad and Hornuf 2019,Huang
et al. 2019, Yermack 2018,Higgins2018, Estrin et al.
2018).
We improve upon current research on adoption of
digital currencies by empirically examining actual
digital currency support over time in a panel data set.
We have location data, covering the entire world, en-
abling the use of geospatial data analysis to investigate
the role of socio-economic and institutional factors, in
parallel to technological and economic factors, in driv-
ing their adoption. Recent literature has emphasized the
need to integrate the social, economic, and cultural
elements when studying entrepreneurial ecosystems
(Spigel 2017). As to the authors’best knowledge, there
are no empirical studies yet that have simultaneously
investigated the relevance of both types of factors for
interest in Bitcoin on a global scale. Our unique context
of cryptocurrency infrastructure allows us to do so.
By increasing our understanding of what socio-
economic and institutional factors that are associated
with the adoption of the infrastructure behind virtual
currencies, the study offers important insights to
scholars seeking to understand the growth of
cryptocurrencies to date. These results are also of po-
tential interest to monetary authorities as well as for
developers and entrepreneurs in the virtual currency
ecosystem, including financial institutions, e-
commerce payment system providers or technology
companies, which are exploring or planning to issue or
accept virtual currencies.
The remainder of this article is organized as fol-
lows. In section 2, we discuss a conceptual frame-
work for our analyses. Section 3 provides a thor-
ough description of our novel data, starting with an
overview of the Bitcoin infrastructure studied. Sec-
tions 4 describes our methodology, and Section 5
explains our results. We conclude and discuss im-
plications of our findings in Section 6.
2Conceptualframework
In considering what socio-economic and institutional
factors may be related to the intensity of Bitcoin infra-
structure adoption, we build on a view of decisions to set
up and operate Bitcoin infrastructure as being related to
a combination of extrinsic and intrinsic motivations.
The launch of the Bitcoin system was embedded in
idealistic notions of providing means to replace existing
financial structures, and nurturing an alternative mone-
tary and financial system that would enable greater
anonymity, privacy, and autonomy (Bashir et al. 2016;
Böhme et al. 2015; Dodd 2018; Shiller 2019). Individ-
uals setting up Bitcoin nodes to grow the peer-to-peer
355Global drivers of cryptocurrency infrastructure adoption
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network would often do so as an expression of support
for Bitcoin as a system and financial phenomenon.
Meanwhile, those running a node do so to verify that
their own transactions are secure, while it may also be
related to bitcoin mining activities, where individuals
seek to earn bitcoins. Merchants’willingness to adopt
technology necessary to accept bitcoins as payment for
their goods is clearly to a large extent driven by business
motivations, while the customer preferences to which
merchants react to, may considerably be driven by their
support for Bitcoin on idealistic grounds. ‘Support for
Bitcoin’is thus in our study a central (albeit not directly
observed) factor.
The growth of Bitcoin infrastructure can be expected
to be directly linked not only to support and positive
attitudes, but also to the actual use of bitcoin (e.g., in
terms of the number of people holding bitcoins). We
expect that in the relatively early stage of development
that we are studying, the use of bitcoin as a currency and
the support for the Bitcoin system are to be understood
as interdependent entities. That is, we expect that posi-
tive attitudes towards the cryptocurrency will translate
into more use, and we expect greater use to spread the
interest for (and general awareness of) Bitcoin, i.e., to
increase the number of people interested in supporting
the Bitcoin network. In developing hypotheses about the
adoption of Bitcoin infrastructure, we hence consider
what set of socio-economic and institutional factors may
be associated with greater support and use of the
cryptocurrency.
2.1 Bitcoin’s differentiating technologies
Cryptocurrencies such as bitcoins have been hailed as
pioneering and potentially disruptive financial technol-
ogies. Bitcoin’s blockchain technology allowed for a
novel way of solving the “double spending problem”
intrinsic to digital currencies, without relying on a cen-
tral clearinghouse or trusted third party (Folkinshteyn
et al. 2015; Böhme et al. 2015). Active support for
Bitcoin may hence be understood as being fundamen-
tally related to opportunities enabled from these differ-
entiating technologies; making it an attractive substitute
to traditional currencies for groups of users (Athey et al.
2016; Ermakova et al. 2017). Opportunities arising due
to the differentiating technology underlying Bitcoin
stem from its ability to remove the need for a trusted
third party or disintermediation, and due to its
cryptographic technology, that enables pseudonymity
in online transactions (Cohen 2017).
6
Preferences for remaining anonymous in financial
transactions may be associated with entirely ideological
views. In an empirical examination of the related phe-
nomenon of ICOs, Fisch et al. (2019)findsuchideo-
logical motives to be among the most important drivers
behind the early development of ICOs. However, pref-
erences for anonymity also arise due to specific inten-
tions to evade legal authorities. Böhme et al. (2015)
document that while consumer payments and buy-and-
hold purposes became important drivers of bitcoin
adoption in later stages, in the early use of bitcoin,
online sale of narcotics, and gambling transfers played
a very important role.
Against this background, it would seem valid to
expect that the global spread of bitcoin has been driven
both by its potential role as a partial substitute role for
traditional financial services and currencies, and by its
potential role as facilitator of illicit activity. In what
follows, we consider these two alternative accounts for
what drives adoption of infrastructure for the supply and
demand of bitcoins.
2.2 Bitcoin as complement or substitute to established
financial systems
The nascent literature on the Bitcoin system and bitcoin
currency indicates their financial potential as (a) a new
exchange/payment system, (b) an investment, or (c) a
speculative trading instrument. Bitcoin’s novelty as a
payment system is due to its relatively low foreign-
exchange transaction costs (Kim 2017), and its indepen-
dence from monetary authorities, governments or pro-
cessing via third-party financial intermediaries. Mean-
while, Glaser et al. (2014) find that uninformed bitcoin
users are primarily interested in bitcoin as an alternative
investment vehicle, rather than an alternative transaction
6
Such opportunities were in line with influential factors at the very
inception of the blockchain within the cyberpunk movement. Bitcoin’s
white paper outlines anonymity and decentralization as influential
factors shaping the evolution of the blockchain sector (Iansiti and
Lakhani 2017). While the developmental motives of the blockchain
were anonymity and decentralization, the practical outcome can more
exactly be described as it providing pseudonymity (Böhme etal. 2015)
and disintermediation, not anonymity and decentralization. Eyal and
Sirer (2014) state that “At this point, the currency is not decentralized
as originally envisioned.”In reality, blockchain has helped create a
disintermediated payment system, as well as a disintermediated method
of raising venture financing via ICOs.
356 E. Saiedi et al.
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system. As an investment it increases opportunities for
portfolio risk management owing to its very limited
correlation with other asset returns such as fiat curren-
cies, stocks, bonds and commodities such as gold (Baur
et al. 2018a; Dyhrberg 2016) and hedging against eco-
nomic uncertainty (Bouri et al. 2017a;2017b).
While previous academic studies have attributed less
(e.g., Blau 2018) or more weight (e.g., Baur et al.
2018b) to speculative trading as the primary use of
Bitcoin, it is clear that speculation is a factor behind
the rising number of individuals holding bitcoin. To the
extent that the adoption of Bitcoin infrastructure is re-
lated to the stock of bitcoin holders, geographical vari-
ation in Bitcoin infrastructure adoption may be related to
geographical differences in the intensity of speculative
trading. In this analysis, however, we are primarily
concerned with analyzing support for Bitcoin infrastruc-
ture in relation to the cryptocurrency’s potential as com-
plement or substitute to more fundamental functions of
the established financial system. This is since node
infrastructure are primarily set up to support bitcoins
as an exchange and payment system. Merchants’adop-
tion of Bitcoin infrastructure may also be understood as
driven by support for Bitcoin, both in regard to the
merchants’own views and in regard to the views of
customer groups that merchants would want to appeal
to.
In the remainder of this section, we describe three
sets of factors that may drive support for Bitcoin as a
financial exchange/payment system. Bitcoin may
substitute for (real or perceived) failings of
established financial systems, due to opportunities
of disintermediation and decentralization technolo-
gies. We first consider bitcoin as a substitute for fiat
currencies in inflation crises. We subsequently con-
sider less acute failings of established financial sys-
tems, in discussing whether Bitcoin can be consid-
ered a viable (potential) substitute to poorly function-
ing national banking markets. Finally, we consider a
more ideological perspective of interest in Bitcoin as
being driven by the spread of general attitudes of
distrust in banks and the established financial system.
When discussing banking market development, we
also acknowledge that financial development may also
be expected to drive the interest in cryptocurrencies. If
this mechanisms dominates, support for Bitcoin and the
growth of Bitcoin infrastructure will be stronger—not
weaker—in countries with well-developed banking
markets.
2.2.1 Inflation
In its capacity as a global currency which is not tied to any
particular economy, bitcoin has the potential to act as a
hedging opportunity against country-specific risk. In par-
ticular, buying bitcoins offers a novel opportunity to hedge
against (very) high inflation, in parallel to how gold and
other assets have been known to function in the past
(Arnold and Auer 2015). Luther (2016) finds that currency
transitions have often occurred during episodes of hyper-
inflation, exemplified by many Bolivians and Peruvians
who switched to US dollars, perceived to be safer, during
such national episodes in the 1980s. People affected by
very high inflation may therefore become more actively
interested in holding and using bitcoin, and in supporting
Bitcoin as an alternative financial system.
Cryptocurrencies have indeed been touted by advo-
cates as a means to a less crisis-prone financial system
(Maurer et al. 2013) and as a counterweight to (hyper)-
inflation (Dierksmeier and Seele 2018). There are also
reports that countries experiencing high inflation have
seen surges in interest in bitcoins. This is visible in the
example of Venezuela, where inflation soared, trust in
the national government policy and currency
plummeted and interest in bitcoins increased, evidenced
by the popularity of bitcoin mining (Kliber et al. 2019).
Another noted example is Cyprus during its 2012–2013
financial crisis (Subramanian and Chino 2015). We
hence expect that high inflation levels or inflation crises
may systematically affect the adoption of the two types
of Bitcoin infrastructure (bitnodes, bitcoin merchants).
Hypothesis 1: The occurrence of inflation crises is
associated with increased adoption of Bitcoin
infrastructure.
2.2.2 Banking market development and competition
There is a potential for financial technologies to substi-
tute for deficient provision of traditional banking ser-
vices, as evidenced, e.g., by the use of mobile money
accounts to transfer money in Sub-Saharan Africa
(Demirguc-Kunt et al. 2018). Digital currencies have
been hailed as a promising means to reach businesses
and people in remote and marginalized areas (Lagarde
2018). Around the world, most existing payment sys-
tems (e.g., credit and debit cards) rely on transactors to
hold bank accounts. It is conceivable that digital curren-
cies could serve as a payment system of choice for the
357Global drivers of cryptocurrency infrastructure adoption
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unbanked (i.e., those without bank accounts). In fact, an
extensive survey of the Bitcoin community finds that
serving the unbanked is one of the foremost purported
promises of this currency (Vigna and Casey 2015;see
Chapter 8 entitled “Unbanked”).
Whereas payment of a minor fee for access to services of
an online trading platform is sufficient for a digital currency
transaction (Dwyer 2015), banking systems impose fees on
businesses and individuals, in the form of depository and
transactional fees. Philippon (2016) finds that the unit cost
of financial intermediation has remained consistently and
surprisingly expensive from 1886 to 2012. Conventional
transactions impose costs ranging from a small percentage
(e.g., 1–3% for credit card purchases) up to highs of one fifth
of transferred amounts (e.g., international remittances; see
Beck and Martínez pería 2011).
By removing the intermediary, the development of
Bitcoin has the potential to do away with these costs.
Transaction fees for bitcoins are in the 0–1% range
(Kaskaloglu 2014). Bitcoin offers a welcome alternative
when high transaction costs of traditional transactions
either disincentives the transaction altogether or diminish
its benefits (Dierksmeier and Seele 2018).Theeaseof
exchange via cryptocurrencies can extend much-needed
liquidity to recipients of micropayments or loans in devel-
oping and underdeveloped countries, offering the world’s
‘unbanked’millions an unprecedented degree of conve-
nience and security (Vigna and Casey 2015). Lacking
access to a financial institution or the needed documenta-
tion to use one, such individuals have to rely on storing
cash, endangering themselves and limiting them to transact
with those within their physical reach (Dierksmeier and
Seele 2018). As long as access to a mobile phone with
SMS technologies or Internet connection exists on any
device, a whole world of transaction and investment pos-
sibilities becomes available (Raymaekers 2015).
Besides the penetration of banking, underdeveloped
competition in the banking market may be another factor
driving interest in Bitcoin as a payment system. Low
competition within the financial system is expected to be
associated with high rents and limited innovation by in-
cumbents, aggravating the frictions of traditional banking
services in terms of costs, service availability, and service
scope. Therefore, low competition may in principle drive
consumers to adopt alternative financial technologies for
reasons parallel to those developed above. Moreover, low
competition in the banking market stimulates investment
in fintech (Thakor 2019). Interest in Bitcoin may follow in
the wake of such activity.
In summary, we suggest the following set of
hypotheses:
Hypothesis 2a: Thelowerthepopulationoffinan-
cially included adults, the greater the adoption of
Bitcoin infrastructure.
Hypothesis 2b: The lower the level of competition in
banking markets, the greater the adoption of Bitcoin
infrastructure.
On the other hand, it is also possible that financial
development is a prerequisite for interest in
cryptocurrencies. Lacking experience with traditional In-
ternet banking services, people may not be prepared to
deal in cryptocurrency. A lack of familiarity with finan-
cial intermediaries and their services may also lead to
little interest in exploring their alternatives. Such a view
would suggest that digital infrastructure aimed at
disrupting banking may develop most strongly in envi-
ronments with the most well-developed banking markets.
Financial literacy and sophistication is a pre-requisite
for taking advantage of financial innovations (Campbell
2006) and engaging in complex financial products, such
as stock market investments (van Rooij et al. 2011)o
r
retirement planning (van Rooij et al. 2012). Financial
literacy has been shown to underlie financial inclusion
and increase the use of financial services (Grohmann
et al. 2018) and hence, it is likely that a high level of
financial literacy and inclusion is required for use of
complex financial innovations such as bitcoins. The use
of electronic payments has been found to be associated
with financial inclusion (c.f. Thakor 2019). Emerging
research points to complementarities between banking
markets and fintech development (Hornuf et al. 2018;
Klus et al. 2019). For example, Gazel and
Schwienbacher (2018) find locations with more bank
headquarters and financial competition attract more
fintech clusters. Such a development can generate ex-
ternalities in the form of greater know-how of financial
technologies, and therebyalsointerestin
cryptocurrencies such as bitcoin. It is also possible that
in a more vibrant banking market, where competition
drives banks to innovate, banking costumers become
less risk-averse towards trying new electronic services.
Together, these arguments suggest that interest in
bitcoin may be highest in countries with well-
developed banking systems and high banking market
competition.
358 E. Saiedi et al.
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We consider the following two competing
hypotheses:
Hypothesis 2c: The greater the population of finan-
cially-included adults, the greater the adoption of
Bitcoin infrastructure.
Hypothesis 2d: The higher the level of competition in
banking markets, the greater the adoption of Bitcoin
infrastructure.
2.2.3 Trust and social attitudes
A prerequisite for economic exchange, trust has been
found to be positively associated with financial devel-
opment and investments. Researchers have explored its
role in online markets such as e-commerce (Ba and
Pavlou 2002), peer-to-peer lending (Duarte et al.
2012), or crowdfunding (Liang et al. 2019;Rau2017).
While the blockchain technology may be perceived as
reducing the need for direct trust in individuals, many
important uses of bitcoin would seem to be positively
associatedwith general trust. We therefore expect higher
levels of trust in others to increase interest in Bitcoin,
e.g., in the form of increased trade in bitcoins for invest-
ment, speculation and online or in-store commerce.
Cohen (2017) documents that bitcoins emerged as
part of the 99% movement—initiated by the Occupy
Wall Street protests—and frustrations with banks that
had become too big to fail.
7
The launch of the Bitcoin
system was embedded in idealistic notions of providing
means to replace existing financial structures, and nur-
turing an alternative monetary and financial system that
enables greater anonymity, privacy, and autonomy
(Bashir et al. 2016; Böhme et al. 2015;Dodd2018;
Shiller 2019). Via enabling cheap and automated verifi-
cation on distributed ledgers, the blockchain technology
underlying cryptocurrencies allows for trust in an inter-
mediary to be replaced by trust in the devised code and
rules that define how the network reaches consensus
(Goldfarb and Tucker 2019). Desire for this replacement
is likely greater where trust in financial intermediaries,
whom traditionally verified payments, is lower, and can
thus motivate the uptake of cryptocurrencies.
While to the best of our knowledge no systematic
investigation has been done regarding distrust to
banks and financial institutions as a driver of support
for Bitcoin, the role of trust to banks has started to be
explored in other fintech settings. Saiedi et al. (2017)
and Bertsch et al. (2017), show that a decline in trust
in banks and financial institutions increases partici-
pation respectively, by lenders and borrowers, in
online peer-to-peer (P2P) lending markets. Theoreti-
cal papers modeling trust-driven substitutions be-
tween intermediaries and fintech firms are also
emerging (e.g., Thakor and Merton 2018). We thus
posit that lower trust in banks and the financial sys-
tem underlies the emergence and adoption of
bitcoins. In our variable definitions, we use distrust
to mean lack of trust for brevity.
Yermack (2015) reports that one of the major
obstacles facing bitcoin in becoming an adopted cur-
rency is its extreme time-series volatility. This high
volatility is a product of the highly speculative nature
of the bitcoin market (Baek and Elbeck 2015).
Yermack (2015) further reports that it is widely un-
derstood that most bitcoin transfers involve transac-
tions between speculative investors. Empirical stud-
ies corroborate the use of bitcoins as a speculative
investment (Baur et al. 2018b; Bouoiyour and Selmi
2015) and that it exhibits speculative bubbles (Cheah
and Fry 2015;Fry2018). It thus seems reasonable to
assume that the interest to buy and hold bitcoins for
speculative purposes, and the willingness to accept
risk when holding bitcoin to be used for transactions,
would require a certain level of risk-willingness. Re-
gional differences in average willingness to take fi-
nancial risks may therefore affect the use of bitcoin.
We expect that the higher the willingness to take risk
in a region, the greater the adoption of bitcoins for
speculative trading.
In summary, we expect that active support for Bitcoin
is associated with a lack of trust in the established
financial system. Such support would be manifested,
e.g., through the running of Bitcoin nodes, and may also
affect merchants’willingness to accept bitcoin. Greater
trust in other people and higher risk-willingness is ex-
pected to facilitate the use of bitcoin, and thereby the
interest in adoption of Bitcoin infrastructure in a
region—in particular in regard to merchants’adoption
of bitcoin payments.
Hypothesis 3a: The greater the level of trust in others,
the greater the adoption of Bitcoin infrastructure.
7
Indeed,in the very first bitcoin mined, its founder, Satoshi Nakamoto,
embedded the message “The Times 03/Jan/2009 Chancellor on brink
of second bailout for banks,”referring to the Times of London’s same-
day headline (Elliott and Duncan 2009).
359Global drivers of cryptocurrency infrastructure adoption
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Hypothesis 3b: The greater the level of distrust in
banks and the financial system, the greater the adoption
of Bitcoin infrastructure.
Hypothesis 3c: The higher the risk-willingness, the
greater the adoption of Bitcoin infrastructure.
2.3 Bitcoin as a facilitator of illicit activity
Bitcoin’s differentiating technology of pseudonymity
makes the cryptocurrency interesting for actors pursuing
activities of illegal trade such as drug trafficking,
weapons trade, and prescription drug trade (Foley
et al. 2019). Böhme et al. (2015) describe that the first
notable adopters of bitcoin sought greater anonymity
and a lack of regulation concerning what could be
purchased using the currency. Bohannon (2016)states
that bitcoin’s purported anonymity made it popular
among criminals. A recent study finds that around
25% of all bitcoin users and 44% of all transactions
relate to trading in illegal activities (Foley et al. 2019).
The source of almost all bitcoins used for illicit pur-
poses and laundered through exchange services are dark-
net marketplaces (Fanusie and Robinson 2018), i.e., mar-
ketplaces on encrypted websites that do not appear
through conventional Internet browsing. Bitcoin is the
primary means of payment on the dark-net (van Slobbe
2016) with estimates showing it accounts for nearly 95%
of the transactions on some dark-net marketplaces (Smith
2019). Moreover, trading in bitcoin is also advantageous
to sellers of illicit goods sold through such marketplaces.
By allowing for pseudonymous exchange, digital curren-
cies such as bitcoin have been identified as ideal money
laundering vehicles (FATF 2014;NDIC2008).
In view of these arguments, we expect the ceteris
paribus interest in bitcoin to be higher in locations with
high activity in trade of illegally acquired goods and
services, and in locations where financial services relat-
ed to such trade take place. In particular, bitcoin has an
important role to play in the trade of illegal drugs online
(Soska and Christin 2015). Bitcoins and online drug
markets have made it possible to sell drugs to unknown
customers, whereas for decades retail drug markets
remained localized, i.e., dealers sold primarily to known
customers (Aldridge and Décary-Hétu 2016a). Using
cryptocurrencies to trade in drugs provides a means for
circumventing state prohibition. Furthermore, the op-
portunity to shift drug trade online, with anonymity
relatively well-preserved due to the use of bitcoins as
means of payment, also allows greater personal security
(threat of violence, etc.) than buying drugs directly from
dealers or contacts (Barratt et al. 2016). Some sellers and
buyers of drugs are therefore actively supporting bitcoin
in view of its role in enabling on-line acquirement of
drugs. It can also be expected that the use of bitcoin for
drug purchases may spill over to other types of use, and
thereby stimulate the general interest and familiarity
with bitcoin as a means of payment.
Even though cryptocurrency markets create a poten-
tially global platform for drug distribution, they tend to
be used for local trade as opposed to cross-border trade
(Demant et al. 2018). Hence, the adoption of Bitcoin
infrastructure can be associated with the level of activity
in on-line drug trade across locations. However, reliable
measures for such activities are hard to come by. We
suggest that the intensity of money-laundering activity
and the effectiveness of law enforcement –two factors
that are being measured with some reliability for a
sufficiently large number of countries for our purpose
–can be used as indicators for such demand.
Money-laundering refers to processes whereby the
proceeds from illicit trade are being transacted through
financial institutions so as to hide its origin in illicit
activities. A substantial share of such activity is directly
related to drug trade. While money laundering may take
place across as well as within nations, locations with
high levels of trade in drugs would typically rate high on
measures of money-laundering activity.
The second factor that we consider, concerns to what
extent drug trade would require the pseudonymity pro-
vided by on-line purchasing using bitcoins. We focus on
the level of law enforcement in a country.
More effective law enforcement may in principle
reduce bitcoin adoption for illicit intent. However, cur-
rent research suggests that contemporary law enforce-
ment does not possess the capacity to control much
Internet-related crime (Holt et al. 2015). In most coun-
tries, no particular Internet-mediated drug trafficking
investigations are carried out (Lavorgna 2014). Despite
law enforcement efforts, such as the closure of Silk
Road and Silk Road 2.0, crypto-markets continue to
proliferate and have proven to be extraordinarily resil-
ient (Aldridge and Décary-Hétu 2016b;Soskaand
Christin 2015). Drug buyers and sellers using the dark
net perceive the likelihood of arrest to be much lower
than alternative means (Aldridge and Décary-Hétu
2016b; Barratt et al. 2016;Ormsby2016; Van Hout
and Bingham 2014).
360 E. Saiedi et al.
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We therefore would not expect that stricter law efforts
may hamper Internet-based drug trade, but it should be
expected to affect other forms of buying and selling
drugs (e.g., openly on the street). Conditional on the
level of demand for illegally acquired goods and ser-
vices, greater perceptions of the quality of police, and
courts could therefore drive illicit activity towards on-
line anonymous (or pseudonymous) activities.
8
In other
words, we expect that the greater the likelihood of arrest
on the streets for traditional drug trafficking, the greater
the interest in selling drugs online using anonymity-
granting technologies. And as argued above, we expect
such interest to generate support for and interest in
Bitcoin.
Therefore, we expect thatthe greater the enforcement
of rule of law, the greater the use of bitcoins for illicit
activity (conditional on the level of activity of a local
drug market). We conclude that the level of drug trade
activity in a location can be expected to be related to the
extent of money-laundering and to law enforcement
effectiveness. On the basis of our expectation that inter-
est in bitcoin is partially driven by on-line drug trading
activity, we thus suggest that both money-laundering
and law enforcement are related to the adoption of
Bitcoin infrastructure.
While we have emphasized above that the level of
money-laundering constitutes a useful proxy for trades
in illicit goods in a country, it should be noted that some
locations may feature as prominent sites for money
laundering activity without necessarily being centers of
illicit trade. Even where this may be the case (primarily
in countries where financial regulation is conducive to
money laundering), we expect money-laundering to be
associated with interest in bitcoin. Consistent with this
expectation, 97% of direct bitcoin payments from iden-
tifiable criminal sources were received by exchanges in
countries with weak anti-money laundering regulations
(CipherTrace 2018). To the extent that they provide
anonymity, bitcoins and other cryptocurrencies can be
exploited for money-laundering purposes (Evans-Pughe
et al. 2014; Moser et al. 2013), e.g., in the form of
automated laundering operations (McGinn et al. 2016).
With the rise of specialized bitcoin money laundering
services on the dark-net, such opportunities are
becoming available to a broader audience (Albrecht
et al. 2019; van Wegberg et al. 2018).
Together, the arguments outlined above can be sum-
marized in the following two hypotheses:
Hypothesis 4: The more money-laundering activities
taking place, the greater the adoption of Bitcoin
infrastructure.
Hypothesis 5: The stronger the rule of law, the great-
er the adoption of Bitcoin infrastructure.
3Data
3.1 Dependent variables
We utilize two global digital infrastructure datasets
supporting the use of Bitcoins encompassing the five-
year period from 2014 to 2018, one of computers that
observably disseminate blockchain information, and
validate and verify transactions on the Bitcoin network
(known as Bitnodes; see Figure 1for validation and
verification actions in a bitcoin transaction life cycle),
and one of merchants accepting bitcoins as payment.
9
A Bitcoin node isany computer or data processor that
connects to the Bitcoin network, in order to receive and
send information relating to Bitcoin’s blockchain (a
growing list of records of bitcoin transactions). Each
bitcoin user represents a node on the network. A subset
of all nodes are Full Bitcoin nodes, which fully down-
load every block and transaction and check them against
all rules of Bitcoin (e.g., no double-spending of
bitcoins). Any decentralized cryptocurrency depends
on such full nodes to ensure its blockchain adheres to
agreed-upon rules. Hence, Full Bitcoin nodes are essen-
tial to ensure security of Bitcoin’s digital ecosystem and
for full removal of a need for a trusted alternative node
or third party. A user becomes a Full Bitcoin node by
running a software, called Bitcoin Core, on her comput-
er. By default, Full Bitcoin nodes accept incoming con-
nections (in technical terms, are reachable or listening)
and upload updated blockchain information to other
peers or nodes on the network. These are termed
Bitnodes, which are reachable full nodes on the net-
work. Our data consists of all Bitnodes operating world-
wide between 2014 and 2018. While there is no reliable
8
In fact, our rule of law variable has a 0.49 correlation with dark-net
market drug purchase statistics in 26 countries, reported by Winstock
et al. (2018).
9
Available at an open data repository associated with this publication.
361Global drivers of cryptocurrency infrastructure adoption
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method to determine if these Bitnodes are businesses,
individuals, exchanges, bitcoin miners, or merchants,
there are indications that they are users or businesses
transacting in bitcoins or volunteers supporting the net-
work (e.g., more nodes are online during US working
hours). The Appendix describes Bitcoin nodes,
Bitnodes, and our dataset preparation in greater detail.
Our dataset allows us to construct measures
reflecting both the total intensity of Bitnode activity
(Bitnode intensity) per country or region and year and
the average number of Unique bitnodes by country or
region and year. Figure 2provides a global depiction of
these variables. We also construct a measure of node
availability by calculating for how many hours there is
at least one Bitnode active in a country or region
(Bitnode hours). For further details, see Table 1.
Our data for merchants accepting payments in bitcoin is
from CoinMap.org. CoinMap is a website where either
business owners or interested customers or users would
add a business that accepts bitcoins (or bitcoins together
with other cryptocurrencies) as payment in exchange for a
service or good. Some of the businesses on these websites
only accept bitcoins on their online websites. Soon after
launching in 2013, CoinMap became the primary website
worldwide, as referred to in online Bitcoin forums, to find
businesses where bitcoin holders can spend them. Figure 3
depicts the global spread of merchants, whereas the
Appendix describes the Bitcoin merchants dataset we use
and our dataset preparation in greater detail.
The accumulated stock of merchants recorded on
CoinMap may not, however, be an entirely accurate repre-
sentation of the situation in that year, as there are numerous
reported cases of merchants added to the database, who
subsequently forego acceptance of bitcoins after months or
years of low-demand (while there is no cost to offering this
service other than training cashiers/personnel). In view of
this, we choose to focus on annual inflow to the database.
Our main dependent variable constructed from this database
is hence the number of new bitcoin-accepting merchants in
any given year in a country, or sub-country state, province or
region. Our dataset does not suffer from survival bias, as
even when merchants are removed from the map by users or
the web admin, they are not dropped from our database.
Our dataset has the limitation that we only observe non-
hidden Full Bitcoin nodes and merchants. Bitnodes, being
reachable full nodes, are a subset of Full Bitcoin nodes
keeping the network secure, as some nodes simply do not
accept incoming connections, primarily due to installation
Fig. 1 Validation and verification role of nodes in the Bitcoin transaction life cycle (see red boxes). Infographic modified from original by
Patrícia Estavão (printed with her permission under license CC BY-SA 4.0), available at patestevao.com/work/
362 E. Saiedi et al.
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of firewalls. Some estimates (e.g., Wang and Pustogarov
2017) indicate as many as 6 to 8 times more nodes on the
network than our data includes. Similarly, Bitcoin mer-
chants do not include merchants who hide their presence
from CoinMap or only sell online. Hence, our study is
focused on digital infrastructure providers who are openly
and visibly supporting the Bitcoin network.
3.2 Explanatory variables
We construct a set of country-level variables related to
Bitcoin’s adoption potential given existing technologi-
cal factors, its role as a potential substitute or comple-
ment to established financial institutions or to the
cryptocurrency’s potential function as a facilitator of
illicit activity.
3.2.1 Financial characteristics
In constructing an indicator for inflation as a driver for use
of bitcoins, we utilize the concept of an Inflation Crisis,
10
defined by Reinhart and Rogoff (2011) as depreciation in
currency value of 20% per annum. For robustness tests, we
also define alternative variables measuring such phenom-
ena called 15% Min Inflation and 25% Min Inflation with
10
It is notable that currency crashes and inflation crises go hand in
hand as shown by Reinhart and Rogoff (2011).
Fig. 2 a Global Map of Bitnode intensity (Monthly measure,
averaged over 2014–2018; See Table 1for variable definition).
Top 15 countries are Monaco, Netherlands, Singapore, Lithuania,
Luxembourg, Germany, Ireland, Switzerland, Sweden, Belize,
Slovenia, Canada, Latvia, Finland, and Hong Kong. b.Global
Map of Unique bitnodes (Averaged for every hour over 2014–
2018; See Table 1for variable definition). Top 15 countries are San
Marino, Austria, Sweden, Germany, Luxembourg, Croatia, Swit-
zerland, Netherlands, Slovenia, Lithuania, Monaco, Australia,
Bulgaria, Finland, and UK
363Global drivers of cryptocurrency infrastructure adoption
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Tabl e 1 Variable description and data sources
Variable Description Source Source variable name
Panel A
Bitnode intensity An intensity measure of the number of
active Bitnodes in a country multiplied
by how many hours each was active in a
month, standardized by the number of
hours in a month and averaged to
produce an annual measure (by taking
the mean of results for the first month of
each quarter). Standardized by dividing
it by the jurisdiction’s population of
Internet users in millions (see Internet
variable).
Bitcoin node data are from
bitnodes.com
Constructed from
API-extracted data in JSON
format
Unique bitnodes Number of unique Bitnodes in a country in
a month. This measure is then made into
an annual measure, by averaging its
value for the first month of each quarter
of a year. Standardized by dividing it by
jurisdiction’s population of Internet
users in millions.
Constructed from
API-extracted data in JSON
format
Bitnode hours Number of hours in a month where at least
one Bitnode from a country is active
Constructed from
API-extracted data in JSON
format
Bitcoin merchants Number of new bitcoin merchants in a
country in a year, as per timing it was
added on CoinMap’smap
Bitcoin Merchant data are
from CoinMap.com
Constructed from
API-extracted data
Bitnode intensity
pc
Similar to Bitnode intensity, but
standardized by population in millions
Bitnodes.com
Unique bitnodes
pc
Similar to Unique bitnode, but
standardized by population in millions
Bitnodes.com
Bitcoin merchants
pc
Similar to Bitnode Merchants, but
standardized by population in millions
CoinMap.com
GDP per capita Log of annual GDP per capita in a country.
GDP is at purchaser’s prices converted
into current US dollars
World Bank WDI (2018) NY.GDP.MKTP.CD
Restrictive regulation An indicator variable equal to one in years
in which hostile or contentious
regulation against the use of bitcoins is
issued. Hostile regulation towards
bitcoin consists of regulatory authorities
imposing full prohibition of its use, or
partial prohibitions such as barring
financial institutions from dealing with
it. Contentious regulation towards
bitcoin consists of some legal
restrictions against use of bitcoin (incl.
Imposition of cumbersome compliance
requirements) or warnings against
bitcoin use by regulatory authorities
going beyond discouragement (incl.
Statements that bitcoin transactions may
cause violation of anti-money
laundering or terrorist financing rules).
Variable equals zero for countries that
have no regulatory framework or a
favorable regulatory framework for
bitcoins.
Hand-collected data from Law
LibraryofCongress(2018),
bitlegal.io, and Wikipedia
364 E. Saiedi et al.
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Tabl e 1 (continued)
Variable Description Source Source variable name
Population Total population in a country in millions.
Mid-year estimates.
World Bank WDI (2018) SP.POP.TOTL
Internet penetration Percentage of inhabitants using the Internet
in a year
International
Telecommunications Unit
(ITU) (2018)Broadband penetration Percentage of inhabitants with fixed
(wired)-broadband subscription in a
year
ICT market development A composite index of a nation’s
development in ICT. The index includes
three aspects of digitalization: ICT
capability (skills and knowledge),
access to ICT infrastructure, and use of
intensity of ICT.
Measuring the Information
Society Reports, ITU
(2015–2018)
ID (ICT Development Index)
Latest technology National average of response to “In your
country, to what extent are the latest
technologies available? [1 = not
available; 7 = widely available]”
World Economic Forum,
Global Competitiveness
Report, Executive Opinion
Surveys (2014–2018)
EOSQ067
Internet servers Secure Internet servers per million people
using encryption technology in Internet
transactions
Wor l d Ban k a nd https://www.
netcraft.com
IT.NET.SECR.P6
Mobile subscriptions The variable measures the number of
mobile telephone subscriptions per 100
adults in the population. A subscription
refers to a public mobile telephone
service that provides access to the
public-switched phone network using
cellular technology, including the count
of pre-paid SIM cards active in the final
three months of the year.
Wor l d
Telecommunication/ICT
Development report and
database
Electricity cost Electricity cost is the total median cost in
percentage of income per capita
associated with completing procedures
to connect a warehouse to electricity.
World Bank Doing Business
Reports (2014–2018)
Inflation crisis An indicator variable equal to one if in a
country the annual decline in the
average Consumer Price Index is greater
than 20% following Reinhart and
Rogoff (2011)
Raw Data are from
International Monetary
Fund (IMF)‘s World
Economic Outlook (2018)
Inflation The average annual change in Consumer
Price Index as a ratio.
Unbanked % aged 15+ without account ownership at
a financial institution and without a
mobile-money-service provider
WB Global Findex Database FX.OWN.TOTL.ZS
Internet banking % aged 15+ who have used the Internet or
a mobile phone to access an account
Fin5.d.1
Five-bank asset concentration Assets offive largest banks as a share of all
banking assets. These include all
earning assets, cash and due from banks,
foreclosed real estate, fixed assets,
goodwill, other intangibles, current tax
assets, deferred tax, discontinued
operations and other assets.
Wor l d Ban k’sGlobal
Financial Development
Index (GFDI 2018); Raw
Data are from Bankscope
and Orbis Bank Focus,
Bureau van Dijk
GFDD.OI.06
Three-bank asset
concentration
Assets of three largest commercial banks
as a share of all commercial banking
assets.
GFDD.OI.01
Rule of law Captures perceptions of the extent to which
agents have confidence in and abide by
RQ.EST
365Global drivers of cryptocurrency infrastructure adoption
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Tabl e 1 (continued)
Variable Description Source Source variable name
the rules of society, and in particular, the
quality of contract enforcement,
property rights, the police, and the
courts, as well as the likelihood of crime
and violence. This variable ranges from
approximately −2.5 (weak) to 2.5
(strong) values for rule of law.
World Bank Worldwide
Governance Indicators
(2018)
Organized crime National average of response to “In your
country, to what extent does organized
crime (mafia-oriented racketeering,
extortion) impose costs on businesses?
[1 =not at all; 7= to a great extent]”
World Economic Forum,
Global Competitiveness
Report, Executive Opinion
Surveys (2014–2018)
EOSQ055
Crime and violence costs National average of response to “In your
country, to what extent does organized
crime (mafia-oriented racketeering,
extortion) impose costs on businesses?
[1 = to a great extent—imposes huge
costs; 7 = not at all—imposes no costs]”
EOSQ034
Money laundering Countries identified as heavily engaged in
“currency transactions involving
significant amounts of proceeds from
international narcotics trafficking.”
US Dept. of State Bureau for
International Narcotics and
Law Enforcement Affairs
(2018)
Panel B
Bitnode intensity An intensity measure of the number of
active bitnodes in a region multiplied by
how many hours each was active in a
month, divided by the number of hours
in a month and averaged to produce an
annual measure (by taking the mean of
results for the first month of each
quarter). Standardized by dividing it by
the jurisdiction’s population of Internet
users in millions (see Internet users
variable).
Bitcoin node data are from
bitnodes.com
Constructed from
API-extracted data
Unique bitnodes Number of unique bitnodes in a statistical
region in a month made into an annual
measure, by averaging its value for the
first month of each quarter of a year.
Standardized by dividing it by
jurisdiction’s population of Internet
users in millions.
Constructed from
API-extracted data
Bitnode hours Number of hours in a month in which at
least one bitnode from a region is active.
Constructed from
API-extracted data
Bitcoin merchants Number of new bitcoin merchants in a
statistical region in a year, as per timing
it was added on CoinMap’smap
Bitcoin Merchant data are
from CoinMap.com
Constructed from
API-extracted data
High risk willingness A region’s average of an indicator variable
for each LiTS III’s survey respondent,
equal to one if response to this question,
is above 8: “Please, rate your
willingnesstotakerisks,ingeneral,ona
scale from 1 to 10, where 1 means that
you are not willing to take risks at all,
and 10 and means that you are very
much willing to take risks.”
EBRD (2016) Life in
Transition Societies (LiTS)
Survey III; US Data are
complemented with same
question in FINRA
(2015)‘s National Financial
Capabilities Study in 2015
Q4.28inLiTSIII;J2inNFCS
Trust in others
366 E. Saiedi et al.
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corresponding alternative thresholds. In further robustness
tests, we utilize a continuous measure of Inflation along
with the square root of Inflation. Inflation data is collected
from the International Monetary Fund.
Data on the proportion of unbanked adults is collect-
ed from the World Bank’s Financial Inclusion Index.
Following Gross et al. (2012), we define the unbanked
as individuals older than 15 years without a checking,
saving or money market account. A subset of those
holding bank accounts, are those with access to the
Internet and mobile banking, which can serve as another
proxy of financial inclusion in a digital age. From the
same source, we obtain an Internet banking variable for
robustness of results.
We extract data on concentration in the banking in-
dustry from the World Bank Global Financial Develop-
ment Index. We primarily use Five-bank asset concen-
tration, the asset concentration of the largest five banks in
each country. For use in robustness testing, we also use
Three-bank asset concentration, which is the asset con-
centration of the largest three banks in each country.
3.2.2 Criminality characteristics
The US’Bureau of International Narcotics and Law
Enforcement Affairs performs an annual assessment of
the risk of money laundering in 200 countries and juris-
dictions. The bureau identifies “major money laundering
countries”as those whose “financial institutions engage
in transactions involving significant amounts of proceeds
from international narcotics trafficking”. The definition
0
We are not aware of any multinational social surveys which is carried
out annually, and even if such a survey existed, social characteristics
typically do not change much 1 year to another (e.g., see Hoffmanet al.
2009 for an explanation of how general trust changes over intermediate
time horizons).
Tabl e 1 (continued)
Variable Description Source Source variable name
Aregion’s average of an index
standardized to run from −1to1,in
answering to “Generally speaking,
would you say that most people can be
trusted, or that you cannot be too careful
in dealing with people? Please answer
on a scale of 1 to 5, where 1 means that
you have complete distrust and 5 means
that you have complete trust.”
EBRD (2016) LiTS Survey
III; US Data are
complemented using the
same question’s response
using NORC (2016)‘s
General Social Survey
(GSS) Geo-sensitive data
Q4.03 in LiTS III; TRUST in
GSS
Distrust in banks & financial
system
A statistical region’s average of an index
standardized to run from −1to1,in
answering to “To what extent do you
trust banks and the financial system?
Please answer on a scale of 1 to5, where
1 means that you have complete distrust
and 5 means that you have complete
trust.”
Q4.04j in LiTS III;
CONFINAN in GSS
Distrust in other institutions A region’s average of indices standardized
to run from −1 to 1, in answering to,
similar to that above, on the following
institutions, other than banks and the
financial system: The
Government/Cabinet of Ministers, the
Parliament, Courts, the Military, The
police, Unions and Religious
Institutions.
Q4.04b,e,f,h,I,m&nin
LiTS III; Trust in the same
institutions in GSS
Bitcoin price Annually-averaged price of Bitcoin in
thousands of USD, from quotes on four
large Bitcoin exchanges of 2014–2018,
namely Bitstamp, Kraken, Coinbase and
Gdax
www.cryptodatadownload.
com/; Accessed on
September 3, 2018
BTC/USD
367Global drivers of cryptocurrency infrastructure adoption
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of “financial institutions”employed by the bureau in the
period we study includes digital currencies and “other
value transfer systems.”We introduce an indicator vari-
able (money laundering) that takes the value one if a
country is included in the bureau’s list in a given year.
As an indicator for the level of law enforcement, we
obtain from the Worldwide Governance Indicators of
the World Bank a rule of law variable. This variable
captures the likelihood of crime and violence, and the
extent to which agents abide by and have confidence in
the rules of society, in particular the quality of police,
courts, contract enforcement and property rights.
3.2.3 Social characteristics
We obtain social characteristic variables of general risk-
taking, general trust, and trust in banks and the financial
system from the Life in Transition (LITS III) survey which
was carried out in 34 countries from 2014 to 2016. While
data used to create most of the independent variables
described previously was only available on the level of
countries, LITS data can be broken down to the (sub-
national) regional level, corresponding to states, provinces
or administrative regions in the 34 countries where this
survey was carried out. The LITS survey was, however,
only carried out once during the period that we investigate
in this paper,
11
meaning that we have no time variation to
exploit for this analysis. In order to utilize as much of the
variation in the data as possible, exploring the role of these
social characteristics is carried out at the regional rather
than national level. This has the consequence that analyses
investigating the role of social characteristics are restricted
to regions in 34 countries (294 regions) predominantly in
Europe, instead of our full set of countries, and these
variables are cross-sectional.
3.3 Control variables
We furthermore construct a number of control variables
related to fundamental economic and legal prerequisites
for the growth of Bitcoin infrastructure.
Participation in any financial system is strongly tied
to the availability of economic resources. In our analysis
of global adoption of Bitcoin infrastructure, variations in
economic strength across countries and regions is there-
fore a natural first factor to consider. We control for
GDP utilizing data from the World Bank.
Alongside economic strength, we also expect tech-
nological development to be strongly related to the
spread of Bitcoin infrastructure. It is therefore important
that our modeling exercise takes the level of technolog-
ical development into account. In particular, as an
Internet-enabled technology, bitcoin adoption is only
feasible for users who have regular access to the Inter-
net. We are, however, reluctant to introduce separate
control variables for such factors in order to avoid
overfitting our models, and in order to avoid introducing
multicollinearity issues (the level of technological de-
velopment across countries is well-known to co-vary
with, e.g., economic and institutional variables
(Czernich et al. 2011; Greenstein and Spiller 1996)
and our main control variable of GDP per capita is
highly correlated (0.89) with Internet penetration in
our dataset). Instead, all our main dependent variables
measuring the penetration of Bitcoin infrastructure are
normalized by Internet penetration, i.e., the percentage
of Internet users in a country.
As a virtual currency with the potential to dis-
rupt existing payment systems and possibly even
monetary systems (Böhme et al. 2015), bitcoin aims
to rival fiat currencies issued by central banks.
Barriers to adoption in each jurisdiction for bitcoins
are largely enforced by regulation. Most regulation
vis-à-vis bitcoins are issued by a country’s financial
regulatory authorities (Law Library of Congress
2018). As suggested by public choice theory
(Stigler 1971), incumbents may lobby for regula-
tion to keep out competitors and create rents for
themselves. To the extent that bitcoin rivals
government-issued currencies, regulatory barriers
on bitcoins by governments can hamper their adop-
tion. We create a longitudinal indicator variable,
Restrictive regulation, equal to one in a country
and for all the years in which restrictive (i.e., con-
tentious or hostile) regulation is in effect in a coun-
try by its financial regulator on either transacting in
bitcoin or buying and selling goods or services in
exchange for bitcoins. Hostile regulation towards
bitcoin consists of regulatory authorities imposing
full prohibition of its use, or partial prohibitions
such as barring financial institutions from dealing
with it. Contentious regulation towards bitcoin con-
sists of some legal restrictions against use of
bitcoin (incl. imposition of cumbersome compliance
requirements) or warnings against bitcoin use by
regulatory authorities going beyond discouragement
(incl. statements that bitcoin transactions may cause
violation of anti-money laundering or terrorist
368 E. Saiedi et al.
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Tabl e 2 Summary statistics for panels A and B
Variable name Obs. Mean Std. dev. Min Median Max
Panel A: Country-year level dataset (2014–-2018; 137 countries)
Dependent variables
Bitnode intensity 685 2.02 4.07 0.0 0.2 34.9
Unique bitnodes 685 15.12 23.45 0.0 3.6 123.4
Bitnode hours 685 431.46 334.30 0.0 608.8 750.0
Bitcoin merchants 685 1.32 5.05 0.0 0.2 114.0
Bitnode intensity
pc
685 1.65 3.52 0.0 0.1 30.8
Unique bitnodes
pc
685 11.70 19.40 0.0 1.9 114.2
Bitcoin merchants
pc
685 0.98 3.99 0.0 0.1 90.9
Control variables
GDP per capita 685 8.80 1.46 5.7 8.8 11.7
Restrictive regulation 685 0.11 0.32 0.0 0.0 1.0
Population 685 51.22 166.39 0.1 11.2 1394.1
Technological variables
Internet penetration 685 54.68 27.71 3.3 59.1 97.6
Broadband penetration 685 14.45 13.79 0.0 9.9 55.7
ICT market development 685 5.27 2.23 1.2 5.3 8.9
Latest technology 685 4.84 0.94 2.3 4.8 6.6
Internet servers 685 4.07 11.24 0.0 0.2 123.1
Mobile subscriptions 685 113.39 34.07 25.0 115.4 259.4
Financial variables
Inflation crisis 685 0.04 0.20 0.0 0.0 1.0
Inflation 685 0.05 0.07 -0.0 0.0 0.4
Unbanked 685 38.56 27.59 0.0 37.9 93.6
Internet banking 685 27.89 21.89 0.4 23.5 85.1
Five-bank asset concentration 685 78.84 16.19 27.5 80.9 100.0
Three-bank asset concentration 685 63.94 18.47 18.4 64.0 100.0
Criminality variables
Rule of law 685 0.06 0.99 −2.3 −0.1 2.1
Organized crime 665 4.77 1.02 1.5 4.8 6.9
Crime and violence costs 685 4.45 1.07 1.5 4.5 6.8
Money laundering 685 0.36 0.48 0.0 0.0 1.0
Alternative variables
Shadow economy 670 30.22 12.76 8.1 30.2 63.3
Taxation 580 25.82 12.49 −1.3 24.7 65.0
Tax burden 665 77.62 12.00 37.2 79.2 99.9
Tax haven 685 0.06 0.23 0.0 0.0 1.0
Stock market return 440 0.67 25.77 −72.1 −1.9 282.9
Crisis stock market return 460 −2.77 19.70 −51.7 −4.6 52.5
Bitcoin mining country 685 0.07 0.26 0.0 0.0 1.0
Bitcoin mining MWs 670 1.85 11.28 0.0 0.0 111.0
Electricity price 685 21.98 76.11 0.7 14.3 1038.0
Electricity cost 685 1329.8 3006.7 0.0 308.2 28,965.9
369Global drivers of cryptocurrency infrastructure adoption
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financing rules). This is a longitudinal measure.
Information about regulation on bitcoin trade is
hand collected from the US Library of Congress,
bitlegal.io, and Wikipedia.
A list of all dependent and explanatory variables used
in the analysis and their definitions are described in
Table 1, where Panel A documents our country-level
analysis and Panel B the regional-level analysis. Sum-
mary statistics for all our variables are presented in
Table 2and a correlation matrix is available in the
Appendix (Table A.I).
4Estimationmethodology
4.1 Country-level analyses
We construct a panel-data structured database of
countries and years for adoption of non-hidden
Bitcoin nodes and Bitcoin merchants, as well as our
independent variables. We limit our database to 2014
to 2018, the period for which full Bitcoin node and
merchant data are available. We regress our measures
of infrastructure adoption over two baseline variables
of GDP per capita and restrictive regulations—
capturing fundamental economic and legal country-
level characteristics—as well as our independent
variables.
In our country-level analyses, we limit our analysis to
137 countries in which all our explanatory variables are
available from their respective data sources for all years
in our database. The variable with the most limited
global coverage, % of unbanked, was restricted due to
the breadth of coverage of the World Bank’s Financial
Inclusion survey. We refer to the resulting database as
Panel A, for which summary statistics are available in
Table 2. Having reduced our sample from 201 countries
respectively for which we have Bitnode and CoinMap
data
12
to 137 countries may introduce a sampleselection
bias. While this choice was driven by a need to have
values for all independent variables used in the analysis,
it is justifiable as the excluded nations are smaller in size
by orders of magnitude and often reliable statistics on
financial and criminal characteristics unavailable for
them.
13
Our dependent variables take the value 0 for a
non-trivial share of our observations (i.e., for
countries and years in which no Bitcoin infrastruc-
ture is detected), and can thus be thought of as
censored variables. In our country-year analyses,
we therefore run random-effects Tobit models
lower-censored at zero with panel-data adjusted
bootstrapped standard errors.
14
We employ the fol-
lowing model specification, where the subscripts i
and t, refer to countries and years respectively:
Panel B: Region-year level dataset (2014–2018; 294 NUTS2 or equivalent regions in 34 countries)
Dependent variables
Bitnode intensity 1470 2.17 4.71 0.0 0.4 59.7
Unique bitnodes 1470 17.37 33.31 0.0 5.9 486.5
Bitcoin merchants 1470 2.03 6.90 0.0 0.0 109.7
Social variables
High risk willingness 1470 0.16 0.12 0.0 0.1 0.7
Trust to Others 1470 −0.05 0.26 −0.8 −0.1 1.0
Distrust in banks & financial system 1470 0.09 0.31 −0.9 0.1 1.0
Distrust in other institutions 1470 0.01 0.24 −0.9 0.0 0.8
Interaction variable
Bitcoin price 1470 2.76 3.21 0.3 0.6 8.5
12
147 countries have been reported to have at least one Bitcoin
merchant, with 28 having only one. We have coded the countries
without reported merchants on CoinMap as having no merchants.
Given that the median GDP per capita of these countries is below
$1000, it is reasonable to assume that no merchants existed in these
nations.
13
In fact, the median population of the excluded 60 countries is less
than 0.3 million while for those included in the analyses it is 11.7
million.
14
This entails blocked bootstrapping with countries as blocks, for the
bootstrapped clustering.
370 E. Saiedi et al.
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Bitcoin Infrastructure Adoption Variableit
¼β0þβ1ln GDPðÞ
it þβ2
Restrictive regulationit þβ3
Inflation crisisit þβ4Unbankedit
þβ5Five bank asset concentrationit
þβ6Money launderingit
þβ7Rule of lawit þWRiþζtþWRiζt
þεit ð1Þ
Our Bitcoin infrastructure adoption variables are bitnode
intensity, unique bitnodes, bitnode hours and Bitcoin mer-
chants. The world region fixed effects (WR
i
) and year fixed
effects (ζ
t
), respectively control for systematic differences
across world regions,
15
and for changes in the price of
bitcoin and in global macroeconomic conditions over time.
Our interactions of world region and year fixed effects
(WR
i
×ζ
t
) absorb the effect of world region-specific shocks
or time trends in adoption of bitcoins. Given the country-
year level variation of our Panel A data, these controls are
stringent enough to tease out the effect of our country-level
variables. The limited variation of our country-level char-
acteristics during the five years of observation does not
allow for the inclusion of country-level fixed effects.
16
We
cluster all regression standard errors at the country level. In
robustness tests, we cluster our results at the country and the
year level, and results do not significantly change.
4.2 Regional level analysis
Our region-year analyses are meant to investigate the
effect of social variables such as general and institutional
trust, and risk willingness on the adoption of Bitcoin
infrastructure. Our social variables are derived from the
Life in Transition Societies (LiTS III) Survey carried out
between 2014 and 2016 of 51,000 households in 34
countries. To this end, we construct a database of averages
of social variables for the 294 statistical regions surveyed,
which are primarily located in nations in Eastern Europe
and Central Asia, as well as Germany, Italy, and Greece.
We make adjustments to ensure our dependent variable
database regions match statistical regions from the LiTS III
survey, and then calculate region-level dependent variables
of bitnode intensity, unique bitnodes and bitnode hours.
We complement this data with population statistics from
European or national statistics agencies for these 34 coun-
tries. Hence, we have effectively restricted our analysis
from 137 countries in the country-year level database in
Panel A, to a subset of 34 countries in the region-year level
database in panel B (with 294 regions). Summary statistics
for Panel B are available in Table 2.
Our region-level analyses use the following random-
effects Tobit regression model, where subscripts jand t,
refer respectively to sub-national administrative or sta-
tistical regions and years:
Bitcoin Infrastructure Adoption Variablejt
¼β8þβ9ln GDPðÞ
it þβ10
Restrictive regulationit þβ11
High risk willingnessjt þβ12
Trust to othersjt þβ13
Distrust in banks and the financial Systemjt
þβ14 Distrust in other institutionsjt þci
þζtþεit
ð2Þ
This equation involves our baseline characteristics of
logged GDP and restrictive regulation to control for the
heterogeneity specific to the economic and legal condi-
tions, with our social characteristic variables following.
Our region-level empirical models using eq. 2control
for systematic differences across nations using country
fixed effects (c
i
) and for macro-economic conditions
using year fixed effects (ζ
t
). Similar to Giannetti and
Wang ( 2016), we control for distrust (lack of trust) in
other institutions unrelated to banks and the financial
system, to separate the specific effect of distrust to banks
from any idiosyncratic relationship between general
institutional sentiments and Bitcoin infrastructure adop-
tion. In additional tests, we interact high risk willingness
with bitcoin price to examine how this social character-
istic varies with price increases.
15
Defined using the World Bank’s division of nations into world
regions of North America, Europe & Central Asia, East Asia & Pacific,
Latin America & Caribbean, Middle East & North Africa, Sub-Saharan
Africa, and South Asia
16
Upon their inclusion, many of our country-level variables would be
omitted, due to their limited time-series variation from 2014 to 2018 in
variables such as Restrictive regulation.
371Global drivers of cryptocurrency infrastructure adoption
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5 Estimation results
5.1 Financial and criminality drivers
Our first set of analyses focus on associations between
use and running of bitnodes or merchants and the crim-
inality and financial characteristics discussed previous-
ly. We first report results on bitnodes and subsequently
on Bitcoin merchants.
Table 3reports results of our random effects regres-
sions for three dependent variables measuring the
frequency of bitnode activity in a country, relative to
the number of Internet users. These dependent variables
are standardized measures described in Table 1,namely
bitnode intensity, unique bitnodes, and bitnode hours. In
all regression models (1) to (4), our primary dependent
variable of bitnode intensity is regressed against a base-
line of controls, GDP per capita and a dummy variable
for restrictive regulation being in effect. Models (2) and
(3) add financial characteristics and criminality charac-
teristics, respectively, and model (4) expands the base-
line estimation to include both sets of financial and
Tabl e 3 Financial and criminality drivers of Bitcoin nodes (Panel A; Country-year level)
(1) (2) (3) (4) (5) (6)
Dependent variables Bitnode intensity Unique bitnodes Bitnode hours
Financial characteristics
Inflation crisis 0.338 0.530+ 7.038
**
50.642
(0.386) (0.306) (2.596) (65.208)
Unbanked −0.006 0.008 −0.189
*
−2.309
(0.013) (0.009) (0.078) (1.421)
Five-bank asset concentration −0.004 0.003 0.011 −5.080
**
(0.015) (0.013) (0.069) (1.676)
Criminality characteristics
Money laundering 1.302
*
1.347
*
6.916
**
176.344
***
(0.568) (0.604) (2.381) (53.490)
Rule of law 1.343
**
1.445
**
9.323
***
88.669+
(0.424) (0.474) (2.557) (45.858)
Baseline controls
GDP per capita 1.425
***
1.338
***
0.617 0.681 0.039 72.001
*
(0.363) (0.378) (0.415) (0.457) (2.209) (34.734)
Restrictive regulation −0.574
+
−0.581
*
−0.553
+
−0.529 3.069
*
32.565
(0.314) (0.291) (0.290) (0.324) (1.347) (39.446)
Constant −11.455
***
−10.185
**
−5.464 −6.624 19.808 382.419
(3.123) (3.616) (3.573) (4.347) (20.597) (431.859)
Year FEs Yes Yes Yes Yes Yes Yes
World Region FEs Yes Yes Yes Ye s Yes Ye s
Year-World Region FEs Yes Yes Yes Yes Yes Yes
Panel Data Specifications Tobit Tobit Tobit Tobit Tobit Tobit
Observations 685 685 685 685 685 685
Number of Countries 137 137 137 137 137 137
Pseudo R-Squared 0.34 0.4 0.34 0.41 0.62 0.22
Log-likelihood −1314.9 −1314.6 −1306.7 −1306.1 −2268.1 −2386.8
RHO 0.74 0.74 0.72 0.72 0.54 0.62
All models are Tobit random effects models lower-censored at 0 and their associated pseudo R-squared are reported. Panel-data adjusted
bootstrapped standard errors, clustered at the country level, are reported in parentheses. The symbols ***, **, *, + mean that the reported
coefficients are statistically different from zero, respectively, at the 0.1, 1, 5, and 10% level. Model 6 is additionally upper-censored at 750
(upper limit of active bitnode hours in a month)
372 E. Saiedi et al.
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criminality characteristics. The magnitude and standard
deviations of coefficients for either sets of variables do
not alter in a significant way and are robust to inclusion
of both. Models (5) and (6) respectively regress unique
bitnodes and bitnode hours on the full set of country-
level characteristics. The Internet Appendix Tables IA.I
to IA.III show the effect of each individual variable
being added separately to the baseline model, for each
of our dependent variables.
Model 1 is our baseline model. Fixed effects for
years, world regions and their interactions together with
our two basic controls account for a substantial fraction
of the variation in Bitnode intensity, corresponding to a
pseudo R
2
of 0.34. As expected, more restrictive regu-
lations vis-à-vis bitcoins are associated with below-
average total intensity of Bitnode activity (models 1 to
4), but with the occurrence of an above-average number
of unique bitnodes. The effect of restrictive regulation is
clearly driven by differences on the intensive margin
(i.e., how many hours a month Bitnodes are available)
rather than difference on the extensive margin (i.e., how
many unique Bitnodes are being run in a country). This
may indicate that while restrictive regulation reduces the
actual use of bitcoins, primarily by making the exchange
of bitcoins to fiat currencies illegal and secondarily by
forbidding the trade of cryptocurrencies, it does not
effectively suppress the general interest in Bitcoin as a
phenomenon, and to contributing to its infrastructure.
17
The occurrence of an inflation crisis is associated
with an increase in the number of unique bitnodes in a
country (p< 0.01). The coefficient estimate indicates
that the onset of an inflation crisis in a country is
associated with an uptake of 47% of the average and
30% of the standard deviation of the number of unique
bitnodes in a country. We run robustness tests by alter-
ing the inflation crisis cut-off to a minimum of 15% or
25% of annual inflation and find similar results (see
Internet Appendix Table IA.IV). This speaks to bitcoins
being utilized for financial hedging where currency
devaluations greatly erode the value of a national cur-
rency, consistent with hypothesis 1.
While a greater fraction of unbanked in a country
is not found to be associated with the intensity of
use of Bitnodes (models 2, 4), it is negatively asso-
ciated with the number of unique Bitnode users
(model 5, p< 0.05). This result goes against the
notion that Bitcoin infrastructure is being set up as
a substitute to the financial system in countries
where financial inclusion and penetration is low,
but rather that Bitcoin infrastructure emerge where
the penetration of the financial system is greater.
This result goes against our hypothesis 2a, in favor
of our counter-hypothesis 2c.
The coefficient on Five-bank asset concentration in
model 6 is negative and significant at 1%, indicating that
greater concentration in the financial sector reduces the
intensity of use of Bitnodes. This result goes against our
hypothesis 2b assertion, in favor of the opposing hy-
pothesis 2d. Together with the findings for unbanked
populations, results point to the penetration of Bitcoin
infrastructure increasing in well-developed banking sys-
tems (hypotheses 2c and 2d), where the general famil-
iarity with online finance is greater and the related
digital entrepreneurial ecosystem is more vibrant.
Countries with high risk of drug-related money laun-
dering tend to exhibit greater Bitnode intensity of activity
(model 4, p< 0.05) and Bitnode adopters (model 5,
p< 0.01) and provide significantly more active hours of
supply of Bitcoin nodes (model 6, p< 0.001). This latter
coefficient estimate for money laundering is significant at
the 1% level and suggests that countries that have a high-
risk of money laundering, all else equal, run Bitcoin
nodes for ~ 7 days more in a month (= 176.3/24) than
other countries. Our hypothesis 4 is therefore validated.
Thevariableruleoflawpertainstosociety’s percep-
tion of the likelihood of crime and violence, as well as
their confidence in the quality of the police, courts, con-
tract enforcement, and property rights in each country. It
is strongly associated with increases in Bitnode use in-
tensity (model 4, p< 0.05) and the number of unique
bitnodes (model 5, p< 0.001). These are in agreement
with our hypothesis 5. We note, however, that our Rule of
law measure could be thought of as having two distinct
components: the dependability of law enforcement, as
well as the likelihood of crime and violence. Our hypoth-
esized relationship relates to the former of these aspects.
We therefore undertake further analysis in order to ensure
that our estimates on rule of law are not driven by the
Bitcoin infrastructure penetration being greater in coun-
tries with more crime and violence.
We use costs to businesses associated with crime and
violence, the sub-index crime and violence costs of the
World Economic Forum’s Global Competitiveness In-
dex, as a proxy for the intensity of crime and violence
in nations. A related variable from the same source, the
17
Plausibly, this increase in the extensive margin may result from
greater publicity of bitcoins following national restrictions.
373Global drivers of cryptocurrency infrastructure adoption
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degree of Organized crime, is also investigated. Inserting
these variables into our main models yields insignificant
estimates throughout. We interpret these results (reported
in Internet Appendix, Table IA.V) to indicate that the
association between rule of law and our dependent vari-
ables is, as argued in the corresponding hypothesis, driv-
en by country-level differences in the reliability and
effectiveness of police and the legal system.
Table 4presents the results of our analyses on deter-
minants of adoption of Bitcoin merchants globally on
the same countries and using the same Panel A database
on which our Table 3results are based on. Similar to
Table 3, models 2 and 3 progressively add national
financial and criminality characteristics to our baseline
model (1). We expand the baseline estimation in model
(4) to include both sets of characteristics.
Model 4 seems to indicate that national financial
characteristics dominate criminality explanations in ac-
ceptance of bitcoins by merchants. Our coefficient esti-
mates on the unbanked fraction (β=−0.048) in model 4
is statistically significant at the 10% level, suggesting
that adoption of bitcoins by merchants is more popular
in locations where adults have bank accounts toa greater
extent in agreement with hypothesis 2c rather than 2a.
Together with the results in the same direction in Table 3
and contrary to a widely purported promise of bitcoins
Tabl e 4 Financial and criminality drivers of Bitcoin merchants (Panel A; Country-year level)
(1) (2) (3) (4)
Dependent variable Bitcoin merchants
Financial characteristics
Inflation crisis −0.418 0.639
(1.175) (1.807)
Unbanked −0.059+ −0.048+
(0.031) (0.029)
Five-bank asset concentration −0.041 −0.037
(0.032) (0.037)
Criminality characteristics
Money laundering 1.163
*
0.761
(0.560) (0.867)
Rule of law 1.448
*
1.303
*
(0.712) (0.628)
Baseline characteristics
GDP per capita 1.401
*
0.481 0.368 −0.234
(0.552) (0.495) (0.531) (0.579)
Restrictive regulation −0.020 −0.667 0.111 −0.388
(0.997) (0.922) (0.736) (0.908)
Constant −12.041
*
1.336 −3.980 6.166
(5.301) (4.511) (4.916) (5.096)
Year FEs Yes Yes Yes Yes
World Region FEs Yes Yes Yes Yes
Year-World Region FEs Yes Yes Yes Yes
Panel Data Specification Tobit Tobit Tobit Tobit
Observations 685 685 685 685
Number of Countries 137 137 137 137
Log-likelihood −1418.2 −1415.8 −1415.1 −1413.7
RHO 0.23 0.23 0.22 0.22
All models are Tobit random effects models lower-censored at 0. Panel-data adjusted bootstrapped standard errors, clustered at the country
level, and are reported in parentheses. The symbols ***, **, *, + mean that the reported coefficients are statistically different from zero,
respectively, at the 0.1, 1, 5, and 10% level
374 E. Saiedi et al.
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(see, e.g., Vigna and Casey 2015), this points to a picture
of Bitcoin infrastructure serving the banked population
more than the unbanked. It is noteworthy that our
Bitcoin merchants variable is more concentrated in cer-
tain countries, especially in the earlier years of the
sample. This has the effect that the broad and stringent
set of fixed effects interacting world regions and year
dummies, and our baseline characteristics, account for a
great deal of the variation in our data, reducing the
variation that can be attributed exclusively to our ex-
ploratory variables.
The coefficient estimate on rule of law is significant
(p< 0.05) in models (3) and (4) where we control for
other covariates and money laundering is only statisti-
cally significant in model (3), in agreement with our
hypothesis 5.
5.2 Social drivers
Table 5presents the results of our region-level
analyses. This set of analysis uses regional-level
versions of the dependent variables and baseline
Tabl e 5 Social drivers of adoption of Bitcoin infrastructure (Panel B; Region-year level)
(1) (2) (3) (4) (5) (6)
Dependent variables Bitnode
intensity
Unique
bitnodes
Bitcoin
merchants
Bitnode
intensity
Unique
bitnodes
Bitcoin
merchants
Social characteristics
High risk willingness 4.848 27.595 17.267
***
6.309
+
38.307 20.057
**
(3.421) −23.031 (4.552) (3.400) (23.387) (6.603)
High risk willingness x Bitcoin
price
−0.581
**
−4.928
*
−1.824
*
(0.000) (0.002) (0.911)
Trust in others 1.804 17.246
+
−0.819 1.945 17.833
+
0.441
(1.229) −9.43 (2.561) (1.186) (9.374) (2.713)
Distrust in banks & financial
system
4.383
**
25.271
*
−0.942 4.266
**
24.855
*
2.581
(1.638) −12.525 (3.210) (1.581) (12.449) (3.310)
Distrust in other institutions −2.827 −18.204 −8.955
*
−2.632 −17.708 −3.834
(2.311) −16.9 (4.354) (2.248) (16.809) (4.351)
Bitcoin price 0.011 −2.985
***
0.269
(0.038) (0.391) (0.169)
Baseline characteristics
GDP per capita −1.886
*
−33.543
***
−6.211 −1.884
*
−33.558
***
−6.341
(0.869) −8.313 (4.548) (0.821) (8.239) (4.225)
Restrictive regulation −0.139 3.468 −0.441 0.049 5.212 0.240
(0.422) −4.051 (2.254) (0.401) (4.036) (2.133)
Constant 8.381 239.257
***
35.253 8.424 239.675
***
32.577
(7.710) −71.928 (38.691) (7.301) (71.287) (35.990)
Year FEs Yes Yes Yes Yes Yes Yes
Country FEs Yes Yes Yes Yes Yes Yes
Panel Data Specifications Tobit Tobit Tobit Tobit Tobit Tobit
Observations 1470 1470 1470 1470 1470 1470
Number of Statistical Regions 294 294 294 294 294 294
Number of Countries 34 34 34 34 34 34
Log-likelihood −2800.7 −5221.2 −2742.9 −2796.7 −5218.4 −2740.9
RHO 0.78 0.59 0.24 0.78 0.59 0.24
Notes: All models are Tobit random effects models, lower censored at 0 and their associated overall R-squared is reported. Their standard
errors are reported in parentheses. The symbols ***, **, *, + mean that the reported coefficients are statistically different from zero,
respectively, at the 0.1, 1, 5 and 10% level
375Global drivers of cryptocurrency infrastructure adoption
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characteristics of Table 3, and in addition includes
social variables and country-level fixed effects in
order to control for unobserved country-level het-
erogeneity. Country-level differences in financial
and criminality factors are absorbed by these fixed
effects and are not included individually in this
analysis. Dependent variables are standardized by
the number of Internet users. Year fixed effects
control for unobserved year-specific events.
Models 1 and 2 present estimates of the model spec-
ification in Eq. (2) for bitnode intensity and unique
bitnodes. In regressing the trust in others variable against
unique bitnodes, the coefficient estimate of model (1) is
insignificant. In model (2), the coefficient of β=17.2is
significant at the 10% level. In terms of magnitude, these
suggest that a one standard deviation decrease in trust in
others is associated with 4.5 fewer unique bitnode
adopters per million Internet users or 31.3% of average
adopters of bitnodes in statistical regions. We thus find
only weak and partial support for hypothesis 3a.
The coefficient estimate for our variable distrust in
banks and the financial system is statistically significant
in models (1) to (2), respectively at the 1% and 5% level.
In terms of magnitude, a one standard deviation increase
in distrust in banks and the financial system equivalent
to 31% more distrustful people
18
is associated with an
increase of 8 unique bitnode adopters per million Inter-
net users (equivalent to 54% of mean adopters in statis-
tical regions), or an increase equivalent to 34% of the
standard deviation of bitnode intensity. These effects are
fairly large, and corroborate the role of crisis-related
factors and distrust in banks being a factor in the early
use of other peer-to-peer fintech technologies (see, e.g.,
Saiedi et al. 2017; Thakor and Merton 2018), in agree-
ment with hypothesis 3b.
19
Model 3 documents region-level social determinants
of the adoption of bitcoin by merchants. Our hypothe-
sized trust variables do not yield any statistically signif-
icant association to merchants’rate of adoption of
bitcoin. However, our measure of self-reported willing-
ness to take risks by individuals is found to be signifi-
cant at the 0.1% level consistent with our hypothesis 3c.
This suggests that adoption of bitcoins by merchants
occurs more where the population have a higher tenden-
cy to engage in risky and speculative behavior.
We conduct a further test regarding the hypothesis 3c.
In developing this hypothesis, we argued that risk-
willingness can be expected to be associated with the
penetration of Bitcoin infrastructure since the level risk-
willingness could be expected to influence how many
people that are interested in holding bitcoin. If this is the
case, we should be able to observe signs of Countercycli-
cal Risk Aversion. Such behavior, which has been identi-
fied as explaining behavior on the stock market (Cohn
et al. 2015;Guisoetal.2018), would imply that when
prices go down, generally risk-averse individuals are even
less willing than before to take on risks. While risk-
willingness would generally be associated with greater
participation in financial markets, this association would
hence in the presence of countercyclical risk aversion be
strengthened in periods when assets trade at a relatively
lower price. We test this prediction in models 4 to 6 which
use the same variables used respectively in models 1
through 3, but add an interaction term of the annually-
av