Global drivers of cryptocurrency infrastructure adoption

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 particular, 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 hyperinflation, 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 advocates 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).

There is a potential for financial technologies to substitute for deficient provision of traditional banking services, 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 systems (e.g., credit and debit cards) rely on transactors to hold bank accounts. It is conceivable that digital currencies could serve as a payment system of choice for the 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). The ease of exchange via cryptocurrencies can extend much-needed liquidity to recipients of micropayments or loans in developing and underdeveloped countries, offering the world’s ‘unbanked’ millions an unprecedented degree of convenience and security (Vigna and Casey 2015). Lacking access to a financial institution or the needed documentation 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 possibilities 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 incumbents, 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:

On the other hand, it is also possible that financial development is a prerequisite for interest in cryptocurrencies. Lacking experience with traditional Internet banking services, people may not be prepared to deal in cryptocurrency. A lack of familiarity with financial 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 environments 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) or 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 externalities in the form of greater know-how of financial technologies, and thereby also interest in 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.

We consider the following two competing hypotheses:

A prerequisite for economic exchange, trust has been found to be positively associated with financial development 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; Rau 2017). 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 associated with 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 investment, 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 nurturing an alternative monetary and financial system that enables greater anonymity, privacy, and autonomy (Bashir et al. 2016; Böhme et al. 2015; Dodd 2018; Shiller 2019). Via enabling cheap and automated verification on distributed ledgers, the blockchain technology underlying cryptocurrencies allows for trust in an intermediary 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 participation respectively, by lenders and borrowers, in online peer-to-peer (P2P) lending markets. Theoretical papers modeling trust-driven substitutions between intermediaries and fintech firms are also emerging (e.g., Thakor and Merton 2018). We thus posit that lower trust in banks and the financial system 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 currency 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 understood that most bitcoin transfers involve transactions between speculative investors. Empirical studies 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; Fry 2018). 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. Regional differences in average willingness to take financial 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 expected 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.

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 phenomena called 15% Min Inflation and 25% Min Inflation with 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 collected 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 industry from the World Bank Global Financial Development Index. We primarily use Five-bank asset concentration, 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 concentration of the largest three banks in each country.

The US’ Bureau of International Narcotics and Law Enforcement Affairs performs an annual assessment of the risk of money laundering in 200 countries and jurisdictions. 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 of “financial institutions” employed by the bureau in the period we study includes digital currencies and “other value transfer systems.” We introduce an indicator variable (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.

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.

In our main models, we account for the role of technological development by normalizing the dependent variables by the number of Internet users. In further work, we construct a range of variables to explore further nuances in the relationship between technological development and the penetration of Bitcoin infrastructure.

Since Information and Communication Technology (ICT) is an enabler and prerequisite for financial technologies (Haddad and Hornuf 2019; Huang et al. 2019), as first order tests, we explore measures accounting for more advanced types of Internet use. Beyond the previously used Internet penetration, we also extract measures of broadband penetration and mobile telephone subscriptions from the International Telecommunications Unit (ITU)’s World Telecommunication/ICT Development reports, respectively defined as percentage of households with access to broadband, and the number of mobile telephone transactions per 100 adults in the population. We obtain ICT capabilities and ICT market development from the ITU’s Measuring the Information Society Reports; the former measures user sophistication as a composite index of adult literacy, and secondary and tertiary education, and the latter measures Information and Communication Technology (ICT) development as a composite index of ICT infrastructure, ICT capabilities (skills and knowledge) and ICT use of intensity. We measure the extent to which the latest technology is available, using the Executive Opinion Survey of the World Economic Forum’s Global Competitiveness Report. We obtain Internet servers data from the World Bank and netcraft.​com, as the number of servers using encryption technology in Internet communication per one million people. We utilize electricity cost and electricity price data from the World Bank’s Doing Business datasets, respectively measuring the median cost in percent terms relative to income per capita of connecting and running a warehouse with electricity, and the price in US cents per kWH of monthly electricity consumption for a warehouse based in the largest business city of a country for the month of March.

Table 8 shows the effect of these variables on per-capita standardized dependent variables. Model 1 shows, for reference, the relationship between GDP per capita and bitnode intensity. In all other models, we do not include GDP per capita due to very high correlations—as high as 0.91—with the technology variables (see Appendix, Table A.I). Models 2 and 3 respectively show that the Internet and broadband penetration are significant (at 0.1% and 5% respectively) in explaining bitnode intensity. Models 4 through to 7 examine the effect of each explanatory variable in Eq. (1) independently on bitnode intensity. ICT market development and latest technology are highly significant (at 0.1%), while secure Internet servers and mobile subscribers are significant at 10% and 5% respectively. While not reported, we also tested ICT capabilities separately which was highly significant at 0.1%. Models 9 through to 12 represent the results of Eq. (1), by progressively regressing all infrastructural variables onto our three per-capita dependent variables. ICT market development and latest technology remain positive and significance in most regressions at 0.1% and below 10% respectively. This aligns with the expectation that Bitcoin infrastructure are more likely to appear in countries where ICT is more advanced. Still, analysis of the pseudo-R²s suggest that economic strength (GDP per capita) is a more efficient predictor of Bitcoin infrastructure penetration than ICT market development. This supports our choice to anchor our main analysis in models controlling for this factor.

We additionally test a range of other variables argued for in the emerging literature to drive the use of cryptocurrencies.²⁰ Since they enable global transactions, digital assets can be used for shadow banking and tax evasion (Van Alstyne 2014). Currently, many national governments and their tax administrations are in the process of penning legislation on assets held in cryptocurrencies, while international task forces are devised to combat cryptocurrency-enabled tax crime (J5 2018). Most countries, however, have still not imposed any regulation or indeed effective regulation (Akins et al. 2015; Marian 2013). Hence, it is conceivable that bitcoins and Bitcoin infrastructure would find the greatest use and support where incentives for tax avoidance and evasion are highest. To test this, in Tables 9 and 10 we separately regress our dependent variables of bitnode intensity and Bitcoin merchants, on measures for the size of the shadow economy relative to the GDP (shadow economy), an indicator variable for whether a jurisdiction is a tax haven (tax haven), tax rates on income, and capital gains (taxation), and a measure of tax burden across countries (tax burden). We maintain the baseline controls, but omit other controls to avoid confounding of variables. We do not find an indication to support the shadow economy and taxation drivers (Tables 9 and 10; Models 1 to 4). This could be attributed either to more hidden uses of bitcoins in such nations or that the new potentials that cryptocurrencies enable for tax evasion and shadow economy, are not yet extensive enough to constitute a large part of bitcoin use.

It is possible that use of Bitcoin infrastructures (esp. Bitcoin nodes) are primarily being driven by cryptocurrency miners vying to reap the financial rewards associated with mining bitcoins. Hence, we explore if bitcoin mining is a major driver of adoption of our variables, by regressing a bitcoin mining country indicator variable for countries identified by the Cambridge Centre for Alternative Finance’s two versions of the Global Cryptocurrency Benchmarking Study (Hileman and Rauchs 2017; Rauchs et al. 2018) as having medium-to-large scale mining operations, and additionally find no significant effect in our robustness tests (Tables 9 and 10, Model 5). We include estimates of mining intensity (in MW) and do not find any significant effect in our robustness tests (Tables 9 and 10, Model 6). We attribute this to operation of Bitcoin nodes being driven by other than purely financial incentives and rewards to mine bitcoins, while this may still be a contributing factor. Bitcoin mining facilities are highly concentrated in certain countries with cheap and reliable electricity, cold climate and friendly regulatory environment (e.g., Rauchs et al. 2018 identify only 128 mining facilities worldwide, where only 7 countries are mining more than 40 MW whereas 1.7GW have been identified worldwide), whereas nodes are less concentrated. In fact, Miller et al. (2018) find only 2% of nodes on the Bitcoin network to account for three quarters of the mining power. We additionally test electricity prices in USD per kWh in different countries and years and similarly find no effect (Tables 9 and 10, Model 7).

One of the touted benefits of cryptocurrencies in the financial literature has been in enabling diversification (Bouri et al. 2019a) or as a hedge against equity markets (Bouri et al. 2019b), although some studies have found them to be a weak hedge for stock markets (Kliber et al. 2019; Shahzad et al. 2019). Certainly bitcoins are held due to their speculative nature (Rauchs et al. 2018; Baur et al. 2018b), in congruence with the reported effects of our high-risk willingness variable, in addition to serving as an alternative investment (Glaser et al. 2014). We test to see if Bitcoin infrastructure has seen an uptick worldwide as a hedge for low returns in national stock markets. Model 8 in Tables 9 and 10 presents results of that and does not show evidence for hedging as a main driver of bitcoin use. In Model 9 of these tables, we additionally test to see if purely low returns during the global financial crisis can be attributable to an upsurge in adoption, and similarly do not find statistically significant effects on our crisis stock market return variable.