Central banks digital currency: Detection of optimal countries for the implementation of a CBDC and the implication for payment industry open innovation

Náñez Alonso, Sergio Luis; Jorge-Vazquez, Javier; Reier Forradellas, Ricardo Francisco Article Central banks digital currency: Detection of optimal countries for the implementation of a CBDC and the implication for payment industry open innovation Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Náñez Alonso, Sergio Luis; Jorge-Vazquez, Javier; Reier Forradellas, Ricardo Francisco (2021) : Central banks digital currency: Detection of optimal countries for the implementation of a CBDC and the implication for payment industry open innovation, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 7, Iss. 1, pp. 1-21, https://doi.org/10.3390/joitmc7010072 This Version is available at: https://hdl.handle.net/10419/241657 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Open Innovation: Technology, Market, and Complexity Article Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation Sergio Luis Náñez Alonso * , Javier Jorge-Vazquez * and Ricardo Francisco Reier Forradellas   Citation: Náñez Alonso, S.L.; Jorge-Vazquez, J.; Reier Forradellas, R.F. Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation. J. Open Innov. Technol. Mark. Complex. 2021,7, 72. https://doi.org/10.3390/ joitmc7010072 Received: 27 January 2021 Accepted: 19 February 2021 Published: 24 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). DEKIS Research Group, Department of Economics, Catholic University of Ávila, 05005 Avila, Spain; ricardo.r[email protected] *Correspondence: [email protected] (S.L.N.A.); [email protected] (J.J.-V.) Abstract: This article analyzes the current situation of Central Bank Digital Currencies (CBDCs), which are digital currencies backed by a central bank. It introduces their current status, and how several countries and currency areas are considering their implementation, following in the footsteps of the Bahamas (which has already implemented them in its territory), China (which has already completed two pilot tests) and Uruguay (which has completed a pilot test). First, the sample of potential candidate countries for establishing a CBDC was selected. Second, the motives for implementing a CBDC were collected, and variables were assigned to these motives. Once the two previous steps had been completed, bivariate correlation statistical methods were applied (Pearson, Spearman and Kendall correlation), obtaining a sample of the countries with the highest correlation with the Bahamas, China, and Uruguay. The results obtained show that the Baltic Sea area (Lithuania, Estonia, and Finland) is configured within Europe as an optimal area for implementing a CBDC. In South America, Uruguay (already included in the comparison) and Brazil show very positive results. In the case of Asia, together with China, Malaysia also shows a high correlation with the three pioneer countries, and finally, on the African continent, South Africa is the country that stands out as the most optimal area for implementing a CBDC. Keywords: CBDC; digital currencies; monetary policy; digital transformation; financial inclusion; access to cash; central bank innovation 1. Introduction It has been more than 12 years since “a new electronic cash system”, baptized as Bitcoin, was born, being “a peer-to-peer electronic cash system” [ 1 ]. All this happened in 2008 after a message was sent to the metzdowd.com cryptocurrency mailing list, signed with the alias Satoshi Nakamoto and titled “Bitcoin P2P e-cash paper” [ 2 ]. Since that time, Bitcoin’s growth has been exponential more as a store of value than as a means of payment for transactions. However, its emergence and the threat it poses by competing with central bank-backed money [ 3 ] have awakened the interest of Central Banks around the world in digital currencies, in this case backed by the central bank. It is therefore the aim of this article to shed light on what would be the optimal country to implement a digital currency backed by the central bank, popularly known as a Central Bank Digital Currency (hereinafter CBDC). For this purpose, a series of motives put forward by the different central banks or monetary authorities was used as a starting point. Once the reasons have been selected, these reasons have been assigned variables that characterize that reason and are compared using bi-variate correlation (Pearson’s correlation, Spearman’s correlation and Kendall’s Tau-b) [ 4 , 5 ] to determine the optimal country or monetary area. For this purpose, we have used data from the Bahamas, which has already implemented its own CBDC in October 2020, China, which has completed two pilot tests, and Uruguay, which also completed a pilot test. The main result has been to generate a list of countries that, J. Open Innov. Technol. Mark. Complex. 2021,7, 72. https://doi.org/10.3390/joitmc7010072 https://www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2021,7, 72 2 of 21 being more closely related to the three previous countries, are considered optimal for the implementation of a CBDC. At the technical level, the SPSS statistical program was used to analyze the correlations between the countries and variables. This article is structured as follows: an introduction highlighting the relevance and importance of CBDCs around the world today. A literature review section where CBDC is defined and its design features are discussed, as well as its differences with cash and cryptocurrencies. The material and results section explains how the motives for implementing CBDCs and the candidate countries are selected, as well as the assignment of variables. The results section shows those countries that have obtained the correlation results closest to those of the Bahamas, China, and Uruguay, and which are therefore optimal for implementing a CBDC. In the conclusion, it is indicated that the results obtained show that the Baltic Sea area (Lithuania, Estonia, and Finland), Uruguay and Brazil, China, Malaysia and finally South Africa stand out as optimal locations for implementing a CBDC. 2. Literature Review: Definition of CBDCs, Differences with Cash and Cryptocurrencies A CBDC is an electronic variant of cash issued by a central bank, which combines cryptography and digital ledger technology to offer this digital money [ 6 ]. It is therefore a central bank liability, which can: - Emulate the characteristics of cash (if held by the public). - Serve as a central bank reserve (if held only by banks and other financial intermediaries that have access to the payment system). The different studies and debates currently revolving around the possible implementation of a CBDC had their origin (as we have already commented in the introduction) in the emergence of Bitcoin and other cryptocurrencies (such as Ripple, Ethereum or Litecoin among others) that, in theory, can perfectly compete with the physical money issued by a central bank [ 3 , 7 ], or as [ 8 ] points out, “Libra is going to challenge the current banking ecosystem”. It should also be considered that traditional cash is costly, as its issuance, circulation and withdrawal require costly infrastructure for the central bank and commercial banks [ 9 ]. It also generates crime (theft) and counterfeiting [ 10 ], and is also the main vehicle for money laundering, tax evasion and terrorist financing [ 11 ]. A CBDC would in principle be more efficient, cleaner, and safer. One of the fundamental questions regarding CBDCs is whether they preserve the anonymity inherent to cash, which will be discussed later in the discussion of technology. As we can see in Figure 1, individuals can currently hold central bank-issued money (central bank cash) in the form of bi-currencies or coins, but only banks and other financial institutions can hold central bank-issued electronic money in the form of reserves. J. Open Innov. Technol. Mark. Complex. 2021, 7, 72 3 of 22 of money that would coexist alongside traditional cash and bank deposits [16], as we can see in Figure 1 containing the Venn diagram of the types of money. Figure 1. Initial classification of a Central Bank Digital Currency (CBDC) using the Venn diagram. Source: Own elaboration based on [12,13]. A CBDC would be denominated in Dollars, Euros, Yen or any other currency, just like banknotes or physical coins, so 10, 50 or 100 monetary units of a CBDC would always have the same equivalence as a banknote of 10, 50 or 100 monetary units of banknotes or traditional physical coins. A CBDC is sometimes considered to be equivalent to a digital banknote, although in practice it may have other characteristics that will depend on its final design [17]. A CBDC requires the creation of a whole new infrastructure by the issuing central bank so that it can be used to make payments digitally. - A database in which the CBDC is registered. - An application through which payments with the CBDC are executed. A CBDC would thus offer users an additional form of payment to complement cash, card, or bank transfer payments. Although the term CBDC includes the words “digital currency”, a CBDC would be somewhat different from “cryptocurrencies”, which in some cases are also called “crypto assets”, such as Bitcoin, Ethereum or others referred to in the introduction. Therefore, the initial classification made of a CBDC should be complemented by the so-called “money flower”. The Venn diagram above shows the characteristics that, according to many authors, money must meet: 1. regarding the issuer of the money, it can be issued by the central bank or not; 2. regarding accessibility, it is to determine whether access will be broad (universal) or restricted (e.g., only to residents of the country or nationals); finally, 3. regarding the type of technology (whether it is account-based). In the diagram, CB means central bank and CBDC means central bank-issued digital currency (excluding central bank digital money that is already available to monetary counterparties and some non-monetary counterparties) [18]. Private (general purpose) digital tokens include crypto assets and currencies, such as Bitcoin among others. Bank deposits are not widely accessible in all jurisdictions [16]. Many crypto assets are privately issued and are not backed by a central bank. Due to the above, these crypto-assets are not considered money because they do not fulfill the essential functions of money that have been previously stated [19]: 1. they are too volatile to be a reliable store of value, i.e., their price oscillates very sharply; 2. they are not widely accepted as a medium of exchange at any time and jurisdiction; and 3. these crypto assets are not used as a unit of account. Figure 1. Initial classification of a Central Bank Digital Currency (CBDC) using the Venn diagram. Source: Own elaboration based on [12,13]. J. Open Innov. Technol. Mark. Complex. 2021,7, 72 3 of 21 Central bank digital currency (CBDC) would be an electronic form of money issued and backed by a central bank, which could be used by households and businesses to make payments and as a store of value. If we focus therefore on the Venn diagram above, a CBDC would thus be central bank-issued money in digital form and universally accessible [ 12 , 13 ]. If a CBDC is implemented, this new “digital cash” may create new opportunities. On the one hand, in payments, and on the other hand, it can directly affect monetary policy and how a central bank can ensure two of its core functions: monetary stability and financial stability. Previous research has explored how wholesale CBDCs could provide better liquidity and serve as a means of payment in financial markets [ 14 ]. However, in this paper we analyze from an innovation perspective the impact of a “retail CBDC”. This retail CBDC, implemented by a central bank, would be aimed at meeting the payment needs of households and businesses. Depending on how the CBDC is ultimately designed, it may (or may not) enable the execution of payments outside the traditional financial sector [ 15 ]. A CBDC issued by a central bank for payments between individuals would be a new form of money that would coexist alongside traditional cash and bank deposits [ 16 ], as we can see in Figure 1containing the Venn diagram of the types of money. A CBDC would be denominated in Dollars, Euros, Yen or any other currency, just like banknotes or physical coins, so 10, 50 or 100 monetary units of a CBDC would always have the same equivalence as a banknote of 10, 50 or 100 monetary units of banknotes or traditional physical coins. A CBDC is sometimes considered to be equivalent to a digital banknote, although in practice it may have other characteristics that will depend on its final design [ 17 ]. A CBDC requires the creation of a whole new infrastructure by the issuing central bank so that it can be used to make payments digitally. - A database in which the CBDC is registered. - An application through which payments with the CBDC are executed. A CBDC would thus offer users an additional form of payment to complement cash, card, or bank transfer payments. Although the term CBDC includes the words “digital currency”, a CBDC would be somewhat different from “cryptocurrencies”, which in some cases are also called “crypto assets”, such as Bitcoin, Ethereum or others referred to in the introduction. Therefore, the initial classification made of a CBDC should be complemented by the so-called “money flower”. The Venn diagram above shows the characteristics that, according to many authors, money must meet: 1. regarding the issuer of the money, it can be issued by the central bank or not; 2. regarding accessibility, it is to determine whether access will be broad (universal) or restricted (e.g., only to residents of the country or nationals); finally, 3. regarding the type of technology (whether it is account-based). In the diagram, CB means central bank and CBDC means central bank-issued digital currency (excluding central bank digital money that is already available to monetary counterparties and some non-monetary counterparties) [ 18 ]. Private (general purpose) digital tokens include crypto assets and currencies, such as Bitcoin among others. Bank deposits are not widely accessible in all jurisdictions [16]. Many crypto assets are privately issued and are not backed by a central bank. Due to the above, these crypto-assets are not considered money because they do not fulfill the essential functions of money that have been previously stated [ 19 ]: 1. they are too volatile to be a reliable store of value, i.e., their price oscillates very sharply; 2. they are not widely accepted as a medium of exchange at any time and jurisdiction; and 3. these crypto assets are not used as a unit of account. There are also so-called stable coins, which are privately issued cryptocurrencies intended to overcome some of the shortcomings we have listed above affecting crypto assets. Fundamentally, they aim to provide stability through some form of backing [ 20 ]. These stable coins have also been classified [ 21 ] or even compared to cases of monetary policy happened in the past [ 20 ]. Depending on the nature of the assets backing the “currency” and how they are held, the stablecoin may be unable to provide value stability and may involve other risks [ 16 ]. In contrast, a CBDC backed by a central bank would be a J. Open Innov. Technol. Mark. Complex. 2021,7, 72 4 of 21 new form of risk-free currency, issued by the central bank, and would therefore fulfill all the essential functions of money. According to [ 22 ], cash is an asset class that combines four characteristics: 1. It is exchanged between peers (without knowledge of the issuer). 2. It is universal (anyone can have it). 3. It is anonymous, and 4. CBDCs are an alternative to cash, and therefore meet the first characteristic mentioned above, but because of their digital nature (and depending on their design) they may differ from the other three characteristics mentioned above. A CBDC can be designed to be universally accessible or restricted to a group of users [ 23 , 24 ]. The CBDC can be designed to be open to the entire public or only available to a specific number of investors or individuals [ 23 – 25 ]. CBDCs can be designed to be anonymous or identified [ 24 ]. Finally, according to [ 22 , 25 ], the decoupling of digital cash from traditional cash (coins and banknotes) “opens up the possibility of including interest as a feature”. 3. Materials and Methods 3.1. Materials Before applying the various statistical tools to analyze the data, it is necessary to select them. First, we start with the motives (and the variables associated with them) for implementing a CBDC for each country, which will serve as input for the model. To this end, we start from the information available on the motives that drive a country to establish a CBDC, based on that collected by [ 9 , 26 ], complemented by the speeches issued by central banks and information also collected by [ 27 ]. Based on the above, the motives present in central bank speeches, reports or briefing notes and the variables associated with them have been selected, as shown in the following table (see Table 1). Therefore, as can be seen in the figure above, we start from the reasons put forward by the different Central Banks for implementing CBDCs in their territory and assign a series of variables. The purpose of the above is to carry out a statistical correlation analysis that allows us to determine a list of optimal countries or currency areas, as well as those that would be optimal (because they are less closely related) for implementing a CBDC. Once this has been done, we have created a matrix (shown in Table A1 in the Appendix A) in which the variables appear for each of the selected countries. The countries have been selected on the basis of the following criterion: the existence of a speech by a member of their central bank giving information on CBDCs, especially if their implementation is being considered in the short term, in the medium term or in the long term. In order to measure geographic dispersion, we have assigned the variable inhabitant per square kilometer [ 28 ]. This variable has been inserted since some countries have difficulties in providing banking services to their population precisely because of this geographical dispersion. Regarding access to banking services, the variable used was commercial bank branches (per 100,000 adults) [ 29 ] to allow us to compare access to this service among the list of selected countries. Regarding the reason for increasing the banking penetration rate, one of the ways to measure this is through financial sector credit to the private sector (% of GDP), selecting data from [ 30 ], as it shows the relationship between credit and GDP. The reason given is that quantifying the credit represents the incidence of the system in the economy. The rationale for implementing a CBDC based on the financial sector not becoming obsolete has been measured through the Digital Readiness Index [ 31 ], which measures seven components that are standardized and summed to obtain an overall score of the digital readiness of each country. The relative motive for implementing a CBDC as consumer protection is determined by the reason that the more online commerce there is, and the more payments made in this way, the more “digital” protection the consumer will need, and the UNCTAD B2C E-commerce Index [ 32 ] has been used as a variable for this purpose. Other central banks have put forward as a reason for implementing CBDCs the power to maintain control over monetary and macroeconomic policy [ 9 ], so to measure this, we have turned to the speeches of governors or members of central banks, reports or technical notes alluding to this specific reason, with its presence or absence in the item J. Open Innov. Technol. Mark. Complex. 2021,7, 72 5 of 21 analyzed as the variable used. The more mobile payment is used or cryptocurrencies are available to the public without a Central Bank alternative, the more risk there is that other forms of payment not controlled by the Central Bank, such as Bitcoin, Ripple and other cryptocurrencies, will be used, and the more difficult it will be to control monetary policy. Many countries, such as Sweden, Norway, or the United Kingdom, have experienced a large drop in the use of cash [ 33 , 34 ]. To measure this reason, the variable used is currency in circulation as a percentage of each country’s GDP [ 35 ]. Another of the reasons used to establish a CBDC is the lower costs and greater efficiency of the banking system [ 34 ], so that the variable used to measure this situation has again been its presence in the speeches of governors or members of central banks, reports or technical notes where this specific reason is alluded to; its presence or not in the item analyzed being the variable used. There is a fundamental motive when it comes to determining the real interest of a central bank or monetary area in establishing its own CBDC: the central bank’s discourse indicating this motive in the short term, medium term or long term (or even its rejection). Table 1. Reasons for establishing a CBDC and associated variables. Reason for Establishing a CBDC Associated Variable Geographic dispersion Inhab/km2 Access to financial services Commercial bank branches (per 100,000 adults) Increase the banking penetration rate Financial sector credit to the private sector (% of GDP) Financial sector does not become obsolete Digital Readiness Index Security reasons: avoid money laundering and terrorism financing Shadow economy, percent of GDP Consumer protection UNCTAD B2C E-commerce Index Maintain control over monetary and macroeconomic policy Fall in use of cash (alternative) Corresponding CB speech Broad money (% of GDP) Lower costs and increased efficiency of the banking system Central Bank Speech Public interest/CBDC 1 Public interest/CBDC 2 CB speech on CBDC CB speech on CBDC Search interest Keyword “CBDC” (2013–2020) annual average of the period. Search Interest topic “CBDC” (2013–2020) annual average of the period Source: Own elaboration based on [9,27–32,35]. To measure this situation, we start from the data of [ 27 ] and its “central_bankers_speech_ stance_index_index_normalized” index. Finally, the reason for establishing a CBDC is the public’s own interest in its acceptance. There are still few studies like that of [ 26 ], so we start from two quantifiable variables: Search Interest Keyword “CBDC” and Search Interest topic “CBDC”, both collected by [ 27 ], but in this case, considering the annual average for the period between 2013 and 2020. All the above allows us to generate the table of countries and variables shown in the Appendix Aas Table A1. 3.2. Methodology Before starting to analyze the relationships between the different countries with the variables that we characterized in the previous section, we performed an analysis of the descriptive statistics for each country (shown in Table A2 in the Appendix A) and also a frequency analysis of the variables (shown in Table A3 in the Appendix A). Once this had been done, we carried out the application of Pearson’s correlation coefficient [ 36 ] using the SPPS statistical program. The objective is to find those countries with the highest degree of correlation [ 37 ], and then Spearman’s Rho and Kendall’s Tab U with the Bahamas in first place, China in second place and Uruguay in third place. The reason for choosing these three countries, as already announced at the beginning of the manuscript, is that they are the countries that have already committed to a CBDC. The Bahamas was one of J. Open Innov. Technol. Mark. Complex. 2021,7, 72 6 of 21 the first countries to launch its own CBDC backed by a Central Bank. The CBOB (Central Bank of the Bahamas) announced via Twitter and the radio following a speech by its Governor, John Rolle, the launch (following pilot tests conducted earlier) of the “Sand Dollar”. Sand Dollar tokens are crypto representations of the Bahamian dollar, issued and regulated by the country’s central bank, which facilitates their integration into existing payment networks. The Sand Dollar is pegged 1:1 to the Bahamian dollar, which, in turn, is pegged to the US dollar [ 38 ]. On the other hand, China already conducted pilot tests in 2019 to establish its own CBDC. However, the final tests were conducted in April 2020. Subsequently, the pilots expanded to nine cities, including Shenzhen, Guangzhou, as well as Hong Kong and Macau. The mass trial in Shenzhen has been conducted through a lottery, where the graceful (50,000 out of 2 million applicants) received 200 digital yuan (about USD 30) to spend at merchants by October 18 (12–18 October 2020) [ 39 , 40 ]. In the case of Uruguay, the Central Bank of Uruguay completed a pilot program with a retail CBDC in April 2018, as part of a broader government financial inclusion program. The pilot test began in November 2017. It proceeded to issue, circulate, and test an e-peso. Thus, “Transfers were made instantly and peer-to-peer, through cell phones using text messages or the e-peso app” [ 41 ]. However, blockchain was not used. Twenty million e-pesos were issued, all of which were cancelled when the pilot ended [ 42 ]. The program is now in an evaluation phase before a decision can be made on further trials and possible issuance. As of today, the Central Bank of Uruguay has not yet decided. In our study, we used Pearson’s correlation coefficient, Spearman’s Rho, and Kendall’s Tau-b first. We used Pearson’s correlation coefficient to measure the degree of relationship of the continuous variables under study, since they are quantitative. However, we complemented this with Spearman’s Rho, which measures the interdependence between two random variables (both continuous and discrete) in case there is any discrepancy. Finally, to complement the study, we used Kendall’s Tau-b, which measures the similarity in the ordering of the data when they are classified in ranks for each of the quantities, selecting Tau-b, since it adjusts for ties. 3.2.1. Pearson’s Correlation Therefore, the aim is to look for those countries that present a higher degree of corelation with the Bahamas, China, and Uruguay as possible optimal candidates to be the next to establish their own CBDC. To this end, we apply the following formula (Equation (1)) to each country, in this case for grouped data: r=n·∑f·dx·dy −(∑f x·dx)(∑f y·dy) rhn·∑f x·dx2−(∑f x·dx)2ihn·∑f y·dy2−(∑f y·dy)2i(1) where n= number of data, which in our case will be the data collected by each country. f= cell frequency, fx = frequency of variable X (which in our case will be the Bahamas, China or Uruguay as appropriate), fy = frequency of variable Y (which in our case will be the rest of the countries excluding the three previous ones), dx = coded or changed values for the intervals of variable X, making sure that the central interval corresponds to dx = 0 to make the calculations easier, and finally, dy = coded or changed values for the intervals of the variable X, ensuring that the central interval corresponds to dy = 0 to make the calculations easier [37,43,44]. The results are shown in Table 2. J. Open Innov. Technol. Mark. Complex. 2021,7, 72 7 of 21 Table 2. Pearson correlation between the Bahamas, China, and Uruguay with the rest of the countries analyzed. Bahamas Pearson’s Correlation China Pearson’s Correlation Uruguay Pearson’s Correlation Lithuania 0.930 Switzerland 0.954 Lithuania 0.956 Uruguay 0.924 Malaysia 0.950 Estonia 0.951 Estonia 0.921 Tunisia 0.929 United States 0.937 Russia 0.918 South Africa 0.922 Canada 0.924 South Africa 0.901 Eurozone 0.891 Bahamas 0.924 Brazil 0.894 Japan 0.850 Russia 0.905 United States 0.886 Kuwait 0.772 Brazil 0.882 Spain 0.838 New Zealand 0.743 Finland 0.827 Malaysia 0.832 United Kingdom 0.730 South Africa 0.819 Finland 0.829 Lithuania 0.706 Iceland 0.793 Iceland 0.807 Ecuador 0.701 Australia 0.751 Tunisia 0.792 Australia 0.699 New Zealand 0.715 Canada 0.780 Senegal 0.675 Malaysia 0.709 Eurozone 0.777 South Korea 0.673 Sweden 0.699 Ecuador 0.774 Bahamas 0.669 Norway 0.676 Ukraine 0.770 Sweden 0.664 Tunisia 0.656 France 0.769 Finland 0.663 Spain 0.585 New Zealand 0.764 Estonia 0.662 China 0.569 Sweden 0.754 Spain 0.637 Ukraine 0.555 Australia 0.753 Norway 0.615 Switzerland 0.549 Norway 0.743 France 0.603 Eurozone 0.531 Madagascar 0.714 Trinidad and Tobago 0.575 France 0.525 China 0.669 Uruguay 0.569 Ecuador 0.455 Switzerland 0.661 Philippines 0.558 Japan 0.422 Swaziland 0.659 Israel 0.552 Madagascar 0.410 United Kingdom 0.620 Jamaica 0.542 Senegal 0.364 Japan 0.596 Ukraine 0.537 United Kingdom 0.327 Kuwait 0.564 Netherlands 0.528 Kuwait 0.281 Senegal 0.549 Swaziland 0.527 Swaziland 0.272 Indonesia 0.465 Ghana 0.454 Indonesia 0.150 Ghana 0.446 Brazil 0.438 South Korea 0.144 South Korea 0.428 Madagascar 0.428 Jamaica 0.132 Jamaica 0.410 India 0.400 Ghana 0.120 Trinidad and Tobago 0.406 Russia 0.384 Israel 0.118 Israel 0.392 United States 0.369 Trinidad and Tobago 0.117 Netherlands 0.355 Indonesia 0.316 Netherlands 0.075 Philippines 0.343 Canada 0.169 Philippines 0.057 India 0.306 Iceland 0.097 India 0.028 Source: Own elaboration using IBM SPSS Statistics 27 and data from Table A1. Following the method described above, there is a perfect positive correlation when the result is equal to 1; if it is between 0.9 and 0.99, it would be a very high positive correlation; and from 0.7 to 0.89, a high positive correlation [ 37 , 44 , 45 ]. In our case, we will select the first ten countries if they meet these requirements. In the results section (Section 4), an analysis of the results is carried out. 3.2.2. Correlation by Spearman’s Rho Secondly, Spearman’s Rho was applied, as before, with the Bahamas in first place, China in second place and Uruguay in third place. The reason for choosing these three countries, as already announced at the beginning of the manuscript, is that they are those countries that have already committed to a CBDC. The ρ (rho) is a measure of the correlation (the association or interdependence) between two random variables (both continuous and discrete). To calculate ρ , the data are sorted and replaced by their respective order [ 46 ], as shown in Equation (2): rs = 1 −6∑21n/(n2−1) (2) J. Open Innov. Technol. Mark. Complex. 2021,7, 72 8 of 21 The application of the above formula has allowed us to obtain the results shown in Table 3. In this case, Spearman’s Rho has been used to contrast the results obtained with Pearson, since Spearman’s correlation coefficient is less sensitive than Pearson’s for values far from the expected [47]. Table 3. Rho Spearman correlation between the Bahamas, China, and Uruguay with the rest of the countries analyzed. Bahamas Rho Spearman Correlation Coefficient China Rho Spearman Correlation Coefficient Uruguay Rho Spearman Correlation Coefficient Uruguay 0.976 Malaysia 0.989 Bahamas 0.976 Brazil 0.942 South Africa 0.962 Estonia 0.976 Estonia 0.936 South Korea 0.955 Lithuania 0.929 Lithuania 0.929 Japan 0.936 Brazil 0.905 South Africa 0.926 Finland 0.900 Russia 0.905 Russia 0.924 Trinidad and Tobago 0.895 South Africa 0.905 Malaysia 0.869 Kuwait 0.883 Finland 0.857 United States of America 0.867 Israel 0.882 United States 0.810 Finland 0.863 Philippines 0.873 Malaysia 0.810 Iceland 0.851 Estonia 0.867 Norway 0.786 Trinidad and Tobago 0.841 Lithuania 0.862 Sweden 0.786 Norway 0.833 Switzerland 0.857 China 0.762 Sweden 0.833 Tunisia 0.857 Iceland 0.762 Philippines 0.830 Sweden 0.842 Jamaica 0.762 South Korea 0.827 Jamaica 0.833 Trinidad and Tobago 0.738 Indonesia 0.827 New Zealand 0.833 Indonesia 0.714 Israel 0.827 United Kingdom 0.806 Israel 0.714 Jamaica 0.827 Bahamas 0.800 Australia 0.690 New Zealand 0.827 Eurozone 0.786 South Korea 0.690 Ecuador 0.816 India 0.782 Ecuador 0.690 Australia 0.815 Uruguay 0.762 Philippines 0.690 Kuwait 0.806 Netherlands 0.755 New Zealand 0.690 Spain 0.802 Ecuador 0.745 Ukraine 0.690 India 0.802 Norway 0.738 India 0.667 China 0.800 Australia 0.736 Tunisia 0.667 Eurozone 0.795 Brazil 0.733 Canada 0.643 Madagascar 0.791 United States 0.727 Spain 0.643 Ghana 0.779 Indonesia 0.718 Madagascar 0.643 France 0.778 Senegal 0.714 Senegal 0.643 Ukraine 0.772 France 0.709 Ghana 0.619 Canada 0.762 Spain 0.700 Eurozone 0.607 Japan 0.733 Ukraine 0.695 Kuwait 0.595 Switzerland 0.729 Ghana 0.661 Netherlands 0.571 Switzerland 0.714 Madagascar 0.611 France 0.548 Swaziland 0.712 Swaziland 0.611 Japan 0.548 Tunisia 0.695 Canada 0.588 Switzerland 0.536 Tunisia 0.690 Russia 0.573 Switzerland 0.464 Senegal 0.619 Iceland 0.545 Swaziland 0.429 Source: Own elaboration using IBM SPSS Statistics 27 and data from Table A1. The results obtained in the table above have been ordered from highest to lowest correlation with the Bahamas, China, and Uruguay. In this case, the interpretation of Spearman’s coefficient is the same as that of Pearson’s correlation coefficient, which we have already analyzed in the previous section. It ranges between − 1 and +1, indicating negative or positive associations, respectively, 0 zero, meaning no correlation, but not independence [43,45]. In the third section, we analyze the results. 3.2.3. Kendall Tau-b Correlation Thirdly, Kendall’s correlation coefficient was applied, as before, with the Bahamas in first place, China in second place and Uruguay in third place. The reason for choosing J. Open Innov. Technol. Mark. Complex. 2021,7, 72 15 of 21 correlation with the three pioneering countries, and finally, on the African continent, South Africa is the country that stands out as an optimal area for implementing a CBDC. The use of these digital currencies backed by the central bank is a further step in the necessary digital transformation of societies. It remains to be seen, however, the degree of acceptance they will have in society, so more research is needed on this issue. However, as technology and consumer tastes change, more research is needed to determine what advantages these CBDCs can bring to users in each country, and whether the effort pays off (given the existence of other digital means of payment). Author Contributions: Conceptualization, S.L.N.A.; Data curation, S.L.N.A.; Formal analysis, S.L.N.A. and J.J.-V.; Funding acquisition, R.F.R.F.; Investigation, S.L.N.A. and J.J.-V.; Methodology, S.L.N.A.; Project administration, S.L.N.A., J.J.-V. and R.F.R.F.; Resources, R.F.R.F.; Software, J.J.-V. and R.F.R.F.; Supervision, J.J.-V.; Visualization, J.J.-V.; Writing—original draft, S.L.N.A.; Writing— review and editing, J.J.-V. and R.F.R.F. All authors have read and agreed to the published version of the manuscript. Funding: The APC was partially funded by the incentive granted to the authors by the Catholic University of Ávila. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: We would like to thank the Catholic University of Avila, which has allowed us to use IBM-SPPS Statistics for the statistical calculations performed in this study. Also, we want to thank Tableu Inc. for allowing us to use Tableau Desktop Professional Edition free of charge for scientific purposes. Conflicts of Interest: The authors declare no conflict of interest. J. Open Innov. Technol. Mark. Complex. 2021,7, 72 16 of 21 Appendix A Table A1. Reasons for establishing a CBDC and associated variables by country. Country/Reason for Establishing CBDC Geographic Dispersion Access to Financial Services Increase the Banking Penetration Rate Financial Sector Not to Become Obsolete Security Reasons: Avoid Money Laundering and Terrorist Financing Consumer Protection Maintain Control over Monetary and Macroeconomic Policy Fall in Use of Cash (Alternative) Lower Costs and Greater Efficiency of the Banking System Central Bank Speech Public Interest/CBDC 1 Public Interest/CBDC 2 Variable Inhab/km2 Commercial bank branches (per 100,000 adults) Financial sector credit to the private sector (% of GDP) Digital Readiness Index Shadow economy, percent of GDP UNCTAD B2C E-commerce Index Corresponding CB speech Broad money (% of GDP) CB speech on CBDC CB speech on CBDC Search interest Keyword “CBDC” (2013–2020) Annual average of the period Search Interest topic “CBDC” (2013–2020) Annual average of the period Australia 3.2 28.2 135.8 17.89 8.1 92 1 122.6 0 0 59.8 48.3 Bahamas 38.5 24.4 45.2 12.74 38.55 65 0 55.7 1 4 Brazil 25.1 18.7 67.3 12.31 35.22 62 0 98.2 1 0 141.5 154.4 Canada 4.1 19.6 17.33 9.42 92 1 122.9 0 0 573.9 461.4 China 148.3 8.9 164.7 13.22 12.11 60 1 197 0 2 39.4 94 South Korea 529.4 15.1 151.7 18.22 19.83 84.3 0 151.8 0 4 108.6 126.1 Ecuador 68.8 10.1 40.2 11.29 30.18 41 0 29.8 0 0 Spain 93.7 49.7 94.7 15.74 22.01 80 1 65.8 1 0 109.1 106.1 United States of America 35.7 30.5 51.9 19.03 7 87 1 92.8 0 −1 163.6 152.8 Estonia 30.4 8.9 59 17.14 18.49 90 1 65.8 1 0 Eurozone 111.8 22 86.3 16.3 17.02 1 65.8 1 0 Philippines 357.7 9.2 48 11.03 28.04 40 0 76.6 1 4 55.6 79 Finland 18.1 3.1 95.1 17.95 13.3 93 1 65.8 1 −4 France 122.3 34.3 107.6 16.25 11.65 90 1 65.8 1 2 141.5 134.8 Ghana 130.8 8.5 12.4 9.55 39.37 35 0 26.9 0 0 India 454.9 14.6 50.2 9.46 17.89 44 1 76.1 0 0 329.8 160.4 J. Open Innov. Technol. Mark. Complex. 2021,7, 72 17 of 21 Table A1. Cont. Country/Reason for Establishing CBDC Geographic Dispersion Access to Financial Services Increase the Banking Penetration Rate Financial Sector Not to Become Obsolete Security Reasons: Avoid Money Laundering and Terrorist Financing Consumer Protection Maintain Control over Monetary and Macroeconomic Policy Fall in Use of Cash (Alternative) Lower Costs and Greater Efficiency of the Banking System Central Bank Speech Public Interest/CBDC 1 Public Interest/CBDC 2 Indonesia 147.8 15.6 32.5 11.68 21.76 36 1 38.8 1 0 189 166.6 Iceland 3.5 30.5 90.6 18.16 12.45 93 1 66.4 0 0 365.1 160.4 Israel 410.5 16.8 65.4 16.67 19.18 81 1 86.8 1 0 43.5 27.3 Jamaica 271 7.3 41.3 11.55 24.97 65 1 50.1 1 0 Japan 347.1 33.9 111.2 17.69 8.19 93.6 1 255 0 −2 63.3 109.8 Kuwait 232.2 23.2 89.3 13.36 21.72 63 1 101.5 0 4 Lithuania 44.7 10.4 38.9 14.78 18.65 79 1 65.8 1 1 Madagascar 45.1 2.4 14.2 6.48 45.29 29 0 24.6 0 0 Malaysia 96 10.1 120.9 14.31 26.07 77 1 123.1 0 1 62.4 79.5 Norway 14.5 128.4 17.98 15.07 96.39 1 66.2 0 0 New Zealand 18.4 25.4 146 17.75 8.97 93 1 103.8 1 −2 Netherlands 511.5 9.2 100 18.66 7.83 95.9 1 65.8 1 0 142.8 123.4 United Kingdom 274.7 133.6 17.86 8.32 95.1 1 141.8 0 0 174 98.4 Russia 8.8 25.6 52.4 13.63 33.72 71 1 58.6 1 0 76 95 Senegal 82.3 5.8 29.3 8.11 33.68 30 0 41.2 0 0 Swaziland 66.1 7 20.9 40.94 29 0 28.4 0 0 South Africa 47.6 9.6 66.7 11.39 21.99 54 0 74.1 0 0 Sweden 25 16.4 132.7 18.42 11.74 94.6 0 74.5 1 3 40.9 51.4 Switzerland 215.5 38.4 18.86 6.94 96.43 0 189.3 1 4 Trinidad and Tobago 270.9 11.5 40.1 12.59 31.4 47.5 0 67.4 0 0 Tunisia 74.4 22.1 64 10.87 30.9 47 0 74.2 0 0 Ukraine 77 0.4 22.8 11.47 42.9 60 0 36.2 1 0 Uruguay 19.7 10 28.1 13.88 20.38 59 0 54.2 0 0 Source: Own elaboration. J. Open Innov. Technol. Mark. Complex. 2021,7, 72 18 of 21 Table A2. Descriptive statistics by country. N Minimum Maximum Mean Mean Deviation Variance Australia 12 0.00 135.80 43.0742 49.33454 Bahamas 10 0.00 65.00 28.5090 23.53701 Brazil 12 0.00 154.40 51.3108 54.48703 Canada 10 0 574 130.16 210.077 China 11 1 197 67.33 72.428 South Korea 12 0.00 529.40 100.7542 147.79425 Ecuador 10 0.00 68.80 23.1370 22.86584 Spain 12 0.00 109.10 53.2375 43.51692 United States of America 12 −1.00 163.60 53.3608 58.33803 Estonia 10 0.00 90.00 29.1730 31.69478 Eurozone 9 0.00 111.80 35.6911 41.61316 Philippines 12 0.00 357.70 59.1808 98.18086 Finland 10 −4.00 95.10 30.4350 38.89312 France 12 1.00 141.50 60.6833 56.04434 Ghana 10 0.00 130.80 26.2520 39.48668 India 12 0.00 454.90 96.5292 147.85946 Indonesia 12 0.00 189.00 55.1450 69.76761 Iceland 12 0.00 365.10 70.0925 105.64317 Israel 12 0.00 410.50 64.0958 113.27797 Jamaica 10 0.00 271.00 47.3220 81.85365 Japan 12 −2.00 347.10 86.5650 110.49680 Kuwait 10 0.00 232.20 54.9280 72.41640 Lithuania 9 1 79 30.48 28.286 Madagascar 10 0.00 45.29 16.7070 18.22546 Malaysia 12 0.00 123.10 50.9483 47.64363 Norway 8 0 128 42.45 48.564 New Zealand 10 −2.00 146.00 41.3320 52.74819 Netherlands 12 0.00 511.50 89.7575 142.89136 United Kingdom 11 0.00 274.70 85.8891 90.36690 Russia 12 0.00 95.00 36.3958 33.28043 Senegal 8 0 82 28.80 26.264 Swaziland 9 0.00 66.10 21.3711 22.55034 South Africa 10 0.00 74.10 28.5380 29.21708 Sweden 11 1 133 42.69 42.137 Switzerland 8 1 216 71.30 86.845 Trinidad and Tobago 10 0.00 270.90 48.1390 81.57949 Tunisia 8 0 74 40.44 28.851 Ukraine 10 0.00 77.00 25.1770 27.87591 Uruguay 8 0.00 59.00 25.6625 20.82992 Source: Own elaboration using IBM SPSS Statistics 27 and data from Table A1. Table A3. Frequency table of the variables. Frequency Percentage Valid Percentage Cumulative Percentage Broad money (% of GDP) 1 8.3 8.3 8.3 Financial sector credit to the private sector (% of GDP) 1 8.3 8.3 16.7 Digital Readiness Index 2019 1 8.3 8.3 25.0 CB’s discourse on CBDC 1 8.3 8.3 33.3 Inhab/km2(2018) 1 8.3 8.3 41.7 Shadow economy, percent of GDP, 2015 1 8.3 8.3 50.0 CB speech on CBDC 2 16.7 16.7 66.7 Search interest Keyword “CBDC” (2013–2020) annual average of the period 1 8.3 8.3 75.0 Search Interest topic “CBDC” (2013–2020) Annual average of the period 1 8.3 8.3 83.3 Commercial bank branches (per 100,000 adults) 1 8.3 8.3 91.7 UNCTAD B2C E-commerce Index, Index 1 8.3 8.3 100.0 Total 12 100.0 100.0 Source: Own elaboration using IBM SPSS Statistics 27 and data from Table A1. J. Open Innov. Technol. Mark. Complex. 2021,7, 72 19 of 21 References 1. Chohan, U.W. A History of Bitcoin. SSRN Electron. J. 2017. [CrossRef] 2. Vigna, P.; Casey, M.J. 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