metadata: authors: 0: author: Graf Von Luckner,Clemens Mathis Henrik
1: author: Reinhart,Carmen M.
2: author: Rogoff,Kenneth S.
count: World,Venezuela, Republica Bolivariana de
docna: 0: docna: Decrypting New Age International Capital Flows
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Investment and Investment Climate,Law Enforcement Systems
teratopic: International Economics and Trade,Macroeconomics and Economic Growth,Law and Development
abstracts: cdata!: This paper employs high frequency
            transactions data on the world’s oldest and most extensive
            centralized peer-to-peer Bitcoin market, which enables trade
            in the currencies of more than 135 countries. It presents an
            algorithm that allows, with high probability, the detection
            of “crypto vehicle transactions” in which crypto currency is
            used to move capital across borders or facilitate domestic
            transactions. In contrast to previous work which has used
            “on-chain” data, this paper’s approach enables one to
            investigate parts of the vastly larger pool of “off-chain”
            transactions. Finding that, as a conservative lower bound,
            over 7 percent of the 45 million trades on the exchange we
            explore represent crypto vehicle transactions in which
            Bitcoin is used to make payments in fiat currency. Roughly
            20 percent of these represent international capital
            flight/flows/remittances.  Although this work cannot be used
            to put a price on cryptocurrencies, it provides the first
            systematic quantitative evidence that the transactional use
            of cryptocurrencies constitutes a fundamental component of
            their value, at least under the current regulatory regime.
geo_reg_key: 119228,517191
repnme: repnme: Decrypting New Age International Capital Flows
display_title: Decrypting New Age International
            Capital Flows
listing_relative_url: /research/2021/10/33467789/decrypting-new-age-international-capital-flows
topicv3: Law Enforcement m1326759 2082,Trade m1326924 698,Digital Currency m1327461 359,Investment Entry m1504647 2544,Cryptocurrencies m1939438 4785
publishtoextweb_dt: 2021-10-05T00:00:00Z
dois: 10.1596/1813-9450-9794
historic_topic: International Economics and Trade,Macroeconomics and Economic Growth,Law and Development
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 58
author_keys: 0: author_key: 1771788
1: author_key: 124384
2: author_key: 883336
model_extractions: Policy Research Working Paper 9794 Decrypting New Age International Capital Flows _Clemens Graf von Luckner Carmen M. Reinhart Kenneth Rogoff_ Development Economics Vice Presidency Office of the Senior Vice President and Chief Economist October 2021
Policy Research Working Paper 9794

# **Abstract**

This paper employs high frequency transactions data on the world’s oldest and most extensive centralized peer-to-peer Bitcoin market, which enables trade in the currencies of more than 135 countries. It presents an algorithm that allows, with high probability, the detection of “crypto vehicle transactions” in which crypto currency is used to move capital across borders or facilitate domestic transactions. In contrast to previous work which has used “on-chain” data, this paper’s approach enables one to investigate parts of the vastly larger pool of “off-chain” transactions. Finding that, as a conservative lower bound, over 7 percent of the 45 million trades on the exchange we explore represent crypto vehicle transactions in which Bitcoin is used to make payments in fiat currency. Roughly 20 percent of these represent international capital flight/flows/remittances. Although this work cannot be used to put a price on cryptocurrencies, it provides the first systematic quantitative evidence that the transactional use of cryptocurrencies constitutes a fundamental component of their value, at least under the current regulatory regime.

This paper is a product of the Office of the Senior Vice President and Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www. worldbank.org/prwp. The authors may be contacted at cgrafvonluckner@worldbank.org and creinhart@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Decrypting New Age International Capital Flows**

Clemens Graf von Luckner<sup>1</sup> , Carmen M. Reinhart<sup>2</sup> and Kenneth Rogoff<sup>3</sup> JEL E42, E51, E58, F21, F32, F38, F24 Keywords: Cryptocurrencies, Bitcoin, International Capital Flows, Transactions, Speculative Bubbles We thank Neil Shephard, Kathryn Holston, Juan Farah Yacoub, Matthew Ferranti and Pierfrancesco Mei for very helpful comments and to the Molly and Dominic Ferrante Fund for research support. The findings, interpretations, and conclusions expressed in this paper are those of the authors. They do not necessarily represent the views of the institutions they are affiliated with.

> 1 Sciences Po & World Bank

> 2 Harvard University, World Bank, CEPR & NBER

> 3 Harvard University & NBER
# **Introduction**

Are cryptocurrencies, particularly as epitomized by Bitcoin, genuinely a potential competitor for fiat money or gold, or are they mainly a speculative bubble akin to 17<sup>th</sup> century Dutch tulips? A critical question is the extent to which there is an underlying transactions demand, now or in the future, to justify their high if wildly fluctuating prices. The results of this paper provide perhaps the strongest quantitative evidence to date demonstrating that, the use of Bitcoin for both domestic and international payments has been significant worldwide.

The vast bulk of media focus has been on cryptocurrencies as an evolving asset class. And although there has been some previous quantitative research providing some insight into the use of Bitcoin in facilitating illegal activities, this work typically concentrates on the analysis of the users, not the type of use, classifying users into legal and illegal agents, rather than distinguishing between investment purchases and transactions use. Furthermore, these analyses have only been applied to on-chain transactions,<sup>1</sup> that is, those that are immutably registered on the blockchain. On-chain transactions, however, constitute only a small share of the universe of all Bitcoin trades, most of which are “off-chain” utilizing some form of exchange, some heavily regulated, some not so much.<sup>2</sup> This paper thus develops a new methodology for examining off-chain transaction data that allows one to probabilistically tag individual trades as “crypto vehicle transactions,” that are clearly distinguished from the use of Bitcoin as an investment vehicle. We do not know whether results such as ours can potentially explain prices for crypto currencies, but they certainly show that the popular media focus on Bitcoin as speculative asset ignores its very active use in payments and capital flows.

> 1  See Chung, 2019; Foley et al, 2019; Framewala et al, 2020; Ron and Shamir, 2013; Yang et al 2019; Zhao and Guan, 2015.

> 2  Though arriving at a precise estimate is difficult, as not all exchanges provide data on trade volume, while other exchanges might have incentives to provide inaccurate data, the off-chain transaction volume involving Bitcoin appears to have been at least 10 times the volume recorded on the Bitcoin blockchain (Sources: CryptoCompare.com API, Blockchain.com API)

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Importantly, the data set we use allows one to track what fiat currency is used for payment (which on-chain transactions do not) and to show that a considerable fraction of crypto vehicle transactions involve cross border flows.

Our laboratory is the world’s largest centralized peer-to-peer exchange market, that includes more than four years of extremely high frequency data for 135 currencies. The data cover all trades made over that window, including currency used, the size of the Bitcoins purchased (down to units of Satoshi – one hundred millionths), and a precise time stamp to the second. By matching identical-size (to eight digits) transactions that take place within a short window, we are able to identify with high probability trades out of one fiat currency into another, as well as transactions that very likely represent domestic payments.<sup>3</sup> We estimate that at least 7.4% of all trades in our data are crypto vehicle transactions; this is only a lower bound, as we only include trades we can identify as likely transactions with very high probability. Of these, around 20% represent international currency movements. These are average numbers; for some currencies (notably the ruble and the renminbi), the share of purely domestic trades is much higher. For others, most notably countries with significant restrictions on international capital flows, the share of international trades is much higher. Our international payments and transfer estimate is especially likely to understate the true flows, as when trades involve countries with relatively inefficient financial systems, the long time lags required to clear fiat currency payments can elude our methodology, which focuses on short time windows. We show, for example, that a much larger share is suggested by the “natural experiment” embodied in the March 7, 2019 widespread power outage in the República Bolivariana de Venezuela that turned out to have massive spillovers on Bitcoin markets in several other Latin American currencies.

The first part of the paper describes our novel core data set, with details provided in the Data Appendix. The second section describes the probabilistic matching methodology we develop. In a significant number of cases, the individual trades we pair are like matching snowflakes that occur nowhere

> 3 The 1 percent fee charged by the exchange we study makes the platform impractical for high-speed arbitrage use.

2
else (or almost nowhere else) in the data set. This is because Bitcoin prices are volatile and the unit of account for most crypto-currency trades is the underlying fiat currency (e.g., where someone sends dollars from the United States to relatives elsewhere). The third section presents the main results, which we interpret as compelling evidence that Bitcoin are being used both for domestic payments and for international transmission of funds (including the large global pool of international remittances). The next section discusses how our methodology can, in principle, be applied (or adapted) to other cryptocurrencies and exchanges, including many classes of stablecoins. This analysis can perhaps guide regulators in identifying the selection of off-chain transactions to focus on when requesting IP addresses for audit. It also highlights how individuals or institutions, who may not have access to any private information, might still be able to draw inferences on the transaction use of cryptocurrencies and cross-border transfers, including capital flight. The final section concludes.

# **I:  Local Bitcoins, and off-chain exchange**

The core data set, described in detail in the Data Appendix (Appendix A.7), makes use of data from LocalBitcoins.com, the world’s largest peer-to-peer (P2P) Bitcoin exchange, which allows Bitcoin to be traded in 135 different fiat currencies. The data encompass more than 45 million trades over the period March 15, 2017 – July 23, 2021.<sup>4</sup> To contextualize, the universe of trades that occurred on-chain over the same period, encompasses 452 million transactions.<sup>5</sup> LocalBitcoins.com is able to operate in such a large number of countries because it only offers deposit services for members’ Bitcoins, but does not involve national fiat currencies, thereby side-stepping national banking regulations. Although other P2P exchanges

> 4 LocalBitcoins has existed since 2012. However, we limit our analysis to the period since March 2017, when the website revamped the exchange’s back-end, guaranteeing consistency in the format of the data.

> 5 One transaction recorded on the Blockchain can include the transfer from one sender to many recipients, thus, when instead counting these as individual transfers, the cumulative number of on-chain transactions from the inception of Bitcoin in 2009 through May 2021 amounts to 862 million (Source: Blockchain.com API, Authors’ Calculations) As noted previously, the universe of off-chain transactions is easily an order of magnitude larger than on-chain transactions.

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have since copied LocalBitcoins’ model, none approach its scope, at least as of this writing. LocalBitcoins, registered in Finland, remains the P2P Bitcoin exchange with the broadest service worldwide.<sup>6</sup> A peer-to-peer transfer is a trade of Bitcoin between two pseudonymous private wallets, typically in direct exchange for goods or fiat currency. Trades within an exchange such as LocalBitcoins are “offchain” in that individuals buy and sell only their claims on Bitcoins that the intermediary houses. Despite its many account holders, LocalBitcoins represents only one node on the blockchain.<sup>7</sup> Nevertheless, with a LocalBitcoins account, it is possible to buy Bitcoin from an account holder in country A using country A’s currency, and sell to a third account holder in country B in exchange for country B’s currency, all within LocalBitcoins and without ever registering on the blockchain.<sup>8</sup> (Intra-country payments and transfers are similarly straightforward.) In principle, since exchanges are required to collect information on account holders (albeit crosscountry standards vary greatly), this means that a determined and well-resourced regulator can track individual activity related to Bitcoin much more easily on an exchange than activity related to a pure “peerto-peer” Bitcoin transfer. However, particularly in the case of an exchange that allows citizens from many countries to trade, such as LocalBitcoins, the international dimension makes this much more difficult. Although access to private information, including IP addresses, would likely be granted by Finnish authorities in egregious cases of crime or terrorism, it is unlikely to be granted on a routine basis. This implies that information on most transactions is generally still beyond the reach of national authorities from other countries (for example to detect evasion of capital controls). Nevertheless, we highlight that the

> 6 That is, LocalBitcoins.com intermediates trades only to extent of that it waits to clear the internal transfer of claims on the exchange to Bitcoin only after payment has been confirmed.  The fact it is only matching parties and not intermediating the fiat money payments is what allows LocalBitcoins to operate outside the financial regulatory framework in most countries, answering only to its home base regulator in Finland.

> 7 Nodes are also referred to as _wallets_ . However, a _keychain_ might be the better analogy, given that one such node can have an infinite number of addresses its users can use to send Bitcoin via the blockchain to their account with LocalBitcoins.

> 8 Because LocalBitcoins, as a whole, represents only one node on the Bitcoin blockchain, the only transfers visible on the public blockchain are thus transfers of Bitcoin to, and withdrawal of Bitcoin from, LocalBitcoins.com.

4
publicly available data alone is sufficient to establish that the market is being used in a significant way as a “crypto vehicle currency” to make fiat currency payments (at home or abroad) or to send capital abroad.

Of course, there is substantial anecdotal evidence on the use of crypto for transactions purposes, and it is well known that Bitcoin is the medium of choice on the Dark Web, not to mention ransomware. But systematic analysis has been lacking to support that claim. Our methodology allows us to systematically establish a lower bound on the use of Bitcoin as a medium of exchange. It is quantitative, rather than anecdotal. In principle, the same algorithm can potentially be applied to data from any P2P exchange, should the researcher or regulator obtain (or demand) similar data to ours. This includes not only P2P exchanges that mainly match buyers and sellers (and hold Bitcoins in escrow until both sides confirm the fiat money payment has cleared), but centralized P2P exchanges as well.<sup>9</sup> Table 1 provides some descriptive statistics of the core data set.

**Table 1: Descriptive Statistics**

|Number of Trades|45 528 193|
|---|---|
|USD Trade Volume|USD 11.0 billion|
|Average Trade Size (USD)|242|
|Average Trade Size (BTC)|0.03734 Bitcoin|
|Largest Trade Size Recorded|USD 2.3 million|
|Number of Fiat Currencies|135|

Source: LocalBitcoins.com API, authors’ calculations

> 9 In a centralized exchange, typically catering only to a single country, the exchange serves as an intermediary, buying and selling on its own account, with price moving to match demand and supply.  Coinbase is the largest such exchange in the United States. Again, it is important to emphasize that although “on-chain” transactions are public and therefore directly observable, the vast majority of off-chain transactions are proprietary information, making the LocalBitcoins data set – which is publicly available –unique at present.

5
For each trade, the data include the time-stamp, trade-size, fiat currency used, and the price paid in fiat currency. (On-chain transactions do not record currency or price.) We note that the average trade sizes are relatively small, in part because agents – who must communicate anyway to make the P2P exchange – have an incentive to shift modalities for large trades after matching. This allows them to engage in a more efficient peer-to-peer exchange, which might involve trading paper currency for crypto in person, thereby avoiding the 1% fee charged by the platform.<sup>10</sup> However, even within the limited trade we can observe, a significant proportion turns out to be transactional, and often cross-border.

# **II:  Algorithm for detecting international crypto vehicle trades**

In this section, we discuss our algorithm for (probabilistic) identification of cases where Bitcoin trades are likely being used for (cross-border) wealth transfers and payments. The mechanics are simple. Suppose an Argentine citizen wants to convert pesos in her Buenos Aires account to dollars in her Miami account, but evade Argentine capital controls. Or alternatively, she might want to buy a painting from a New York gallery to give to her sister who lives in New Jersey. Traditional interbank markets are often expensive and subject to capital controls, and whereas there are ways to avoid capital controls other than using crypto, many “traditional” ones have significant barriers to entry (e.g. trade mis-invoicing).<sup>11</sup> With the rise of Crypto markets all around the globe, there today exists a relatively easily and widely accessible alternative: The Argentine citizen can simply trade pesos for Bitcoin through an exchange or P2P platform (presumably from an Argentine resident), and then turn around and sell the same amount in exchange for dollars, presumably from an American resident. The dollars can then be used to make payments or be deposited in an American bank. The reverse process (from New York to Buenos Aires or elsewhere) encompasses the vast transactions associated with remittances. The process is illustrated in Figure 1. Crypto

> 10 We have already distinguished P2P matching services such as LocalBitcoins from decentralized peer-to-peer trades which go through the blockchain; these involve paying miners a fee to verify the transaction, with the fees endogenously depending on congestion, but there is no centralized authority of any sort.

> 11 Haibo 2008; Aizenman 2008; Coppola et al. 2020; Schneider 2003.

6
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million sides.<sup>12</sup> We verify that a significant share of the 8-digit trades we document appear only twice in the data set, and often within a very short time window. Because our data set contains quantity (in Satoshi), time stamp, price and fiat currency used, we can also see the flow of funds across countries and currencies, as well as use in domestic transactions.<sup>13</sup> Table 2 illustrates with an example from the data.

**Table 2: Extract from the data**

|Timestamp|Trade Size|Price (Local|Fiat Currency|
|---|---|---|---|
|||Currency/Bitcoin)||
|2021-03-15 14:42:22|0.00098037|1.02E+11|Venezuelan Bolivar|
|2021-03-15 14:42:24|0.01157996|60449.26|US Dollar|
|2021-03-15 14:42:27|0.00022173|4509989.50|Indonesian Rial|
|2021-03-15 14:42:27|0.00047619|42000.04|British Pound|
|2021-03-15 14:42:28|0.00093023|6450017.50|Kenyan Shilling|
|2021-03-15 14:42:29|0.00063638|4321317.50|Russian Ruble|
|2021-03-15 14:42:33|0.00039107|1554708.87|Ukrainian Hryvnia|
|...|...|...||
|2021-03-15 15:28:53|0.01157996|1.04E+11|Venezuelan Bolivar|

> 12  More precisely, when considering that a Bitcoin trade-size can indeed be greater than 1 Bitcoin, the “dice” theoretically has 21 quadrillion sides - 21 million Bitcoins that can be mined, times 100 million Satoshi or decimal places.

13 As noted earlier, “on-chain” trades contain only the addresses involved, Bitcoin size and time, but not the fiat currency used or the price paid.

8
# Source: LocalBitcoins.com API

LocalBitcoins.com does charge a commission (averaging 1%) on both buy and sell trades, but this is generally paid directly by the market maker and does not affect the Bitcoin trade quantity or price reported, and therefore does not interfere with our matching algorithm.<sup>14</sup> With this preamble, we now turn to the algorithm we use to identify crypto vehicle trades. Taking into account the large size of the data set, even with trade sizes documented out to eight digits, there is still a possibility of two identical-sized trades randomly appearing close together, especially as some trade sizes appear somewhat more frequently than others. The goal of our identification methodology is to arrive at an algorithm that identifies crypto vehicle transactions (both domestic and international) with a 95% confidence level. We aim to estimate the probability that two matching trades represent a crypto-currency vehicle trade, if they occur within, say, a five-hour window. The choice of window gives rise to the usual Type I and Type II errors trade-off. The shorter the window, the more currency vehicle trades we miss, the longer the window the more likely we are counting a random reoccurrence of an eight-digit match as and “in and out” vehicle trade. Our main results will turn out to be quite robust to the window choice; this is in part because the probabilistic approach directly controls for changes in the time window.

Our algorithm is constructed to generate an unbiased estimate of the share of trades on LocalBitcoins that are Crypto Vehicle Trades, while controlling for potential false discoveries Let _S_ be the set of all _I_ individual trades in our dataset, _i_ , each of which has a trade-size, _xi_ . Distinct trade sizes, denoted by , are assumed be an element of kk xx (1) 1 }, KK XX= {xx , ... , xx

> 14 The 1% transaction fee (2% to both buy and sell) makes LocalBitcoins.com a relatively unattractive vehicle for arbitrageurs, and in any event they would most likely be larger market makers whose trades our algorithm misses anyway because sizes would no longer match because of fees.

9
which is fixed and known, .<sup>15</sup> The number of distinct trade sizes K will be massively large, in our data greater than 7 million. Our null hypothesis (HKK ≤ II 0) corresponds to a model of what one would expect if there were no vehicle trades, and any exact matching transactions were solely random.

**<u>Assumption 1 (Null Model)</u>** _Assume that under the model implied by the null hypothesis, trades of any given size appear as an independent Poisson process. The number of times any unique trade size, occurs_ kk _from time 0 to time t is thus defined as_ P _), where and_ ≥0 _._<sup>_16_</sup> _The Poisson process’ intensity,_ xx kk kk , _is the product of_ , _the probability of any new trade having the size_ (θθpp θθ> 0 pp _, such that_ ∑ KK kk kk kk kk=1 kk<sup>= 1,</sup><sup>_and_</sup> θθpp pp xx pp _the number of arrivals of trades over the time period of interest._ θθ, Note that we will estimate on the basis of our data set, making use of the frequency of each individual kk trade. The probability of a trade size that never occurs in the data set, thus has a probability,  pp , equal to kk zero.<sup>_17_</sup> pp Consider the benchmark case of a 5-hour period following any given trade _i_ , where total trades happen ii and let enote the number of times trade size occurs. Under the null hypothesis, NN follows a single ii ii ii multinomial draw, nn dd xx nn

(2) 1 ~ M M 1 , ) II ii ii KK (nn , ... , nn ) | NN MMNN(NN ; pp ... , pp

This is because conditioning on removes from the conditional probability distribution.<sup>18</sup> ii, NN θθ

> 15 We denote trade sizes by subscript k, as certain trade sizes occur more than once in the data so that . 16 We thus arrive at a K-dimensional vector of independent Poisson Processes for each trade size . 17 In contrast, when applying an alternative Bayesian approach, one might instead attach positive probabilities even II≥KK kk to trades that did not occur. xx

> 18 For every trade i, we are interested in the number of times the corresponding trade size occurs in the subsequent five-hour windows. We denote this . Since is a function of the trade-size, and all trade-sizes that occur within ii the five hours after a trade, is distinct to each trade i, and not solely a function of the trades-size xx . ii ii nn nn nnii xxii 10

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Rejection of the null thus implies the presence of vehicle trades in the data. To assess whether the null hypothesis holds or not, define (3) > 1) | ) ∈[0,1] . ii ii ii θθ = PP (( nn NN Note that without imposing any underlying structure to the data, can only be observed as a categorical ii variable, taking on the values {0,1}. Meanwhile, under the null model, the multinomial structure implies θθ that > 1) | ) would equal ii ii PP (( nn NN (4) ∗ ) ) −1 19 NNii NNii ii ii ii ii ii θθ = 1 −{(1 − pp + NN pp } (1 −pp , MM= 1, ... , II. When _K_ is very large, is relatively modest (typically we looking at window of 2, 5 or 10 hours out of ii ∗ several years of data), and NN is very small (since prices are in Satoshi), then ) as the NNii ii ii ii chance > 2 is close to zero. pp θθ ≅1 −(1 −pp ii nn

To detect vehicle trades, we test for departures from the model under the null hypothesis of purely random pairings.<sup>20</sup> Note that if is not very small (as we shall see later, in Figure 2 below, certain size trades are common), ii then > 1 becomes much more likely under the null. We formalize this in the following way. pp ii nn

> 19 Note that , as is drawn from _X_ with probability 20 As a robustness check, the Appendix introduces a similar approach using independent, distinct time windows, which ii jj ii jj are thus not prone to potential serial correlation.pp = pp xx pp

11
**<u>Definition 2</u>** _Let_ Θ ∈ _[0; 1] be some preset number. The trade i is not a candidate for a statistical vehicle_ θθ _trade of size , if_ ii xx

_Otherwise i is potentially a statistical vehicle trade of size ._ ii xx Recalling that _I_ is the total number of trades in the entire data set, the “vehicle trade share estimand” is thus Note that because every vehicle trade consists of two trade legs and since our algorithm identifies the first leg of any vehicle trade, the share of trades that we identify is multiplied by two.

Under the null hypothesis = 0, since captures the excess clustering of trades at particular trade sizes, that is, in excess of what one would expect underφφ φφ H0.

We set Θ to 0.05 to remove trades which are relatively common across the entire data set, so = 2 θθ ii provides little evidence of a vehicle trade. nn Given that is large, it is important to recognize there are hypotheses, relating our approach to the multiple hypotheses testing literature (Efron (2007), Wakefield, J. (2007)), which has been extensively applied to II II genomic sequencing and chromosome segmentation. However, in this literature, because of 12
technological/economic constraints, one is unlikely to possess the population of the underlying data. By contrast, in this exercise we have access of the full distribution of trades (the full LocalBitcoins trade data set). This allows us to control for “false positives” by estimating the expected share misidentified as vehicle trades by our algorithm through randomized sampling from our overall data set, in a way that by construction, does not contain any vehicle trades.

For each trade _i_ , this discovery algorithm is a single hypothesis test with a size (under the null hypothesis) of ∗. ii θθ To control for false “discoveries”, and to establish our estimate of the share of crypto vehicle trades, , we introduce a measure of the number of trades one would expect to falsely discover as vehicle trades, φφ. ii cc where ∗. Over _i_ , describes the probabilities of a trade seeing a random match within five ii ii ii ii hours under the null model. Summing cc = αα θθ cc over _i_ thus represents a measure of the expected number of ii matches, assuming the null model holds. cc 13
**<u>Theorem 4</u>** _Under an arbitrary data generating process for_ 1 ) _,_ II (nn , ... , nn

# **_Proof of Theorem 4:_**

Where we make use of the fact that for any single _i,_ ). ii ii ii ii EE [ φφ | NN ] = 1 ∗ PP (( nn > 1) | NN

The algorithm’s capacity to identify capital flows (cross-border transactions) thus relies on two assumptions: First, for trades to be matched with a degree of confidence, individual crypto trade sizes must be sufficiently unique. If the majority of the trades had the exact same nominal size, the matching algorithm would be of limited use. This feature does not, however, characterize our data, where there exist 7.4 million different trade sizes (more precisely 7,405,680).<sup>21</sup> More than two-thirds of these occur twice or less. The distribution of trade sizes for the data set is illustrated in Figure 2.

> 21 The value which occurs most often in the dataset is 0.00010000 BTC, with 44,544 trades having that nominal value.

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To highlight how volatile Bitcoin prices are, Figure 3 shows the intraday volatility of Bitcoin markets since 2016, measured by the daily maximum drawdown – that is, what share of one’s investment one could have lost if one happened to buy at the high and sell at the low on any given day – compared to the same measure for two highly liquid FX-pairs. The point made by Figure 3 is in line with recent studies finding Bitcoin-fiat volatility to exceed the volatility of major currency pairs by a factor of ten (Baur and Dimpfl, 2021).<sup>22</sup> Even when assessing longer time windows, the volatility of Bitcoin markets, and thus the risk of holding Bitcoin for extended time periods, remains high in comparison: The annualized standard deviation of the USD/BTC since 2014 has been 93%, compared to 8% and 12% for the exemplary USD/EUR and USD/MXN exchange rate pairs. Our assumption that market participants not interested in exposure to Bitcoin per se, would try to trade in and out of the digital currency as quickly as possible, is thus likely to hold in most cases.<sup>23</sup>

> 22 Of course, especially in the post-pandemic context, there exist some developing economy currencies which are equally spectacularly volatile.

> 23 It is certainly possible that some percentage of agents using Bitcoin mainly as transfer vehicle do not mind – or possibly even prefer -- some exposure to price volatility.  To the extent we are too conservative in picking a relatively short time window, this constitutes another reason why our estimates are a lower bound on crypto vehicle trades.

16
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robustness checks. Further, by means of a Monte Carlo simulation, applying this methodology to a randomly shuffled data set, we will present evidence on how applying different time windows affects the likelihood of false positives produced by the algorithm.

# **III:  Results**

Table 3 gives results for our matching algorithm using a five-hour window to identify crypto vehicle trades. Of the 45 million trades, 10.5% or 4.78 million trades have an exact match (or are matched) in terms of Satoshi size. However, running our algorithm very conservatively excludes 0.89 million of these trades because the trade size is sufficiently common (for example 0.1 Bitcoin) that the algorithm cannot attach a 95% confidence interval to a matched pair of being a crypto vehicle trade. Further, we deduct the number of trades one would expect to match with a 95% confidence interval even in a data set without any real vehicle trades (i.e. the False Discovery Rate). This brings the percentage of crypto vehicle trades in the data set down to a conservative lower bound of 7.4%.

**Table 3: Crypto Vehicle Trades**

|Total Number of trades|45 528 193|
|---|---|
|Number of trades with one match <br>|4 782 374|
|Number of trades identified as vehicle<br>trades ((P(Match is Random)_<_0.05),di<br>|3 895 362|
|Expected “False Discoveries” in a data set<br>without vehicle trades,ci<br>|522 251|
|<br>Share of total trades identified as crypto<br>vehicle trades,φ|**7.4%**|

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|Share of trade volume identified as vehicle<br>trades,γ(net of False Discoveries)<sup>25</sup>|**3.8%**|
|---|---|
|Cross-border trades<sup>26</sup>/ identified crypto vehicle-<br>trades|17%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume)|20%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume) when excluding RMB<br>and RUB|27%|

Source: LocalBitcoins.com API, Authors’ Calculations Table 3 also shows that of the 7.4% of all trade we identify as crypto vehicle trades, just under one-fifth are cross-currency/cross border; that is, where two different fiat currencies are used in the two transactions of exactly matching Satoshi size. In Appendix A.2, we perform a Monte Carlo simulation where we draw random samples of trades from the data set.<sup>27</sup> Applying the algorithm on these randomly constructed data sets, only about 1% are individually identified as vehicle trades. And exactly 0% remain identified as vehicle trades after deducting the false discovery control discussed in section II.

Table A.3 in the Appendix looks at the robustness of our results to the time window, using twohour, five-hour and ten-hour windows. As can be seen in the table, as the time-window increases, the number of candidate vehicle trades increases. However, because the number of trades encompassed

> 25 See the Appendix for an explanation of how the methodology is adjusted to calculate the share of the trade volume.

> 26 Note that here we consider only trades with two different currencies as cross-border. However, during limited field experiments one of the authors found the US dollar to be used for cross-border crypto vehicle trades in countries whose primary legal tender is not the US dollar.

> 27 For details see Appendix A2.

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increases as increases, ceteris paribus, P increases, so that the number of trades that we identify as ii matched vehicle trades with at least a 95% confidence level decreases (Appendix A.1, Figure A.2) NN In our baseline methodology, to assess the unconditional probability of any trade size occurring, we apply a uniform historical distribution of trade sizes over 2017-2021. One might conjecture that this distribution could be affected by changing dynamics over time (see Appendix A.3) and that the methodology should potentially control for this by embedding a time-series dimension in the assessment of the values of �. To assess whether such a dynamic is likely to significantly affect our point jj QQ estimates, we compare the share of trades that are identified by our core methodology (uniform �pp , ... , pp distribution of trade-sizes) versus vehicle trades for each calendar year. We derive the p-values from a year-specific distribution function. Table 4 presents evidence that the impact of such dynamic is indeed limited, and that the computationally significantly more efficient approach applying a uniform tradesize distribution appears to provide a close enough approximation.

**Table 4: Comparing results using uniform and year-specific trade-size distributions**

||(1)<br>Uniform trade-size<br>distribution|(2)<br>Year-specific trade size<br>distribution|
|---|---|---|
|2017|6.3 %|5.8 %|
|2018|8.1 %|7.6 %|
|2019|7.8 %|7.5 %|
|2020|6.6 %|6.3 %|
|2021|7.4 %|6.9 %|

Source: LocalBitcoins.com API, Authors’ Calculations 20
<!-- Start of picture text --> Domestic Vehicle Trades International Vehicle Trade<br>105 105<br>104 104<br>103 103<br>102 102<br>0 50 100 150 200 250 300 0 50 100 150 200 250 300<br>Minutes between Trade-legs Minutes between Trade-legs<br><!-- End of picture text -->
Table 5 breaks down the 3.9 million crypto vehicle trades we have identified into the most common origin-destination pairs. Notably, the Russian Federation and China stand out as having an exceptionally high share of same currency trades, that is where the ruble or renminbi, respectively, is both the origin and destination currency, accounting for 89.8% and 84% of all trades involving these currencies. In the case of Russia, US financial sanctions may be responsible for the popularity of Bitcoin as a vehicle for domestic transactions, and it may also offer some limited capacity to evade state surveillance. In the case of the US dollar, euro and pound, internal transactions are still quite significant, accounting for around 70% of all transactions.<sup>29</sup> Because these are international currencies, it is a possible that a percentage of these represent transactions involving residents of other countries.

> 29 Note that the instances where the same currency occurs twice are also unlikely to be simply due to tumbling – the practice of mixing bitcoins in a pool so to obscure a transactional trail. Because LocalBitcoins represents a WebWallet, transactions by multiple customers can be arriving or departing from the same node on the blockchain. Hence, simply sending Bitcoins to one’s LocalBitcoins account, and later withdrawing the Bitcoins from the account already suffices to tumble the coins. In other words, the tumbling does not require a transaction involving any fiat currencies.

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# **Table 5: Largest Currency Pairs in Crypto Vehicle Trades**

|Origin Currency|Destination Currency|Trade Volume as share of total<br>identified vehicle trade volume in<br>Origin Currency (in %)|
|---|---|---|
|Russian Ruble|Russian Ruble|89.8|
|Venezuelan Bolivar|Venezuelan Bolivar|51.7|
|Yuan Renminbi|Yuan Renminbi|84.0|
|US Dollar|US Dollar|60.1|
|Euro|Euro|76.5|
|Colombian Peso|Colombian Peso|62.1|
|Pound Sterling|Pound Sterling|76.9|
|US Dollar|Nigerian Naira|24.3|
|Nigerian Naira|Nigerian Naira|26.3|
|Colombian Peso|Venezuelan Bolivar|24.8|

Source: LocalBitcoins.com API, Authors’ Calculations Out of the 10 top crypto vehicle trades listed in Table 5, two are cross border: US dollars to Nigerian naira, and Colombian pesos to Venezuelan bolivars, both likely capturing remittance flows around fairly strict Venezuelan controls (although these can also represent official Venezuelan entities avoiding US sanctions.) Figure 5 below and Table A1 in the Appendix expands the list to the top 10 cross currency crypto vehicle trades, with Peru, Chile and Argentina alongside Colombia, Venezuela and Nigeria.

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**Table 6: Ten most active markets, scaled by population, 2018-2020.**

|**2018**|**2019**|**2020**|
|---|---|---|
|Russian Ruble|Venezuelan Bolivar|Venezuelan Bolivar|
|Venezuelan Bolivar|Russian Ruble|Russian Ruble|
|Belarusian Ruble|Belarusian Ruble|Belarusian Ruble|
|Swedish Krona|Panamanian Balboa|Colombian Peso|
|Panamanian Balboa|Peruvian Sol|Chilean Peso|
|British Pound|Swedish Krona|Peruvian Sol|
|Peruvian Sol|Colombian Peso|Swedish Krona|
|New Zealand Dollar|Chilean Peso|Panamanian Balboa|
|Colombian Peso|British Pound|British Pound|
|Nigerian Naira|Kazakhstani Tenge|Argentine Peso|

Source: LocalBitcoins.com API, Authors’ Calculations The question of the determinants of crypto use in different markets merits further investigation, which will be facilitated by expanding the number of exchanges incorporated, as comparable data becomes available. Yet, the notion that the use of crypto currencies is changing the economics of capital control evasion, and that, in turn, capital control evasion is an important driver for the expansion of crypto markets is broadly in line by the list of countries in Table 6. Though many factors render a more systematic analysis impossible,<sup>30</sup> Figure 6 presents the results of an exemplary case study, further illustrating the link between capital controls and crypto market

> 30 The most important factor being the small sample size of countries introducing capital controls over the period of our sample, Additionally, when demand for crypto currencies rises, as the need to evade capital controls becomes imminent, often, alternative and more cost-efficient exchanges open, which then reduces trade volumes on LocalBitcoins.com. Finally, one of the authors’ field research suggests that the recent rise of stable coins has at least in some cases, such as Lebanon, led to the former taking over the role of Bitcoin as the major crypto vehicle to evade capital..

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currencies with the highest share of cross-country transactions align well with countries that have had significant capital controls throughout the period, for example, Argentina and Nigeria. In Venezuela, although there are capital controls in place, that domestic transactions are as significant as international ones comes as little surprise, given the unfolding hyperinflation and strict bankwithdrawal limits, which make Bitcoin an attractive alternative to the local currency and domestic banking channels for domestic payments and transactions.

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1.0 wo€ 0.8 2 fe) > w ne} © 5 > 0.6 oO = =] c oO 3 o 62<sup>0.4</sup> -fo} L o = vn 0.2

<!-- Start of picture text --> 0.0 £oO fo]@ Ss8 & 8 =&@ fo]8 uis§ oe§ 2 8B- &§& F Bte wo0D § F o€ ‘6€ Fwo<br>o Oo o a aD vo 2 w oO S Oo = = Q = = ° = 5<br>2 a Cc a 5 c a sS rad a = o 5 wu = $ iS a<br>c w2 © c wz c c a c & a a 2 c<br>rd(4 a] &3 ® a fadc S ac =o c© sa) & vn c c a < oO =5<br>o 2 Fd 2 & 2 G BW 2 2 a & £ & ke) 3 « rd<br>>a oDo a.o ()= 3s.hUA oOa ce)iEo ad& SSo£ €GS ao5 3== 4sSs FAif c©2 aa3<br>= a oO s 8 “ 3 n ><br>= Oo>c vnfo}3 ¢<br><!-- End of picture text --> Share of identified Vehicle Trade Volume that is Cross-Border Mim Share of identified Vehicle Trade Volume that is Domestic
**The P2P platform (LocalBitcoins in this application) used only for one of the two legs**

**The amount in Bitcoin of the two legs might differ.**

**The two trades lie farther apart than our time window (five hours in the baseline case) allows** .

**The payment technology in (at least) one country involved is slow.**

**The trade is matched but wrongfully disregarded, because of the probability of it being matched by chance**

**The parties involved in a planned trade cancel the transaction to complete it in cash and avoid the escrow fees**

Imagine a remittance sending agent, working and living in a country with centralized exchanges and sending his remittances to a developing country, where the only platform available for Bitcoin trades is the P2P exchange. It might be cost efficient to purchase the Bitcoin on the lower cost centralized exchange of the sending country, transfer it to the e-wallet of the P2P platform, and then sell it on the P2P platform in the destination country. Our algorithm would not detect this vehicle trade, as only one of two trades is recorded in our P2P data.

The algorithm only identifies a vehicle trade where the amount of Bitcoin for both two legs is identical. Yet, some cases might exist, where an agent uses his wallet both for vehicle trades and speculative purposes, so the two amounts might differ. The same can happen when the best P2P price offer of the second leg is limited to a trade-size Z < X, when the agent to might split the second trade into two.

Our algorithm does not match any two trades when the time difference between them exceeds 300 minutes (for a five-hour window). If the trader has access to a perpetual futures contract, that allows her to wait for better terms of trade on the destination’s country P2P market, this can allow her to accept much longer periods of exposure to Bitcoin price volatility.

Because the clearing time of the two trades is highly dependent on transfer technologies in the countries involved, our methodology might not capture trades from countries where the prevalent money transaction technology has a clearance time that exceeds the chosen window.

The algorithm applies a fairly conservative methodology in identifying trades, skewed towards reducing Type 1 errors (FalseIdentification as vehicle trades) at the cost of larger Type 2 Errors (disregarding a trade that indeed was a vehicle trade).

Whereas a 1% fee for the transaction amount of 100 USD hardly creates an incentive to face the potential risk and nuisance of a arranging an in person transaction, anecdotal evidence suggests that agents seeking to make larger trades often seek to circumvent the fees by cancelling trades on the LocalBitcoins.com and instead use the messenger function on LocalBitcoins.com to organize an 29
exchange of the crypto currency for cash and in person. Offers by sellers - who as market makers pay the 1% fees, proposing to share the amount saved by avoiding the fees are indeed common on the platform.

**The sending party is a market maker in one of the two trade-legs**

Being the market maker in a trade, in our application, means fees will be deducted from the nominal Bitcoin amount, and a division of the total nominal and multiple smaller trades is more likely.

# **Section IV: Event Study: República Bolivariana de Venezuela**

To underscore that our crypto cross-country currency vehicle estimates are lower bounds, it is interesting to consider the spillover effects to global LocalBitcoins trade of a massive unanticipated three-day power outage that took place in the Venezuela, starting March 7<sup>th</sup> , 2019.

If a significant share of the trades are indeed paired to implement cross-border transfers, an exogenous shock that constrains the ability to trade Bitcoins in one currency should impact Bitcoin trade in other currencies that are a major destination for/origin for cross-border-flows from the affected country.

A major power-cut in the Venezuela that began on March 7, 2019, provides an interesting natural experiment.<sup>31</sup> The power-cut, caused by an incident at the country’s major hydro-electric power plant at Guri Dam, left more than 30 million Venezuelans without electricity for more than 72 hours. Many Bitcoin trades were obviously halted. A question worth asking is how this affected Venezuela’s main trading partners’ (as identified by our algorithm) Bitcoin trade volume.

> 31 See The Guardian, “Venezuela: huge power outage leaves much of country in the dark”, from March 8th, 2019 or BBC News

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<!-- Start of picture text --> 160<br>140 y—\N<br>120 A<br>80<br>60 Currency<br>= / | —— Number of Trades in VES<br>y —— Number of Trades in COP<br>40 Number of Trades in CLP<br>Number of Trades in PEN<br>20 Number of Trades in MXN<br>2019-03-04 2019-03-06 2019-03-08 2019-03-10 2019-03-12 2019-03-14 2019-03-16<br><!-- End of picture text -->
for Mexico, Peru, Chile and Colombia. (For Venezuela, the drop also reflects a fall in internal payments.) It is instructive to compare Figure 7 and Figure 8. Figure 7 shows at the share of cross-currency trade pairs (within the five hour window) out of all trades identified as transactions related, and shares for Mexico, Peru, Chile and Colombia are also quite high, ranging from 40% to 70%, But in Figure 8 the drop in these same countries represents a fall as a share of _all_ P2P trades, which is an order of magnitude larger.

Consider the case of Peru, where the algorithm produces a lower bound of 7.5% for the share of all trades involving the Peruvian sol identified as crypto vehicle trades.<sup>33</sup> Of the 7.5% percent, 38% represent trades where the other fiat currency involved is the Venezuelan bolivar.<sup>34</sup> If this lower-bound estimate captured most of the action, then we would expect the collapse of the Venezuelan market due to the electricity shutoff to lead to an approximately 3% reduction in LocalBitcoins trades in Peruvian sol during the course of the blackout (38% of 7.5%). Instead, as Figure 8 shows, the actual drop in trade volume was 65%.

The same can be shown for other currencies our algorithm identifies as equally important senders or receivers of crypto vehicle trades from or to Venezuela. In addition to the Peruvian sol, Mexican peso and the Chilean peso, these also include Argentine peso and the Panamanian balboa. We note, however, that the gap between our algorithm’s lower bound estimate and the true measure of crypto cross-country vehicle trades is likely to be particularly large for transactions involving Venezuela’s currency, where lags in fiat money payments are likely to take much longer than for most other countries, given the country’s economic dysfunction during this period. Thus, our maximum 5-hour window (and even the 24-hour window results included in the appendix) is likely to miss the majority of crypto-vehicle transactions involving Venezuela.

> 33 In Mexico the equivalent amounts to 6.6%, in Colombia 6.5% and in Chile 7.2%.

> 34 In Mexico the share of identified trade volume that moves to or originates fromVenezuela amounts to 42%. In Colombia and Chile the same amounts to 30% and 35% respectively.

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# **V: Extensions and applications**

We have used comprehensive off-chain transactions data from what has been the world’s largest peer-to-peer crypto exchange platform over the past five years. The analysis provides evidence that strongly suggests that Bitcoin is used actively as a vehicle currency in international transactions; in most countries it is also used extensively as vehicle for domestic currency transactions. Thus, it runs counter the oft-expressed view of crypto currencies as a purely speculative asset class. As we have emphasized, the nature of the data and, in particular, our ability to trace transactions price and currency is completely different than for research analyzing on-chain transactions.

Nevertheless, as computationally intensive as our exercise may be, the LocalBitcoins.com data set represents only a small share of the universe of off-chain crypto transactions when one includes centralized exchanges (where the exchange acts as financial intermediary).

In principle, our methodology can be applied for any more targeted investigation in any particular country/region, as well as to data from any exchange that identifies trades in terms of the fiat currency used to purchase crypto, as long as a number of minimum conditions are fulfilled (see Appendix A.6 for the precise description). Of course, when applying our methodology to data from other exchanges, one must account of their individual structure and features, including the average speed of clearing fiat money payments, and how to factor in pro-rata transactions fees. Also, in some centralized exchanges, the fee structure makes it more feasible to engage in very high-frequency speculative trading, although these almost invariably involve buying and selling in the same currency and one can thus exclude the impact of such trades on aggregates by exclusively analyzing international crypto vehicle currency trades.<sup>35</sup> Although data from many exchanges is private, regulators can typically access data from centralized exchanges in their own local jurisdiction, or potentially beyond that given sufficient

> 35 Similarly Wash trades, a fairly common and unregulated phenomenon in crypto markets (Cong et al, 2020; Le Pennec et al., 2021), can also be controlled, by assessing international capital flows exclusively.

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international cooperation. Regulators might, for example, use this algorithm to identify suspicious cases for which they can make targeted requests for IP addresses from exchanges. Note that our methodology can also be applied to assess the probability of a single pair of trades constituting a Crypto Vehicle Transaction for any time window (which could be one minute or one week) and for any probability threshold (which could be, say 80% instead of 95%). Our approach also allows researchers to show how cryptocurrencies are used for off-chain capital flows and transactions, without requiring knowledge of private data.

In principle, the methodology can also be applied to identify/estimate international capital transfers where an agent buys Bitcoin on an exchange then transfers the funds out of the exchange’s wallet to their own personal wallet, that is a combination of off-chain and on-chain transaction. The on-chain data is already public, so one needs only access to the data from the exchange where the off-chain transaction was executed.

Moreover, rather than estimating aggregates of (cross-border) capital flow volumes, the methodology can also be applied to assess the individual probability of two trades constituting a capital transfer. While arguably less important for macroeconomic research, such an application could be very valuable for regulators and law enforcement. Appendix A.6 describes such a procedure in detail.

Looking forward, Bitcoin remains by far the largest cryptocurrency, but the same general approach can be applied to other high volatility cryptocurrencies. It may even be possible to adapt the approach to certain classes of stablecoins, whose characteristics render them particularly well-suited for use as vehicles in capital transfers. For example, the leading stablecoin Tether (USDT) is measured to 8 decimal places, and although its price is far less volatile than Bitcoin, it still has experienced significant periodic price fluctuations. True, the lower volatility reduces the incentive to trade in and out quickly, but presumably most vehicle trades would still take place fairly quickly, given the risks. The _p_ values might be higher for 34
the matching algorithm, but it would not necessarily render the methodology inapplicable.<sup>36</sup> In any event, our analysis suggests that this approach could prove useful for both regulatory authorities and researchers.

# **VI: Conclusions**

The results of this paper challenge the dominant view that Bitcoin is little used for transactions purposes (other than buying other cryptocurrencies), and that its value is almost entirely based on speculation. Exploiting extraordinarily rich and detailed public data on trades in 135 fiat currencies, we have presented what we believe to be the most systematic and detailed evidence to date on how crypto is used as a vehicle for international capital transfers, as well as for domestic transactions. Our main identification technique takes advantage of the fact that trades are reported to eight decimal places, and the data set gives a timestamp, price, and currency for each trade. To bolster our analysis, we have also looked an interesting natural experiment of a three-day power outage in the Venezuela, where crypto has been a popular vehicle for private citizens to evade draconian capital controls, and perhaps also for official entities to avoid US sanctions. We find that the power cut had a massive footprint not only in the Venezuelan Bitcoin market, but also in the Bitcoin markets of its major destination and remittance-sending countries. Indeed, although we do not know whether the Venezuelan experience can be extrapolated to other markets, it suggests that our estimate of share of trades that are crypto vehicle trades is very much a lower bound. As such, our work complements and considerably extends previous studies that indirectly attempt to identify transactions use based on network analysis of on-chain transactions, which do not include payment currency and are only a small share of the Bitcoin trade universe.

Even though crypto exchanges are subject to regulation in the country where they are based, offchain transactions, particularly smaller ones, are difficult to systematically audit, especially where the regulator in one country needs information on a centralized exchange in another, Finland in the case of

> 36 A similar comment applies to those exchanges that report holdings only to six digits instead of eight digits.

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LocalBitcoins. We do not comment here on the future of Bitcoin regulation, but one can certainly infer from these results that any country aiming to institute or maintain capital controls will also need to find a way to prevent these from being circumvented via crypto (in addition to the plethora of “traditional” methods), and that regulation will be much more effective if there is widespread international cooperation.

If there are controls on crypto, these need to be included in any “new age” measure of controls on international capital movements. For example, the International Monetary Fund has recently argued for explicitly incorporating controls into fully defining a country’s exchange rate regime (see also Ilzetzki, Reinhart and Rogoff (2019), Erten et al (2021) and Basu et al (2020)). If so, then differences in restrictions on transfers via cryptocurrencies increasingly need to be accounted for.

To do so, it will be important to extend the kind of analysis illustrated here to a wider range of crypto markets. Doing so will also give researchers, investors and policy makers a much better handle on “new age” international capital flows, both for purposes of determining the crisis vulnerability of individual countries (particularly EMDEs), as well as developing a deeper understanding of how international capital flows are impacting broader cross-country macroeconomic transmission.

Much more work will be needed to establish a metric for whether current crypto prices are too high or too low, given current regulation and expectations of future regulation. However, in parallel to Garber’s classic (1989) analysis of the tulip mania, the transaction usage documented here should give one pause from drawing any strong conclusions yet on whether underlying payments (illicit or not) or international capital transfer use (in violation of capital controls or not) can eventually underpin prices.<sup>37</sup> 37 Although our analysis suggests that crypto prices are not a pure speculative bubble, analyzing just one exchange does not allow us to infer anything about the level of Bitcoin prices, which at a bare minimum would require applying our algorithm to a much broader set of crypto markets. Obviously, as active as the developing transactional use of Bitcoin may be, the potential value can be quite high if regulators permit extensive transactions use, or cannot stop it. Suppose, for example, we use a back of the envelope estimate of the world underground economy to be 20% of global GDP, or roughly 18 trillion dollars (see Medina and Schneider (2018), Rogoff (2016) for further discussion). Then if, say Bitcoin, established itself as a major player in transactions alongside paper currency, and its transactions value were equal to just 0.5%, that would amount to 90 billion dollars per year.

36
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