If you’ve been in the crypto space for long enough, you’re more than familiar with the term “Bitcoin Maxi”. This term describes someone who isn’t pro-crypto, they are pro-Bitcoin, and they will often make sure to define just how import that distinction is to them. Bitcoin, to a maxi, is the one true crypto currency.

     Why does such a subset of people exist? Well, as a monetary economist, I would argue Bitcoin maxis are mostly correct. Bitcoin getting the “digital gold” tag isn’t too far off. In short, Bitcoin’s supply is fixed, the mechanics of “mining” that supply closely model gold’s stock-to-flow mechanics, and no one owns it.

     Not to mention, it’s also accurate that Bitcoin has been a serious outperformer versus the rest of the crypto market. You can see this via the Bitcoin Dominance chart (BTC.D), where Bitcoin weathers the storm of bear markets and leads the charge of bull markets.

     Despite tens of thousands of new coins, memes, and stablecoins, Bitcoin sits pretty at 59% of the total market cap of the entire crypto asset class. One asset has more capital allocated into it than every other competitor in the market, and it’s not even close. If you had sold every altcoin you had for Bitcoin as the 2022 bear market began to set in, you would’ve outperformed your altcoin portfolio by 55% on average. That’s crazy outperformance by investing in a lower-risk asset. No brainer.

     With all of this said, I think we’re about to begin the phase of the market cycle where BTC.D goes down. Quickly. The crypto community calls this “Alt Season”. This is where life changing money is made, and often lost. The high of this 6-ish month market environment gets chased for years after the fact, while BTC.D climbs up higher. The altcoin trades people took losses on make new Bitcoin maxis. The spirits of aspiring traders get broken, down 90% on fartcoin. They come to realize sticking with Bitcoin was the better bet all along, and take those lessons with them for the next bull market.

     Here’s where I think Bitcoin maxis are wrong, and why I am not one, even though Bitcoin makes up the vast majority of my crypto portfolio.

     Cryptocurrency is a bad term to describe the assets. Stocks have prices, and are traded, but these properties don’t make them currency per se. I think that’s why Bitcoin maxis get so disgruntled with the rest of the crypto market. They’ve figured out Bitcoin has unique, money-like properties that no other blockchain tech perfectly matches, so they stick their nose up at any other cryptocurrency. Yet, for most of these other assets, acting as currency really isn’t the aim. They’re better described as blockchain based software companies. These companies, for certain, take monetary economics into account when designing and issuing their assets, but again, that doesn’t mean they’re currency.

     Yet, these blockchain based companies are doing incredible things, of very high importance and value. This is where the maxis get it totally wrong. It might have been difficult to fully demonstrate where this is all headed in terms of utility 6 years ago, but it is abundantly clear today. This list could encapsulate 50 more Mirror entries, but I want to highlight a few of my favorite concepts.

     Base, Coinbase’s flagship L2 built on Ethereum, has potential within it to scale into an institutional giant. A reality exists where credit card processing runs on base, taking a 3-5% fee to less than a penny per transaction. With Coinbase playing ball with institutions and regulators, Base is a leading candidate for the path to tokenize real world assets. Hello, 24/7 stock market. Of course, Base is also capable of creating environments for DeFi, lending, and more. If successful, Base will create billions of dollars in value that doesn’t exist today. It’s also worth noting that Base isn’t a tradeable asset, which I believe was an incredibly wise choice, and further proves my point that cryptocurrency is a horrible name to describe this novel technology.

     Stablecoins are also an incredibly interesting innovation, one that has now seen tens of trillions of dollars in trading volume. Every day, people, institutions, and AI agents are transacting stablecoins over fiat alternatives. This isn’t a cute coincidence, the trading volume has eclipsed that of GDPs of developed nations.

     May you also turn your attention to Polymarket, which predicted the election through wagers better than legacy polling companies. Many users don’t even realize that Polymarket is built on blockchain technology. Polymarket is allowing the market to serve as guidance for what is likely to occur, what might be real. In a world where data and narratives manipulate reality and create misinformation, I expect tools like Polymarket to help people determine what is real and what isn’t.

     These are a few recent and interesting examples, but the well is deep. You don’t have to search very far to find the real world economic value blockchain technology is creating. Of course, there are scams, memes, NFTs, and all the like. It can be disheartening at times to see the sheer volume of junk and quick-dollar ploys in this market. However, the technology creating actual value will endure, while everything else will fall to the wayside. Despite critiques to the contrary, markets are genuinely good at clearing junk out over time.

     Bitcoin paved the way to a new era in banking, finance, and payment systems. It started with Bitcoin, but then came everything else. “Everything else” isn’t a currency the same way that Bitcoin is, but everything else is capable of financial innovation that far exceeds what Bitcoin could do alone.

     This was once a problem I didn’t spend much time thinking about, and now I think it’s THE problem we need to be solving for in order to make the world a better place: How do we enable people to participate in an economy that they are currently excluded from?

     I first heard of Bitcoin in 2015. There was a Twitter account with a profile picture of a Guy Faux mask that was talking about how it was the future of money. Bitcoin was about $300 at the time, and I was a sophomore in high school that had 0 idea how to acquire such an asset anyways. I’m not sure where the guy who ran that account ended up, but he was a left-anarchist that identified with the anonymous/occupy wall street crowds. This was right before politics became super loaded, so while I didn’t agree with all of his posts, I appreciated the sentiments of freedom, justice, and equality. The Guy Faux mask twitter profile was a fitting introduction to Bitcoin, I can think of 10 V for Vendetta quotes that fit the narrative behind this entire asset class. I hope he’s doing well, and I hope he kept his Bitcoin.

     The next time people started talking about Bitcoin often was Q4 of 2017. At that point, I had very little conversations of substance about how Bitcoin actually worked. I was interested in monetary economics, but I was completely ignorant, and conflated Bitcoin with any other magic internet coin out there. At the time, Bitcoin, Dogecoin, or any others were all the same to me: different names for the same underlying technology. I did try to buy some near the top, and in hindsight I’m thankful that Comerica Bank blocked me from doing so (Rare W from my tradfi bank). It wasn’t 3 months later that it was down over 70%, and I felt like I dodged a bullet investing into a hype-driven asset bubble.

     In October of 2019, I attended a conference hosted by the Cato Institute in Washington, DC. I met a very bright individual by the name of Martin, who proceeded to tell me how he was bullish on Bitcoin. I was a lot more savvy to economics and finance at this point, yet I scoffed at him. I recall confidently telling him something along the lines of “It’s never coming back”. I thought he was just another smart person caught up in an asset bubble. I’m incredibly glad he defended his stance. We talked about Bitcoin’s proof of work system, CBDCs, fiat currency, and all the technical mechanics that I was woefully ignorant on. It was my first proper Bitcoin education, albeit many have tried before (looking at you, Jota).

     Like an idiot, and not uncommon, the first crypto I ever bought was Dogecoin in late 2019. It was summer of 2020 that I finally made my first significant Bitcoin purchase. By that point, we were deep in lockdowns and I was sending applications to attend George Mason University for an MA in Economics the following year. I hosted livestreams talking about economics, I was super concerned about inflation, and I finally boarded the Bitcoin hype train, like many of us did around this time. Interest in Bitcoin turned into obsession. I taught myself technical analysis, I ate up every piece of content you could find, and sought to educate myself as much as possible on Bitcoin and the rest of the cryptocurrency asset class. I made a ton of mistakes and was surely suffering from the Dunning-Kruger effect, but I was trying hard to make up for lost time as well. I really owe a lot of credit to my mentor, Jota (alias). In the midst of my poor decision making, he gracefully and patiently taught me how to trade. They weren’t really lessons, more like “here’s what I do, you’re smart enough, try to keep up”. Frankly, I’d argue a trial by fire in that vein is the only tried and true method into getting into trading. You don’t have to be a trader to purchase Bitcoin, of course, but man am I happy it’s a skill that I’ve added to my toolkit.

     In 2021, I started my first semester at George Mason. I was temporarily embarrassed about this, but now I am not, I was THE resident crypto bro at George Mason. Any former classmates reading this will probably chuckle, because they’re well aware that I was SO annoying. I really couldn’t shut up about it. I was so bullish on Bitcoin, that I put laser eyes on my Twitter profile, and announced to the world I would not be removing them until Bitcoin hit $100,000 in price.

     I thought we would hit $100,000 that year. Then came the bear market, then came Terra/Luna, Voyager, and FTX. Through all of that, I was just as passionate about the space as I was previously. I wrote multiple papers that I am still quite proud of on the asset class. I studied under the great Lawrence White, who was the first person to ever mention Bitcoin in congress. The entire way, I sat around and chucked some spare cash into Bitcoin each month. As much as I was late to DCA’ing Bitcoin from when I first invested, 2022 was a great time to pick up the habit. The bear market got so bad that people on Twitter would tease me for posting my TA charts. My super supportive fiancé once asked me if I thought Bitcoin was ever going to go up again. There were probably moments that she thought I was setting our money on fire along the way, which was totally valid, I would’ve said the same thing to someone not 3 years earlier. Throughout all of it, I kept my laser eyes on. I outlasted Tom Brady, Anthony Pompliano, and all the other influencers that spent the 2021 bullmarket touting their support for the crypto asset class. They all took their laser eyes off. I’m not sure if they were embarrassed or lacked conviction, or maybe it was just a bad look for how much capital they got wrapped up in the wrong stuff. While they were moving on to other topics to chase, I was releasing Bitcoin themed music in the depths of the bear market. Conviction strong, I released “Laser Eyes ‘Til $100K” in April of 2022.

     Now, we’re finally here, Bitcoin is at $100,000. This post was meant to summarize my crypto journey for myself, while also noting just how much education, conviction, and time on task it took for it to “pay off”. It seems like a silly concept to start, and then the more you look into it the more you realize what significant potential it has to create positive change in the world. I’m now the first to tell my friends, this industry isn’t for the faint of heart, and frankly I’m not sure that I openly recommend it for most. I’d rather people understood it as a concept rather than had an interest in putting their money towards it.

     Despite the price volatility, though, I very strongly believe that Bitcoin is an innovation that points to a better monetary future. My interest in it has continued to grow as I see potential for economic empowerment at a scale we don’t even fully realize yet. People across the globe will no longer be bound by the economy they are born into, the government that sets the rules they live under, or the foundation their local currencies are built on. Most people don’t spend a lot of time thinking about how their money works, and when questioned on it they don’t often have articulate answers. This is exactly why Bitcoin has taken over 16 years for people to start to take seriously. The problems Bitcoin solves for aren’t widely understood problems, but they are still problems people feel every day. They feel it with inflated currency, they feel it with interest rates, they feel it when there are barriers to transacting with people. They feel it when on the phone with their bank, dealing with exchange rates, or trying to do business with someone far away. They feel spied on, over taxed, and over regulated. To assume this is the best we can do with our money and banking isn’t thinking big enough. Money is an eternally evolving technology, and we might be on the verge of a major breakthrough.

     The anon on Twitter that I followed in 2015 wasn’t worried about the price, he was interested in the technology that had the potential to free people from a dated and centralized monetary system. If that use case spikes your interest, maybe do some research. If the research is compelling, consider forgoing pizza delivery once per month to buy some Bitcoin.

“It might make sense just to get some in case in catches on”.

Jon Vokal

George Mason University

Stablecoins, a popular blockchain-based technology within the cryptocurrency industry, are tokens designed to be pegged to the value of some other asset, most commonly the United States Dollar. There are a variety of ways to design a stablecoin to achieve this peg. Some of these designs have failed, causing the token to “depeg” from the underlying asset. The most notable of these failures is Terra (UST), a USD pegged “algorithmic” stablecoin by Terraform Labs. Terra was pegged to USD not by reserves, but by a second token, called LUNA, meant to absorb volatility and stabilize the value of the coin. Terra depegged on May 9th, 2022, less than 2 years after its launch. It essentially hit zero on May 13th, and was shut down on May 27th. This paper will compare the designs of Terra to other popular stablecoins, such as US Dollar Coin (USDC), Tether USD (USDT) and MakerDAO/DAI (DAI) to assess why Terra-Luna failed while others have not.

USDC & USDT

USDC is a stablecoin distributed by the payments company Circle. The design and structure of USDC can be compared to a bank or warehouse that runs on the Ethereum Network. Circle backs every USDC in circulation 1:1 with a mixture of federal reserve notes and US treasuries (Circle, n.d.). Circle, unlike other stablecoins, is regulated as a money transmitter and undergoes regular audits that are filed with the SEC. Circle also publishes monthly attestation reports. As of their 11/10/2022 report, Circle held $11.1B in cash and $32.6B in treasures ($43.8B in total) to match the $43.6B USDC in circulation.

This model closely resembles that of a bank. The bank takes client assets into their reserves, and makes loans in order to generate interest on those reserves. A simplified banking balance sheet can be denoted as:

R + L = D + N + K

Where R = Reserves, L = Loans for assets, where liabilities are D = Deposits, N = Notes in Circulation, K = Equity Capital.

If a bank’s liabilities are greater than its assets, it cannot repay its depositors and is therefore insolvent. Circle’s transactions execute on the blockchains it functions on, rather than on a private or centralized system like a bank, or other transaction services such as Paypal. As long as this remains the case, and the blockchains USDC executes on remain functional, then it is unlikely USDC would fail. One risk to Circle, however, is that they are not a member of the banking system, and therefore they do not rely on the federal reserve to act as a lender of last resort.

Tether, or USDT, boasts a similar design as USDC. However, there is greater alarm pertaining to Tether’s solvency than USDC, even though Tether makes similar promises. Tether claims to back its assets 1:1, users can redeem their USDT for fiat currency at any time using tether.io, and Tether undergoes regular audits to show that their assets match their liabilities. The reason why USDT is under greater scrutiny is because Tether is not as transparent about the state of their reserves. Unlike USDC, Tether states all USDT is backed by cash or cash-like equivalents, but has not previously elaborated on what exactly those “cash-like equivalents” are. Assuming their auditors are not participating in or ignoring fraudulent behavior, Tether has taken steps to increase their transparency. As of December 2021, Tether has posted quarterly audits that break down the “cash-like equivalents” in their reserves. Tether’s September 2022 shows that they hold $39B in US treasury bills, $7B in money market funds, $6B in cash, $3B in reverse repo agreements, $3B in corporate bonds, $2.6B in other investments, and $6B in secured loans. The assets held total $65.7B. The total number of USDT in circulation is roughly $65.5B (Tether Limited, 2022). While there are still some questions about the truthfulness of their reporting versus Circle, Tether’s reporting as of 2021 does make it appear that the company is solvent. However, Tether does not file these audits with the SEC like Circle does, and is not regulated as a money transmitter. Fraudulent behavior has occurred with several cryptocurrency firms and there is always risk to holding assets with these firms, even if they claim they are solvent and undergo audits.

Using the same general principles to assess bank solvency, both Circle and Tether appear to be in sound financial standing. Additionally, both companies have developed technology that are interoperable with dozens of popular blockchain networks. Because these stablecoins are not “algorithmic” pegs to the United States Dollar, but rather issued on a basis of reserves, the underlying blockchain technology that executes the transactions is simple. Both USDC and USDT would not be expected to fail if they maintain their current standards and continue to back their issued tokens 1:1 with liquid assets. One reason that other firms may want to adopt different models for their stablecoins is that Circle and Tether are both centralized. Many proponents of cryptocurrency maintain that one of the most critical value propositions of the technology is that the systems are decentralized. LUNA and DAI use different designs for their stablecoins that both claim to be decentralized. This claim of decentralization can be easy to make, but proves difficult to properly implement.

Terra/LUNA

Terraform Labs launched its algorithmic stablecoin, Terra (UST), in September of 2020. Terra achieves its peg to the United States Dollar through miners absorbing demand volatility for UST. This is accomplished by miners staking Terra’s sister coin, LUNA, which in turn is used to mint UST.  From there, price fluctuations in UST can be resolved by the miners arbitraging the delta from the target price. For example, an arbitarguer can extract a profit when 1 TerraSDR = .9 SDR by trading TerraSDR for 1 SDR’s worth of LUNA, instead of the .9 SDR they would receive for it on the open market. When 1 TerraSDR = 1.1 SDR, they can trade 1 SDR worth of LUNA to the system to receive 1.1 SDR of UST (Terraform Labs et al., n.d.). This implies that demand volatility for UST is removed from the price by the minting and burning of LUNA supply in the system. This concept is similar to that of Maker/DAI, but there are issues with this design that actually make UST/LUNA function quite differently. First is the need for stable mining/staking demand and increasing UST demand, second is the introduction of leverage with LUNA as the only collateral.

1. Need for stable mining demand

Stable mining demand is required for the protocol to function properly, which Terraform Labs originally achieves through two methods, transaction fees and seigniorage paid by burning LUNA. The transaction fees vary from .1-1% based on transaction volume. Seigniorage is earned through LUNA burn. When demand for UST increases, the system mints new UST and receives LUNA in return. The system then burns LUNA, making mining power from LUNA staking more scarce, increasing the rewards paid by staking and transaction fees. Thus, the profit function earned for miners is as follows:

Where t is the work period.

In theory, the total rewards paid should increase as the economy on the network grows, with more transaction fees going out to miners and LUNA supply being burned. The inverse for both is true when the economy shrinks. This concept begins to illuminate a primary concern for this design: rising UST demand is the only way for profit from mining rewards to increase. Terraform Labs attempted to compensate for this by introducing “stability levers” that are adjusted to control for transaction fees and mining rewards to the amount of activity on the network. The equation for this is as follows:

Where f is transaction fees, b is burned supply, (1+g) is a growth constant, and R is mining rewards paid.

This implies that fees and supply burn increase inverse to mining rewards. If network activity decreases, fees and supply burn would increase. Terraform Labs claims this is a stabilizing mechanic that will allow for mining rewards to remain constant, even amidst heavy demand volatility on the network. They provide the following charts, which they claimed were subject to “extensive simulations and stress testing”, to demonstrate the concept:

Terraform Labs mention in their whitepaper that the target growth rate for staking rewards is 15%. This highly attractive rewards model was meant to keep mining demand stable. What this design overlooks is that even with low transaction volume, this model would still require continuous new liquidity flowing into the ecosystem to keep miner payouts stable. The second chart even implies a LUNA supply burn rate of over 100% to stabilize mining rewards during a long bear market, which isn’t sustainable.

1. Introduction of leverage with LUNA as the only collateral

Terraform labs making LUNA the only collateral to its ecosystem was most likely the reason this design collapsed on itself. The only reason the LUNA token had value is that it was required for mining/UST stability and because of speculators purchasing the token. This became especially risky when Terraform Labs launched the Anchor Protocol, a platform built on Terra that allows investors to earn high yield on staked deposits and borrowing against their crypto holdings. Many products on the platform offered 20%+ APY for yield farming. Just like the mining mechanics, this design can only be functional if there is continuous new liquidity coming into the system to pay out the people that were there earlier. This led to UST’s market cap growth outpacing the market cap of LUNA in early 2021. The LUNA/UST market cap ratio peaked at over 6:1 in November of 2021, but the immense amount of yield paid out in UST from the Anchor Protocol led to this ratio being less than 1.5:1 the month before the collapse (Alden, 2022). Terraform labs would either need to keep injecting liquidity to pay these yields, or let the yields fall, reducing demand for UST.

Cyrus Younessi, head of risk at MakerDAO, pointed out many of the risks of Terra/LUNA’s design in 2018. “Terra is like the Maker model, if the only collateral you could post to create DAI is the MKR token itself… If Terra were to fall and break the peg, then it would depend on LUNA to save Terra. But, Luna would fall as investors would panic, and then Terra would continue to fall’ (Younessi et al, 2022). The “death spiral” that Younessi describes is exactly what occurred in May 2022 that led to the ecosystem's demise. UST depegging caused LUNA demand to decrease, and the panic set the collapse of the system in motion.

Unlike Maker/DAI, the entire ecosystem's collateral was built entirely on itself. There were no other tokens other than UST/LUNA that were holding up the system, and the promises made to investors and miners alike were unsustainable. If such an unwise loan with poor collateralization were to be made on Maker/DAI, they would be liquidated before any systemic risk is posed toward the system. Additionally, there are no 20%+ yield farms paid out in DAI. The primary profit opportunity is arbitrage, not high-yield staking. Terraform Labs identified this risk the month before the collapse, and launched the “Luna Foundation Guard” to hold over 80,000 Bitcoin (at the time over $3B) as a second layer to defend the peg. They bought no USD, USDC, or USDT. This entire position was liquidated trying to defend the peg. The Terra/LUNA ecosystem was not decentralized, it did not back its UST assets 1:1, and it was highly leveraged with insufficient collateral before its failure.

Maker/DAI

MakerDAO and DAI, like USDC, are ERC-20 tokens that are native to the Ethereum blockchain. The original design for DAI stabilizes as a “soft-peg” to the United States Dollar through a mechanism called a collateralized debt position, or a CDP for short. Originally, the only collateral one could put up for a CDP on the system was Ethereum. The ecosystem now allows for multi-collateral CDPs that accept BAT, USDC, and wBTC as additional options. These loans are over-collateralized, meaning that one must lock significantly more ETH in the CDP than there is DAI minted from the CDP. Maker is the governance token for the Maker/DAI system, and is not a part of the CDP system in order to avoid the possibility of a “death spiral” or other conflicts of interest. DAI is not an algorithmic stablecoin, as there are no equations trying to smooth the mining nor minting incentives to control supply on the platform.

DAI is pegged to the United States Dollar because there are profit opportunities from arbitrage as DAI rises above or falls below the value of $1. The cost of borrowing DAI becomes more expensive when the price falls below $1. This decreases the supply, causing the price to return to its target. Inversely, the cost of minting new DAI reduces when the price rises above $1, increasing the supply and causing the price to return to its target. The profit opportunity arises because those who hold a CDP can pay off their loan at a discount when DAI falls below $1, taking the necessary DAI out of circulation in order to cause the price to increase. When the price is above $1, ETH holders can create DAI for $1 but sell it for greater than $1, increasing the supply (Walters, 2022).

If the value of the ETH locked into a CDP were to drop, the user would have the option to pay off their debt at a premium to exit the CDP, or be liquidated of their position plus a 13% penalty. “Keepers” on the system run algorithms in order to spot risky CDPs to liquidate. MakerDAO claims that this system can withstand price volatility of almost any kind, suggesting that the system will remain functional as long as Ethereum is worth more than 0 and that the price of Ethereum doesn’t drop 50% within 5 minutes (Walters, 2022). As volatility picks up in the market, the collateral ratios for a CDP begin to increase. This is a clever defense mechanism of the system, whereas Terra/LUNA was more concerned about defending miner profits through bearish market conditions.

Maker/DAI is also the most decentralized stablecoin system of the four covered. First, there is no high-yield staking involved that requires MakerDAO to go searching for outside liquidity to try to collateralize the system. CDP holders are paid .5% APY on their loan, which transaction and liquidation fees cover with ease. Additionally, anyone that holds MKR tokens can participate in the governance system, making it a true decentralized autonomous organization. Even the process of liquidating bad CDPs on the platform is decentralized. By combining a decentralized system with proper incentives, Maker/DAI seems to have achieved what the other 3 designs have not: a properly collateralized, decentralized stablecoin.

Conclusion

Like any bank, exchange, or business, the primary defining factor for the success of DAI, USDC, and USDT is solvency. Technological design is a factor for success, but solvency is vital. The designs of these three tokens make sure that assets in the system match liabilities, where Terra/LUNA did not do proper diligence to ensure this. The promises made to miners, LUNA stakers, and yield farmers were far too unrealistic for the algorithmic design to be viable. It would not be outrageous to suggest that the design of Terra/LUNA and the actions of Terraform Labs were a ponzi scheme. While Circle and Tether are not decentralized systems, both firms demonstrate that they have taken the necessary steps to ensure that the currency in circulation matches that of the assets they hold in reserves. Maker/DAI ensures that the system is solvent through over-collateralization of the CDP loans, while also going great lengths to achieve a decentralized system. Assuming the design of USDC, USDT, and DAI remain the same, one could expect that these systems are unlikely to fail in the catastrophic manner that Terra/LUNA did.

Works Cited

Alden, L. (2022, May 15). Digital Alchemy: A Post-Mortem of the Crypto Crash. Lyn Alden. Retrieved November 25, 2022, from https://www.lynalden.com/digital-alchemy/

Circle. (n.d.). USD Coin (USDC) | Crypto that’s held to a higher standard. Circle. Retrieved November 22, 2022, from https://www.circle.com/en/usdc

Sandor, K., & Genç, E. (2022, May 20). The Fall of Terra: A Timeline of the Meteoric Rise and Crash of UST and LUNA. CoinDesk. Retrieved November 25, 2022, from https://www.coindesk.com/learn/the-fall-of-terra-a-timeline-of-the-meteoric-rise-and-crash-of-ust-and-luna/

Terraform Labs, Kereiakes, E., Kwon, D., Maggio, M. D., & Platias, N. (n.d.). Terra Money: Stability and Adoption. Terra Money: Stability and Adoption. Retrieved November 25, 2022, from https://assets.website-files.com/611153e7af981472d8da199c/618b02d13e938ae1f8ad1e45_Terra_White_paper.pdf

The Maker Team. (2017, December). The​​Dai​​Stablecoin​​System. MakerDAO. Retrieved November 26, 2022, from https://makerdao.com/whitepaper/DaiDec17WP.pdf

Tether Limited. (2022, November 18). Transparency. Tether. Retrieved November 23, 2022, from https://tether.to/en/transparency/

Tether Limited Inc. (2014). Tether White Paper. Tether: Fiat currencies on the Bitcoin blockchain. Retrieved November 23, 2022, from https://assets.ctfassets.net/vyse88cgwfbl/5UWgHMvz071t2Cq5yTw5vi/c9798ea8db99311bf90ebe0810938b01/TetherWhitePaper.pdf

Walters, S. (2022, July 7). What is The DAI? Complete Beginners Guide to Stablecoins. The Coin Bureau. Retrieved November 25, 2022, from https://www.coinbureau.com/education/what-is-dai-coin/

One of the main propositions for cryptocurrency adoption is that it is an alternative to bad monetary policy and helps facilitate economic growth when a country’s institutions fail to do so. Bitcoin bull, Anthony Pompliano, states “We’re going to get to a situation where you need a decentralized, non-censored, non-seizable, non-inflatable, or non-debaseable asset. And when you go looking for it, you'll find Bitcoin. I believe Bitcoin’s best ally is just time. And as more people get put in situations where it could be valuable to them, they will opt in” (Gladstein). This facilitates the question: do dire economic situations actually accelerate cryptocurrency adoption? This paper aims to assess if there is any evidence that cryptocurrency is truly becoming an alternative store of value or medium of exchange in countries that have faced high inflation, poor economic conditions, or catastrophic events such as war.

The results that this study yielded were very interesting. The vast majority of the regressions ran appeared to validate the hypothesis. Not only are there significant findings in peer-to-peer transaction volume and different forms of economic stress, but there was a divergence in these findings when comparing peer-to-peer transactions and overall cryptocurrency adoption.

Methodology

This study has compiled data from 149 regions. The economic datasets used for this study originate from the International Monetary Fund. This data is mostly complete, but some patchwork needed to be done on a few regions due to instability and civil war, namely Afghanistan, Syria, and Lebanon, which all had missing data points pertaining to metrics such as inflation and GDP. Data that did not come directly from the International Monetary Fund is listed in the dataset, which is attached in the bibliography. Statistics from the International Monetary Fund includes GDP growth rate, nominal GDP denoted in USD, and inflation. Datasets range from 2009-2021, as 2009 was the first year of Bitcoin’s existence. Additionally, averages for the listed figures from 2009-2021 have been calculated.

Also listed are the rank and score from each country’s 2022 economic freedom index, created by the Heritage Foundation. This index scores countries based on an average of grades given for property rights, government integrity, judicial effectiveness, tax burden, government spending, fiscal health, along with business, labor, monetary, trade, investment, and financial freedom. Cryptocurrency adoption data comes from Chainalysis’ 2021 Geography of Cryptocurrency study. Metrics include each country's cryptocurrency adoption score, cryptocurrency adoption rank, and peer-to-peer transaction volume rank. Studying potential correlations with these datasets aid in identifying if cryptocurrency adoption does accelerate under poor economic conditions. There are two major hypotheses that this study will assess.

1. If cryptocurrency is considered an alternative in the face of high inflation, poor economic output in terms of GDP, or other forms of economic duress, then peer-to-peer transaction volume should be higher in such countries.

2. Peer-to-peer transaction volume should be a better indicator than overall cryptocurrency adoption, because many people under quality economic conditions use cryptocurrency as a risk-on investment asset that has a number of other use cases. Hobbyist crypto users are also interested in non-fungible tokens, enjoy the humorous factor of buying certain currencies, or are interested in any number of other use cases of blockchain technology outside of being a monetary vehicle. On the other hand, peer-to-peer transaction volume suggests that cryptocurrency is being used as a medium of exchange, implying there is some level of transactional economic activity being performed, rather than being used for the other reasons stated. Due to this, the hypothesis would suggest that there may be a divergence between overall adoption data and peer-to-peer crypto adoption data as it pertains to undesirable economic situations.

Additionally, naturalistic studies on countries who have suffered from sudden economic duress such as Libya, Venezuela, and others will be researched on their cryptocurrency adoption versus neighboring regions. Lastly, this study will aim to quantify how war impacts cryptocurrency adoption. There have been multiple claims that cryptocurrency has proved itself useful during the war between Ukraine and Russia, such as Russia using cryptocurrency to avoid sanctions and Ukrainian refugees carrying their savings denoted in Bitcoin on USB drives. Are these claims accurate and, if yes, what is the scale at which these situations are occurring?

Caveats and Other Considerations

Missing Regions

Regions that were not included in the dataset, due to missing cryptocurrency adoption data or general economic data include the following: Andorra, Antigua and Barbuda, Aruba, Bhutan, Burundi, Central African Republic, Chad, Comoros, Republic of Congo, Djibouti, Dominica, Equatorial Guinea, Eritrea, Eswatini, Grenada, Guinea, Guinea-Bissau, Guyana, Kiribati, Kosovo, Lesotho, Liberia, Marshall Islands, Mauritania, Micronesia, Nauru, Niger, Palau, Papua New Guinea, Samoa, San Marino, São Tomé and Príncipe, Sierra Leone, Somalia, Solomon Islands, South Sudan,  St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Taiwan Province of China, Timor-Leste, Tonga, Tuvalu, Vanuatu, West Bank and Gaza, and Yemen.

Controlling For The 3 Inflation Outliers

Three countries had substantially higher inflation than the others. These countries were Argentina, Zimbabwe, and Venezuela. For this reason, additional regressions are included to help provide context and further validate the regressions that include these countries. Some of the regressions include them in the form of a dummy variable, other regressions exclude them altogether. The regressions will be annotated accordingly on how these countries are controlled for.

Controlling for Venezuela

Another consideration for this study is the data around Venezuela. Starting in about 2016, Venezuela went through a true hyperinflation event in which the Venezulian Bolivar completely collapsed. Due to this, the average inflation rate for Venezuela from 2009-2021 is so extreme that it hardly makes contextual sense for the purposes of this paper. The average inflation rate for this time period is stated at over 7000%, for reference, the two next highest average inflation rates for this time period are Zimbabwe at 77% and Argentina at 44.5%. Due to this, I have normalized Venezuela's inflation rate at 111%, which is the estimated inflation rate in 2015 prior to the currency collapse event. There are three reasons why this was an appropriate adjustment to make. First, Venezuela breaks the scaling on any of the scatter plot graphs due to how much of an outlier it is. This makes it very difficult to illustrate some of the trends in the data. Second, the 7000% inflation rate will only help to further validate the hypothesis that peer-to-peer transactions increase in times of economic duress, as Venezuela is one of the top ranking countries for peer-to-peer transactions. The only issue that will be caused by this normalization from a statistical results perspective is that it will reduce some of the general variance in the data. However, regressions that exclude the three outliers and regressions with dummy variables are provided, which will control for the adjustment made on Venezuela's inflation number. Third, inflation metrics stop being relevant in events of complete currency collapse as the currency becomes completely useless. The difference from an actual use perspective between a currency that has inflated 5000, 7000, or 10,000 percent is just documentation. Any currency that is inflating at such levels is almost impossible to use for any relevant economic purposes.

Controlling for Vietnam

Vietnam is an outlier in regressions pertaining to overall cryptocurrency adoption score, as Vietnam has a perfect score in the Chainalysis data, but no other country has a perfect score. For this reason, regressions with the cryptocurrency adoption score will have versions that include Vietnam and versions that do not include Vietnam.

Inflation Dummy Variable Setup

Some regressions have the average inflation data serving as a dummy variable, where 0 equals “not high inflation” and 1 equals “high inflation”. For the purposes of this study, any country with above 8% average inflation from 2009-2021 was denoted as “high inflation”. 8% is the threshold because, historically, the United States Federal Reserve has made very concentrated efforts in raising interest rates when year over year inflation has trended toward this metric. While the United States Federal Reserve is not necessarily the standard in monetary policy, one can consider that the Federal Reserve taking strong actions to combat inflation as it has neared 8% levels in a 12 month period would suggest that the inflation rate is unsustainably high and would cause substantial economic harm if left unchecked. To illustrate this, below is a chart of the United States Inflation Rate overlaid with the Federal Funds Rate. Ceteris Paribus, Federal Reserve tends toward hiking interest rates when inflation consistently remains higher than its 2% target. Treating inflation as a dummy variable will help further control for outliers in the data.

Crime and Illicit Activity

The last consideration for the provided data is illicit activity. When assessing peer-to-peer transaction volume in countries that are less developed, it’s important to consider the amount of crime and illicit activity occurring within the cryptocurrency market. Cryptocurrency use could potentially be higher in countries with higher crime, which also tend to be countries that have less developed economies (Alborg). There is unfortunately no way to control for what amount of peer-to-peer transactions are being used for illicit activity, but there are transactions that are being used for crime. While black markets can still count as economic activity, this is not activity that is desirable for assessing if cryptocurrency is actually used as a monetary alternative for countries in poorly developed economic situations,

Regression Analysis

In total, 18 regressions are listed. There are three primary crypto adoption metrics that are measured against three different economic metrics, totaling for 9 primary regressions. The other regressions listed are derivatives of these metrics as to control potential outliers in the data and provide additional insight. Each of the regressions will receive their own annotations on how they relate to the hypothesis, as well as how they relate to one another.

Overall, the majority of the regressions appear to have confluence with the hypothesis. Peer-to-peer transaction volume does appear higher in countries under different forms of economic stress. While the strength of the outputs vary, the trend is consistent when measuring the peer-to-peer data against inflation, GDP, and economic freedom. Additionally, while the results vary, the trends pertaining to overall crypto adoption appear to be weaker than that of the peer-to-peer data, which is consistent with the inferences of the hypothesis. This suggests that economic stress is potentially a catalyst to encourage someone to adopt cryptocurrency as a medium of exchange, while it does not encourage people to adopt cryptocurrency for the other uses that have more enthusiast qualities.

Inflation Regressions

The first and most extensive set of regressions are measuring the three cryptocurrency adoption parameters against inflation. This set of regressions has the most variations provided to attempt to control for the high inflation outliers in two separate ways. The first is controlling for inflation by using a dummy variable, where any country with over 8% average yearly inflation from 2009-2021 is deemed “high inflation”, while all others are “not high inflation”. In addition to this, separate regressions are provided simply by removing the three outliers of Venezuela, Argentina, and Zimbabwe from the data.

This regression of peer-to-peer transaction data and average inflation rates shows that there is a positive trend in peer-to-peer transaction ranking in countries with higher inflation. The regression is significant (P = .004). While cryptocurrency is not often regarded as an inflation hedge in developed nations, this regression suggests that less developed nations with high inflation do find use of cryptocurrency as a medium of exchange. Controlling for outliers, the above regression includes inflation as a dummy variable, where the metric for “high inflation” was any country that had higher than 8% average yearly inflation from 2009-2021. This regression yields the same positive trend between peer-to-peer transactions and high inflation. It is significant (P = .076) as well.

Controlling for outliers using another method, the above regression has removed Venezuela, Argentina, and Zimbabwe from the regression. These three countries were the largest outliers in terms of their inflation rate, and all three countries rank highly for peer-to-peer transaction volume. Venezuela ranks 6th, Argentina 33rd, and Zimbabwe 9th. Controlling for these countries, the results do still show a significant trend (P = .097).

All three variations of the peer-to-peer ranking measured against average inflation display results that are consistent with the hypothesis.

When measuring overall crypto adoption ranking against inflation, which includes measurements for general on-chain value received along with peer-to-peer adoption, the results are significant (P =.035). However, the P value of this regression is less significant than the P value of the peer-to-peer / average inflation regression of P = .004. For the regressions to be consistent with the hypothesis, it would be ideal that the P values for overall crypto adoption ranking and crypto adoption score are generally less significant than the peer-to-peer P values.

When running the same regression, but with inflation set as a dummy variable where the metric for “high inflation” is any country that had higher than 8% average yearly inflation from 2009-2021. The results for this regression are not significant (P = .304). The P value for this regression for peer-to-peer transaction ranking was P = .076.

Above is the overall crypto adoption ranking and average inflation regression, but controlling for the outliers of Venezuela, Argentina, and Zimbabwe once again by removing them. The results for this regression are not significant (P = .364). Additionally, the P value of this regression is less than the peer-to-peer regression where P = .097. It is worth noting that the R-squared for this regression is very low, adding support to the idea that these two metrics are not strong predictors of one another.

While the other regressions measure how countries rank comparatively to one another, regressions measuring the overall crypto adoption score measure the data based on the actual metric that the rankings come from. While the score for peer-to-peer transaction volume is not listed in the Chainalysis data, it is still helpful to see how the overall crypto adoption score compares to the economic data. This regression measuring the overall cryptocurrency adoption score and average yearly inflation from 2009-2021 is significant (P = .045). Once again, however, the P value for this regression is less significant than the P value for the peer-to-peer regression of P = .004.

Measuring overall crypto adoption score against the high inflation dummy variable does not yield significant results (P = 0.425). This is another instance in which peer-to-peer transaction volume yielded more significant results with a P value of .076 when regressed with the high inflation dummy variable.

The above regression shows the cryptocurrency adoption score regressed with the inflation data, controlling for the outlier inflation countries of Venezuela, Argentina, and Zimbabwe. The results were not significant (P = .253), and were comparatively less significant than the peer-to-peer transaction data which produced a P value of .097.

In conclusion of inflation related regressions, the data presents a comparison of the overall cryptocurrency adoption score with the Vietnam outlier removed to the average inflation rate. These results are significant (P = .002). Removing Vietnam allowed for the positive trend pertaining to inflation to become much more apparent, as Vietnam doesn’t have a very high average inflation rate from 2009-2021, but has the highest cryptocurrency adoption score. Vietnam’s average inflation rate from 2009-2021 is 5.51%.

Economic Freedom Regressions

The next set of regressions pertain to the economic freedom index score of each country. The economic freedom index by the Heritage Foundation measures several indicators that might signal economic stress in ways other than inflation. Some countries may have lower levels of inflation, but still rank very low on metrics such as business freedom or property rights. While the economic freedom index is more arbitrary than hard inflation numbers, the basis for which each country is graded is the same, and therefore can provide helpful comparative context when trying to measure alternative forms of economic stress. Just like the inflation regressions, it is ideal to see a confluence of countries that rank highly in peer-to-peer transaction volume having lower levels of economic freedom in order to be consistent with the hypothesis. Additionally, it is ideal if the peer-to-peer regressions are more statistically significant than the general cryptocurrency adoption regressions.

The regression measuring peer-to-peer transaction volume ranking and economic freedom index score is very significant (P = .001). It is also worth noting that the R-squared measures at .071. The data suggests countries that rank poorly in terms of economic freedom tend to rank higher in their respective peer-to-peer transaction volumes.

Overall cryptocurrency adoption ranking is not significant (P =.304). This measurement diverges from the peer-to-peer transaction volume, which has a P value of .001. This divergence suggests that economic freedom might be a good indication of whether or not one is to use cryptocurrency as a monetary alternative, but it does not suggest whether or not someone is likely to adopt cryptocurrency for its other uses.

The results for the overall cryptocurrency adoption score are significant (P =.08). However, these results are much less significant than the peer-to-peer transaction volume ranking in which the P value is .001. Just like the inflation data, the overall cryptocurrency adoption regressions fail to produce more significant results than peer-to-peer data.

The last analyzed dataset is the overall cryptocurrency adoption score controlling for the outlier of Vietnam. When controlling for Vietnam, the crypto adoption score is still significant (P = .011). It is more significant than the regression not controlling for Vietnam (P =.08)

GDP Regressions

The final set of regressions compares the cryptocurrency adoption metrics with a nation's average GDP from 2009-2021, denoted in United States dollars. While GDP isn’t an inherent indicator of a healthy or stressed economy, a 12-year average of GDP data does provide insight on what countries are comparatively producing less output than others. This is another hard dataset that can be resourceful in providing insight on how a country reacts to cryptocurrency. Ideally, countries with lower GDP are more likely to adopt cryptocurrency for peer-to-peer transactions in order to be consistent with the hypothesis. It is also ideal if peer-to-peer regressions produce more statistically significant results than overall cryptocurrency adoption.

The regression of peer-to-peer transaction volume ranking and GDP is significant (P = 0.00). This suggests that countries with low GDP tend to rank higher in peer-to-peer transactions than countries with high GDP. The R-Squared is .118, which is also a highly supportive metric.

The results are also significant (P = 0.00) in regards to the regression of overall cryptocurrency adoption ranking and GDP. They are equally as significant as the peer-to-peer GDP regression. However, the data presents an upward grouping of the data, suggesting that countries with high GDP are more likely to adopt cryptocurrency for its other purposes than countries with lower GDP. Both of these results being equally as significant with a P value of 0.00, but with differing trends would suggest that there is a divergence between peer-to-peer transaction volume rankings under economic stress and overall crypto adoption under economic stress. While both developed and undeveloped countries are adopting cryptocurrency, these regressions may suggest that it is being adopted for entirely different uses and reasons.

The overall cryptocurrency adoption score regressed with GDP yields a significant result (P = .072). However, this result is less significant than the results of peer-to-peer transaction volume ranking and overall cryptocurrency adoption ranking.

The next regression is the same cryptocurrency adoption score with GDP, but with Vietnam removed. The result is significant (P = .01). Vietnam appeared to have been a strong outlier in this particular case, as the removal of Vietnam greatly increased the significance of the results of this regression. However, the result is still not as significant as peer-to-peer transaction volume ranking nor overall crypto adoption ranking.

Overall Regression Findings

After analyzing the regressions, there is an abundance of evidence that they are supportive of both of the hypotheses. First, peer-to-peer transactions rankings favored countries under the measured forms of economic stress. The regressions suggest that countries with higher inflation tended to rank higher in peer-to-peer transaction volume use. Additionally, countries with lower GDP output and lower economic freedom scores also tended to rank higher in their peer-to-peer transaction volume. The majority of these results also produced adequate P values to suggest statistical significance. There is also a divergence present between the peer-to-peer transaction results and the overall crypto adoption results. While several overall crypto adoption regressions did produce significant results, several others did not. Additionally, the P values of these regressions were consistently lower than their peer-to-peer transaction counterparts in every regression except overall cryptocurrency adoption ranking with GDP, which produced the same P value of 0.00 as peer-to-peer transaction ranking and GDP. However, the results of this particular regression actually showed a trend of countries with higher GDP output favoring higher overall cryptocurrency adoption. The peer-to-peer version of this regression produced an opposite trend, in which countries with lower GDP output favored peer-to-peer transactions. Both of these results being equally as significant has promising implications toward the hypotheses.

What Would Improve The Results?

There are two measures that may further improve these results in the future. The first is using 2022 cryptocurrency adoption rankings. Chainalysis’ Geography of Cryptocurrency study was published in October of 2021. Having more recent numbers to compare with inflation data would be incredibly beneficial, especially considering the higher-than-average amount of economic turmoil that occurred through 2021 into 2022. This includes historic global inflation, a historic rise in global commodities prices such as wheat and oil, and increased potential of recessions in countries across the globe. Ideally, the results of these measurements should increase in their significance with updated economic and cryptocurrency adoption data for 2022 given all of the economic volatility that has occurred.

The second measure that would improve the accuracy of this study involves receiving a peer-to-peer transaction score to run regressions along with the peer-to-peer transaction ranking. Unfortunately, the Chainalysis study only provided how countries comparatively rank with one another for their respective peer-to-peer transaction volumes. Only overall cryptocurrency adoption metrics were accompanied by a score as well as a ranking. Assigning an actual data point or score would help provide additional context to a country's peer-to-peer transaction volume behavior. Additionally, having datasets that span several years backward can also help identify trends in this data. For example, was it not until Venezuela's currency collapse event that it began to rank highly in peer-to-peer transaction volume, or did this trend start years before or after hyperinflation began in 2016? Some insights from analyst Matt Alborg will provide helpful context to this further along in the study, but having access to Chainalysis data on this topic would help provide more context to the regression results as well.

Natural Studies

The countries selected for natural studies include post-2016 Venezuela, post-2021 Turkey, and post-2018 Zimbabwe. All of these selections suffered from dramatic inflation events.. Venezuela and Zimbabwe rank highly for peer-to-peer transaction volume, while Turkey ranks highly for overall adoption. Turkey ranks near the bottom of the list for peer-to-peer transactions, however, because Turkey has made such transactions illegal.

Along with natural studies pertaining to economic turmoil with inflation or low economic output, there is also the prevalent and current case of Ukraine and Russia. In February of 2022, Russia launched a full scale invasion of Ukraine. During the invasion, two narratives emerged pertaining to cryptocurrency. One narrative suggested that Russia was using cryptocurrency as a means to reconcile the impact of sanctions that were placed on them by countries around the globe. Russia was removed from SWIFT, the international system that connects financial institutions worldwide. Russia’s ban from SWIFT made it significantly harder for Russia to move money. The narrative that Russia had transitioned to using cryptocurrency more frequently emerged as a result from this ban. The second narrative involved Ukraine using cryptocurrency during the invasion. Many claims were made that cryptocurrency was used for citizens to easily evacuate with their savings, as well as collect revenue to fund Ukrainian war efforts faster than traditional banking systems could allow for. Are these narratives true, and if yes, to what extent? Looking further into the use of cryptocurrency in volatile geopolitical and economic situations may lead to more clues on the incentive structure of crypto users in dire situations. This includes the inflation events in Venezuela, Turkey, and Zimbabwe, as well as the outbreak of wars in Ukraine, Russia, and the Middle East.

Conclusion

While there is still more to do with data reporting quality and geopolitical developments in relation to peer-to-peer cryptocurrency use, this analysis has suggested evidence that P2P usage does indeed increase in dire situations with regards to inflation, economic freedom, and economic output. The body of regressions analyzed, overall, are promising in suggesting significance in the data for how users transact economically depending on the conditions of their environment.

Works Cited

“2022 Index of Economic Freedom.” Index of Economic Freedom: Promoting Economic Opportunity and Prosperity by Country, https://www.heritage.org/index/.The economic freedom index compiles a number of economic indicators and scores countries based on their performance. The economic freedom score is made by measuring a country's respective policies on factors such as rule of law, government size, regulatory efficiency, and market freedom pertaining to factors such as trade and investment.

Goyeneche, Ainhoa, and Dana Khraiche. “Lebanon's Inflation Rate Is Worse than Zimbabwe's and Venezuela's.” Business and Economy News | Al Jazeera, Al Jazeera, 21 Sept. 2021, https://www.aljazeera.com/economy/2021/9/21/lebanons-inflation-rate-is-worse-than-zimbabwes-and-venezuelas. Used to retrieve recent inflation data in Lebanon

International Monetary Fund. “World Economic Outlook Database.” 2022, https://www.imf.org/en/Publications/WEO/weo-database/2021/October/weo-report. Accessed 15 Apr. 2022.This dataset compiles the necessary inflation data to conduct the study. Along with the economic freedom index, it will serve as the primary point of reference for all non-crypto related economic data for the purpose of the study.

“The Blockchain Data Platform.” Chainalysis: The 2021 Geography Of Cryptocurrency Report, Chainalysis , 30 Mar. 2022, https://www.chainalysis.com/.Chainalysis' 2021 Geography of Cryptocurrency report provides all of the data necessary to analyze and rank countries by both their cryptocurrency and defi adoption rates. This report does not provide adoption and use rates for specific projects, such as Bitcoin or Ethereum, which would be necessary to calculate money velocity. It is worth noting that many of the rankings provided in this report adjust the metrics to internet access, normalizing the data for countries less technologically fortunate than others.

“Afghanistan GDP2022 Data - 2023 Forecast - 1960-2021 Historical - Chart - News.” Afghanistan GDP - 2022 Data - 2023 Forecast - 1960-2021 Historical - Chart - News, https://tradingeconomics.com/afghanistan/gdp#:\~:text=GDP%20in%20Afghanistan%20is%20expected,according%20to%20our%20econometric%20models. Used to retrieve GDP data on Afghanistan.

“Afghanistan Inflation Rate 2022: Consumer Price Index.” Take Profit Afghanistan Inflation, https://take-profit.org/en/statistics/inflation-rate/afghanistan/#:\~:text=What%20is%20Afghanistan%20Core%20Inflation,and%20minimum%20was%20%2D3.69%20%25. Used to retrieve inflation data on Afghanistan.

Ahlborg, Matt. “Nuanced Analysis of LocalBitcoins Data Suggests Bitcoin Is Working as Satoshi Intended.” Medium, Medium, 20 May 2019, https://medium.com/@mattahlborg/nuanced-analysis-of-localbitcoins-data-suggests-bitcoin-is-working-as-satoshi-intended-d8b04d3ac7b2. Used to study geographical trends in cryptocurrency, and add additional context from a previous analyst who has researched this topic in the past.

“Argentina Inflation RateJune 2022 Data - 1944-2021 Historical - July Forecast.” Argentina Inflation Rate - June 2022 Data - 1944-2021 Historical - July Forecast, https://tradingeconomics.com/argentina/inflation-cpi. Used to retrieve recent inflation data for Argentina.

“GDP Growth (Annual %) - Syrian Arab Republic.” Syrian Inflation Data, 2022, https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end=2019&locations=SY&start=2003. Used to retrieve GDP data for Syria.

Gladstein, Alex, and Anthony Pompliano. “Currency of Last Resort.” Bitcoin Magazine - Bitcoin News, Articles and Expert Insights, Bitcoin Magazine - Bitcoin News, Articles and Expert Insights, 4 May 2022, https://bitcoinmagazine.com/culture/currency-of-last-resort.

“Pakistan GDP2022 Data - 2023 Forecast - 1960-2021 Historical - Chart - News.” Pakistan GDP - 2022 Data - 2023 Forecast - 1960-2021 Historical - Chart - News, 2022, https://tradingeconomics.com/pakistan/gdp#:\~:text=GDP%20in%20Pakistan%20is%20expected,according%20to%20our%20econometric%20models. Used to retrieve GDP data for Pakistan.

“Syria Nominal GDP.” Syria | Nominal GDP | 1990 – 2022 | CEIC Data, https://www.ceicdata.com/en/indicator/syria/nominal-gdp. Used to retrieve GDP data for Syria.

Themes-Lab. “ .” UsefulTulips, Matt Alborg, https://www.usefultulips.org/. Several of Matt's metrics were used to cross-reference the data I presented. Additionally, Matt put together some visualizations on the geographical progress of peer-to-peer transactions. Matt and I met via videochat to go over several of his findings while researching similar topics, and gave me permission to cite his works for the purposes of this study.

Annotated Dataset

This is the file used that contains all of the data that was used for the regressions ran for this study. Any data that was altered or came from different sources is annotated.