There’s a new field of economics in the blockchain space known as ‘token economics’ or ‘tokenomics’. This refers to how crypto-tokens or cryptoassets have value and interact within an ecosystem. Many startups are creating their own cryptoassets or tokens and utilizing traditional economic theories (such as the Equation of Exchange) to map out their new blockchain economies and how token value will be managed.
In principle, this is good practice. Cryptoassets do not operate in an ethereal sphere all of their own and still conform to traditional economic and financial modelling rules. However, robustly applying economic rules to new digital ecosystems comes with a few major flaws… Get ready for a dense read.
Let’s start with traditional fiat currencies. A fiat currency is a currency denominated and supported by a country, such as the USD, Euro, Pound, AUD etc. For a fiat currency to be an effective store of value it generally must have a few common traits: scarcity, liquidity, durability and fungibility.
Most cryptoassets exhibit these same traits.
- Scarcity – Cryptoassets have a finite supply. For example, there will only ever be 21 million Bitcoin in existence.
- Liquidity – The ‘popular’ cryptoassets, such as Bitcoin and Etherem have high levels of liquidity making them good sources of value. Many cryptoassets suffer from considerable variances in liquidity creating high variance and instability. If an asset has greater liquidity it becomes more attractive as there is a free-flowing exchange of value.
- Durability – Cryptoassets can be traded multiple times without suffering from wear-and-tear. After all, these assets are just strings of digital characters recorded on a digital ledger.
- Fungibility – Cryptoassets are also fungible. Much like any fiat currency, a single Bitcoin is indistinguishable from any other Bitcoin regarding form and value, thus making them mutually interchangeable.
If we establish cryptoassets conform to the same fundamental principles of traditional fiat currencies, we can look at applying other financial techniques and rules to cryptoassets (I appreciate this may be a stretch but stay with me).
For anyone who’s had the pleasure of studying economics, you may be familiar with the Monetary Theory of Inflation or Equation of Exchange, MV = PQ (or MV=PT) initially put forth by economist Irving Fisher in the early 1900s.
Equation of Exchange
M = the total monetary size of the resource base,
V = the velocity of the resource (or how quickly the resource changes hands),
P = the price of the resource, and
Q = quantity of the resource (in circulation).
Here are a few rough examples of how this equation works in practice.
Example 1: Printing money
First, if we re-arrange the equation for P, we get P = MV / Q.
In large scale economies, we can assume the velocity of the resource and the total amount in circulation remains relatively constant.
When we print money, we increase the quantity of the resource (Q). Increasing Q (quantity) without affecting M (monetary size) or V (velocity) results in a net decrease in P (price). This is a very straightforward example of inflation.
The hyperinflation of the Venezuelan bolívar is a prime example of a failure to abide by economic inflationary principles (although there are a number of additional factors at work).
Venezuela: Inflation rate from 2015 to 2018 (compared to the previous year)
Inflation rate compared to the previous year
Example 2: Monetary rebates
In 2009, amidst fears of a pending recession, the Australian Government issued a $900 bonus one-off payment for Australians earning less than $100,000 a year. In the leadup to any recession, one of the first things people do is stop spending money. The decrease in transactions is very bad for the economy. In our equation, this results in a net reduction in V (velocity) and consequently P (price).
Putting aside the controversy surrounding the initiative, the theory behind it was a one-off payment would incentivize spending, thereby increasing V (velocity) and maintaining P (price) and M (monetary base).
Now the fun stuff. How does this work when dealing with cryptoassets and digital ecosystems?
Let’s start with Bitcoin. In 2016, the Bitcoin blockchain processed an average of $160 million in estimated USD transactions per day, for a total of $58 billion in the year. This starting point forms our PQ calculation.
The average size of the Bitcoin asset base for the 2016 year was $8.9billion (M).
Consequently, our V (velocity) = PQ/M or $58B / $8.9B = 6.5 (approx.).
Meaning in 2016, on average, each Bitcoin changed hands 6.5 times (in theory). For anyone looking for a more detailed explanation of this as a valuation method, please check out Chris Burniske’s article here.
However, Bitcoin is an isolated example and its use case is limited to a payment system. Many other cryptocurrencies and tokens exist with a whole range of use cases such as a privacy token, a data tracking token, a token representing off-chain security interests, etc.… To clarify, a ‘token’ is a cryptoasset that’s hosted on a blockchain platform that already exists (such as the Ethereum blockchain). Cryptocurrencies generally have their own blockchain such as Bitcoin (BTC), Litecoin (LTC) or Ethereum (ETH).
I’m going to deep dive on what’s commonly known as a ‘utility token’. For a token to be classified as a utility token it should have a clear functional use within a network or platform, such as:
- Influencing information & content,
- Providing future work,
- Participating in governance,
- Maintaining a data footprint for credit or reputation, or
- Gaining access to goods and services.
We can think of utility tokens as tokens that operate in ‘micro-economies’. These are separate networks with a clear functional purpose. Remember, these micro-economies operate amidst a more extensive sea of cryptoassets as well as even broader global economies (including fiat, commodity & equity markets).
It’s important to note that unlike traditional business models, the interests of both the business and the user are usually economically aligned. The value of both interests becomes dependent on the value of the native token being used and consequently the value of the ecosystem.
Token Economics in Practice: The ‘CapgeminiCoin’ (CC)
Let’s pretend I’ve created a cryptoasset, the CapgeminiCoin (‘CC’). The CC is a fictional utility token. It’s a token that’s been completely minted, and we currently hold the total supply of 100million CCs.
The CC will be used to power the new Capgemini Events platform. This platform facilitates events in the technology space where various professionals can network and discuss upcoming innovations.
The CC will be used to pay for registration, upgrades, special features and VIP status at events. It can also be used to pay for travel and accommodation. In other words, it provides access to goods and services.
To distribute the CC coin, we might have an initial coin offering (‘ICO’) where we offer the coin to the public in exchange for Bitcoin or Ethereum contributions. This serves a dual purpose:
- We raise capital to pay for the development, marketing and operational costs of the platform; and
- We distribute the CC into the hands of token holders.
Let’s fast-forward a few months. We’ve now developed a working platform, have a large base of token holders and are ready to deploy our product.
The issue is our token value has decreased due to the lack of products being released and waning confidence in our platform.
So, how can we regain value in our new ecosystem?
Moderating our ecosystem
- Staking tokens. Users need to be incentivized to remove tokens from circulating supply by holding on to them or staking them for rewards or participation in the governance of the platform. Reducing Q (quantity) without changing any other figures will increase P (price). We could do this by creating a ‘CC vault’, where users can lock up their tokens in a vault and receive free airdrops of more CC tokens every month that they have tokens in the vault.
- Rewards programs. Users need to maintain and increase V (velocity) of transactions. If users hold onto their tokens, the value of the ecosystem will drop (not dissimilar to the recession example above). We need to incentivize circulation of the CC by providing token rewards and ‘cashback’ for attending events. This helps maintain velocity within the ecosystem.
- Burn functions. To combat the increase in Q (quantity) distributed via rewards or staking programs, it is also necessary to offset the inflation caused by releasing more tokens into circulation. For every token distributed by the rewards pool, we would also need to destroy an equivalent amount from our internal asset pool. This burn function can be automatically executed via smart contracts.
- Net result. Reducing Q and increasing V and M increases P. By raising the price of our CC token, we are boosting the value of our ecosystem. In theory, this results in the co-creation of value, and everyone wins.
The Equation of Exchange is an increasingly popular tool for projects utilizing cryptoassets. The equation is used to justify decisions relating to token governance and project features. The net result of these decisions ‘should’ be an increase in both P (price) and M (monetary base) which keeps all the token holders happy. I’ll stress this again, the interests of both the business and token holders are aligned meaning these activities result in a net co-creation of value.
For a real-world example of these principles in practice, check out this article by Dion-Dalton Bridges and the team at Canya, here.
I don’t believe the MV=PQ model is gospel. There are many concerns surrounding its application to tokenized ecosystems. After all, it was designed as a theoretical framework to balance economies at scale where the ‘MV’ represents the monetary value of an ecosystem which should equal ‘PQ’, the economic value of resources within that ecosystem.
One of my key concerns is circularity. Many projects utilize this model to determine everything from expected token value, market cap and ecosystem valuations.
The issue is we can’t use the MV=PQ model to value a token without referring to a relative asset that already exists. In a vacuum, cryptoassets are just magical internet money with no tangible value. Every crypto-project first needs to assume their ecosystem will capture a certain percentage of market share that has an equivalent fiat (usually $USD) value. i.e. We might project our CC coin to capture 5% of the global technology events industry within 5 years.
This percentage share forms the ‘expected future value’ of our token. This expectation should drive demand and trading of a token. In the same way we trade shares, we hope the business will capture greater market share over time, thereby increasing in value. This ‘expectation’ forms the backbone of our valuation. But, our MV=PQ model now derives its initial assessment from a $USD trading value. This model means our current trading price is denoted in $USD, our target token price is in $USD and the total value of our ecosystem and resource base is also in $USD. We have now created circularity.
Additionally, there is no way to get our token into the hands of token holders without users buying it an equivalent $USD value (even if they are trading Bitcoin or Ethereum). In practice, all the circulating value within cryptoasset ecosystems are generally denoted in a $USD (or other fiat) equivalent value.
Given all this dependence on $USD trading pairs, it begs the question, why even have a cryptoasset at all? Let’s just tokenize the $USD… (yes, this is already being done by a few projects).
My response to all this is quite simple. The feasibility of tokenized ecosystems is not sustainable with this dependency. Eventually, we will need to move to an alternative valuation standard.
I have two theories about this:
- We revert back to the gold standard. Gold is already a globally accepted asset that can be unified as a point of reference for value that is far less susceptible to regulatory intervention like fiat currencies. It is also far less vulnerable to overspending and debt (let’s not talk about the amount of debt leveraged against the $USD).
- Cryptoassets adopt a reference point all of their own. The ecosystem becomes so large and takes up such a large percentage of global GDP that it reaches a form of equilibrium. The real-world implication is this solves volatility and your morning coffee might no longer be advertised at $3.50, but instead cost 0.00001 Bitcoin. If this were to happen, I suspect it would be many years into the future and would require significant fractionalization of fiat economies.
There is an increasing reliance on the rudimentary MV=PQ model to justify token valuations and project features. Whilst there are reservations about this method in practice, cryptoassets do not operate in a new sphere of economics all on their own. They still conform to traditional finance principles and therefore will behave similarly to traditional value storing assets.
Additionally, new age business models will no longer serve shareholders but instead rely on co-creation of value where the business and the customers’ interests are intimately aligned through the circulation of a native token. Token economics will be critical to these models as they provide methods of aligning these interests within new ecosystems.
Finally, the prevalence of the $USD as a benchmark to value cryptoassets limits the principles we can work with. The rise of cryptoassets will require a shift in how we think about money and how we think about value. The winds of change are blowing, and finance as we know it might be set for a real shake-up.
 The value of ‘PQ’ within the MV=PQ equation denotes the total circulating value of an ecosystem. Over a year, we can take an average of all transactions to find an equivalent value.
 Minting (much like traditional industrial production) is the process of creating coins, or digital tokens.
 Many tokens will list their token on secondary exchanges by this point. This allows more token holders to get access to the CC that did not participate in the ICO. The value of a token in this phase can fluctuate wildly based on projected valued, speculation, hype and expected usability. We’ll deal with this another time…