With Darcy WE Allen, Aaron M Lane and Patrick A. McLaughlin. Published in Australian Journal of Public Administration, 18 September 2020
Abstract: The problem of regulatory accumulation has increasingly been recognised as a policy problem in its own right. Governments have then devised and implemented regulatory reform policies that directly seek to ameliorate the burdens of regulatory accumulation (e.g. red tape reduction targets). In this paper we examine regulatory reform approaches in Australia through the lens of policy innovation. Our contributions are twofold. We first examine the evolutionary discovery process of regulatory reform policies in Australia (at the federal, intergovernmental and state levels). This demonstrates a process of policy innovation in regulatory mechanisms and measurements. We then analyse a new measurement of regulatory burden based on text analytics, RegData: Australia . RegData: Australia uses textual analysis to count “restrictiveness clauses” in regulation—such as “must”, “cannot” and “shall”—thereby developing a new database. We place this “restrictiveness clauses” measurement within the context of regulatory policy innovation, and examine the potential for further innovation in regulatory reform mechanisms.
With Alastair Berg. Published in Cosmos + Taxis, Volume 8, Issue 8-9, 2020
Abstract: This paper offers a new framework to understand institutional change in human societies. An ‘institutional fork’ occurs when a society splits into two divergent paths with shared histories. The idea of forking comes from the open-source software community where developers are free to copy of a piece of software, alter it, and release a new version of that software. The parallel between institutional choice and software forking is made clear by the function and politics of forking in blockchain implementations. Blockchains are institutional technologies for the creation of digital economies. When blockchains fork they create two divergent communities with shared transaction ledgers (histories). The paper examines two instances of institutional forks. Australia can be seen as a successful fork of eighteenth-century Britain. The New Australia settlement in Paraguay can be seen as an unsuccessful fork of nineteenth century Australia.
With Darcy W E Allen, Kiersten Jowett, Mikayla Novak, and Jason Potts. Published in in Cosmos + Taxis, Volume 8, Issue 8 + 9, 2020
Abstract: We explore the connection between new decentralised data infrastructure and the spatial organisation of cities. Recent advances in digital technologies for data generation, storage and coordination (e.g. blockchain-based supply chains and proof-of-location services) enables more granulated, decentralised and tradeable data about city life. We propose that this new digital infrastructure for information in cities shifts the organisation and planning of city life downwards and opens new opportunities for entrepreneurial discovery. Compared to centralised governance of smart cities, crypto-cities are more emergent orderings. This paper introduces this research agenda on the boundaries of spatial economics, the economics of cities, information economics, institutional economics and technological change.
This essay follows an RMIT Blockchain Innovation Hub workshop on defi. Contributions by Darcy WE Allen, Chris Berg, Sinclair Davidson, Oleksii Konashevych, Aaron M Lane, Vijay Mohan, Elizabeth Morton, Kelsie Nabben, Marta Poblet, Jason Potts, and Ellie Rennie. Originally a Medium post.
The financial sector exists solely to smooth economic activity and trade. It is the network of organisations, markets, rules, and services that move capital around the global economy so it can be deployed to the most profitable use.
It has evolved as modern capitalism has evolved, spreading with the development of property rights and open markets. It has grown as firms and trade networks became globalised, and supercharged as the global economy became digitised.
Decentralised finance (defi) is trying to do all that. But just since 2019, and entirely on the internet.
Any business faces the question of “how do I get customers to pay for my product?” Similarly consumers ask the question, “Where and how can I pay for the goods and services I want to buy?” For the decentralised digital economy, defi answers this question. Defi provides the “inside” money necessary to facilitate transactions.
But what in traditional, centralised finance looks like banks, stock exchanges, insurance companies, regulations, payments systems, money printers, identity services, contracts, compliance, and dispute resolution systems — in defi it’s all compressed into code.
From a business perspective trade needs to occur in a trusted and safe environment. For the decentralised digital economy, that environment is blockchains and the dapps built on top.
And as we can see, defi doesn’t just finance individual trades or firms — it finances the trading environment, in the same way that taxes finance regulators and inflation finances central banks. If blockchain is economic infrastructure, defi is the funding system that develops, maintains and secures it.
These are heavy, important words for something that looks like a game. The cryptocurrency and blockchain space has always looked a little game-y, not least with its memes and “in-jokes”. The rise of defi has also had its own cartoonified vibe and it has been somewhat surreal to see millions of dollars of value pass through tokens called ‘YAMs’ and ‘SUSHI’.
Games are serious things though. A culture of gaming provides a point around which all participants can coordinate activity and experimentation — what we’re seeing in defi is the creation of a massive multiplayer online innovation system. The “rules” of this game are minimal, there are no umpires, and very little recourse, where the goal is the creation and maintenance of decentralised financial products, and willing players can choose (if and) to what extent they participate.
Because there is real value at stake, the cost of a loss is high. Much defi is tested in production and the losses from scams, unethical behaviour, or poor and inadequately audited coding are frequent.
On the other side, participation in the game of defi is remarkably open. There are few barriers to entry except a small amount of capital that players are willing to place at risk. Once fiat has been converted into cryptocurrency, the limit on participation in decentralised finance isn’t regulatory or institutional — it is around knowledge. (Knowledge is a non-trivial barrier, excluding people who could be described as naive investors. This is important for regulatory purposes.)
This is starkly different from the centralised financial system, where non-professional participants have to typically go through layers of gatekeepers to experiment with financial products.
The basic economics of defi
The purpose of defi is to ensure the supply of an ‘inside money’ — that is, stablecoins — within decentralised digital platforms and to provide tools to manage finance risks.
In the first instance defi is about consumer finance. It answers basic usability questions in the blockchain space: How do users of the platform pay native fees? Which digital money is deployed as a medium of exchange or unit of account on the platform?
In the second instance defi concerns itself with the operation of consensus mechanisms — particularly proof of stake mechanisms and their variants. The problem here is how to capture financial trust in a staking coin and then how to use that trust to generate “trust” on a blockchain. Blockchains need mechanisms to value and reward these tokens. Given the (potential) volatile nature of these tokens, risk management instruments must exist in order to efficiently allocate the underlying risk of the trading platform.
As we see it, the million yam question is whether the use of these risk management tools undermine trust in the platform itself. It is here that governance is important.
Which governance functions should attach to staking tokens and when should those functions be deployed? Should they be automated or should voting mechanisms be used? If so, which voting mechanisms and what level of consensus is appropriate for decision making.
Finally defi addresses the existence of stablecoin and staking tokens from an investor perspective. Again there are some significant questions here that the defi space has barely touched. How do these instruments and assets fit into existing investment strategies? How will the tax function respond? How much of existing portfolio theory and asset pricing applies to these instruments and assets?
Of course, we already have a complex and highly evolved centralised financial system that can provide much of the services that are being built from the ground up in defi. So why bother with defi?
The most obvious reason is that the blockchain space has a philosophical interest in decentralisation as a value in and of itself. But decentralisation addresses real world problems.
First, centralised systems can have human-centric cybersecurity vulnerabilities. The Canadian exchange QuadrigaCX lost everything when the only person with access to the cryptographic keys to the exchange died (lawyers representing account holders have requested that the body be exhumed to prove his death). Decentralised algorithmic systems have their own vulnerabilities (need we mention yams again?) but they are of a different character and unlike human nature they can be improved.
Second, centralised systems are exposed to regulation — for better or worse. For example, one of the arguments for UniSwap is that it is more decentralised than EtherDelta. EtherDelta was vulnerable to both hackers (its order book website was hacked) and regulators (its designer was sued by SEC).
Third, digital business models need digital instruments that can both complement and substitute for existing products. Chain validation instruments and the associated risk management tools presently do NOT have real world equivalent products.
Fourth and finally, the ability to digitise, fractionalise, and monetise currently illiquid real-world assets will require a suite of instruments and digital institutions. Defi is the beginning of that process.
In this sense, the defi movement is building a set of financial products and services that look superficially familiar to the traditional financial system using a vastly different institutional framework — that is, with decentralisation as a priority and without the layers of regulation and legislation that shape centralised traditional finance.
Imagine trying to replicate the functional lifeforms of a carbon-based biochemical system in a silicon based biochemical system. No matter how hard you tried — they’d look very different.
Defi has to build in some institutions that mimic or replicate the economic function provided by central banks, government-provided identity tech, and contract enforcement through police, lawyers and judges. It is the financial sector + the institutions that the traditional finance sector relies on. So, initially, it’s going to look more expensive, relative to “finance”. But the social cost of the traditional finance sector is much larger — a full institutional accounting for finance would have to include those courts and regulations and policymakers and central banks that it relies on.
Thus defi and centralised finance look very different in practice. Consider exchanges. Traditional financial markets can either operate as organised exchanges (such as the New York Stock Exchange) or as over-the-counter (OTC peer-to-peer) markets. The characteristics of those types of market are set out below.
Defi exchanges represent an attempt to combine the characteristics of both organised exchanges and over-the-counter markets. In the very instance, of course, they are decentralised markets governed by private rules and not (necessarily) public regulation. They aim to be peer-to-peer markets (including peer-to-algorithm markets in the case of AMM).
But at the same time they aim to be anonymous (in this context meaning that privacy is maintained), transparent, highly liquid, and with less counterparty risk than a traditional OTC market.
Where is defi going?
Traditional finance has been developing for thousands of years. Along with secure private property rights and the rule of law, it is one of the basic technologies of capitalism. But of those three, traditional finance has the worst reputation. It has come to be associated with city bros and the “Wolf of Wall Street”, and the Global Financial Crisis. Luigi Zingales has influentially argued that the traditional finance system has outgrown the value it adds to society, in part because of the opportunities of political rent seeking.
This makes defi particularly interesting. Defi is for machines. Not people. It represents the automation of financial services.
A century ago agriculture dominated the labour force. The heavy labour needs of farming are one of the reasons we were poor back then. As we added machines to agriculture — as we let machines do the farming — we reduced the need to use valuable human resources. Defi offers the same thing for finance. Automation reduces labour inputs.
Automation of course has been increasingly common in financial systems since at least the 1990s. But it could only go so far. A lot of the reason that finance (and many sectors, including government and management) resisted technological change and capital investment, was at the bottom, there had to be a human layer of trust. Now that we can automate trust through blockchains, we can move automation more deeply into the financial system.
Of course, this is in the future. Right now defi is building airplanes in 1902 and tractors in 1920. They’re hilariously bad and horses are still better. But that’s how innovation works. We’re observing the creation of the base tools for entrepreneurs to create value. Value-adding automated financial products and services comes next.
Economists have long warned that a great deal of regulation that is supposedly in the public interest acts in practice as a transfer of wealth from one private interest to another. Policy makers are usually demure enough to hide this. It is rare that rent-seeking is as explicit and open as the Australian government’s current attempt to expropriate money from Facebook and Google—money that will be directly paid to the traditional news outlets that these tech companies have disrupted.
This is much more than just a regional policy fight; what is happening in Australia tells us a lot about how the changing economics of media are feeding the bipartisan war on the tech industry in the United States and globally. Much of what is dressed up as populist anti-tech policy is in fact an attempt by legacy media companies to weaponize confused regulators against their competitors.
The Australian government wants Facebook and Google to sign onto what they call a “News Media Bargaining Code,” which will require tech companies to pay news organizations when news content is “included” on the digital platforms. The exact price to be paid is meant to be negotiated by media companies and digital platforms. The rationale offered by Australian antitrust regulators is that there is a “bargaining power imbalance” between tech and media companies, hence the need for government action to get negotiations started.
You may be tempted to read that paragraph again. Don’t bother. The government’s argument is euphemistic and its reasoning is obscure. The most obvious problem is that neither Facebook nor Google “include” news content on their platform. The dispute is not about intellectual property theft; all tech companies are doing is linking to news sites. Facebook allows users to share links. Google offers links through its general search function as well as its Google News search service. The upshot is that the Australian government wants Facebook and Google to pay news organizations for the privilege of linking to their content.
Much like the U.S., the Australian media sector is in economic freefall. And like the U.S., both Australian progressives and conservatives have been regarding tech companies with increasing skepticism. We usually think of anti-tech skepticism as ideological—conservatives are at odds with socially liberal Silicon Valley types while progressives look at Silicon Valley as the new robber barons. But in Australia, it is strikingly obvious how the economic collapse of traditional media causes anti-tech sentiment.
Throughout the 20th century, the newspaper business model was simple: Newspapers sought to match readers with advertisers. Advertising paid for journalism, which attracted readers, which then attracted more advertising, and so on. The more readers the better. So newspapers tried to appeal to the median reader.
The history of the media in the last two decades is the slowly unfolding consequences of advertising migrating from the print media to the internet. This creative destruction not only undermined business models that paid for mass-market journalism, but reshaped how public debate is conducted. The growth in media partisanship in recent years could be because media outlets no longer try to appeal to the median reader—they try to engage a passionate few who will stump up a subscription fee. That is, media partisanship has economic causes.
The other consequence is political backlash against the big tech companies.
For generations, the art of politics involved serenading the local press, getting an audience with the regional media mogul, or building a relationship with a sympathetic journalist who could be relied on to get a political message out.
Now those friendly moguls and journalists are on the backfoot, shocked by the extraordinary growth of the digital platforms that seem to have ripped both the economic high ground from under them. And the political class is starting to respond to the new media normal the best way they know how: with threats of regulation.
It is easy to laugh at the odd populist attempts to tame digital platforms, such as Sen. Josh Hawley’s (R–Mo.) 2019 proposal to ban autoplay videos and infinite scrolling. But the Australian experience shows that the greater threat to digital platforms comes from antitrust regulators, dressed up in bizarre claims about “bargaining power imbalances.”
Antitrust policy in the 21st century is particularly vulnerable to this sort of strange thinking. Antitrust was conceived in a world of monolithic corporate hierarchies—factories that built physical things and distributed those things using physical infrastructure like rails and ports. Digital platforms are hard to understand through the traditional antitrust lens.
The more users a digital platform has—the more it dominates a market segment—the more valuable that platform is to those users. We want to use the social media network that everyone else is using. And to get more users, platforms often provide access to one side of the market for free. For regulators used to hunting for predatory pricing, this just looks weird. At the same time these digital markets are highly dynamic; firms tend to dominate them, but not for long. This too leaves regulators in a bind. They’ve spent more than a century warning of the dangers of monopolies, but now they’re struggling to identify the actual harm these digital platform “monopolies” are causing.
Australian regulators are confused as to what to do and have proposed everything from regulating Facebook and Google’s algorithms, to enacting new privacy laws, to giving more money to public broadcasters.
It’s clear that U.S. regulators are confused too. In early September, The New York Times reported that while the Department of Justice plans to imminently bring an antitrust case against Google’s parent company, Alphabet, there is much internal disagreement about what the grounds of the government complaint should be.
As the Australian experience shows, this combination of confused regulators and aggrieved, politically connected industries is a dangerous thing.
What we’ve discovered from working with the Agoric team is the possibilities of driving markets down into machines. Mark Miller’s groundbreaking work with Eric Drexler explored how property rights and market exchange can be used within computational systems. Agoric starts economics where we start economics — with the institutional framework that secures property rights.
This has been one of the most intellectually stimulating collaborations of each of our careers, and has shaped much of how we think about the economics of frontier technologies.
We first met the Agoric team through Bill Tulloh at the Crypto Economics Security Conference at Blockchain @ Berkeley in 2017, just as we were forming the RMIT Blockchain Innovation Hub. CESC was the first serious attempt we were aware of to bring the blockchain industry and social science together — such as our disciplines of economics and political economy.
In the presentation to CESC, we applied some of Oliver Williamson’s thinking to understand the economic properties of tokens and cryptocurrencies.
Bill — who had thought along similar lines — came over to chat during a break. We met again at the 2018 Consensus Conference in New York. Bill introduced us to Mark Miller. What started out as a quick chat to say hello over breakfast turned into a long discussion about Friedrich Hayek, Don Lavoie, and market processes in computer science. Through Bill and Mark we then met Kate Sills and Dean Tribble.
It is true that economic thinking is everywhere in the blockchain and cryptocurrency community. There’s a lot of lay reasoning about Austrian economics, monetary policy, central banks, and inflation. These ideas have brought a lot of people into the cryptocurrency space. Some of the thinking that brought them here is good economics (we’re very passionate about how Austrian economics can inform the blockchain industry ourselves — see here and our colleague Darcy Allen here) but unfortunately a lot of it is not-so-good economics. Many developers have self-taught economics, many have intuited economics from first principles, and we have observed a combination of brilliant insight, economic fallacy, and knowledge gaps.
Developers, however, tend to be very good at game theory; if only because unlike our colleagues in academia, the blockchain community is testing the assumptions of game theory and applying it in the real world for business models with real value at stake. Reality can be bracing. Only invest what you can afford to completely lose. This is still a highly experimental industry.
But economics has much, much more to contribute to our understanding of the blockchain economy than just Hayekian monetary theory and textbook game theory. Our friends at Agoric know this — they already had an economist in their team. They know and understand that it isn’t enough to have good code — to succeed, you need to have economically coherent code.
To that end, we have developed a new field of economics: institutional cryptoeconomics. In this field, we apply the transaction cost economics of Ronald Coase and Oliver Williamson to explore blockchain as an economic institution competing with and complementing the schema of firms, markets, states, clubs and the commons.
The economic foundation of our institutional cryptoeconomics is broad and solid. In addition to economics Nobel laureates like Hayek, Ronald Coase, and Oliver Williamson, we have also incorporated the work of other laureates such as Herbert Simon, Douglass North, Elinor Ostrom, and Jean Tirole into our blockchain research. Then we’’ve drawn on should-have-been-laureates such as Joseph Schumpeter, William Baumol, Armen Alchian, and Harold Demsetz are included. Economists such as Andrei Shleifer and Israel Kirzner could still win a Nobel.
Merton Miller — himself an economics laureate — once argued that there was nothing more practical than good theory. Our experience working with Agoric has convinced us of the value of very good theory. We have had plenty of help — actual practitioners trying to solve immediate real-world problems are hard task masters. Ideas cannot remain half-baked — they must be fully explained and articulated. Working with Agoric has been an intellectually intense, extended interactive academic seminar where ideas are taken from vague hunch to ‘how can this be implemented’ and back again. From whiteboard to business model.
As academics we have learned which ideas, models and tools are of immediate use and value in the blockchain world. There have been some surprises here. Whoever would have thought that edgeworth boxes would have a practical real world application? Or indifference curves? But here we are. When building an entire economic ecosystem — the Agoric economy — we have had to draw upon the full breadth of our economic training. We suspect that having an economics team on board will become an industry standard in the years to come.
We have benefited as educators too. Of course, explaining complex ideas to highly intelligent laypeople is a large part of our day job. The stakes, however, are much higher. The Agoric team aren’t seeking information to pass a class test. They are seeking information to pass a market test — that the market will grade. As another favorite economist of ours Ludwig von Mises explained, consumers are hard task masters.
Our own students particularly have benefited from our Agoric experience. We now have a deeper understanding of industry needs and thought in the blockchain space. We know which ideas interest them and which don’t. The Agoric team questioned us closely on some topics. Our students will know how to answer those questions.
It also turns out that financial engineering is far more important than we thought it would be when we first started working on blockchain economics. The work with Agoric has coincided with the defi boom — a richly anarchic and innovative movement within the blockchain space. As a consequence, the blockchain for business degree programs that we have launched at RMIT have huge dollops of finance in them.
We share with Agoric a vision of the future where technology leads to an improvement in human flourishing and an enhancement of our capacity to lead full lives.
With Darcy WE Allen and Sinclair Davidson. American Institute for Economic Research, 2020
We are on the cusp of a dramatic wave of technological change – from blockchain to automated smart contracts, artificial intelligence and machine learning to advances in cryptography and digitisation, from Internet of Things to advanced communications technologies.
These are the new technologies of freedom. These tools present a historical unprecedented opportunity to recapture individual freedoms in the digital age – to expand individual rights, to protect property, to defend our privacy and personal data, to exercise our freedom of speech, and to develop new voluntary communities.
This book presents a call to arms. The liberty movement has spent too much time begging the state for its liberties back. We can now use new technologies to build the free institutions that are needed for human flourishing without state permission.
Let’s take a birds’ eye view of the Australian economy. What do we produce? In order: iron ore, coal, and credentials.
Tertiary education is Australia’s third-largest export industry. And Australia is the third-largest education exporter in the world, behind the US and UK.
The world’s skilled labour markets are dependent upon proof of identity, experience and skills, including education qualifications, trade certification and occupational licensing. The smooth operation of these markets relies on the technical infrastructure that supports those credentials: a continually updated, reliable, trusted and efficient public registry of qualifications and skills.
We call this intersection of the education sector and access points into global labour markets the credentialing industry.
We’re university academics, but the credentialing industry encompasses much more than universities. In fact, it’s about more than just education. A surprisingly large fraction of the economy supplies and deliver credential services.
High school education and equivalencies (completion certification)
Credentials prove skills and qualities, and trusted claims of skills and capabilities are an input into contracts and jobs. They are an institutional token that carries trusted information that facilitates transactions in almost every labour market, many service markets, and all professional markets in an economy.
As the economy becomes more complex, the workforce will need to be more highly skilled and globally oriented. This means that credentials will be a more important output (from the credentialing industry) and input (into labour markets).
Credentials are a key institution in a modern economy. The more complex and developed the economy, the more it depends on efficient and effective credential infrastructure and production.
These certifications benefit consumers, facilitating trust in professional and trade services, and employers, facilitating trusted information about skills and capabilities. The more complex the economy, the more important and valuable are credentials.
But what exactly is a credential? Credentials are a type of institutional technology that is produced by the education sector, by professional and trade associations, and by government, often jointly.
So from a public policy perspective, it can be hard to tell where the rules that govern the credential come from — are they from government regulation, or the private imposition of standards by a professional association. Richard Wagner calls these sorts of intermingling public/private rules entangled political economy.
From a technology perspective, a credential is a bundle of:
Identity (who does it attach to)
Registry (what is the content of claims made)
Assessment and evaluation (how have they been verified, and by whom)
Storage, maintenance and recall (an effective transactional database)
A credential has institutional and technical properties:
Trust and transparency
Security and auditability
Transactional value in use
A credential is essentially an entry in a ledger. But centralized credential technology has limitations in all of the above dimensions. Blockchain technology presents an opportunity to revolutionise the credential sector by offering a more effective, scalable and secure platform for the production and use of credentials.
For Australia, innovation in blockchain credentials, will benefit a major export industry, increasing administrative efficiency and facilitating adoption of digital technology in tertiary education, as well as improve the functioning of labour markets in Australia and around the world, increasing the quality of job matching and lowering the cost of employment. We expect blockchain adoption in the credentialing industry is expected to drive economic growth, exports, and jobs.
At least in Australia, every government on both sides of politics has promised some form of deregulation as part of their election agenda.
It turns out that deregulation is hard. It’s hard for two reasons:
First: it is hard to identify regulatory reductions that are strongly beneficial but at the same time politically easy. Few regulations do not have a passionate constituency behind them. Few regulations are completely pointless. But in aggregate, regulation adds to a significant economic burden.
Second: policymakers might talk a good game about deregulation, but have little incentive to deregulate. All the incentives go in the other direction — towards more rules, more regulatory interventions.
To solve the identification and incentive problems of reform, governments have increasingly implemented ‘regulatory policies’. That is, policies and mechanisms directed at the regulatory process itself.
For example, sunset clauses and regulatory impact statements force legislators to more closely assess or revisit the burden of a given regulation. But these don’t propel forward deregulation. Other approaches try to use the flow of new regulation to reduce the stock of existing regulation. New regulations can be made subject to ‘regulatory budgets’ or ‘1-in, n-out’ policies. But these are highly sensitive to how we measure regulations and their burdens.
Deregtech can be directed at regulatory analyticsto better measure and grasp the extent of the regulatory state. The RegData project, for instance, counts the number of ‘restrictive clauses’ in regulations. New measurements encourage innovation in regulatory policies and make new mechanisms possible. RegData’s associated QuantGov initiative provides the tools for open-source policy analysis, rather than this being done within government departments.
With strong regulatory analytics — analytics that can be verified by observers from outside the government — tighter controls over a regulatory budget can be introduced, and affected industries can have a better understanding of the state of regulation.
These systems are not just a way to monitor regulation — they are an infrastructure on which deregulatory efforts can be built.
For instance, can we apply machine learning to identify regulatory changes that are unobservable to humans? Can technologies be leveraged to create new measurements to analyse regulatory overlap?
At the same time deregtech can encourage regulatory experimentation to reveal the costs and benefits of rules in practice. New technologies can act as the foundation for regulatory experimentation, encouraging competition between jurisdictions.
Inserting greater competition into governance, such as in special economic zones and charter cities, encourages discovery. On a more granular level, regulatory sandboxes attempt to reveal local knowledge about where regulatory burdens fall and what they are inhibiting.
Deregtech also encompasses more extensive regulatory automation, driving the enforcement and governance of regulations into private platforms. Blockchain platforms, for instance, enable complete enforceability of particular trading rules by incorporating them into the platform.
Deregtech will reduce the regulatory barriers that hamper our transition to a decentralised, automated and digital post-pandemic economy. The technological inputs of deregtech are here with us today. Now we just need to build them.
Everybody, whatever side of politics they are on, generally agrees that the media is one of the reasons that politics is so polarised right now.
Agreeing on why the media has driven this is a little harder. Yes, the newspaper and print industry has been disrupted, thanks to the internet. And yes, it seems like newspapers are more desperate for readers.
But underlying these surface level observations is the fact that newspapers are undergoing a fundamental structural shift between two organisational types — from platforms to factories.
Let’s call what’s happened to the newspaper industry multi-sided market collapse. Understanding the industry this way clarifies how today’s media environment is so different from that of the twentieth century — and offers a warning for other platform industries that face disruption in the future.
(I’m going to focus here on the newspaper industry, because the dynamics are most obvious there. But we can use this framework to understand how media economics effects media content in everything from talk radio to cable television.)
The twentieth century newspaper was a particular type of economic organisation: a platform that serviced a multi-sided market.
Platform economics is interesting because market participants want to use the platform that everybody else is using. We want to buy the video game console that has the most games — and developers want to design for the console that has the most users. We want to use the ridesharing app that has the most drivers — and drivers want to drive for the app with the most riders.
This desire to go where the crowd already is leads to some curious pricing structures. Platforms typically feature complex cross-subsidies. One side of the multi-sided market might be given access to the platform for free, or given heavy discounts, while the other faces high charges.
For the traditional newspaper industry, the market participants are advertisers and readers. Readers want content, and advertisers want eyeballs. Revenue from advertising paid for the production of news content, which attracted readers, which attracted more advertisers, and so on.
The cross-subsidies were straightforward. Advertisers were charged relatively large fees for access (very large in the case of fullpage advertising, and relatively large in the case of classifieds). Readers were charged small fees (through either subscription or individual sales), or even no fees (such as the free newspaper model or free distribution locations like stadiums and railway stations).
The need to get as many readers as possible onto the platform didn’t just shape pricing — it shaped decisions about what content would be published.
Newspapers sought to cater for as wide an audience as possible. On the op-ed page newspapers would strive for a rough balance. They’d match one opinion piece from the ideological right with one opinion piece from the ideological left. Let’s call this liberal balancing theory — all voices get heard.
In the news pages they’d adopt a perspective that wouldn’t excessively upset any particular side of politics. Let’s call this median reader theory. The combination of these two approaches has given us the twentieth century model of journalistic objectivity, view-from-nowhere journalism, the idea of newspaper-as-public-square etc.
The arrival of the internet disrupted the underlying newspaper business model.
Newspapers first sought to continue the existing model in an online world by offering their content for free supported by banner ads or cross subsidised by print sales.
However, much advertising — particularly but not only classified advertising — migrated to dedicated digital platforms. To be more specific, the advertising migrated to digital platforms that didn’t use journalism as a way to attract eyeballs.
Within the space of a decade, the cross-subsidies that sustained the newspaper business model evaporated. But the demand for journalism has not. Newspapers have responded to this reduction in revenue from advertising by increasing the cost to readers. Newspaper websites now charge for access. Newspaper subscription prices went up.
Journalism is now predominantly paid for by fees from the readers that demand that journalism, rather than indirectly through advertising. This shift represents a change from a platform servicing a multi-sided market to a something that looks more like a production process servicing a single sided market. Less an advertising platform, and more a journalism factory.
In other words, what we’ve seen in the newspaper industry is multi-sided market collapse. (I would prefer to call it deplatforming — but that word has already been taken.)
Now let’s think through what this means for newspaper content and journalism.
Higher subscription fees imply a smaller readership. This is less of a problem than it appears — newspapers no longer have the same need to deliver huge readership numbers to advertisers. Instead, newspapers need to convince readers to pay more for what a product they used to get cheaply or even free.
The strategy newspapers have pounced upon is specialisation. Newspapers now seek readers who have more emotionally invested in that particular newspaper brand. They’re the ones more likely to pay the higher subscription fees.
Ideology is a specialisation. Partisanship is a specialisation.
In other words, multi-sided market collapse explains the dominance of ideologically driven media outlets in the digital age.
And if newspapers are no longer trying to appeal to the median reader, why should they continue producing bland ‘view-from-nowhere’ content? The news pages have become more passionate, more opinionated, more self-aware. Newspapers now focus on what their most dedicated readers actually want — not just what the median reader in the population will accept.
Converting a business from a platform to a factory is hard. If, presented with this argument before the internet existed, you tried to make predictions about what would happen to the newspaper industry should its platform model collapse, you’d likely predict:
1. Lots of newspapers fail to make the transition and massive business failure.
2. Lots of new media organisations be established that are structured around the new factory model.
Which is of course exactly what we have seen.
There are lots of implications of the idea of multi-sided market collapse. Here are a few. For instance, it demonstrates clearly that lot of our current debate about platform ‘monopolies’ like Facebook and Google is deeply confused about platform economics.
The multi-sided market collapse model shows that there has been no ‘expropriation’ of advertising from newspapers to digital platforms. Rather, as platform businesses, newspapers have been outcompeted. “Readers” (in this case, social media users and webpage searchers) and advertisers want the platforms they use to be as big as possible. Advertisers were attracted to newspapers because they were big platforms. Now advertising has migrated to different (digital) platforms. Nothing nefarious has occurred.
What does this mean for future technological disruption? If the analysis here is correct, it’s not obvious that new platform technologies like blockchain pose a threat to the new business model of journalism. They’re just not platforms anymore.
If we’re looking for blockchain use cases in journalism we should be thinking of them more along the lines of the factory/production process/supply chain model (focusing on provenance, track and trace) rather than the matching service performed by platforms.
Platforms are one of the dominant organisational structures of the digital economy. They rely on their ability to cross-subsidise one side of a market with another. And society invested heavily in newspapers as platforms — not just investments in terms of capital, but in cultural and political significance.
But when you work for a platform company it is easy to be confused about what your company’s competitive advantage actually is. In truth that advantage was not journalism, but matching. Newspapers were outcompeted by competitors that were better at matching.
The partisanship and fervour we’re seeing in media content right now is just the most visible symptom of an entire industry trying to restructure itself in real-time.