What we think we know about defi

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.

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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.

Australia’s Confused Tech Regulators Are Cracking Down on Google for Using Links

Published in Reason, 10 September 2020

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. 

Unsurprisingly, Facebook has said if it is forced to pay it will simply block Australians from sharing news links. Google might also shut down its Google News service—that’s what happened when a similar policy was introduced in Spain in 2014.

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 learned from working with Agoric

With Sinclair Davidson and Jason Potts. Originally a Medium post.

Since 2017 we (along with our colleague Joe Clark) have been working with Agoric, an innovative and exciting smart contract team, who are about to launch a token economy model we helped design.

At the RMIT Blockchain Innovation Hub we’ve long been thinking about how blockchain can drive markets deeper into firms, resolving the electronic markets hypothesis and giving us new opportunities for outsourcing corporate vertical integration.

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.

In a new book published by the American Institute for Economic Research we’ve argued that blockchain and other frontier technologies offer us the tools to actively take back liberties we may have lost.

With Agoric, it is incredibly exciting to be able to actually build the economy of the future that we’ve been studying.

How to understand the credentialing industry

With Jason Potts. Originally a Medium post.

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)
  • University credentials (course credits, graduate certificates, diplomas, bachelors degrees, higher degrees)
  • Vocational education credentials and trade certifications (hairdressing, electrician, builder, etc.)
  • Industry and professional association based qualifications (e.g. accounting [CPA, ACA], law [the Bar], finance [FINSIA], etc.)
  • Proficiency qualifications (languages, driving, etc.)
  • Occupational licenses (surgeons, pilots, dentists, teaching, etc.)

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.

Deregtech: using technology to deregulate the economy

With Darcy WE Allen and Aaron M Lane. Originally a Medium post.

The Australian Prime Minister Scott Morrison wants to make deregulation and cutting red tape the centrepiece of the COVID-19 economic recovery.

This focus is not just welcome, but essential. Entrepreneurs need room to experiment with new business models without being held back by unnecessary rules — as we argue in our recent book Unfreeze: How to Create a High Growth Economy After the Pandemic.

But at the same time, developed world governments have spent at least three decades trying to cut burdensome red tape — with little obvious to show for it. The regulatory state just keeps expanding.

Existing regulatory policies such as sunset clauses have not worked to stem the steady growth in regulation and regulatory complexity. We need a new deregulation strategy.

The term ‘regtech’ describes how technologies such as blockchain, artificial intelligence and the internet of things can assist with regulatory compliance.

Now is the time for deregtech: using frontier technologies to identify, coordinate and incentivise deregulation.

Deregtech leverages technology not for compliance, but for policy reform.

Adopting the principles of permissionless innovationinstitutional diversity, and private governance will be vital if we are to get a rapid recovery from the COVID-19 crisis. Deregtech is about designing specific mechanisms that governments can deploy to identify and encourage that deregulation.

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.

The economy is a dynamic complex adaptive system, and as it evolves so too must the regulatory state. In the wake of the pandemic, which has accelerated many economic and technological trends, this has never been more necessary.

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.

Multi-sided market collapse in the newspaper industry

Originally a Medium post. Published in the Spectator Australia as on 9 July 2020 as ‘The death – and rebirth – of the newspaper?

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 basics

The twentieth century newspaper was a particular type of economic organisation: a platform that serviced a multi-sided market.

The idea of a multi-sided market platform was first developed in detail by Jean-Charles Rochet and Jean Tirole in 2003. It’s intuitive: we want to make trades with each other, and a platform helps match us together.

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.

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The newspaper as platform

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 collapse

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.)

The adjustment

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 specialisationNewspapers 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.

It helps explain controversies like that which greeted the Tom Cotton opinion piece published in the New York Times in June 2020. Why should ideologically-motivated readers pay higher prices for content intended to appeal to their ideological opponents?

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.

The future

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.

Blockchain innovation and public policy

Introduction to Journal of Entrepeneurship and Public Policy special issue ‘Blockchain innovation and public policy’, with Jason Potts and Sinclair Davidson. Available at Emerald.

Blockchain, or distributed ledger technology, invented by Satoshi Nakamoto (2008), has quickly and somewhat surprisingly emerged as one of the most disruptive new technologies of the early twenty-first century; it is facilitating an entirely new decentralised architecture of economic organization (Narayanan et al., 2016; Davidson et al., 2018; Rauchs et al., 2018; Werbach, 2018; Berg et al., 2019). While still an experimental technology, shrouded in technological, economic, regulatory and legal uncertainty, blockchain is nevertheless moving from being a proof-of-concept innovation to early-stage pilots that will likely significantly disrupt sector after sector in the coming years. This process of what Joseph Schumpeter called “creative destruction” first started with money (with Bitcoin, the world’s first cryptocurrency) and then payments, and is now moving through banking and finance (decentralised finance, or defi), logistics, health, and generally across the digital economy. Like other digital and internet-based technologies, such as virtual reality and machine learning, we are still in the early phases of an economy-wide disruption that is being driven and shaped by new entrepreneurial startups (since 2017 funded through initial coin offerings, although increasingly now through venture capital financing) and also by industry dominant firms who are working to reimagine and rebuild their business models and services on a more decentralised organisational architecture and business infrastructure (Rauchs et al., 2019).

A key challenge for all entrepreneurs, whether in start-ups or in large incumbent firms, is policy uncertainty in relation to this radical new technology. Blockchain technology facilitates an entirely new architecture for money and payments, for establishing ownership and storing value, for making contracts and recording data and facts. This means that legal and regulatory frameworks, tax models and economic policy settings are not designed for this technology and will need to be adapted (De Filippi and Wright, 2018).

This special issue aligns scholarship and analysis towards a better understanding of the nature of entrepreneurship in relation to the development and innovation of this new technology, and the way in which that entrepreneurship interacts with current public policy settings. The papers in this special issue broadly seek to explore particular problem domains where public policy is either failing or succeeding in this context, and also to explore new frameworks for public policy that are conducive to entrepreneurship and innovation.

These papers cover a broad set of questions, ranging from consideration about the shifting role of government and economic policy in a world with widespread blockchain adoption, to seeking to provide a global map of the policy dimensions upon which governments are acting with respect to blockchain technology, to exploring how public policy interacts with entrepreneurial discovery of blockchain use cases and commercial applications. Papers also explore the implications for constitutional experimentations and monetary policy reform.

In the first paper in this special issue, Berg, Davidson and Potts explore the long run policy equilibrium associated with the consequences of wide-spread blockchain adoption, drawing on theories of institutional cryptoeconomics (Berg et al., 2019). They argue that the long run policy implication of the industrial revolution and the era of modern economic growth through the twentieth century was for competition policy and industry policy to counterbalance the power of large hierarchical organizations (or the rise of very large firms as a basic dynamic of industrial capitalism). Berg, Davidson and Potts argue that blockchain technology predicts both market disintermediation and organizational “dehierarchicalisation”, which they then infer unwinds the economic justification for a large range of economic policies implemented through the twentieth century that sought to control the effects of market power and organizational hierarchy. “Capitalism after Satoshi” predicts widespread blockchain technology adoption could reduce the need for counter-veiling economic policy, and therefore shrinking the role of government, and therefore a new public policy equilibrium with reduced demand for economic policy. This shows the long-run relationship between digital technological innovation and the regulatory state.

In “Cryptofriendliness”, Mikayla Novak explores the chief aspects of policy interest in blockchain technology, and maps these to an index-based policy measure that she calls “cryptofriendliness” (see Novak et al. 2018). Novak is particularly interested in using national case studies of blockchain policies to identify “policy entrepreneurship” that seeks to foster and promote the discovery and development of entrepreneurial opportunities in the emerging, but still nascent, blockchain economy. Novak argues that so-called “crypto-friendly” jurisdictions are more likely to attract entrepreneurs and investors in the crypto-economic blockchain space.

Brendan Markey-Towler builds on the idea of blockchain as an “institutional technology”, a concept first developed by Davidson et al. (2018), in order to propose an evolutionary model of institutional competition. Markey-Towler shows how blockchain development is a form of institutional evolution that then interacts with national systems of innovation (which are themselves institutional systems), furnishing a macro-level concept of how blockchain technology interacts not only with economic administrative and organizational infrastructure (e.g. money and payments, supply chains, and specific sectors), but also with higher-order knowledge and innovation institutions. He argues that institutional competition from blockchain technology predicts superior performance from national systems of innovation, which in turn predicts greater opportunity space for entrepreneurs.

In “Governing entrepreneurial discovery” Darcy Allen explores how entrepreneurs discover opportunities in blockchain applications, which is a specific instance of the general problem of entrepreneurial discovery in early stage technologies. Allen focuses on the institutional mechanisms that facilitate the pooling of the broad information set that entrepreneurs require, and how policy choices that affect the institutional environment in turn affect entrepreneurial transaction costs. Elaborating on Novak’s argument that specific policy choices shape the viability of blockchain entrepreneurial development (what she calls crypto-friendly policy), Allen further argues that an important way that crypto-friendly policy is operationalized is through channels that lower the cost of opportunity discovery for entrepreneurs.

In “The market for rules” Nick Cowen builds on the constitutional tradition in economics (as pioneered by James Buchanan as a hybrid of New Institutional Economics and political theory) to observe that the entrepreneurial opportunity space of blockchain is fundamentally in the provision of rules for governance that are in effect hard-coded into blockchain platform infrastructure. Cowen therefore argues that blockchain technology facilitates competition between the entrepreneurial supply of governance rules – encoded in “private order” platform or protocol mechanisms – with the government or legislator supply of “public order” policy rules. Whereas Davidson, Berg and Potts argue in “Capitalism after Satoshi” that blockchain technology will reduce demand for public policy, via the mechanism of disintermediation and dehierarchicalisation, Cowen makes a different argument but with the same broad direction of prediction, namely, that competition from private-order rules (what Cowen calls “the market for rules”) will reduce demand for public-order rules.

In “Cryptoliquidity”, James Caton examines the connection between blockchain technology adoption and broad monetary stability. Caton observes that macroeconomic fluctuations tend to be in significant part a monetary phenomena, and therefore monetary policy stabilisation works through exogenous changes in money supply. He then shows that cryptocurrencies can create endogenous liquidity creation mechanisms through rules-based asset liquidation (assuming real-asset backed cryptocurrencies) as triggered by changes in macroeconomic variables. Entrepreneurial development of novel cryptocurrency instruments such as stablecoins can therefore also be potentially developed at the level of monetary aggregates in order to automate the supply of liquidity. This predicts that blockchain technologies can further facilitate the evolution of market economy institutions.

The six separate and distinct papers in this special issue each deal with different aspects that connect the economic study of entrepreneurship to both the immediate practical implications (e.g. Novak, 2019; Allen, 2019) and broadly philosophical implications (e.g. Berg et al., 2019; Cowen, 2019) of blockchain adoption for public policy. Yet taken together these papers all broadly point in the same direction, in terms of the predicted effect: blockchain technology, which is an institutional technology, offers institutional competition with public policy rules, and this entrepreneurial competition is expected to improve the overall quality of economic rules and governance. Taken together, these six papers predict that blockchain technology will, on the whole, induce a better institutional environment for entrepreneurial action.

References

Allen, D. (2020), “Governing the entrepreneurial discovery of blockchain applications’”, Journal of Entrepreneurship and Public Policy, Vol. 9 No. 2, pp. 194-212.

Berg, C., Davidson, S. and Potts, J. (2020), “Capitalism after Satoshi”, Journal of Entrepreneurship and Public Policy, Vol. 9 No. 2, pp. 152-164.

Berg, C., Davidson, S. and Potts, J. (2019), The Blockchain Economy: Introduction to Institutional Cryptoeconomics, Edward Elgar, Cheltenham.

Cowen, N. (2020), “The market for rules: the promise and peril of blockchain distributed governance”, Journal of Entrepreneurship and Public Policy, Vol. 9 No. 2, pp. 213-226.

Davidson, S., de Filippi, P. and Potts, J. (2018), “Blockchains and the economics institutions of capitalism”, Journal of Institutional Economics.

De Filippi, P. and Wright, A. (2018), Blockchain and the Law: The Rule of Code, Harvard University Press, Cambridge, MA.

Nakamoto, S. (2008), “Bitcoin: a peer-to-peer electronic cash system”, available at: https://bitcoin.org/bitcoin.pdf

Narayanan, A., Bonneau, J., Felten, E., Miller, A. and Goldfeder, S. (2016), Bitcoin and Cryptocurrency Technologies, Princeton University Press, Princeton, NJ.

Novak, M. (2020), “Cryptofriendliness: understanding blockchain public policy”, Journal of Entrepreneurship and Public Policy, Vol. 9 No. 2, pp. 227-252.

Novak, M., Davidson, S. and Potts, J. (2018), “The cost of trust: a pilot study”, Journal of British Blockchain Association, doi: 10.31585/jbba-1-2-(5)2018.

Rauchs, M., Glidden, A., Gordon, B., Pieters, G., Recanatini, M., Rostand, F., Vagneur, K. and Zhang, B. (2018), Distributed Ledger Technology Systems, Cambridge institute for Alternative Finance, University of Cambridge.

Rauchs, M., Blandin, A., Bear, K. and McKeon, S. (2019), “2nd Global Enterprise blockchain benchmarking study”, Cambridge institute for Alternative Finance, University of Cambridge.

Werbach, K. (2018), The Blockchain and the New Architecture of Trust, MIT Press, Cambridge, MA.

Further reading

Catalini, C. and Gans, J. (2017), “Some simple economics of the blockchain”, MIT Sloan Research Paper No. 5191-16, available at: https://ssrn.com/abstract=2874598

Caton, J. (2019), “Cryptoliquidity: how innovation and blockchain and public policy can promote monetary stability”, Journal of Entrepreneurship and Public Policy.

Markey-Towler, B. (2020), “Blockchains and institutional competition in innovation systems”, Journal of Entrepreneurship and Public Policy, Vol. 9 No. 2, pp. 185-193.

The digital consequences of the pandemic

With Darcy WE Allen, Sinclair Davidson, Aaron M Lane, and Jason Potts. Originally a Medium post.

The global policy response to the COVID-19 pandemic has been extraordinary. We’ve seen a massive increase in government spending and social welfare programs, heavy handed policing, and some less remarked on crisis deregulation.

But the long run effect of the pandemic will be even more substantial. COVID-19 is driving far deeper, and profound, changes in the economy.

Some of these changes we can start to see already, but their full implications are still murky and distant. Nonetheless, as we argue in our book Unfreeze: How to Create a High Growth Economy After the Pandemic, the economy will not simply snap back into place. The post-COVID-19 economy will not look like the pre-COVID-19 economy.

Here we offer seven changes that have big consequences for policymakers, entrepreneurs, and employees.

1 — Digital acceleration

COVID-19 has massively accelerated the adoption of digital technology to facilitate work from home. But also shop from home, school from home, telehealth, and so on.

This digital shift is often remarked on but not well understood. Technology adoption normally follows a particular diffusion trajectory. Digital technologies that have significant scale effects must overcome behavioural and institutional resistance, and they can get stuck at take-off. This means that the productivity benefits from widespread technology adoption, especially infrastructural and production technology, can be very slow to realise.

COVID-19 arrived at a critical time in the history of technology — when a supercluster of digital technologies were forming, poised to disrupt the underlying infrastructure of the economy. This suite of digital platforms and technologies had been developing for the past several decades. But they had run into innovation constraints caused by coordination adoption problems and regulatory barriers.

In March 2020, many of these constraints suddenly vanished. The spread of online education and telemedicine, which had been until then a multi-decade process, occurred in a matter of weeks.

This was a massive, global, multisector, virtually-instantaneous coordinated adoption of digital technology. That’s utterly incredible — and perhaps unique in the history of technology adoption.

A major problem with platform technologies is to drive coordinated adoption. The pandemic did in a few weeks what decades of government effort had failed to do. Long-run that is very good. But short-run it is highly disruptive.

2 — A need for massive entrepreneurial adjustment

In Unfreeze we argue that there is an urgent need for entrepreneurs to adapt to the post-COVID-19 world. Economies are made of connections, information, contracts, webs of value, relationships. When we try to restart the economy, much of this connective tissue will be gone.

The rapid technological acceleration driven by the crisis creates its own unique needs for adaptation. We’re already seeing the formation of new consumer preferences, new types of jobs, new types of business models with new cost and demand structures, new patterns of supply, and new regulatory and legal uncertainties.

But this implies that a significant amount of human capital and physical capital (built for industrial era technologies and business models) has rapidly devalued.

The first priority for entrepreneurs in the post-COVID-19 economy will be understanding how particular markets and jobs and administrative functions have changed. For example, many restaurants have moved to take-away only. Will consumers expect those new services to continue? Much of the white-collar economy has moved to work from home. Will employees demand that continues?

Entrepreneurial skills are essential during periods of rapid change. Entrepreneurship is not something that can be supplied by governments. But it can be inhibited. Policymakers have to make sure they are facilitating — not impeding — entrepreneurial adaptation to the accelerated digital adoption triggered by COVID-19.

3 — Decentralised production and innovation

One consequence of this sudden digital uptake is increased decentralisation. With the rapid adoption of work from home — not just the technologies but the social practices — we’ve seen a shift in the locus of much economic activity from offices into homes.

This shift has several implications. One, it facilitates greater co-production of value. More household resources, including especially local information, are being mixed into production.

Two, this also shifts the sites of innovation, facilitating greater household innovation and user innovation. More innovation occurs in the commons rather than in markets and organisations. This in turn increases the need for trusted decentralised networks and, in turn, increases the demand for and use of distributed innovation technology and institutions.

Three, distributed production will require more distributed dispute resolution mechanisms. Traditional courts have been slow to adapt to the digital environment and parties will be looking to more agile forms of alternative dispute resolution.

Four, because more production and innovation is occurring in households and in the commons, this means that it is harder to measure value creation and improvements in these non-market contexts. The non-market part of the economy will increase in apparent scale. So our industrial era measurements of economic activity (like GDP) will need to catch up with these new digital era realities of value creation.

This new institutional economic order will require a new economics to make sense of these new patterns of consumption and production, and new digital forms of capital and value creation.

4 — Powered-up economic evolution

The pandemic is a selection filter. As the precursor and mechanism of many of these changes, the economic consequence of the economic policy response to the viral pandemic is a powerful evolutionary selection mechanism passing over the global economy and through each sector.

This brutal selection mechanism is causing job losses, contract terminations or renegotiations, demand reductions, business closures and bankruptcy, fire sales, credit shrinkage, asset repricing, factor substitution, and other distinct forms of economic destruction that will play out over the coming months and years.

This hard evolutionary selection mechanism is also a filter. It will kill off some things disproportionately and let other things pass through. Most obviously, digitally enabled businesses and sectors will do better, because they are more well-adapted to the new environment. Bigger firms with better capitalisation (or better political connections) will do better, and smaller firms will be selected against.

In labour markets some positions are more vulnerable than others, particularly part-time workers or contractors. While many workers and firms are on temporary support through public sector subsidy of wages or quasi-partial nationalisations, a proportion of those positions or organisations being kept alive will die as soon as support is removed. There are many zombies already.

Similarly, there will be a lot of bad debt on company books (and thereby in banks) that will be realised in market revaluations over coming periods. These collapses will release resources for subsequent entrepreneurial reconstitution and reinvention.

But we should also expect consolidation of existing markets and resources among surviving players. This may actually result in higher growth and profits among large adaptive companies — particularly technology driven companies. So a period of global economic destruction is not inconsistent with a booming share market.

5 — The twilight of conventional macroeconomic policy

At the same time, COVID-19 looks to fundamentally break the standard monetary and fiscal policy levers that have been used to manage business cycles over the twentieth century.

From a public finance perspective, the magnitude of the committed policy actions is already unprecedented. The levels of public debt that are planned in order to deal with this crisis — the policies to subsidise wages, provide rent and income relief, bail out companies, etc in order to avoid market catastrophe — are the largest that has ever been experienced. Moreover, these actions are being taken during a massive collapse in tax receipts. The implications for public finance are catastrophic, with a huge increase in public debt, a vastly worse central bank balance sheet, and looming inflation.

The result is a policy challenge that far exceeds capabilities of traditional monetary and fiscal levers. We will require institutional policy reforms to deal with the crisis. But institutional policy designed to free-up the supply side of the economy, to lower the costs and constraints on businesses, is politically much harder to achieve.

Indeed, the limits of these policy levers reveals the extent to which government administration (e.g. of money, of asset and property registries, of identity, of regulation and governance) is still the foundation of a modern economy. The pandemic has brought into sharp relief the limits and constraints of this centralised public infrastructure and the technocratic foundations of the macroeconomic policy mechanisms built upon them.

The real alternative to conventional policy levers isn’t different policies (like quantitative easing, negative interest rates, or universal basic income) but better institutional technologies. We’ve been looking in the past few years at distributed digital technology (that is, blockchain) that offers a new administrative and governance base layer of the economy (see herehereherehere and here to start).

A digital infrastructure base layer of industry utilities and digital platforms would provide a far more agile foundation for targeted economic policy and entrepreneurial adaptation.

6 — A new global trading order

One of the most powerful institutional forces over the past several centuries, and which has underpinned global economic prosperity in the industrial era, was the development of global trading infrastructure for commodities and capital. It was built around the Westphalian system of nation-state record-keeping and intra-nation state treaty-based institutional governance (i.e. trade zones). But it has come to a virtual halt in the crisis.

In the short and medium term the global trading order will rebuild around a different order, namely provable health identity and data to facilitate the safe movement and interaction of people. Where that can safely happen, so can economic activity. Health zones can become the basis for trade zones. Australia and New Zealand are already talking about a “health bubble”. It would be easy to include other highly successful health economies — Taiwan, Japan, Germany, potentially Hong Kong and Singapore, some Pacific Island nations.

Green zones (or cordon sanitaire) have long been used in pandemics and have once again been proposed as a way to exit lockdown. As the health zone grows, so can the trade zone. Economic zones can then free ride on the decentralised identity and data infrastructure created to build a health zone. The result will be the redrawing of physical and network boundaries, even eliminating artificial economic borders, to create integrated trade zones.

7 — A new political order

The costs of COVID-19 do not fall evenly across the population. The health risks fall heavily on some groups (the elderly and those with co-morbidities), and the costs of economic lockdown fall on different groups and will be felt differently. The differential impact by sector, jobs, education, human capital investments or physical or financial capital write-downs shape how the costs are distributed across society.

The virus imposes huge private costs that will be in part socialised through political bargaining. The outcome of these politically mediated bargains and transfers that will shape politics for years to come.

But the pandemic also shifts some of the anchor points of political economy. The sudden growth of the welfare state, of unemployment insurance and wage-support, of healthcare provision and childcare, even of social housing are unlikely to be easily rolled back. So there will be a higher demand for social welfare safety nets.

But to pay for this, along with the urgent need to address the huge deterioration of public balance sheets, economic policy will need an aggressive pro-market agenda to unleash economic growth. Politically, this is a pivot to the centre with very ‘dry’ economic policy and ‘wet’ social policy — what was called ‘third way’ in the 1990s.

The counterpoint to that centre-pivot is that many of the high-cost political projects of both the right and the left will be abandoned. Reduced economic growth means we can afford fewer of the luxuries of advanced capitalism.

This is a vision of a new kind of social-digital capitalism to be built after the reset — from the government-led physical infrastructure of the industrial era, to a digital era built on private, open and communally developed technology platforms.

Finally

The economic consequences of the COVID-19 pandemic are mostly currently being discussed as a macro policy response to dealing with the economic destruction that the public health strategy necessitates. This is talk of the V-shaped, U-shaped, L-shaped or W-shaped recoveries. In Unfreeze we wrote of the need for a square root shaped recovery — after the reopening, we’ll need a long period of high economic growth to return to the prosperity of 2019.

But here we’ve gone further. COVID-19 is driving structural evolutionary change in the economy. The accelerated adoption of digital economic infrastructure during the crisis will leave a lasting mark on the political and economic system of the future.

Look at our history: protectionism doesn’t work

With Vijay Mohan

We rarely think about supply chains – those immensely complex networks of production and logistics that structure the economy. 

That has changed. Early in the COVID-19 crisis, we learned that Australia imports much of its basic medical equipment like facemasks and other protective gear. As borders were being closed importing this high-demand equipment got suddenly very hard.  

Now there is an unsurprising clamour for the government to take more of an interest in how our supply chains actually work, and to use the traditional tools of protectionism to encourage domestic production of medical equipment and pharmaceuticals.  

Prime Minister Scott Morrison said in April that “we need to look very carefully at our domestic economic sovereignty”. 

But neo-protectionism to secure Australia’s supply chains would be a grave mistake – and it fundamentally gets the supply chain challenge wrong. 

First, the obvious but necessary point. We actually had a protectionist economy for most of the twentieth century. And we didn’t build facemasks. We built cars. We built cars because cars had a certain romance in the twentieth century and Labor and the union movement wanted to lock in prestigious manufacturing jobs for their supporters. 

This has always been one of the central planks of the case against protectionism. The choice of what industries to protect is not made by all-knowing and benevolent leaders, but by self-interested politicians. They get to the top of their profession not because they are skilled production managers or supply chain coordinators, but because they’re great at navigating political factions and going on television. 

Of course, our national leaders will come out of this crisis more focused on the risk of future pandemics, and more motivated to prepare our economy for this now-known risk. But as they say in the military, generals too often prepare for the last war, not the next one. We don’t need an economic system that is prepared for a crisis that looks exactly like COVID-19. We need an economic system that is prepared for an unexpected crisis – which, definitionally, could be anything. 

Indeed, it is the fact that the pandemic was unexpected to most in government that makes the strongest case for free trade. The crisis has caused a lot of market disruption. But global supply chains have adjusted remarkably well to new demands and routed around new constraints. For example, airlines have been doing temporary conversions of passenger planes to cargo planes – particularly important because medical equipment, which in normal times would be leisurely transported by ship, needs to get to new COVID-19 hot spots urgently. 

Protectionism invariably makes the industries it protects brittle and highly politicised, not agile and adaptable to sudden economic shocks. And it is a fantasy to suggest that a small, wealthy, highly-educated nation like Australia could or should ever be self-reliant in the production of all low-value goods that might be needed in unexpected crises. 

There are things the government can do to be prepared for the next crisis. Rather than making essential products, we can buy them and store them. This requires no more foresight than full-blown protectionism and is a lot cheaper. The idea of keeping extensive national stockpiles of equipment for emergencies is uncontroversial. By all accounts, the National Medical Stockpile has been an immensely valuable asset during COVID-19. 

With our RMIT colleague Marta Poblet, we have been looking at the problems consumers had getting reliable information on supply chain security in the first weeks of the crisis.  

Before the pandemic, Australian industry was interested in using new technologies (such as blockchain, 5G communication, and smart devices) to better combat food fraud in export markets or to how to prove to their customers that their products were organic or fair trade certified.  

But the pandemic revealed a more basic problem with about supply chain information. Consumers were not worried about quality or fraud. They were worried there were not enough goods available to meet demand at all – hence the panic buying of toilet paper, hand sanitizer, and dried pasta.  

This panic buying looked a lot like the sort of panic withdrawals you see in a bank run. If depositors aren’t convinced their bank is solvent, they rush to be the first to get their money out. And as we saw, Scott Morrison was no better able to convince shoppers that there were adequate domestic supplies of toilet paper in March 2020 than South Australian premier Don Dunstan was able to convince the customers of the Hindmarsh Building Society that there were adequate funds to cover deposits October 1974 — despite standing in the street outside its headquarters with a megaphone.  

In moments of high-stress consumers just don’t trust the political assurances they are given. Do we really blame them? 

Ultimately within a few weeks supply chains adjusted. Coles and Woolworths lifted their toilet paper sale limits. 

But the toilet paper panic symbolises the choice we now face when it comes to supply chain resilience. To go protectionist would be to trust our supply chains to the same political class that we simultaneously accuse of being underprepared for COVID-19. Or we could lean into free trade and open markets. We should encourage entrepreneurs to adapt rapidly to new circumstances, to experiment with new technology, and let them figure out how to operate in a disrupted global economy. 

Australia has a long history of protectionism. Let’s try to remember what we learned. 

The COVIDSafe app was just one contact tracing option. These alternatives guarantee more privacy

With Kelsie Nabben

Since its release on Sunday, experts and members of the public alike have raised privacy concerns with the federal government’s COVIDSafe mobile app.

The contact tracing app aims to stop COVID-19’s spread by “tracing” interactions between users via Bluetooth, and alerting those who may have been in proximity with a confirmed case.

According to a recent poll commissioned by The Guardian, 57% of respondents said they were “concerned about the security of personal information collected” through COVIDSafe.

In its coronavirus rewhy sponse, the government has a golden opportunity to build public trust. There are other ways to build a digital contact tracing system, some of which would arguably raise fewer doubts about data security than the app.

All eyes on encryption

Incorporating advanced cryptography into COVIDSafe could have given Australian citizens a mathematical guarantee of their privacy, rather than a legal one.

A team at Canada’s McGill University is working on a solution that uses “mix networks” to send cryptographically “hashed” contact tracing location data through multiple, decentralised servers. This process hides the location and time stamps of users, sharing only necessary data.

This would let the government alert those who have been near a diagnosed person, without revealing other identifiers that could be used to trace back to them.

It’s currently unclear what encryption standards COVIDSafe is using, as the app’s source code has not been publicly released, and the government has been widely criticised for this. Once the code is available, researchers will be able to review and assess how safe users’ data is.

COVIDSafe is based on Singapore’s TraceTogether mobile app. Cybersecurity experts Chris Culnane, Eleanor McMurtry, Robert Merkel and Vanessa Teague have raised concerns over the app’s encryption standards.

If COVIDSafe has similar encryption standards – which we can’t know without the source code – it would be wrong to say the app’s data are encrypted. According to the experts, COVIDSafe shares a phone’s exact model number in plaintext with other users, whose phones store this detail alongside the original user’s corresponding unique ID.

Tough tech techniques for privacy

US-based advocacy group The Open Technology Institute has argued in favour of a “differential privacy” method for encrypting contact tracing data. This involves injecting statistical “noise” into datasets, giving individuals plausible deniability if their data are leaked for purposes other than contact tracing.

Zero-knowledge proof is another option. In this computation technique, one party (the prover) proves to another party (the verifier) they know the value of a specific piece of information, without conveying any other information. Thus, it would “prove” necessary information such as who a user has been in proximity with, without revealing details such as their name, phone number, postcode, age, or other apps running on their phone.

Not on the cloud, but still an effective device

Some approaches to contact tracing involve specialised hardware. Simmel is a wearable pen-like contact tracing device. It’s being designed by a Singapore-based team, supported by the European Commission’s Next Generation Internet program. All data are stored in the device itself, so the user has full control of their trace history until they share it.

This provides citizens a tracing beacon they can give to health officials if diagnosed, but is otherwise not linked to them through phone data or personal identifiers.

Missed opportunity

The response to COVIDSafe has been varied. While the number of downloads has been promising since its release, iPhone users have faced a range of functionality issues. Federal police are also investigating a series of text message scams allegedly aiming to dupe users.

The federal government has not chosen a decentralised, open-source, privacy-first approach. A better response to contact tracing would have been to establish clearer user information requirements and interoperability specifications (standards allowing different technologies and data to interact).

Also, inviting the private sector to help develop solutions (backed by peer review) could have encouraged innovation and provided economic opportunities.

How do we define privacy?

Personal information collected via COVIDSafe is governed under the Privacy Act 1988 and the Biosecurity Determination 2020.

These legal regimes reveal a gap between the public’s and the government’s conceptions of “privacy”.

You may think privacy means the government won’t share your private information. But judging by its general approach, the government thinks privacy means it will only share your information if it has authorised itself to do so.

Fundamentally, once you’ve told the government something, it has broad latitude to share that information using legislative exemptions and permissions built up over decades. This is why, when it comes to data security, mathematical guarantees trump legal “guarantees”.

For example, data collected by COVIDSafe may be accessible to various government departments through the recent anti-encryption legislation, the Assistance and Access Act. And you could be prosecuted for not properly self-isolating, based on your COVIDSafe data.

A right to feel secure

Moving forward, we may see more iterations of contact tracing technology in Australia and around the world.

The World Health Organisation is advocating for interoperability between contact tracing apps as part of the global virus response. And reports from Apple and Google indicate contact tracing will soon be built into your phone’s operating system.

As our government considers what to do next, it must balance privacy considerations with public health. We shouldn’t be forced to choose one over another.