Category: Economics

A distributed resilience among darknet markets?

You may have seen the news earlier this year that two large darknet marketplaces, Alphabay and Hansa, have been taken down by international law enforcement. Particularly interesting about these takedowns is that they were deliberately structured to seed distrust among market participants: after Alphabay closed many traders migrated to Hansa, not aware that it had already covertly been taken over by the police. As trading continued on this smaller platform, the Dutch police and their peers kept track of account logins, private messages, and incoming orders. Two weeks later they also closed Hansa, and revealed their successful data collection efforts to the public. Many arrests followed. The message to illicit traders: you can try your best to stay anonymous, but eventually we will catch you.

By coincidence, our small research team of Joss Wright, Mark Graham, and me had set out earlier in the year to investigate the economic geography of darknet markets. We had started our data collection a few weeks earlier, and the events took us by surprise: it doesn’t happen every day that a primary information source gets shut down by the police… While we had anticipated that some markets would close during our investigations, it all happened rather quickly. On the other hand, this also gave us a rare opportunity to observe what happens after such a takedown. The actions by law enforcement were deliberately structured to seed distrust in illicit trading platforms. Did this effort succeed? Let’s have a look at the data…

The chart above shows weekly trading volumes on darknet markets for the period from May to July 2017. The black line shows the overall trading volume across all markets we observed at the time. Initially, Alphabay (in blue) represented a significant share of this overall trade, while Hansa (in yellow) was comparably small. When Alphabay was closed in week 27, overall sales dropped: many traders lost their primary market. The following week, Hansa trading volumes more than doubled, until it was closed as well. More important however is the overall trend: while the takedowns lead to a short-term reduction in trade, in the longer term, people simply moved to other markets. (Note that we estimate trading volumes from buyer reviews, which are often posted days or weeks after a sale. The apparent Alphabay decline in weeks 25-27 is likely attributable to this delay in posting feedback: many reviews simply hadn’t been posted yet by the time of the market closure.)

In other words, within less than a month, overall trading volumes were back to previous levels. This matches prior research findings after similar takedown efforts — see below for links to some relevant papers. But does this suggest that the darknet market ecosystem as a whole has a kind of distributed resilience against interventions? This remains to be seen. While the demand for illicit goods appears unchanged, these markets are under increasing pressures. Since the two takedowns, there have been reports of further market closures, long-running distributed denial of service attacks, extortion attempts, and other challenges. As a result, there is renewed uncertainty about the long-term viability of these platforms. We’ll keep monitoring…

Further reading (academic):

Further reading (popular):

Mapping Fentanyl Trades on the Darknet

My colleagues Joss Wright, Martin Dittus and I have been scraping the world’s largest darknet marketplaces over the last few months, as part of our darknet mapping project. The data we collected allow us to explore a wide range of trading activities, including the trade in the synthetic opioid Fentanyl, one of the drugs blamed for the rapid rise in overdose deaths and widespread opioid addiction in the US.

The above map shows the global distribution of the Fentanyl trade on the darknet. The US accounts for almost 40% of global darknet trade, with Canada and Australia at 15% and 12%, respectively. The UK and Germany are the largest sellers in Europe with 9% and 5% of sales. While China is often mentioned as an important source of the drug, it accounts for only 4% of darknet sales. However, this does not necessarily mean that China is not the ultimate site of production. Many of the sellers in places like the US, Canada, and Western Europe are likely intermediaries rather than producers themselves.

In the next few months, we’ll be sharing more visualisations of the economic geographies of products on the darknet. In the meantime you can find out more about our work by Exploring the Darknet in Five Easy Questions.

Follow the project here: https://www.oii.ox.ac.uk/research/projects/economic-geog-darknet/

Twitter: @OiiDarknet

Introducing Martin Dittus, Data Scientist and Darknet Researcher

We’re sitting upstairs, hunched over a computer, and Martin is showing me the darknet. I guess I have as good an idea as most people what the darknet is, i.e. not much. We’re looking at the page of someone claiming to be in the UK who’s selling “locally produced” cannabis, and Martin is wondering if there’s any way of telling if it’s blood cannabis. How you would go about determining this? Much of what is sold on these markets is illegal, and can lead to prosecution, as with any market for illegal products.

But we’re not buying anything, just looking. The stringent ethics process governing his research means he currently can’t even contact anyone on the marketplace.

[Read more: Exploring the Darknet in Five Easy Questions]

Martin Dittus is a Data Scientist at the Oxford Internet Institute, and I’ve come to his office to find out about the OII’s investigation (undertaken with Mark Graham and Joss Wright) of the economic geographies of illegal economic activities in anonymous Internet marketplaces, or more simply: “mapping the darknet”. Basically: what’s being sold, by whom, from where, to where, and what’s the overall value?

Between 2011 and 2013, the Silk Road marketplace attracted hundreds of millions of dollars worth of bitcoin-based transactions before being closed down by the FBI, but relatively little is known about the geography of this global trade. The darknet throws up lots of interesting research topics: around traffic in illegal wildlife products, the effect of healthcare policies on demand for illegal prescription drugs, whether law enforcement has (or can have) much of an impact, questions around the geographies of trade (e.g. sites of production and consumption), and the economics of these marketplaces — as well as the ethics of researching all this.

OII researchers tend to come from very different disciplinary backgrounds, and I’m always curious about what brings people here. A computer scientist by training, Martin first worked as a software developer for Last.fm, an online music community that built some of the first pieces of big data infrastructure, “because we had a lot of data and very little money.” In terms of the professional experience he says it showed him how far you can get by being passionate about your work — and the importance of resourcefulness; “that a good answer is not to say, ‘No, we can’t do that,’ but to say: ‘Well, we can’t do it this way, but here are three other ways we can do it instead.’”

Resourcefulness is certainly something you need when researching darknet marketplaces. Two very large marketplaces (AlphaBay and Hansa) were recently taken down by the FBI, DEA and Dutch National Police, part-way through Martin’s data collection. Having your source suddenly disappear is a worry for any long-term data scraping process. However in this case, it raises the opportunity of moving beyond a simple observational study to a quasi-experiment. The disruption allows researchers to observe what happens in the overall marketplace after the external intervention — does trade actually go down, or simply move elsewhere? How resilient are these marketplaces to interference by law enforcement?

Having originally worked in industry for a few years, Martin completed a Master’s programme at UCL’s Centre for Advanced Spatial Analysis, which included training in cartography. The first time I climbed the three long flights of stairs to his office to say hello we quickly got talking about crisis mapping platforms, something he’d subsequently worked on during his PhD at UCL. He’s particularly interested in the historic context for the recent emergence of these platforms, where large numbers of people come together over a shared purpose: “Platforms like Wikipedia, for example, can have significant social and economic impact, while at the same time not necessarily being designed platforms. Wikipedia is something that kind of emerged, it’s the online encyclopaedia that somehow worked. For me that meant that there is great power in these platform models, but very little understanding of what they actually represent, or how to design them; even how to conceptualise them.”

“You can think of Wikipedia as a place for discourse, as a community platform, as an encyclopaedia, as an example of collective action. There are many theoretical ways to interpret it, and I think this makes it very powerful, but also very hard to understand what Wikipedia is; or indeed any large and complex online platform, like the darknet markets we’re looking at now. I think we’re at a moment in history where we have this new superpower that we don’t fully understand yet, so it’s a time to build knowledge.” Martin claims to have become “a PhD student by accident” while looking for a way to participate in this knowledge building: and found that doing a PhD was a great way to do so.

Whether discussing Wikipedia, crisis-mapping, the darknet, or indeed data infrastructures, it’s great to hear people talking about having to study things from many different angles — because that’s what the OII, as a multidisciplinary department, does in spades. It’s what we do. And Martin certainly agrees: “I feel incredibly privileged to be here. I have a technical background, but these are all intersectional, interdisciplinary, highly complex questions, and you need a cross-disciplinary perspective to look at them. I think we’re at a point where we’ve built a lot of the technological building blocks for online spaces, and what’s important now are the social questions around them: what does it mean, what are those capacities, what can we use them for, and how do they affect our societies?”

Social questions around darknet markets include the development of trust relationships between buyers and sellers (despite the explicit goal of law enforcement agencies to fundamentally undermine trust between them); identifying societal practices like consumption of recreational drugs, particularly when transplanted into a new online context; and the nature of market resilience, like when markets are taken down by law enforcement. “These are not, at core, technical questions,” Martin says. “Technology will play a role in answering them, but fundamentally these are much broader questions. What I think is unique about the OII is that it has a strong technical competence in its staff and research, but also a social, political, and economic science foundation that allows a very broad perspective on these matters. I think that’s absolutely unique.”

There were only a few points in our conversation where Martin grew awkward, a few topics he said he “would kind of dance around“ rather than provide on-record chat for a blog post. He was keen not to inadvertently provide a how-to guide for obtaining, say, fentanyl on the darknet; there are tricky unanswered questions of class (do these marketplaces allow a gentrification of illegal activities?) and the whitewashing of the underlying violence and exploitation inherent to these activities (thinking again about blood cannabis); and other areas where there’s simply not yet enough research to make firm pronouncements.

But we’ll certainly touch on some of these areas as we document the progress of the project over the coming months, exploring some maps of the global market as they are released, and also diving into the ethics of researching the darknet; so stay tuned!

Until then, Martin Dittus can be found at:

Web: https://www.oii.ox.ac.uk/people/martin-dittus/
Email: martin.dittus@oii.ox.ac.uk
Twitter: @dekstop

Follow the darknet project at: https://www.oii.ox.ac.uk/research/projects/economic-geog-darknet/

Twitter: @OiiDarknet

Exploring the Darknet in Five Easy Questions

Many people are probably aware of something called “the darknet” (also sometimes called the “dark web”) or might have a vague notion of what it might be. However, many probably don’t know much about the global flows of drugs, weapons, and other illicit items traded on darknet marketplaces like AlphaBay and Hansa, the two large marketplaces that were recently shut down by the FBI, DEA and Dutch National Police.

We caught up with Martin Dittus, a data scientist working with Mark Graham and Joss Wright on the OII’s darknet mapping project, to find out some basics about darknet markets, and why they’re interesting to study.

Firstly: what actually is the darknet?

Martin: The darknet is simply a part of the Internet you access using anonymising technology, so you can visit websites without being easily observed. This allows you to provide (or access) services online that can’t be tracked easily by your ISP or law enforcement. There are actually many ways in which you can visit the darknet, and it’s not technically hard. The most popular anonymising technology is probably Tor. The Tor browser functions just like Chrome, Internet Explorer or Firefox: it’s a piece of software you install on your machine to then open websites. It might be a bit of a challenge to know which websites you can then visit (you won’t find them on Google), but there are darknet search engines, and community platforms that talk about it.

The term ‘darknet’ is perhaps a little bit misleading, in that a lot of these activities are not as hidden as you might think: it’s inconvenient to access, and it’s anonymising, but it’s not completely hidden from the public eye. Once you’re using Tor, you can see any information displayed on darknet websites, just like you would on the regular internet. It is also important to state that this anonymisation technology is entirely legal. I would personally even argue that such tools are important for democratic societies: in a time where technology allows pervasive surveillance by your government, ISP, or employer, it is important to have digital spaces where people can communicate freely.

And is this also true for the marketplaces you study on the darknet?

Martin: Definitely not! Darknet marketplaces are typically set up to engage in the trading of illicit products and services, and as a result are considered criminal in most jurisdictions. These market platforms use darknet technology to provide a layer of anonymity for the participating vendors and buyers, on websites ranging from smaller single-vendor sites to large trading platforms. In our research, we are interested in the larger marketplaces, these are comparable to Amazon or eBay — platforms which allow many individuals to offer and access a variety of products and services.

The first darknet market platform to acquire some prominence and public reporting was the Silk Road — between 2011 and 2013, it attracted hundreds of millions of dollars worth of bitcoin-based transactions, before being shut down by the FBI. Since then, many new markets have been launched, shut down, and replaced by others… Despite the size of such markets, relatively little is known about the economic geographies of the illegal economic activities they host. This is what we are investigating at the Oxford Internet Institute.

And what do you mean by “economic geography”?

Martin: Economic geography tries to understand why certain economic activity happens in some places, but not others. In our case, we might ask where heroin dealers on darknet markets are geographically located, or where in the world illicit weapon dealers tend to offer their goods. We think this is an interesting question to ask for two reasons. First, because it connects to a wide range of societal concerns, including drug policy and public health. Observing these markets allows us to establish an evidence base to better understand a range of societal concerns, for example by tracing the global distribution of certain emergent practices. Second, it falls within our larger research interest of internet geography, where we try to understand the ways in which the internet is a localised medium, and not just a global one as is commonly assumed.

So how do you go about studying something that’s hidden?

Martin: While the strong anonymity on darknet markets makes it difficult to collect data about the geography of actual consumption, there is a large amount of data available about the offered goods and services themselves. These marketplaces are highly structured — just like Amazon there’s a catalogue of products, every product has a title, a price, and a vendor who you can contact if you have questions. Additionally, public customer reviews allow us to infer trading volumes for each product. All these things are made visible, because these markets seek to attract customers. This allows us to observe large-scale trading activity involving hundreds of thousands of products and services.

Almost paradoxically, these “hidden” dark markets allow us to make visible something that happens at a societal level that otherwise could be very hard to research. By comparison, studying the distribution of illicit street drugs would involve the painstaking investigative work of speaking to individuals and slowly trying to acquire the knowledge of what is on offer and what kind of trading activity takes place; on the darknet it’s all right there. There are of course caveats: for example, many markets allow hidden listings, which means we don’t know if we’re looking at all the activity. Also, some markets are more secretive than others. Our research is limited to platforms that are relatively open to the public.

Finally: will you be sharing some of the data you’re collecting?

Martin: This is definitely our intention! We have been scraping the largest marketplaces, and are now building a reusable dataset with geographic information at the country level. Initially, this will be used to support some of our own studies. We are currently mapping, visualizing, and analysing the data, building a fairly comprehensive picture of darknet market trades. It is also important for us to state that we’re not collecting detailed consumption profiles of participating individuals (not that we could). We are independent academic researchers, and work neither with law enforcement, nor with platform providers.

Primarily, we are interested in the activity as a large-scale global phenomenon, and for this purpose, it is sufficient to look at trading data in the aggregate. We’re interested in scenarios that might allow us to observe and think about particular societal concerns, and then measure the practices around those concerns in ways that are quite unusual, that otherwise would be very challenging. Ultimately, we would like to find ways of opening up the data to other researchers, and to the wider public. There are a number of practical questions attached to this, and the specific details are yet to be decided — so stay tuned!

Martin Dittus is a researcher and data scientist at the Oxford Internet Institute, where he studies the economic geography of darknet marketplaces. More: @dekstop

Follow the project here: https://www.oii.ox.ac.uk/research/projects/economic-geog-darknet/

Twitter: @OiiDarknet

 

Further reading (academic):

Further reading (popular):

Martin Dittus was talking to OII Managing Editor David Sutcliffe.

Considering the Taylor Review: Ways Forward for the Gig Economy

The Taylor Review of Modern Working Practices in the UK was published last week. The review assesses changes in labour markets and employment practices, and proposes policy solutions. One of the big themes in the report is the rise of platform-mediated gig work. I have been doing research on platform-mediated work for a few years now, and am currently leading a major European Research Council funded research project on the topic. This article is my hot take on some of the topics covered in the report. Overall the report takes a relatively upbeat view of the gig economy, but engages with its problematic points as well.

A third way in employment classification

In the U.S. policy debate around the gig economy, many have called for a ‘third category’ between protected employment and unprotected self-employment. The interesting thing is that in the UK such a category already exists. An employment tribunal decision last year determined that Uber drivers were not employees or contractors, but ‘workers’, enjoying some of the benefits of employment but not all. The review recommends making use this ‘worker’ category and renaming it ‘dependent contractor’.

The review calls for greater emphasis on control over one’s work as a factor in determining whether someone is a ‘dependent contractor’ or genuinely self-employed. The question of control has featured prominently in recent research on gig economy platforms (see, for example: Rosenblat & Stark 2016, Graham et al. 2017). Uber promises freedom, but in practice uses a variety of nudges and constraints to manage workers quite closely. Platforms for digitally delivered work like graphic design don’t necessarily try to control the workers in the same way at all. So focusing on control can help distinguish between the employment status implications of different platforms, which can be quite different.

Of course, the fact that someone is genuinely self-employed doesn’t necessarily mean that they are well off. Self-employed people are often relatively poor and suffer from unpredictability of income. So it’s good that the report also calls for extending more safety nets and other support to self-employed people (p. 74-81).

The report also calls for greater clarity in law, and for alignment of the definitions between different branches of law (employment law and tax law, p. 38). This seems like such an obvious thing to do. As someone coming from a civil law system, I have always marvelled at common law’s ability to evolve through court decisions, but that spontaneous and complex evolution has a price. As the Review states, many people in Britain don’t know their rights, and even if they do, it is often prohibitively expensive to pursue them.

Fair piece rates

The Review’s section on piece rates (p. 38) is very interesting and in many ways forward-looking, but likely to cause contention.

Piece rates mean that workers are paid on the basis of the number of tasks completed (e.g. meals delivered) rather than on the basis of hours worked. This is how many gig work platforms function today. The Review suggests that platforms be required to use their data to calculate how much a worker can earn per hour from such piece rates, given what they know about the demand for the tasks and how long it usually takes to complete them. Based on this calculation, platforms would be required to set their piece rate so that on average it produces an hourly rate that clears the National Minimum Wage with a 20% margin of error.

One argument likely to be put forward in opposition is that since platforms have all the data necessary to calculate the average hourly rate, why don’t they just pay the average hourly rate instead of the piece rate? As the Review notes, piece rates are used in work where the employer cannot monitor the hours worked, such as for people who fill envelopes with information for mailshots from home. Platforms usually monitor their pieceworkers intensively, so they could just as well pay hourly rates.

I think this is a fairly strong argument, but not without its limits. Piece rates are a substitute for more direct managerial control. Employers who pay hourly rates are pickier about whom they accept into their ranks in the first place, whereas one of the strengths of these platforms is that essentially anyone can sign up and start working right away with a minimal hurdle. And workers who are paid on an hourly basis usually cannot take breaks quite as easily as pieceworkers. This low entry barrier and potential for almost minute-by-minute flexibility are genuine features of platform-based piecework, and some workers value them.

I say potential for flexibility, because actual flexibility for the worker depends on how much work there is available on the platform, as I discuss in an upcoming paper. Pieceworkers also have to put more effort into managing their own time than regular workers, though platform design can ameliorate this.

Flexibility or erosion?

The Review moreover suggests that platforms should be allowed to offer piecework at times when demand is so low as to result in hourly earnings below minimum wage, as long as the worker is fully informed of this. To quote: “If an individual knowingly chooses to work through a platform at times of low demand, then he or she should take some responsibility for this decision.” (p. 38) This is likely to be a very contentious point.

On the one hand, the report is using an old trope of laissez-faire labour policy: if the worker chooses to work for such low pay, or in such terrible conditions, who are we to stop them? Yet such choices are not independent, but shaped by and constitutive of wider structural forces. If there is nothing else on offer, of course the worker will rather accept a pittance than starve; but if every labourer accepted a pittance, soon employers would find it necessary to offer little else. The minimum wage must thus remain inviolable as a bulwark against exploitation, goes the labour movement refrain.

On the other hand, it is probably also true that much of the work that is available on platforms during off-hours will simply not be done if the cost is higher (and indeed was not done before platforms arrived). Eaters will cook at home or pick up a meal themselves instead of paying double for delivery. Part of the value of platforms is that they make marginal, low-value transactions at least somewhat feasible by matching interested parties and bringing down transaction costs. In doing so they grow the total pie of work available. As an incremental source of income for someone with another job or studies, these edges of the pie may be very appealing.

The challenge for policymakers is to prevent what is intended to be a side gig for students from becoming the desperate sustenance of families. In 1999, Japan deregulated the use of temporary contract workers, partly with the aim of helping students and housewives gain work experience and earn additional income to supplement the salaries of the male breadwinners, who enjoyed life-long employment. Less than a decade later, almost a third of the labour force found themselves on such contracts, including millions of breadwinners (Imai 2011).

The same pros and cons also apply to the idea of the third ‘dependent contractor’ category: it could help employers accommodate more diverse life situations and business models, but it could also represent an erosion of rights if regular employees eventually find themselves in that category. Early results from our ongoing research suggest that some Fortune 500 companies that are experimenting with online gig work platforms are not doing so with the intention of replacing regular employees, but as a complement and substitute to temporary staffing agencies. But statistics will be necessary to evaluate the wider impacts of platforms on labour markets and society.

Statistics on the gig economy

When it comes to statistics, the Review points out that “official data is not likely to include the increasing number of people earning additional money in a more casual way, through the use of online platforms for example” (p. 25). This is a real problem: official labour market statistics don’t capture platform-based work, or when they do, they don’t make it possible to distinguish it from ordinary self-employment income. This makes it impossible to properly evaluate the role that platforms are taking in the modern labour market.

To help address this paucity of data, we have created the Online Labour Index, the first economic indicator that provides an online gig economy equivalent of conventional labour market statistics. It shows that the online gig economy grew by a whopping 26 percent over the past year, and that UK-based employers are among its leading users in the world. By online gig economy we refer to digitally delivered platform work like design, data entry, and virtual assistant services, rather than local services like delivery. The index is constructed by ‘scraping’ all the gigs from the six biggest platforms in real time and calculating statistics on them; a similar approach could possibly be used to create new statistics on the local gig economy, to complement inadequate official labour market statistics.

Open issues

There is much more in the 116-page Review. For instance, the issue of flexibility gets a lot of attention, and is something that colleagues and I are also doing research on. The question of “flexibility for whom – workers or employers” will no doubt continue to feature in the debates on the future of work and employment.

I hope you enjoyed my hot take, and I hope to return to these topics in a future blog post!

***

Prof. Vili Lehdonvirta is an economic sociologist who studies the design and socioeconomic implications of digital marketplaces and platforms, using conventional social research methods as well as novel data science approaches. He is the Principal Investigator of iLabour, a 5-year research project funded by the European Research Council. @ViliLe

Can universal basic income counter the ill-effects of the gig economy?

Platforms like eBay, Uber, Airbnb, and Freelancer are thriving, growing the digital economy and disrupting existing business. The question is how to ensure that the transformations they entail have a positive impact on society. Here, universal basic income may have a role to play.

Few social policy ideas are as hot today as universal basic income. Social scientists, technologists, and politicians from both ends of the political spectrum see it as a potential solution to the unemployment that automation and artificial intelligence are expected to create.

It has also been floated as a potential solution to the rise of the gig economy, where work is centred around on-demand tasks and short-term projects as opposed to regular full-time employment. This is the kind of employment that platforms like Uber and Freelancer are based on.

Automation and the gig economy are actually closely linked. Isolating and codifying a job task in such a way that it can be outsourced to a gig worker can be the first step towards automating that task. Once a task has been automated, gig workers are used to train and supervise the algorithm. Meanwhile, expert online contractors are hired to fine-tune the technology. More often than not, a finished artificial intelligence system is actually an ensemble of machines and human workers acting in concert.

Basic income is an interesting solution for the gig economy, because it addresses its problems from a new angle. One of the most problematic aspects of the gig economy has to do with its negative job characteristics. Though gig work can provide autonomy and good earnings for some, it also involves uncertainty and insecurity, and for many can entail working antisocial hours for little pay.

A sort of default policy response therefore tends to be to regulate gig work back into the mould of standard employment, consisting of things like guaranteed working hours and notice periods. Basic income takes a different angle. It provides workers with a level of security and predictability over their income that is independent of work.

Plus, by providing workers with a fallback option, a sufficiently high basic income empowers them to turn down bad gigs. So, rather than regulating employer-employee relations, basic income allows them to negotiate terms on a more level playing field. This is why the idea has found favour on both sides of the left-right divide.

Paying the cost

But is basic income viable? One of the big questions is of course its cost. Giving every citizen enough money every month to pay for their essential living costs is no mean feat. Even if it replaced needs-based benefits, it would still probably entail a largescale redistribution of income. The economics of this continue to be vigorously debated, but on a macro-level it may be basically viable.

Yet if basic income is intended as a corrective to economic inequality resulting from new technologies, then the micro-level details of how it is funded are also important. Silicon Valley technology companies, regardless of all the wonderful services they provide us with, have also been notoriously good at avoiding paying taxes. Had they paid more taxes, there would probably be less economic inequality to grapple with now in the first place.

So when some of the same technologists now suggest that states should address mounting inequality with basic income, it is pertinent to ask who will pay for it. Colleagues and I have previously looked into novel ways of taxing the data economy, but there are no easy solutions in sight.

Beyond the basics

Another frequently cited set of questions has to do with basic income’s expected effects on society. Sceptics fear that given a free income, most people would simply stay home and watch YouTube while society crumbles. After all, employment is tightly bound with people’s sense of identity and self-worth, and provides time structure for each day, week, and year.

Proponents, however, have faith that most would want to better themselves or help others, even if they were not explicitly paid to do so. The idea is that a guaranteed income would free people to pursue societally valuable activities that markets won’t pay them to pursue, and that current test-based benefits may even hinder them from pursuing.

But all of these activities require not just food and shelter that basic income can buy, but also other resources, such as skills, knowledge, connections, and self-confidence. The most important means through which many of these resources are cultivated is education. Deprived of this, people with nothing but a basic income may well end up sitting at home watching YouTube.

Research that colleagues and I have carried out looking at online gig workers in Southeast Asia and Sub-Saharan Africa suggests that it is not the most resource-poor who are able to benefit from new online work opportunities, but more typically those with a good level of education, health, and other resources.

The ConversationSo universal basic income is a very interesting potential solution to the rise of the gig economy and more entrepreneurial working lives in general. But whenever basic income is discussed, it is important to ask who exactly would be paying for it. And it is important to recognise that more than just money for basic necessities is needed – unless the intention is simply to store away surplus people in YouTube-enabled homes. Apart from universal basic income, we might therefore want to talk about universal basic resources, such as education as well.

Vili Lehdonvirta, Associate Professor and Senior Research Fellow, Oxford Internet Institute, University of Oxford

This article was originally published on The Conversation. Read the original article.

Can universal basic income counter the ill-effects of the gig economy?

Platforms like eBay, Uber, Airbnb, and Freelancer are thriving, growing the digital economy and disrupting existing business. The question is how to ensure that the transformations they entail have a positive impact on society. Here, universal basic income may have a role to play.

Few social policy ideas are as hot today as universal basic income. Social scientists, technologists, and politicians from both ends of the political spectrum see it as a potential solution to the unemployment that automation and artificial intelligence are expected to create.

It has also been floated as a potential solution to the rise of the gig economy, where work is centred around on-demand tasks and short-term projects as opposed to regular full-time employment. This is the kind of employment that platforms like Uber and Freelancer are based on.

Automation and the gig economy are actually closely linked. Isolating and codifying a job task in such a way that it can be outsourced to a gig worker can be the first step towards automating that task. Once a task has been automated, gig workers are used to train and supervise the algorithm. Meanwhile, expert online contractors are hired to fine-tune the technology. More often than not, a finished artificial intelligence system is actually an ensemble of machines and human workers acting in concert.

Basic income is an interesting solution for the gig economy, because it addresses its problems from a new angle. One of the most problematic aspects of the gig economy has to do with its negative job characteristics. Though gig work can provide autonomy and good earnings for some, it also involves uncertainty and insecurity, and for many can entail working antisocial hours for little pay.

A sort of default policy response therefore tends to be to regulate gig work back into the mould of standard employment, consisting of things like guaranteed working hours and notice periods. Basic income takes a different angle. It provides workers with a level of security and predictability over their income that is independent of work.

Plus, by providing workers with a fallback option, a sufficiently high basic income empowers them to turn down bad gigs. So, rather than regulating employer-employee relations, basic income allows them to negotiate terms on a more level playing field. This is why the idea has found favour on both sides of the left-right divide.

Paying the cost

But is basic income viable? One of the big questions is of course its cost. Giving every citizen enough money every month to pay for their essential living costs is no mean feat. Even if it replaced needs-based benefits, it would still probably entail a largescale redistribution of income. The economics of this continue to be vigorously debated, but on a macro-level it may be basically viable.

Yet if basic income is intended as a corrective to economic inequality resulting from new technologies, then the micro-level details of how it is funded are also important. Silicon Valley technology companies, regardless of all the wonderful services they provide us with, have also been notoriously good at avoiding paying taxes. Had they paid more taxes, there would probably be less economic inequality to grapple with now in the first place.

So when some of the same technologists now suggest that states should address mounting inequality with basic income, it is pertinent to ask who will pay for it. Colleagues and I have previously looked into novel ways of taxing the data economy, but there are no easy solutions in sight.

Beyond the basics

Another frequently cited set of questions has to do with basic income’s expected effects on society. Sceptics fear that given a free income, most people would simply stay home and watch YouTube while society crumbles. After all, employment is tightly bound with people’s sense of identity and self-worth, and provides time structure for each day, week, and year.

Proponents, however, have faith that most would want to better themselves or help others, even if they were not explicitly paid to do so. The idea is that a guaranteed income would free people to pursue societally valuable activities that markets won’t pay them to pursue, and that current test-based benefits may even hinder them from pursuing.

But all of these activities require not just food and shelter that basic income can buy, but also other resources, such as skills, knowledge, connections, and self-confidence. The most important means through which many of these resources are cultivated is education. Deprived of this, people with nothing but a basic income may well end up sitting at home watching YouTube.

Research that colleagues and I have carried out looking at online gig workers in Southeast Asia and Sub-Saharan Africa suggests that it is not the most resource-poor who are able to benefit from new online work opportunities, but more typically those with a good level of education, health, and other resources.

The ConversationSo universal basic income is a very interesting potential solution to the rise of the gig economy and more entrepreneurial working lives in general. But whenever basic income is discussed, it is important to ask who exactly would be paying for it. And it is important to recognise that more than just money for basic necessities is needed – unless the intention is simply to store away surplus people in YouTube-enabled homes. Apart from universal basic income, we might therefore want to talk about universal basic resources, such as education as well.

Vili Lehdonvirta, Associate Professor and Senior Research Fellow, Oxford Internet Institute, University of Oxford

This article was originally published on The Conversation. Read the original article.

Could data pay for global development? Introducing data financing for global good

“If data is the new oil, then why aren’t we taxing it like we tax oil?” That was the essence of the provocative brief that set in motion our recent 6-month research project funded by the Rockefeller Foundation. The results are detailed in the new report: Data Financing for Global Good: A Feasibility Study.

The parallels between data and oil break down quickly once you start considering practicalities such as measuring and valuing data. Data is, after all, a highly heterogeneous good whose value is context-specific — very different from a commodity such as oil that can be measured and valued by the barrel. But even if the value of data can’t simply be metered and taxed, are there other ways in which the data economy could be more directly aligned with social good?

Data-intensive industries already contribute to social good by producing useful services and paying taxes on their profits (though some pay regrettably little). But are there ways in which the data economy could directly finance global causes such as climate change prevention, poverty alleviation and infrastructure? Such mechanisms should not just arbitrarily siphon off money from industry, but also contribute value back to the data economy by correcting market failures and investment gaps. The potential impacts are significant: estimates value the data economy at around seven percent of GDP in rich industrialised countries, or around ten times the value of the United Nations development aid spending goal.

Here’s where “data financing” comes in. It’s a term we coined that’s based on innovative financing, a concept increasingly used in the philanthropical world. Innovative financing refers to initiatives that seek to unlock private capital for the sake of global development and socially beneficial projects, which face substantial funding gaps globally. Since government funding towards addressing global challenges is not growing, the proponents of innovative financing are asking how else these critical causes could be funded. An existing example of innovative financing is the UNITAID air ticket levy used to advance global health.

Data financing, then, is a subset of innovative financing that refers to mechanisms that attempt to redirect a slice of the value created in the global data economy towards broader social objectives. For instance, a Global Internet Subsidy funded by large Internet companies could help to educate and and build infrastructure in the world’s marginalized regions, in the long run also growing the market for Internet companies’ services. But such a model would need well-designed governance mechanisms to avoid the pitfalls of current Internet subsidization initiatives, which risk failing because of well-founded concerns that they further entrench Internet giants’ dominance over emerging digital markets.

Besides the Global Internet Subsidy, other data financing models examined in the report are a Privacy Insurance for personal data processing, a Shared Knowledge Duty payable by businesses profiting from open and public data, and an Attention Levy to disincentivise intrusive marketing. Many of these have been considered before, and they come with significant economic, legal, political, and technical challenges. Our report considers these challenges in turn, assesses the feasibility of potential solutions, and presents rough estimates of potential financial impacts.

Some of the prevailing business models of the data economy — provoking users’ attention, extracting their personal information, and monetizing it through advertising — are more or less taken for granted today. But they are something of a historical accident, an unanticipated corollary to some of the technical and political decisions made early in the Internet’s design. Certainly they are not any inherent feature of data as such. Although our report focuses on the technical, legal, and political practicalities of the idea of data financing, it also invites a careful reader to question some of the accepted truths on how a data-intensive economy could be organized, and what business models might be possible.

Read the report: Lehdonvirta, V., Mittelstadt, B. D., Taylor, G., Lu, Y. Y., Kadikov, A., and Margetts, H. (2016) Data Financing for Global Good: A Feasibility Study. University of Oxford: Oxford Internet Institute.

The blockchain paradox: Why distributed ledger technologies may do little to transform the economy

Bitcoin’s underlying technology, the blockchain, is widely expected to find applications far beyond digital payments. It is celebrated as a “paradigm shift in the very idea of economic organization”. But the OII’s Professor Vili Lehdonvirta contends that such revolutionary potentials may be undermined by a fundamental paradox that has to do with the governance of the technology.

 

I recently gave a talk at the Alan Turing Institute (ATI) under the title The Problem of Governance in Distributed Ledger Technologies. The starting point of my talk was that it is frequently posited that blockchain technologies will “revolutionize industries that rely on digital record keeping”, such as financial services and government. In the talk I applied elementary institutional economics to examine what blockchain technologies really do in terms of economic organization, and what problems this gives rise to. In this essay I present an abbreviated version of the argument. Alternatively you can watch a video of the talk below.

 

[youtube https://www.youtube.com/watch?v=eNrzE_UfkTw&w=640&h=360]

 

First, it is necessary to note that there is quite a bit of confusion as to what exactly is meant by a blockchain. When people talk about “the” blockchain, they often refer to the Bitcoin blockchain, an ongoing ledger of transactions started in 2009 and maintained by the approximately 5,000 computers that form the Bitcoin peer-to-peer network. The term blockchain can also be used to refer to other instances or forks of the same technology (“a” blockchain). The term “distributed ledger technology” (DLT) has also gained currency recently as a more general label for related technologies.

In each case, I think it is fair to say that the reason that so many people are so excited about blockchain today is not the technical features as such. In terms of performance metrics like transactions per second, existing blockchain technologies are in many ways inferior to more conventional technologies. This is frequently illustrated with the point that the Bitcoin network is limited by design to process at most approximately seven transactions per second, whereas the Visa payment network has a peak capacity of 56,000 transactions per second. Other implementations may have better performance, and on some other metrics blockchain technologies can perhaps beat more conventional technologies. But technical performance is not why so many people think blockchain is revolutionary and paradigm-shifting.

The reason that blockchain is making waves is that it promises to change the very way economies are organized: to eliminate centralized third parties. Let me explain what this means in theoretical terms. Many economic transactions, such as long-distance trade, can be modeled as a game of Prisoners’ Dilemma. The buyer and the seller can either cooperate (send the shipment/payment as promised) or defect (not send the shipment/payment). If the buyer and the seller don’t trust each other, then the equilibrium solution is that neither player cooperates and no trade takes place. This is known as the fundamental problem of cooperation.

There are several classic solutions to the problem of cooperation. One is reputation. In a community of traders where members repeatedly engage in exchange, any trader who defects (fails to deliver on a promise) will gain a negative reputation, and other traders will refuse to trade with them out of self-interest. This threat of exclusion from the community acts as a deterrent against defection, and the equilibrium under certain conditions becomes that everyone will cooperate.

Reputation is only a limited solution, however. It only works within communities where reputational information spreads effectively, and traders may still defect if the payoff from doing so is greater than the loss of future trade. Modern large-scale market economies where people trade with strangers on a daily basis are only possible because of another solution: third-party enforcement. In particular, this means state-enforced contracts and bills of exchange enforced by banks. These third parties in essence force parties to cooperate and to follow through with their promises.

Besides trade, another example of the problem of cooperation is currency. Currency can be modeled as a multiplayer game of Prisoners’ Dilemma. Traders collectively have an interest in maintaining a stable currency, because it acts as a lubricant to trade. But each trader individually has an interest in debasing the currency, in the sense of paying with fake money (what in blockchain-speak is referred to as double spending). Again the classic solution to this dilemma is third-party enforcement: the state polices metal currencies and punishes counterfeiters, and banks control ledgers and prevent people from spending money they don’t have.

So third-party enforcement is the dominant model of economic organization in today’s market economies. But it’s not without its problems. The enforcer is in a powerful position in relation to the enforced: banks could extract exorbitant fees, and states could abuse their power by debasing the currency, illegitimately freezing assets, or enforcing contracts in unfair ways. One classic solution to the problems of third-party enforcement is competition. Bank fees are kept in check by competition: the enforced can switch to another enforcer if the fees get excessive.

But competition is not always a viable solution: there is a very high cost to switching to another state (i.e. becoming a refugee) if your state starts to abuse its power. Another classic solution is accountability: democratic institutions that try to ensure the enforcer acts in the interest of the enforced. For instance, the interbank payment messaging network SWIFT is a cooperative society owned by its member banks. The members elect a Board of Directors that is the highest decision making body in the organization. This way, they attempt to ensure that SWIFT does not try to extract excessive fees from the member banks or abuse its power against them. Still, even accountability is not without its problems, since it comes with the politics of trying to reconcile different members’ diverging interests as best as possible.

Into this picture enters blockchain: a technology where third-party enforcers are replaced with a distributed network that enforces the rules. It can enforce contracts, prevent double spending, and cap the size of the money pool all without participants having to cede power to any particular third party who might abuse the power. No rent-seeking, no abuses of power, no politics — blockchain technologies can be used to create “math-based money” and “unstoppable” contracts that are enforced with the impartiality of a machine instead of the imperfect and capricious human bureaucracy of a state or a bank. This is why so many people are so excited about blockchain: its supposed ability change economic organization in a way that transforms dominant relationships of power.

Unfortunately this turns out to be a naive understanding of blockchain, and the reality is inevitably less exciting. Let me explain why. In economic organization, we must distinguish between enforcing rules and making rules. Laws are rules enforced by state bureaucracy and made by a legislature. The SWIFT Protocol is a set of rules enforced by SWIFTNet (a centralized computational system) and made, ultimately, by SWIFT’s Board of Directors. The Bitcoin Protocol is a set of rules enforced by the Bitcoin Network (a distributed network of computers) made by — whom exactly? Who makes the rules matters at least as much as who enforces them. Blockchain technology may provide for completely impartial rule-enforcement, but that is of little comfort if the rules themselves are changed. This rule-making is what we refer to as governance.

Using Bitcoin as an example, the initial versions of the protocol (ie. the rules) were written by the pseudonymous Satoshi Nakamoto, and later versions are released by a core development team. The development team is not autocratic: a complex set of social and technical entanglements means that other people are also influential in how Bitcoin’s rules are set; in particular, so-called mining pools, headed by a handful of individuals, are very influential. The point here is not to attempt to pick apart Bitcoin’s political order; the point is that Bitcoin has not in any sense eliminated human politics; humans are still very much in charge of setting the rules that the network enforces.

There is, however, no formal process for how governance works in Bitcoin, because for a very long time these politics were not explicitly recognized, and many people don’t recognize them, preferring instead the idea that Bitcoin is purely “math-based money” and that all the developers are doing is purely apolitical plumbing work. But what has started to make this position untenable and Bitcoin’s politics visible is the so-called “block size debate” — a big disagreement between factions of the Bitcoin community over the future direction of the rules. Different stakeholders have different interests in the matter, and in the absence of a robust governance mechanism that could reconcile between the interests, this has resulted in open “warfare” between the camps over social media and discussion forums.

Will competition solve the issue? Multiple “forks” of the Bitcoin protocol have emerged, each with slightly different rules. But network economics teaches us that competition does not work well at all in the presence of strong network effects: everyone prefers to be in the network where other people are, even if its rules are not exactly what they would prefer. Network markets tend to tip in favour of the largest network. Every fork/split diminishes the total value of the system, and those on the losing side of a fork may eventually find their assets worthless.

If competition doesn’t work, this leaves us with accountability. There is no obvious path how Bitcoin could develop accountable governance institutions. But other blockchain projects, especially those that are gaining some kind of commercial or public sector legitimacy, are designed from the ground up with some level of accountable governance. For instance, R3 is a firm that develops blockchain technology for use in the financial services industry. It has enrolled a consortium of banks to guide the effort, and its documents talk about the “mandate” it has from its “member banks”. Its governance model thus sounds a lot like the beginnings of something like SWIFT. Another example is RSCoin, designed by my ATI colleagues George Danezis and Sarah Meiklejohn, which is intended to be governed by a central bank.

Regardless of the model, my point is that blockchain technologies cannot escape the problem of governance. Whether they recognize it or not, they face the same governance issues as conventional third-party enforcers. You can use technologies to potentially enhance the processes of governance (eg. transparency, online deliberation, e-voting), but you can’t engineer away governance as such. All this leads me to wonder how revolutionary blockchain technologies really are. If you still rely on a Board of Directors or similar body to make it work, how much has economic organization really changed?

And this leads me to my final point, a provocation: once you address the problem of governance, you no longer need blockchain; you can just as well use conventional technology that assumes a trusted central party to enforce the rules, because you’re already trusting somebody (or some organization/process) to make the rules. I call this blockchain’s ‘governance paradox’: once you master it, you no longer need it. Indeed, R3’s design seems to have something called “uniqueness services”, which look a lot like trusted third-party enforcers (though this isn’t clear from the white paper). RSCoin likewise relies entirely on trusted third parties. The differences to conventional technology are no longer that apparent.

Perhaps blockchain technologies can still deliver better technical performance, like better availability and data integrity. But it’s not clear to me what real changes to economic organization and power relations they could bring about. I’m very happy to be challenged on this, if you can point out a place in my reasoning where I’ve made an error. Understanding grows via debate. But for the time being, I can’t help but be very skeptical of the claims that blockchain will fundamentally transform the economy or government.

The governance of DLTs is also examined in this report chapter that I coauthored earlier this year:

Lehdonvirta, V. & Robleh, A. (2016) Governance and Regulation. In: M. Walport (ed.), Distributed Ledger Technology: Beyond Blockchain. London: UK Government Office for Science, pp. 40-45.

The blockchain paradox: Why distributed ledger technologies may do little to transform the economy

Bitcoin’s underlying technology, the blockchain, is widely expected to find applications far beyond digital payments. It is celebrated as a “paradigm shift in the very idea of economic organization”. But the OII’s Professor Vili Lehdonvirta contends that such revolutionary potentials may be undermined by a fundamental paradox that has to do with the governance of the technology.

 

I recently gave a talk at the Alan Turing Institute (ATI) under the title The Problem of Governance in Distributed Ledger Technologies. The starting point of my talk was that it is frequently posited that blockchain technologies will “revolutionize industries that rely on digital record keeping”, such as financial services and government. In the talk I applied elementary institutional economics to examine what blockchain technologies really do in terms of economic organization, and what problems this gives rise to. In this essay I present an abbreviated version of the argument. Alternatively you can watch a video of the talk below.

 

[youtube https://www.youtube.com/watch?v=eNrzE_UfkTw&w=640&h=360]

 

First, it is necessary to note that there is quite a bit of confusion as to what exactly is meant by a blockchain. When people talk about “the” blockchain, they often refer to the Bitcoin blockchain, an ongoing ledger of transactions started in 2009 and maintained by the approximately 5,000 computers that form the Bitcoin peer-to-peer network. The term blockchain can also be used to refer to other instances or forks of the same technology (“a” blockchain). The term “distributed ledger technology” (DLT) has also gained currency recently as a more general label for related technologies.

In each case, I think it is fair to say that the reason that so many people are so excited about blockchain today is not the technical features as such. In terms of performance metrics like transactions per second, existing blockchain technologies are in many ways inferior to more conventional technologies. This is frequently illustrated with the point that the Bitcoin network is limited by design to process at most approximately seven transactions per second, whereas the Visa payment network has a peak capacity of 56,000 transactions per second. Other implementations may have better performance, and on some other metrics blockchain technologies can perhaps beat more conventional technologies. But technical performance is not why so many people think blockchain is revolutionary and paradigm-shifting.

The reason that blockchain is making waves is that it promises to change the very way economies are organized: to eliminate centralized third parties. Let me explain what this means in theoretical terms. Many economic transactions, such as long-distance trade, can be modeled as a game of Prisoners’ Dilemma. The buyer and the seller can either cooperate (send the shipment/payment as promised) or defect (not send the shipment/payment). If the buyer and the seller don’t trust each other, then the equilibrium solution is that neither player cooperates and no trade takes place. This is known as the fundamental problem of cooperation.

There are several classic solutions to the problem of cooperation. One is reputation. In a community of traders where members repeatedly engage in exchange, any trader who defects (fails to deliver on a promise) will gain a negative reputation, and other traders will refuse to trade with them out of self-interest. This threat of exclusion from the community acts as a deterrent against defection, and the equilibrium under certain conditions becomes that everyone will cooperate.

Reputation is only a limited solution, however. It only works within communities where reputational information spreads effectively, and traders may still defect if the payoff from doing so is greater than the loss of future trade. Modern large-scale market economies where people trade with strangers on a daily basis are only possible because of another solution: third-party enforcement. In particular, this means state-enforced contracts and bills of exchange enforced by banks. These third parties in essence force parties to cooperate and to follow through with their promises.

Besides trade, another example of the problem of cooperation is currency. Currency can be modeled as a multiplayer game of Prisoners’ Dilemma. Traders collectively have an interest in maintaining a stable currency, because it acts as a lubricant to trade. But each trader individually has an interest in debasing the currency, in the sense of paying with fake money (what in blockchain-speak is referred to as double spending). Again the classic solution to this dilemma is third-party enforcement: the state polices metal currencies and punishes counterfeiters, and banks control ledgers and prevent people from spending money they don’t have.

So third-party enforcement is the dominant model of economic organization in today’s market economies. But it’s not without its problems. The enforcer is in a powerful position in relation to the enforced: banks could extract exorbitant fees, and states could abuse their power by debasing the currency, illegitimately freezing assets, or enforcing contracts in unfair ways. One classic solution to the problems of third-party enforcement is competition. Bank fees are kept in check by competition: the enforced can switch to another enforcer if the fees get excessive.

But competition is not always a viable solution: there is a very high cost to switching to another state (i.e. becoming a refugee) if your state starts to abuse its power. Another classic solution is accountability: democratic institutions that try to ensure the enforcer acts in the interest of the enforced. For instance, the interbank payment messaging network SWIFT is a cooperative society owned by its member banks. The members elect a Board of Directors that is the highest decision making body in the organization. This way, they attempt to ensure that SWIFT does not try to extract excessive fees from the member banks or abuse its power against them. Still, even accountability is not without its problems, since it comes with the politics of trying to reconcile different members’ diverging interests as best as possible.

Into this picture enters blockchain: a technology where third-party enforcers are replaced with a distributed network that enforces the rules. It can enforce contracts, prevent double spending, and cap the size of the money pool all without participants having to cede power to any particular third party who might abuse the power. No rent-seeking, no abuses of power, no politics — blockchain technologies can be used to create “math-based money” and “unstoppable” contracts that are enforced with the impartiality of a machine instead of the imperfect and capricious human bureaucracy of a state or a bank. This is why so many people are so excited about blockchain: its supposed ability change economic organization in a way that transforms dominant relationships of power.

Unfortunately this turns out to be a naive understanding of blockchain, and the reality is inevitably less exciting. Let me explain why. In economic organization, we must distinguish between enforcing rules and making rules. Laws are rules enforced by state bureaucracy and made by a legislature. The SWIFT Protocol is a set of rules enforced by SWIFTNet (a centralized computational system) and made, ultimately, by SWIFT’s Board of Directors. The Bitcoin Protocol is a set of rules enforced by the Bitcoin Network (a distributed network of computers) made by — whom exactly? Who makes the rules matters at least as much as who enforces them. Blockchain technology may provide for completely impartial rule-enforcement, but that is of little comfort if the rules themselves are changed. This rule-making is what we refer to as governance.

Using Bitcoin as an example, the initial versions of the protocol (ie. the rules) were written by the pseudonymous Satoshi Nakamoto, and later versions are released by a core development team. The development team is not autocratic: a complex set of social and technical entanglements means that other people are also influential in how Bitcoin’s rules are set; in particular, so-called mining pools, headed by a handful of individuals, are very influential. The point here is not to attempt to pick apart Bitcoin’s political order; the point is that Bitcoin has not in any sense eliminated human politics; humans are still very much in charge of setting the rules that the network enforces.

There is, however, no formal process for how governance works in Bitcoin, because for a very long time these politics were not explicitly recognized, and many people don’t recognize them, preferring instead the idea that Bitcoin is purely “math-based money” and that all the developers are doing is purely apolitical plumbing work. But what has started to make this position untenable and Bitcoin’s politics visible is the so-called “block size debate” — a big disagreement between factions of the Bitcoin community over the future direction of the rules. Different stakeholders have different interests in the matter, and in the absence of a robust governance mechanism that could reconcile between the interests, this has resulted in open “warfare” between the camps over social media and discussion forums.

Will competition solve the issue? Multiple “forks” of the Bitcoin protocol have emerged, each with slightly different rules. But network economics teaches us that competition does not work well at all in the presence of strong network effects: everyone prefers to be in the network where other people are, even if its rules are not exactly what they would prefer. Network markets tend to tip in favour of the largest network. Every fork/split diminishes the total value of the system, and those on the losing side of a fork may eventually find their assets worthless.

If competition doesn’t work, this leaves us with accountability. There is no obvious path how Bitcoin could develop accountable governance institutions. But other blockchain projects, especially those that are gaining some kind of commercial or public sector legitimacy, are designed from the ground up with some level of accountable governance. For instance, R3 is a firm that develops blockchain technology for use in the financial services industry. It has enrolled a consortium of banks to guide the effort, and its documents talk about the “mandate” it has from its “member banks”. Its governance model thus sounds a lot like the beginnings of something like SWIFT. Another example is RSCoin, designed by my ATI colleagues George Danezis and Sarah Meiklejohn, which is intended to be governed by a central bank.

Regardless of the model, my point is that blockchain technologies cannot escape the problem of governance. Whether they recognize it or not, they face the same governance issues as conventional third-party enforcers. You can use technologies to potentially enhance the processes of governance (eg. transparency, online deliberation, e-voting), but you can’t engineer away governance as such. All this leads me to wonder how revolutionary blockchain technologies really are. If you still rely on a Board of Directors or similar body to make it work, how much has economic organization really changed?

And this leads me to my final point, a provocation: once you address the problem of governance, you no longer need blockchain; you can just as well use conventional technology that assumes a trusted central party to enforce the rules, because you’re already trusting somebody (or some organization/process) to make the rules. I call this blockchain’s ‘governance paradox’: once you master it, you no longer need it. Indeed, R3’s design seems to have something called “uniqueness services”, which look a lot like trusted third-party enforcers (though this isn’t clear from the white paper). RSCoin likewise relies entirely on trusted third parties. The differences to conventional technology are no longer that apparent.

Perhaps blockchain technologies can still deliver better technical performance, like better availability and data integrity. But it’s not clear to me what real changes to economic organization and power relations they could bring about. I’m very happy to be challenged on this, if you can point out a place in my reasoning where I’ve made an error. Understanding grows via debate. But for the time being, I can’t help but be very skeptical of the claims that blockchain will fundamentally transform the economy or government.

The governance of DLTs is also examined in this report chapter that I coauthored earlier this year:

Lehdonvirta, V. & Robleh, A. (2016) Governance and Regulation. In: M. Walport (ed.), Distributed Ledger Technology: Beyond Blockchain. London: UK Government Office for Science, pp. 40-45.