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):

• Décary-Hétu & Giommoni (2017), “Do police crackdowns disrupt drug cryptomarkets? A longitudinal analysis of the effects of Operation Onymous”
• Ladegaard (2017). “We Know Where You Are, What You Are Doing and We Will Catch You: Testing Deterrence Theory in Digital Drug Markets.“
• Duxbury & Haynie (2017), “Building them up, breaking them down: Topology, vendor selection patterns, and a digital drug market’s robustness to disruption”

Further reading (popular):

• Wired: “Global police spring a trap on thousands of dark web users”
• Krebs on Security: “Exclusive: Dutch Cops on AlphaBay ‘Refugees’”

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

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

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):

• Dolliver et al. (2016), “A Geographic Analysis of Drug Trafficking patterns on the Tor Network”. Geographical Review DOI: 10.1111/gere.12241
• Buskirk et al. (2016), “Who Sells What? Country Specific Differences in Substance Availability on the Agora Cryptomarket”. International Journal of Drug Policy 35: 16-23.
• Broséus et al. (2017), “A Geographical Analysis of Trafficking on a Popular Darknet Market”. Forensic Science International 277:88-102.

Further reading (popular):

• Wired: Dark net drug markets kept alive by great customer service
• The Guardian: Dark web marketplaces AlphaBay and Hansa shut down

Martin Dittus was talking to OII Managing Editor David Sutcliffe.

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

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.

So 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.

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.

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.

Homejoy was slated to become the Uber of domestic cleaning services. It was a platform that allowed customers to summon a cleaner as easily as they could hail a ride. Regular cleanups were just as easy to schedule. Ratings from previous clients attested to the skill and trustworthiness of each cleaner. There was no need to go through a cleaning services agency, or scour local classifieds to find a cleaner directly: the platform made it easy for both customers and people working as cleaners to find each other. Homejoy made its money by taking a cut out of each transaction. Given how incredibly successful Uber and Airbnb had been in applying the same model to their industries, Homejoy was widely expected to become the next big success story. It was to be the next step in the inexorable uberization of every industry in the economy.

On 17 July 2015, Homejoy announced that it was shutting down. Usage had grown slower than expected, revenues remained poor, technical glitches hurt operations, and the company was being hit with lawsuits on contractor misclassification. Investors’ money and patience had finally ran out. Journalists wrote interesting analyses of Homejoy’s demise (Forbes, TechCrunch, Backchannel). The root causes of any major business failure (or indeed success) are complex and hard to pinpoint. However, one of the possible explanations identified in these stories stands out, because it corresponds strongly with what theory on platforms and markets could have predicted. Homejoy wasn’t growing and making money because clients and cleaners were taking their relationships off-platform: after making the initial contact through Homejoy, they would simply exchange contact details and arrange further cleanups directly, taking the platform and its revenue share out of the loop. According to Forbes, only 15-20 percent of customers came back to Homejoy within a month to arrange another cleanup.

According to the theory of platforms in economics and management studies literature, platforms solve coordination problems. Digital service platforms like Uber and Airbnb solve, in particular, the problem of finding another party to transact with. Through marketing and bootstrapping efforts they ensure that both buyers and sellers sign up to the platform, and then provide match-making mechanisms to bring them together. They also provide solutions towards the problem of opportunism, that is, how to avoid being cheated by the other party. Rating systems are their main tool in this.

Platforms must compete against the existing institutional arrangements in their chosen industry. Uber has been very successful in taking away business from government-licensed taxicabs. Airbnb has captured market share from hotels and hotel booking sites. Both have also generated lots of new business: transactions that previously didn’t happen at all. It’s not that people didn’t already occassionally pay a highschool friend to give them a ride home from a party, or rent a room for the weekend from a friend of a friend who lives in New York. It’s that platforms make similar things possible even when the highschool friend is not available, or if you simply don’t know anyone with a flat in New York. Platforms coordinate people to turn what is otherwise a thin market into a thick one. Not only do platforms help you to find a stranger to transact with, but they also help you to trust that stranger.

Now consider the market for home cleaning services. Home cleaning differs from on-demand transport and short-term accommodation in one crucial way: the service is typically repeated. Through repeated interactions, the buyer and the seller develop trust in each other. They also develop knowledge capital specific to that particular relationship. The buyer might invest time into communicating their preferences and little details about their home to the seller, while the seller will gradually become more efficient at cleaning that particular home. They have little need for the platform to discipline each individual cleanup; relationships are thus soon taken off-platform. Instead of an all-encompassing Uber-style platform, all that may be needed is a classifieds site or a conventional agency that provides the initial introduction and references. Contrast this with on-demand transport and short-term accommodation, where each transaction is unique and thus each time the counterparty is a stranger — and as such a potential cheat or deadbeat. Here the platform continues to provide security after the parties have been introduced.

The case of Homejoy and the economic theory on platforms thus suggest that there are fundamental limits to the uberization of the economy. Digital service platforms can be very successful at mediating one-off transactions, but they are much less useful in industries where the exact same service is repeated many times, and where buyers and sellers develop assets specific to the relationship. Such industries are more likely to continue to be shaped by hierarchies and networks of personal relationships.

There are probably other dimensions that are also pivotal in predicting whether an industry is susceptible to uberization. Geographical span is one: there are efficiencies to be had from particular cleaners specializing in particular neighbourhoods. Yet, at the same time, online labour platforms like Upwork cater to buyers and sellers of software development (and other digitally mediated contract work) across national boundaries. I will discuss this dimension in detail in a future blog post.

Vili Lehdonvirta is a Research Fellow at the OII. He 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. Read Vili’s other Policy & Internet Blog posts on Uber and Airbnb:

Uber and Airbnb make the rules now – but to whose benefit?

Why are citizens migrating to Uber and Airbnb, and what should governments do about it?

Should we love Uber and Airbnb or protest against them?

An angry crowd has attacked Uber cars with bars and stones outside Mexico City airport, the latest in a series of worldwide protests against the ride-hailing app. More than 1,000 taxi drivers blocked streets in Rio de Janeiro a few days ago, and the service has been restricted or banned in the likes of France, Germany, Italy and South Korea. Protests have also been staged against Airbnb, the platform for renting short-term accommodation.

Neither platform shows any signs of faltering, however. Uber is available in 57 countries and produces hundreds of millions of dollars in revenues. Airbnb is available in more than 190 countries, and boasts more than 1.5 million rooms.

Journalists and entrepreneurs have been quick to coin terms that try to capture the social and economic changes associated with such platforms: the sharing economy; the on-demand economy; the peer-to-peer economy; and so on. Each perhaps captures one aspect of the phenomenon, but doesn’t make sense of all its potentials and contradictions, including why some people love it and some would smash it into pieces.

Economic sociologists believe markets are always based on an underlying infrastructure that allows people to find out what goods and services are on offer, agree prices and terms, pay, and have a reasonable expectation that the other party will honour the agreement. The oldest example is the personal social network: traders hear what’s on offer through word of mouth and trade only with those they personally know and trust.

In the modern world we can do business with strangers, too, because we have developed institutions to make this reliable, like private property, enforceable contracts, standardised weights and measures, and consumer protection. They are part of a long historical continuum, from ancient trade routes with their customs to medieval fairs with codes of conduct to the state-enforced trade laws of the early industrial era.

Natural selection

Institutional economists and economic historians theorised in the 1980s that these have gradually been evolving towards ever more efficient forms through natural selection. People switch to cheaper, easier, more secure and more efficient institutions as new technology and organisational innovations make them possible. Old and cumbersome institutions fall into disuse, says the theory, and society becomes more efficient and economically prosperous as a result.

It is easy to frame platforms as the next step in such a process. Even if they don’t replace state institutions, they can plug gaps. For example enforcing a contract in court is expensive and unwieldy. Platforms provide cheaper and easier alternatives through reputation systems where participants rate each other’s conduct and view past ratings.

Uber does this with government-licensed taxi infrastructures, for instance, addressing everything from quality and discovery to trust and payment. Airbnb provides a similarly sweeping solution to short-term accommodation rental. The sellers on these platforms are not just consumers seeking to better use their resources, but also firms and professionals switching over from the state infrastructure. It is as if people and companies were abandoning their national institutions and emigrating en masse to Platform Nation.

Downside or upside?

The natural selection theory argues that the government shouldn’t try to stop people from using the likes of Uber and Airbnb, nor impose its evidently less efficient norms on them. Let people vote with their feet. But is that an oversimplification?

If buyers switch to new institutions, for instance, sellers may have little choice but to follow. Even if taxi drivers don’t like Uber’s rules, they may find there is little business to be had outside the platform and switch anyway. In the end, whether the market shifts can boil down to power rather than choice.

Even when everyone participates willingly, the arrangement might be bad for society. It might adversely affect third parties, for example, such as Airbnb guests annoying neighbours through noise, traffic or being unfamiliar with the local rules. In the worst case, a platform can make society less efficient by creating a “free-rider economy”.

If these kinds of conflicting interests are reconciled, it is through the political institutions that govern the markets. Social scientists can often find out more about a market by looking at its political institutions than comparative efficiency. Take the hotel industry. Local governments try to balance the interests of hoteliers and their neighbours by limiting hotel business to certain zones. Airbnb has no such mandate to address the interests of third parties on an equal footing. Perhaps because of this, 74% of Airbnb properties are not in the main hotel districts, but often in ordinary residential blocks.

Of course, government regulators are at risk of being captured by incumbents, or at the very least creating rules that benefit incumbents to the detriment of possible future participants. An example would be taxi-licensing systems that strictly limit the numbers of cab operators. Whatever quality assurance this offers customers, among the main losers are excluded would-be drivers.

Against this background, platforms can look like radical reformers. For example Uber aims to create 1m jobs for women by 2020, a pledge that would likely not be possible if it adhered to government licensing requirements, as most licences are owned by men. Having said that, Uber’s definition of a “job” is much more precarious and entrepreneurial than the conventional definition. My point here is not to take sides, but to show that their social implications are very different. Both possess flaws and redeeming qualities, many of which can be traced back to their political institutions and whom they represent.

What kind of new economic institutions are platform developers creating? How efficient are they? What other consequences do they have? Whose interests are they geared to represent? These are the questions that bureaucrats, journalists, and social scientists ought to be asking. I hope we will be able to discover ways to take what is good from the old and the new, and create infrastructure for an economy that is as fair and inclusive as it is efficient and innovative.

Ride-hailing app Uber is close to replacing government-licensed taxis in some cities, while Airbnb’s accommodation rental platform has become a serious competitor to government-regulated hotel markets. Many other apps and platforms are trying to do the same in other sectors of the economy. In my previous post, I argued that platforms can be viewed in social science terms as economic institutions that provide infrastructures necessary for markets to thrive. I explained how the natural selection theory of institutional change suggests that people are migrating from state institutions to these new code-based institutions because they provide a more efficient environment for doing business. In this article, I will discuss some of the problems with this theory, and outline a more nuanced theory of institutional change that suggests that platforms’ effects on society will be complex and influence different people in different ways.

Economic sociologists like Neil Fligstein have pointed out that not everyone is as free to choose the means through which they conduct their trade. For example, if buyers in a market switch to new institutions, sellers may have little choice but to follow, even if the new institutions leave them worse off than the old ones did. Even if taxi drivers don’t like Uber’s rules, they may find that there is little business to be had outside the platform, and switch anyway. In the end, the choice of institutions can boil down to power. Economists have shown that even a small group of participants with enough market power — like corporate buyers — may be able to force a whole market to tip in favour of particular institutions. Uber offers a special solution for corporate clients, though I don’t know if this has played any part in the platform’s success.

Even when everyone participates in an institutional arrangement willingly, we still can’t assume that it will contribute to the social good. Cambridge economic historian Sheilagh Ogilvie has pointed out that an institution that is efficient for everyone who participates in it can still be inefficient for society as a whole if it affects third parties. For example, when Airbnb is used to turn an ordinary flat into a hotel room, it can cause nuisance to neighbours in the form of noise, traffic, and guests unfamiliar with the local rules. The convenience and low cost of doing business through the platform is achieved in part at others’ expense. In the worst case, a platform can make society not more but less efficient — by creating a ‘free rider economy’.

In general, social scientists recognize that different people and groups in society often have conflicting interests in how economic institutions are shaped. These interests are reconciled — if they are reconciled — through political institutions. Many social scientists thus look not so much at efficiencies but at political institutions to understand why economic institutions are shaped the way they are. For example, a democratic local government in principle represents the interests of its citizens, through political institutions such as council elections and public consultations. Local governments consequently try to strike a balance between the conflicting interests of hoteliers and their neighbours, by limiting hotel business to certain zones. In contrast, Airbnb as a for-profit business must cater to the interests of its customers, the would-be hoteliers and their guests. It has no mechanism, and more importantly, no mandate, to address on an equal footing the interests of third parties like customers’ neighbours. Perhaps because of this, 74% of Airbnb’s properties are not in the main hotel districts, but in ordinary residential blocks.

That said, governments have their own challenges in producing fair and efficient economic institutions. Not least among these is the fact that government regulators are at a risk of capture by incumbent market participants, or at the very least they face the innovator’s dilemma: it is easier to craft rules that benefit the incumbents than rules that provide great but uncertain benefits to future market participants. For example, cities around the world operate taxi licensing systems, where only strictly limited numbers of license owners are allowed to operate taxicabs. Whatever benefits this system offers to customers in terms of quality assurance, among its biggest beneficiaries are the license owners, and among its losers the would-be drivers who are excluded from the market. Institutional insiders and outsiders have conflicting interests, and government political institutions are often such that it is easier for it to side with the insiders.

Against this background, platforms appear almost as radical reformers that provide market access to those whom the establishment has denied it. For example, Uber recently announced that it aims to create one million jobs for women by 2020, a bold pledge in the male-dominated transport industry, and one that would likely not be possible if it adhered to government licensing requirements, as most licenses are owned by men. Having said that, Uber’s definition of a ‘job’ is something much more precarious and entrepreneurial than the conventional definition. My point here is not to side with either Uber or the licensing system, but to show that their social implications are very different. Both possess at least some flaws as well as redeeming qualities, many of which can be traced back to their political institutions and whom they represent.

What kind of new economic institutions are platform developers creating? How efficient are they? What other consequences, including unintended ones, do they have and to whom? Whose interests are they geared to represent — capital vs. labour, consumer vs. producer, Silicon Valley vs. local business, incumbent vs. marginalized? These are the questions that policy makers, journalists, and social scientists ought to be asking at this moment of transformation in our economic institutions. Instead of being forced to choose one or the other between established institutions and platforms as they currently are, I hope that we will be able to discover ways to take what is good in both, and create infrastructure for an economy that is as fair and inclusive as it is efficient and innovative.

Vili Lehdonvirta is a Research Fellow and DPhil Programme Director at the Oxford Internet Institute, and an editor of the Policy & Internet journal. He is an economic sociologist who studies the social and economic dimensions of new information technologies around the world, with particular expertise in digital markets and crowdsourcing.

Cars were smashed and tires burned in France last month in protests against the ride hailing app Uber. Less violent protests have also been staged against Airbnb, a platform for renting short-term accommodation. Despite the protests, neither platform shows any signs of faltering. Uber says it has a million users in France, and is available in 57 countries. Airbnb is available in over 190 countries, and boasts over a million rooms, more than hotel giants like Hilton and Marriott. Policy makers at the highest levels are starting to notice the rise of these and similar platforms. An EU Commission flagship strategy paper notes that “online platforms are playing an ever more central role in social and economic life,” while the Federal Trade Commission recently held a workshop on the topic in Washington.

Journalists and entrepreneurs have been quick to coin terms that try to capture the essence of the social and economic changes associated with online platforms: the sharing economy; the on-demand economy; the peer-to-peer economy; and so on. Each perhaps captures one aspect of the phenomenon, but doesn’t go very far in helping us make sense of all its potentials and contradictions, including why some people love it and some would like to smash it into pieces. Instead of starting from the assumption that everything we see today is new and unprecedented, what if we dug into existing social science theory to see what it has to say about economic transformation and the emergence of markets?

Economic sociologists are adamant that markets don’t just emerge by themselves: they are always based on some kind of an underlying infrastructure that allows people to find out what goods and services are on offer, agree on prices and terms, pay, and have a reasonable expectation that the other party will honour the agreement. The oldest market infrastructure is the personal social network: traders hear what’s on offer through word of mouth and trade only with those whom they personally know and trust. But personal networks alone couldn’t sustain the immense scale of trading in today’s society. Every day we do business with strangers and trust them to provide for our most basic needs. This is possible because modern society has developed institutions — things like private property, enforceable contracts, standardized weights and measures, consumer protection, and many other general and sector specific norms and facilities. By enabling and constraining everyone’s behaviours in predictable ways, institutions constitute a robust and more inclusive infrastructure for markets than personal social networks.

Modern institutions didn’t of course appear out of nowhere. Between prehistoric social networks and the contemporary institutions of the modern state, there is a long historical continuum of economic institutions, from ancient trade routes with their customs to medieval fairs with their codes of conduct to state-enforced trade laws of the early industrial era. Institutional economists led by Oliver Williamson and economic historians led by Douglass North theorized in the 1980s that economic institutions evolve towards more efficient forms through a process of natural selection. As new institutional forms become possible thanks to technological and organizational innovation, people switch to cheaper, easier, more secure, and overall more efficient institutions out of self-interest. Old and cumbersome institutions fall into disuse, and society becomes more efficient and economically prosperous as a result. Williamson and North both later received the Nobel Memorial Prize in Economic Sciences.

It is easy to frame platforms as the next step in such an evolutionary process. Even if platforms don’t replace state institutions, they can plug gaps that remain the state-provided infrastructure. For example, enforcing a contract in court is often too expensive and unwieldy to be used to secure transactions between individual consumers. Platforms provide cheaper and easier alternatives to formal contract enforcement, in the form of reputation systems that allow participants to rate each others’ conduct and view past ratings. Thanks to this, small transactions like sharing a commute that previously only happened in personal networks can now potentially take place on a wider scale, resulting in greater resource efficiency and prosperity (the ‘sharing economy’). Platforms are not the first companies to plug holes in state-provided market infrastructure, though. Private arbitrators, recruitment agencies, and credit rating firms have been doing similar things for a long time.

What’s arguably new about platforms, though, is that some of the most popular ones are not mere complements, but almost complete substitutes to state-provided market infrastructures. Uber provides a complete substitute to government-licensed taxi infrastructures, addressing everything from quality and discovery to trust and payment. Airbnb provides a similarly sweeping solution to short-term accommodation rental. Both platforms have been hugely successful; in San Francisco, Uber has far surpassed the city’s official taxi market in size. The sellers on these platforms are not just consumers wanting to make better use of their resources, but also firms and professionals switching over from the state infrastructure. It is as if people and companies were abandoning their national institutions and emigrating en masse to Platform Nation.

From the natural selection perspective, this move from state institutions to platforms seems easy to understand. State institutions are designed by committee and carry all kinds of historical baggage, while platforms are designed from the ground up to address their users’ needs. Government institutions are geographically fragmented, while platforms offer a seamless experience from one city, country, and language area to the other. Government offices have opening hours and queues, while platforms make use of latest technologies to provide services around the clock (the ‘on-demand economy’). Given the choice, people switch to the most efficient institutions, and society becomes more efficient as a result. The policy implications of the theory are that government shouldn’t try to stop people from using Uber and Airbnb, and that it shouldn’t try to impose its evidently less efficient norms on the platforms. Let competing platforms innovate new regulatory regimes, and let people vote with their feet; let there be a market for markets.

The natural selection theory of institutional change provides a compellingly simple way to explain the rise of platforms. However, it has difficulty in explaining some important facts, like why economic institutions have historically developed differently in different places around the world, and why some people now protest vehemently against supposedly better institutions. Indeed, over the years since the theory was first introduced, social scientists have discovered significant problems in it. Economic sociologists like Neil Fligstein have noted that not everyone is as free to choose the institutions that they use. Economic historian Sheilagh Ogilvie has pointed out that even institutions that are efficient for those who participate in them can still sometimes be inefficient for society as a whole. These points suggest a different theory of institutional change, which I will apply to online platforms in my next post.

Vili Lehdonvirta is a Research Fellow and DPhil Programme Director at the Oxford Internet Institute, and an editor of the Policy & Internet journal. He is an economic sociologist who studies the social and economic dimensions of new information technologies around the world, with particular expertise in digital markets and crowdsourcing.

While these problems could be addressed by new approaches and technologies for financing dynamism in Europe’s economies, financing of (potentially risky) innovation could also be held back by financial regulation that focuses on stability, avoiding forum shopping (i.e., looking for the most permissive regulatory environment), and preventing fraud, to the exclusion of other interests, particularly innovation and renewal. But the role of finance in enabling the development and implementation of new ideas is vital — an economy’s dynamism depends on innovative competitors challenging, and if successful, replacing complacent players in the markets.

However, newcomers obviously need capital to grow. As a reaction to the markets having priced risk too low before the financial crisis, risk is now being priced too high in Europe, starving the innovation efforts of private financing at a time when much public funding has suffered from austerity measures. Of course, complementary (non-bank) sources of finance can also help fund entrepreneurship, and without that petrol of money, the engine of the new technology economy will likely stall.

The Internet has made it possible to fund innovation in new ways like crowd funding — an innovation in finance itself — and there is no reason to think that financial institutions should be immune to disruptive innovation produced by new entrants that offer completely novel ways of saving, insuring, loaning, transferring and investing money. New approaches such as crowdfunding and other financial technology (aka “FinTech”) initiatives could provide depth and a plurality of perspectives, in order to foster innovation in financial services and in the European economy as a whole.

The time has come to integrate these financial technologies into the overall financial frameworks in a manner that does not neuter their creativity, or lower their potential to revitalize the economy. There are potential synergies with macro-prudential policies focused on mitigating systemic risk and ensuring the stability of financial systems. These platforms have great potential for cross-border lending and investment and could help to remedy the retreat of bank capital behind national borders since the financial crisis. It is time for a new perspective grounded in an “innovation-friendly” philosophy and regulatory approach to emerge.

Crowdfunding is a newcomer to the financial industry, and as such, actions (such as complex and burdensome regulatory frameworks or high levels of guaranteed compensation for losses) that could close it down or raise high barriers of entry should be avoided. Competition in the interests of the consumer and of entrepreneurs looking for funding should be encouraged. Regulators should be ready to step in if abuses do, or threaten to, arise while leaving space for new ideas around crowdfunding to gain traction rapidly, without being overburdened by regulatory requirements at an early stage.

The interests of both “financing innovation” and “innovation in the financial sector” also coincide in the FinTech entrepreneurial community. Schumpeter wrote in 1942: “[the] process of Creative Destruction is the essential fact about capitalism. It is what capitalism consists in and what every capitalist concern has got to live in.” An economy’s dynamism depends on innovative competitors challenging, and if successful, taking the place of complacent players in the markets. Keeping with the theme of Schumpeterian creative destruction, the financial sector is one seen by banking sector analysts and commentators as being particularly ripe for disruptive innovation, given its current profits and lax competition. Technology-driven disintermediation of many financial services is on the cards, for example, in financial advice, lending, investing, trading, virtual currencies and risk management.

The UK’s Financial Conduct Authority’s regulatory dialogues with FinTech developers to provide legal clarity on the status of their new initiatives are an example of good practice , as regulation in this highly monitored sector is potentially a serious barrier to entry and new innovation. The FCA also proactively addresses enabling innovation with Project Innovate, an initiative to assist both start-ups and established businesses in implementing innovative ideas in the financial services markets through an Incubator and Innovation Hub.

By its nature, FinTech is a sector that can benefit and benefit from the EU’s Digital Single Market and make Europe a sectoral global leader in this field. In evaluating possible future FinTech regulation, we need to ensure an optimal regulatory framework and specific rules. The innovation principle I discuss in my article should be part of an approach ensuring not only that regulation is clear and proportional — so that innovators can easily comply — but also ensuring that we are ready, when justified, to adapt regulation to enable innovations. Furthermore, any regulatory approaches should be “future proofed” and should not lock in today’s existing technologies, business models or processes.

Read the full article: Zilgalvis, P. (2014) The Need for an Innovation Principle in Regulatory Impact Assessment: The Case of Finance and Innovation in Europe. Policy and Internet 6 (4) 377–392.

Pēteris Zilgalvis, J.D. is a Senior Member of St Antony’s College, University of Oxford, and an Associate of its Political Economy of Financial Markets Programme. In 2013-14 he was a Senior EU Fellow at St Antony’s. He is also currently Head of Unit for eHealth and Well Being, DG CONNECT, European Commission.

Ed: Firstly, what is a ‘virtual’ economy? And what exactly are people earning or exchanging in these online environments?

Vili: A virtual economy is an economy that revolves around artificially scarce virtual markers, such as Facebook likes or, in this case, virtual items and currencies in an online game. A lot of what we do online today is rewarded with such virtual wealth instead of, say, money.

Ed: In terms of ‘virtual earning power’ what was the relationship between character gender and user gender?

Vili: We know that in national economies, men and women tend to be rewarded differently for the same amount of work; men tend to earn more than women. Since online economies are such a big part of many people’s lives today, we wanted to know if this holds true in those economies as well. Looking at the virtual economies of two massively-multiplayer online games (MMOG), we found that there are indeed some gender differences in how much virtual wealth players accumulate within the same number of hours played. In one game, EVE Online, male players were on average 11 percent wealthier than female players of the same age, character skill level, and time spent playing. We believe that this finding is explained at least in part by the fact that male and female players tend to favour different activities within the game worlds, what we call “virtual pink and blue collar occupations”. In national economies, this is called occupational segregation: jobs perceived as suitable for men are rewarded differently from jobs perceived as suitable for women, resulting in a gender earnings gap.

However, in another game, EverQuest II, we found that male and female players were approximately equally wealthy. This reflects the fact that games differ in what kind of activities they reward. Some provide a better economic return on fighting and exploring, while others make it more profitable to engage in trading and building social networks. In this respect games differ from national economies, which all tend to be biased towards rewarding male-type activities. Going beyond this particular study, fantasy economies could also help illuminate the processes through which particular occupations come to be regarded as suitable for men or for women, because game developers can dream up new occupations with no prior gender expectations attached.

Ed: You also discussed the distinction between user gender and character gender…

Vili: Besides occupational segregation, there are also other mechanisms that could explain economic gender gaps, like differences in performance or outright discrimination in pay negotiations. What’s interesting about game economies is that people can appear in the guise of a gender that differs from their everyday identity: men can play female characters and vice versa. By looking at player gender and character gender separately, we can distinguish between how “being” female and “appearing to be” female are related to economic outcomes.

We found that in EVE Online, using a female character was associated with slightly less virtual wealth, while in EverQuest II, using a female character was associated with being richer on average. Since in our study the players chose the characters themselves instead of being assigned characters at random, we don’t know what the causal relationship between character gender and wealth in these games was, if any. But it’s interesting to note that again the results differed completely between games, suggesting that while gender does matter, its effect has more to do with the mutable “software” of the players and/or the coded environments rather than our immutable “hardware”.

Ed: The dataset you worked with could be considered to be an example of ‘big data’ (ie you had full transactional trace data people interacting in two games) — what can you discover with this sort of data (as opposed to eg user surveys, participant observation, or ethnographies); and how useful or powerful is it?

Vili: Social researchers are used to working with small samples of data, and then looking at measures of statistical significance to assess whether the findings are generalizable to the overall population or whether they’re just a fluke. This focus on statistical significance is sometimes so extreme that people forget to consider the practical significance of the findings: even if the effect is real, is it big enough to make any difference in practice? In contrast, when you are working with big data, almost any relationship is statistically significant, so that becomes irrelevant. As a result, people learn to focus more on practical significance — researchers, peer reviewers, journal editors, funders, as well as the general public. This is a good thing, because it can increase the impact that social research has in society.

In this study, we spent a lot of time thinking about the practical significance of the findings. In any national economy, a 11 percent gap between men and women would be huge. But in virtual economies, overall wealth inequality tends to be orders of magnitude greater than in national economies, so that a 11 percent gap is in fact relatively minuscule. Other factors, like whether one is a casual participant in the economy or a semi-professional, have a much bigger effect, so much so that I’m not sure if participants notice a gender gap themselves. Thus one of the key conclusions of the study was that we also need to look beyond traditional sociodemographic categories like gender to see what new social divisions may be appearing in virtual economies.

Ed: What do you think are the hot topics and future directions in research (and policy) on virtual economies, gaming, microwork, crowd-sourcing etc.?

Vili: Previously, ICT adoption resulted in some people’s jobs being eliminated and others being enhanced. This shift had uneven impacts on men’s and women’s jobs. Today, we are seeing an Internet-fuelled “volunterization” of some types of work — moving the work from paid employees and contractors to crowds and fans compensated with points, likes, and badges rather than money. Social researchers should keep track of how this shift impacts different social categories like men and women: whose work ends up being compensated in play money, and who gets to keep the conventional rewards.

Read the full article: Lehdonvirta, V., Ratan, R. A., Kennedy, T. L., and Williams, D. (2014) Pink and Blue Pixel$: Gender and Economic Disparity in Two Massive Online Games. The Information Society 30 (4) 243-255.

Vili Lehdonvirta is a Research Fellow and DPhil Programme Director at the Oxford Internet Institute, and an editor of the Policy & Internet journal. He is an economic sociologist who studies the social and economic dimensions of new information technologies around the world, with particular expertise in digital markets and crowdsourcing.

Vili Lehdonvirta was talking to blog editor David Sutcliffe.

The excitement over the potentially transformative effects of the internet in low-income countries is nowhere more evident than in East Africa – the last major populated region of the world to gain a wired connection to the internet.

Before 2009, there wasn’t a single fibre-optic cable connecting the region to the rest of the world. After hundreds of millions of dollars of investment, cables were laid to connect the region to the global network. Prices for internet access went down, speeds went up, and the number of internet users in the region skyrocketed.

Politicians, journalists and academics all argued that better connectivity would lead to a blossoming of economic, social, and political activity – and a lot of influential people in the region made grand statements. For instance, former Kenyan president Mwai Kibai stated:

I am gratified to be with you today at an event of truly historic proportions. The landing of this fibre-optic undersea cable project in Mombasa is one of the landmark projects in Kenya’s national development story.

Indeed some have compared this to the completion of the Kenya-Uganda railway more than a century ago. This comparison is not far-fetched, because while the economies of the last century were driven by railway connections, the economies of today are largely driven by internet.

The president of Rwanda, Paul Kagame, also spoke about the revolutionary potentials of these changes in connectivity. He claimed:

In Africa, we have missed both the agricultural and industrial revolutions and in Rwanda we are determined to take full advantage of the digital revolution. This revolution is summed up by the fact that it no longer is of utmost importance where you are but rather what you can do – this is of great benefit to traditionally marginalised regions and geographically isolated populations.

As many who have studied politics have long since noted, proclamations like these can have an important impact: they frame how scarce resources can be spent and legitimise actions in certain areas while excusing inaction in others.

Two moments of change

Because the internet is so frequently talked about in revolutionary terms, colleagues Casper Andersen and Laura Mann and I decided to compare in a paper the many hopes, expectations and fears written about the internet with those from another transformational moment in East Africa’s history: the construction of the Uganda Railway.

The Uganda Railway was built from 1896 to 1903 between Mombasa and Lake Victoria, connecting parts of East Africa to each other and the region to the wider world. There were strong views at the time of what this could bring. The journalist and explorer Henry Morten Stanley claimed:

I seemed to see in a vision what was to happen in the years to come. I saw steamers trailing their dark smoke over the waters of the lake; I saw passengers arriving and disembarking; I saw the natives of the east making blood brotherhood with the natives of the west. And I seemed to hear the sound of church bells ringing at great distance afar off.

A young Winston Churchill waxed lyrical on the new railway:

What a road it is! Everything is apple-pie order. The track is smoothed and weeded and ballasted as if it were London and North-Western. Every telegraph post has its number; every mile, every hundred yards, every change of gradient, has its mark … Here and there, at intervals which will become shorter every year, are plantations of rubber, fibre and cotton, the beginnings of those inexhaustible supplies which will one day meet the yet unmeasured demand of Europe for those indispensable commodities… In brief, one slender thread of scientific civilisation, of order, authority, and arrangement, drawn across the primeval chaos of the world.

Learning from expectations

After a full analysis of the historical and contemporary texts, we can make two key points.

The hopes and fears people hold about changes to how they connect with other people and places are surprisingly similar across generations. But there are notable differences between these two moments, a century apart.

The arrival of the railway revolved around the use of technology to integrate an empire and open up new lands to imperial ambitions. By framing the arrival of the railway as allowing the core to extend its dominion over the periphery, what was said and written at the time served to legitimise the extension of colonialism.

The arrival of fibre-optic cables, however, presents a different story. Instead of a world of shrinking space between the core and periphery, it tends to lean more on the idea of a “global village.” The need to connect everyone to the global economy overrides concepts of self sufficiency, local economies, or trade outside the global marketplace.

These visions matter because they leave little room for alternatives. Just as dominant narratives around the arrival of the railway presented a worldview amenable to colonialism, contemporary dominant narratives offer a convenient justification of globalised capitalism and neo-liberalism.

How people imagine they are connected to the world matters. They shape how we make sense of the world and ultimately what steps we take to re-shape the world. We should therefore look to the past, and not just the future, when we examine the effects of the changing ways in which we are connected.

Mark: We are studying online freelancing. In other words this is digital or digitised work for which professional certification or formal training is usually not required. The work is monetised or monetisable, and can be mediated through an online marketplace.

Freelancing is a very old format of work. What is new is the fact that we have almost three billion people connected to a global network: many of those people are potential workers in virtual production networks. This mass connectivity has been one crucial ingredient for some significant changes in how work is organised, divided, outsourced, and rewarded. What we plan to do in this project is better map the contours of some of those changes and understand who wins and who doesn’t in this new world of work.

Ed: Are you able to define what comprises an individual contribution to a ‘virtual production network’ — or to find data on it? How do you define and measure value within these global flows and exchanges?

Mark: It is very far from easy. Much of what we are studying is immaterial and digitally-mediated work. We can find workers and we can find clients, but the links between them are often opaque and black-boxed. Some of the workers that we have spoken to operate under non-disclosure agreements, and many actually haven’t been told what their work is being used for.

But that is precisely why we felt the need to embark on this project. With a combination of quantitative transaction data from key platforms and qualitative interviews in which we attempt to piece together parts of the network, we want to understand who is (and isn’t) able to capture and create value within these networks.

Ed: You note that “within virtual production networks, are we seeing a shift in the boundaries of firms” — to what extend to you think we seeing the emergence of new forms of organisation?

Mark: There has always been a certain spatial stickiness to some activities carried out by firms (or within firms). Some activities required the complex exchanges of knowledge that were difficult to digitally mediate. But digitisation and better connectivity in low-wage countries has now allowed many formerly ‘in-house’ business processes to be outsourced to third-parties. In an age of cloud computing, cheap connectivity, and easily accessible collaboration tools, geography has become less sticky. One task that we are engaged in is looking at the ways that some kinds of tacit knowledge that are difficult to transmit digitally offer some people and firms (in different places) competitive advantages and disadvantages.

This proliferation of digitally mediated work could also be seen as a new form of organisation. The organisations that control key work marketplaces (like oDesk) make decisions that shape both who buyers and sellers are able to connect with, and the ways in which they are able to transact.

Ed: Does ‘virtual work’ add social or economic value to individuals in low-income countries? ie are we really dealing with a disintermediated, level surface on a global playing field, or just a different form of old exploitation (ie a virtual rather than physical extraction industry)?

Mark: That is what we aim to find out. Many have pointed to the potentials of online freelancing to create jobs and bring income to workers in low-income countries. But many others have argued that such practices are creating ‘digital sweatshops’ and facilitating a race to the bottom.

We undoubtedly are not seeing a purely disintermediated market, or a global playing field. But what we want to understand is who exactly benefits from these new networks of work, and how.

Ed: Will you be doing any network analysis of the data you collect, ie of actual value-flows? And will they be geolocated networks?

Mark: Yes! I am actually preparing a post that contains a geographic network of all work conducted over the course of a month via oDesk (see the website of the OII’s Connectivity, Inclusion, and Inequality Group for more..).

Mark Graham was talking to blog editor David Sutcliffe.

Mark Graham is a Senior Research Fellow at the OII. His research focuses on Internet and information geographies, and the overlaps between ICTs and economic development.

Digital gaming, once a stigmatized hobby, is now a mainstream cultural activity. According to the Oxford Internet Survey, more than half of British Internet users play games online; more in fact, than watch films or pornography online. Most new games today contain some kind of a virtual economy: that is, a set of processes for the production, allocation, and consumption of artificially scarce virtual goods. Often the virtual economy is very simple; sometimes, as in massively multiplayer online game EVE Online, it starts to approach the scale and complexity of a small national economy.

Just like national economies, virtual economies incentivize certain behaviours and discourage others; they ask people to make choices between mutually exclusive options; they ask people to coordinate. They can also propagate value systems setting out what modes of participation are considered valuable. These virtual economies are now built into many of the most popular areas of the Internet, including social media sites and knowledge commons — with their systems of artificially scarce likes, stars, votes, and badges. Understanding these economies is therefore crucial to anyone who is interested in the social dynamics and power relations of digital media today.

But a question I am asked a lot is: what can ‘real’ economies and the economists who run them learn from these virtual economies? We might start by imagining how a textbook economist would approach the economy of an online game. In EVE Online, hundreds of thousands of players trade minerals, spaceship components and other virtual commodities on a number of regional marketplaces. These marketplaces are very sophisticated, resembling real commodity spot markets.

Our economist would doubtless point out several ways its efficiency could be radically improved. For example, EVE players can only see prices quoted in their current region, likely missing a better deal available elsewhere. (In physical commodity markets, prices are instantly broadcast worldwide: you wouldn’t pay more for gold in Tokyo than you would in New York.) Our economist knows that providing more information to market participants increases the market’s efficiency, and might therefore suggest modifying the game such that all players gain instant and galaxy-wide access to the same price information. This would improve the overall efficiency of the galactic market.

This change would obviously be a blow to those players who have specialized in gathering and trading this price information. It would also reduce the opportunities for arbitrageurs: players who rummage the galaxy for underpriced goods, transporting them to regions where they will fetch a profit. Of course, these players could always turn themselves into haulers, the space equivalent of truck drivers. Increased efficiency would probably increase cross-regional trade, meaning a boom-time for haulers.

But wait – realizing the infinite malleability of virtual economies, the textbook economist might decide to eliminate regions altogether. Distance is what economists refer to as a transaction cost: the economy would run much more efficiently without the need to transport things around. In a virtual environment goods and characters could be instantly teleported, or the galaxy simply collapsed into a single, dimensionless point. The efficiency of the virtual economy would certainly be greatly improved. But who would pay a subscription fee to participate in such a boring economy!

Why did our strawman economist make such a horrible mess of the game economy? Conventional economic laws are work equally well in virtual environments: the equilibrium price of a commodity in a competitive market is determined by the interaction of supply and demand, regardless of whether you are in the market for magic swords or soya beans. The crucial difference is in the objectives the economy is intended to fulfil. When conventional economists design and analyse economies, they take it as read that the purpose of the economy and its institutions is to solve the so-called economic problem: the allocation of limited resources so as to best satisfy human needs. Microeconomists do this by designing mechanisms that are as efficient as possible, while macroeconomists are concerned with maximizing economic output.

But in game economies, the economic problem doesn’t really exist. The needs that players experience are contrived, created by positioning otherwise useless goods (magic swords) as desirable status items. The scarcity of resources is likewise artificial, enforced through programme code. If games designers wanted to solve the economic problem, they could do it with a few keystrokes; no markets or other economic institutions are required for this purpose.

Different multiplayer game economies have different aims, but one key objective stands out: the economy helps create and hold together the social fabric of the game. Regular interaction generates interpersonal ties and trust. Having people consume the fruits of one’s digital labour generates a sense of meaning, a sense of a role to play in the community. Division of labour and the resulting mutual interdependence moreover creates solidarity and social cohesion. In short, the economy can act as a wonderful glue holding people together.

The social fabric is important to game developers, because the stronger the ties between players, the longer the players will keep playing (and paying fees). Some games developers expend considerable resources in their own style of economic research, experimenting with different exchange mechanisms and institutions to find the designs that really strengthen the social fabric. When we examine the resulting virtual economies we can see that their design choices are often very different from the choices that a conventional economist would make.

I will give an example. One aspect of designing a market is designing an exchange mechanism: the concrete mechanism through which the buyer and the seller meet, settle on a price and quantity, and execute the transaction. The simplest exchange mechanism is two people meeting face to face to negotiate a trade, and then exchanging the goods on the spot. A more sophisticated mechanism is an online auction, like eBay. Stock markets use an even more sophisticated mechanism, where participants submit buy and sell offers, these are matched by an algorithm, and trades are executed automatically.

Given that many exchange mechanisms are possible, what kind of an exchange mechanism should be build into your market? When governments and companies create markets they usually turn to microeconomists specializing in this kind of mechanism design. The microeconomist’s answer is that you should choose the exchange mechanism that is most efficient, in the sense of allocating goods optimally and minimizing all transaction costs: in the best case it may not even be necessary for the buyer and the seller to know each others’ identities.

Games economists, in contrast, tend to favour exchange mechanisms that involve social interaction; often through a virtual face-to-face meeting, where the tedious parts (explaining item characteristics) are automated, but negotiation over prices and quantities is conducted manually. Some locations in the virtual world often spontaneously emerge as sort of bazaars, where buyers and sellers congregate to search for deals. These double as social hubs where people come to meet friends and put on performances and displays, thereby building social capital. One might later consider one’s trading acquaintances when putting together a team for some quest.

More sophisticated exchange mechanisms, such as auction houses and the commodity spot markets in EVE Online, are also common in games, but they also avoid completely displacing social trade networks. In EVE Online, players must either move around in space or use their social networks to obtain price information from neighboring localities. This way, EVE Online’s developers have struck a balance between efficiency and social ties. One thing that virtual economies can teach is to look for other objectives besides efficiency and output as variables that need to be maximized in an economic system.

I would argue that a focus on social fabric — rather than just on efficiency and output – can usefully inform national economies. First, in today’s affluent societies, we are close to solving the economic problem: in the United Kingdom or the United States the need for life-sustaining material basics is all but fulfilled. Keynes predicted 90 years ago that the economic problem will be solved within 100 years; in the affluent parts of the world, it looks like he may have been right. The greatest problem faced by the UK and US today is not the economic problem, but the disintegration of the social fabric. Virtual economies show how economic institutions could be arranged so as to strengthen it.

Second, even in that greater part of the world where the economic problem still remains acute, it is not the case that we should focus on it exclusively. Poor countries should not have to go through social disintegration to reach economic affluence. Third, as I have already mentioned, games and virtual economies have become significant phenomena in their own right. Their creators are smart people who have developed many economic insights of their own. They are eager for knowledge on how to better design and operate these economies, but conventional economic advice that focuses solely on efficiency fails to address their needs. Economists and economic sociologists should widen their research to develop answers that satisfy the needs of virtual economy designers – and also of a more ‘social’ national economy.

Now, to be fair, the fact that markets and other modern economic institutions can serve important social functions has been known to sociologists since Émile Durkheim. But this has been regarded as something of a side effect, and certainly not the purpose for which these institutions are created. Game economies are radical in this respect – that they are created entirely to serve these other functions, rather than any material function. Economic anthropologist Karl Polanyi argued in The Great Transformation that in the transition from a traditional to a market society, social structure was rearranged to serve the needs of the economy. What game economies do is in some ways the opposite: they rearrange the economy to serve the needs of the social structure. And that would seem to be a very worthwhile endeavor.

Read more: Vili Lehdonvirta and Edward Castronova (2014) Virtual Economies: Design and Analysis. MIT Press.

Vili Lehdonvirta is a Research Fellow at OII. He is an economic sociologist who studies the social and economic dimensions of new information technologies around the world. His particular areas of expertise are virtual goods, virtual currencies, and digital labour. Vili’s book Virtual Economies: Design and Analysis (with Edward Castronova) is published by MIT Press.

Ed: Concerns have been expressed (eg by Hillary Clinton and David Cameron) about the detrimental role China may play in African media sectors, by increasing authoritarianism and undermining Western efforts to promote openness and freedom of expression. Are these concerns fair?

Iginio: China’s initiatives in the communication sector abroad are burdened by the negative record of its domestic media. For the Chinese authorities this is a challenge that does not have an easy solution as they can’t really use their international broadcasters to tell a different story about Chinese media and Chinese engagement with foreign media, because they won’t be trusted. As the linguist George Lakoff has explained, if someone is told “Don’t think of an elephant!” he will likely start “summoning the bulkiness, the grayness, the trunkiness of an elephant”. That is to say, “when we negate a frame, we evoke a frame”. Saying that “Chinese interventions are not increasing authoritarianism” won’t help much. The only path China can undertake is to develop projects and use its media in ways that fall outside the realm of what is expected, creating new associations between China and the media, rather than trying to redress existing ones. In part this is already happening. For example, CCTV Africa, the new initiative of state-owned China’s Central Television (CCTV) and China’s flagship effort to win African hearts and minds, has developed a strategy aimed not at directly offering an alternative image of China, but at advancing new ways of looking at Africa, offering unprecedented resources to African journalists to report from the continent and tapping into the narrative of a “rising Africa”, as a continent of opportunities rather than of hunger, wars and underdevelopment.

Ed: Ideology has disappeared from the language of China-Africa cooperation, largely replaced by admissions of China’s interest in Africa’s resources and untapped potential. Does politics (eg China wanting to increase its international support and influence) nevertheless still inform the relationship?

China’s efforts in Africa during decolonisation were closely linked to its efforts to export and strengthen the socialist revolution on the continent. Today the language of ideology has largely disappeared from public statements, leaving less charged references to the promotion of “mutual benefit” and “sovereignty and independence” as guides of the new engagement. At the same time, this does not mean that the Chinese government has lost interest in engaging at the political/ideological level when the conditions allow. Identity of political views is not a precondition for engagement anymore but neither is it an aspiration, as China is not necessarily trying to influence local politics in ways that could promote socialism. But when there is already a resonance with the ideas embraced by its partners, Chinese authorities have not shied away from taking the engagement to a political/ideological level. This is demonstrated for example by party to party ties between the Communist Party of China (CUC) and other Socialist parties in Africa, including the Ethiopian People’s Revolutionary Democratic Front. Representative of the CUC have been invited to attend the EPRDF’s party conferences.

Ed: How much influence does China have on the domestic media / IT policies of the nations it invests in? Is it pushing the diffusion of its own strategies of media development and media control abroad? (And what are these strategies if so?)

Iginio: The Chinese government has signalled its lack of interest in exporting its own development model, and its intention to simply respond to the demands of its African partners. Ongoing research has largely confirmed that this ‘no strings attached’ approach is consistent, but this does not mean that China’s presence on the continent is neutral or has no impact on development policies and practices. China is indirectly influencing media/IT policies and practices in at least three ways.

First, while Western donors have tended to favour media projects benefiting the private sector and the civil society, often seeking to create incentives for the state to open a dialogue with other forces in society, China has exhibited a tendency to privilege government actors, thus increasing governments’ capacity vis-à-vis other critical components in the development of a media and telecommunication systems.

Second, with the launch of media projects such as CCTV Africa China has dramatically boosted its potential to shape narratives, exert soft power, and allow different voices to shape the political and development agenda. While international broadcasters such as the BBC World Service and Aljazeera have often tended to rely on civil society organisations as gatekeepers of information, CCTV has so far shown less interest in these actors, privileging the formal over the informal and also as part of its effort to provide more positive news from the continent.

Third, China’s domestic example to balance between investment in media and telecommunication and efforts to contain the risks of political instability that new technologies may bring, has the potential to act as a legitimising force for other states that share concerns of balancing both development and security, and that are actively seeking justifications for limiting voices and uses of technology that are considered potentially destabilising.

Ed: Is China developing tailored media models for abroad, or even using Africa as a “development lab”? How does China’s interest in Africa’s mediascape compare with its interest in other regions worldwide?

Iginio: There are concerns that, just as Western countries have tried to promote their models in Africa, China will try to export its own. As mentioned earlier, no studies to date have proved this to be the case. Rather, Africa indeed seems to be emerging as a “development lab”, a terrain in which to experiment and progressively find new strategies for engagement. Despite Africa’s growing importance for China as a trading and geostrategic partner, the continent is still perceived as a space where it is possible to make mistakes. In the case of the media, this is resulting in greater opportunities for journalists to experiment with new styles and enjoy freedoms that would be more difficult to obtain back in China, or even in the US, where CCTV has launched another regional initiative, CCTV America, which is more burdened, however, by the ideological confrontation between the two countries.

As part of Oxford’s Programme in Comparative Media Law and Policy‘s (PCMLP’s) ongoing research on China’s role in the media and communication sector in Africa, we have proposed a framework that can encourage understanding of Chinese engagement in the African mediasphere in terms of its original contributions, and not simply as a negative of the impression left by the West. This framework breaks down China’s actions on the continent according to China’s ability to act as a partner, a prototype, and a persuader, questioning, for example, whether or not media projects sponsored by the Chinese government are facilitating the diffusion of some aspects that characterise the Chinese domestic media system, rather than assuming this will be the case.

China’s role as a partner is evident in the significant resources it provides to African countries to implement social and economic development projects, including the laying down of infrastructure to increase Internet and mobile access. China’s perception as a prototype is linked to the ability its government has shown in balancing between investment in media and ICTs and containment of the risks of political instability new technologies may bring. Finally, China’s presence in Africa can be assessed according to its modality and ability to act as a persuader, as it seeks to shape national and international narratives.

So far we have employed this framework only to look at Chinese engagement in Africa, focusing in particular on Ghana, Ethiopia and Kenya, but we believe it can be applied also in other areas where China has stepped up its involvement in the ICT sector.

Ed: Has there been any explicit conflict yet between Chinese and non-Chinese news corporations vying for influence in this space? And how crowded is that space?

Iginio: The telecommunication sector in Africa is increasingly crowded as numerous international corporations from Europe (e.g. Vodafone), India (e.g. Airtel) and indeed China (e.g. Huawei and ZTE) are competing for shares of a profitable and growing market. Until recently Chinese companies have avoided competing with one another, but things are slowly changing. In Ethiopia, for example, after an initial project funded by the Chinese government to upgrade the telecommunication infrastructure was entirely commissioned to Chinese telecom giant ZTE, which is partially owned by the state, now ZTE has entered in competition with its Chinese (and privately owned) rival, Huawei, to benefit from an extension of the earlier project. In Kenya Huawei even decided to take ZTE to court over a project its rival won to supply the Kenyan police with a communication and surveillance system. Chinese investments in the telecommunication sectors in Africa have been part of the government’s strategy of engagement in the continent, but profit seems to have become an increasingly important factor, even if this may interfere with this strategy.

Ed: How do the recipient nations regard China’s investment and influence? For example, is there any evidence that authoritarian governments are seeking to adopt aspects of China’s own system?

Iginio: China is perceived as an example mostly by those countries that are seeking to balance between investment in ICTs and containment of the risks of political instability new technologies may bring. In a Wikileaks cable reporting a meeting between Sebhat Nega, one of the Ethiopian government’s ideologues, and the then US ambassador Donald Yamamoto, for example, Sebhat was reported to have openly declared his admiration for China and stressed that Ethiopia “needs the China model to inform the Ethiopian people”.

Iginio Gagliardone is a British Academy Post-Doctoral Research Fellow at the Centre for Socio-Legal Studies, University of Oxford. His research focuses on the role of the media in political change, especially in Sub-Saharan Africa, and the adaptation of international norms of freedom of expression in authoritarian regimes. Currently, he is exploring the role of emerging powers such as China in promoting alternative conceptions of the Internet in Africa. In particular he is analysing whether and how the ideas of state stability, development and community that characterize the Chinese model are influencing and legitimizing the development of a different conception of the information society.

Iginio Gagliardone was talking to blog editor David Sutcliffe.

Ed: What persuasive technologies might we routinely meet online? And how are they designed to guide us towards certain decisions?

There’s a broad spectrum, from the very simple to the very complex. A simple example would be something like Amazon’s “one-click” purchase feature, which compresses the entire checkout process down to a split-second decision. This uses a persuasive technique known as “reduction” to minimise the perceived cost to a user of going through with a purchase, making it more likely that they’ll transact. At the more complex end of the spectrum, you have the whole set of systems and subsystems that is online advertising. As it becomes easier to measure people’s behaviour over time and across media, advertisers are increasingly able to customise messages to potential customers and guide them down the path toward a purchase.

It isn’t just commerce, though: mobile behavior-change apps have seen really vibrant growth in the past couple years. In particular, health and fitness: products like Nike+, Map My Run, and Fitbit let you monitor your exercise, share your performance with friends, use social motivation to help you define and reach your fitness goals, and so on. One interesting example I came across recently is called “Zombies, Run!” which motivates by fright, spawning virtual zombies to chase you down the street while you’re on your run.

As one final example, If you’ve ever tried to deactivate your Facebook account, you’ve probably seen a good example of social persuasive technology: the screen that comes up saying, “If you leave Facebook, these people will miss you” and then shows you pictures of your friends. Broadly speaking, most of the online services we think we’re using for “free” — that is, the ones we’re paying for with the currency of our attention — have some sort of persuasive design goal. And this can be particularly apparent when people are entering or exiting the system.

Ed: Advertising has been around for centuries, so we might assume that we have become clever about recognizing and negotiating it — what is it about these online persuasive technologies that poses new ethical questions or concerns?

The ethical questions themselves aren’t new, but the environment in which we’re asking them makes them much more urgent. There are several important trends here. For one, the Internet is becoming part of the background of human experience: devices are shrinking, proliferating, and becoming more persistent companions through life. In tandem with this, rapid advances in measurement and analytics are enabling us to more quickly optimise technologies to reach greater levels of persuasiveness. That persuasiveness is further augmented by applying knowledge of our non-rational psychological biases to technology design, which we are doing much more quickly than in the design of slower-moving systems such as law or ethics. Finally, the explosion of media and information has made it harder for people to be intentional or reflective about their goals and priorities in life. We’re living through a crisis of distraction. The convergence of all these trends suggests that we could increasingly live our lives in environments of high persuasive power.

To me, the biggest ethical questions are those that concern individual freedom and autonomy. When, exactly, does a “nudge” become a “push”? When we call these types of technology “persuasive,” we’re implying that they shouldn’t cross the line into being coercive or manipulative. But it’s hard to say where that line is, especially when it comes to persuasion that plays on our non-rational biases and impulses. How persuasive is too persuasive? Again, this isn’t a new ethical question by any means, but it is more urgent than ever.

These technologies also remind us that the ethics of attention is just as important as the ethics of information. Many important conversations are taking place across society that deal with the tracking and measurement of user behaviour. But that information is valuable largely because it can be used to inform some sort of action, which is often persuasive in nature. But we don’t talk nearly as much about the ethics of the persuasive act as we do about the ethics of the data. If we did, we might decide, for instance, that some companies have a moral obligation to collect more of a certain type of user data because it’s the only way they could know if they were persuading a person to do something that was contrary to their well-being, values, or goals. Knowing a person better can be the basis not only for acting more wrongly toward them, but also more rightly.

As users, then, persuasive technologies require us to be more intentional about how we define and express our own goals. The more persuasion we encounter, the clearer we need to be about what it is we actually want. If you ask most people what their goals are, they’ll say things like “spending more time with family,” “being healthier,” “learning piano,” etc. But we don’t all accomplish the goals we have — we get distracted. The risk of persuasive technology is that we’ll have more temptations, more distractions. But its promise is that we can use it to motivate ourselves toward the things we find fulfilling. So I think what’s needed is more intentional and habitual reflection about what our own goals actually are. To me, the ultimate question in all this is how we can shape technology to support human goals, and not the other way around.

Ed: What if a persuasive design or technology is simply making it easier to do something we already want to do: isn’t this just ‘user centered design’? (ie a good thing?)

Yes, persuasive design can certainly help motivate a user toward their own goals. In these cases it generally resonates well with user-centered design. The tension really arises when the design leads users toward goals they don’t already have. User-centered design doesn’t really have a good way to address persuasive situations, where the goals of the user and the designer diverge.

To reconcile this tension, I think we’ll probably need to get much better at measuring people’s intentions and goals than we are now. Longer-term, we’ll probably need to rethink notions like “design” altogether. When it comes to online services, it’s already hard to talk about “products” and “users” as though they were distinct entities, and I think this will only get harder as we become increasingly enmeshed in an ongoing co-evolution.

Governments and corporations are increasingly interested in “data-driven” decision-making: isn’t that a good thing? Particularly if the technologies now exist to collect ‘big’ data about our online actions (if not intentions)?

I don’t think data ever really drives decisions. It can definitely provide an evidentiary basis, but any data is ultimately still defined and shaped by human goals and priorities. We too often forget that there’s no such thing as “pure” or “raw” data — that any measurement reflects, before anything else, evidence of attention.

That being said, data-based decisions are certainly preferable to arbitrary ones, provided that you’re giving attention to the right things. But data can’t tell you what those right things are. It can’t tell you what to care about. This point seems to be getting lost in a lot of the fervour about “big data,” which as far as I can tell is a way of marketing analytics and relational databases to people who are not familiar with them.

The psychology of that term, “big data,” is actually really interesting. On one hand, there’s a playful simplicity to the word “big” that suggests a kind of childlike awe where words fail. “How big is the universe? It’s really, really big.” It’s the unknown unknowns at scale, the sublime. On the other hand, there’s a physicality to the phrase that suggests an impulse to corral all our data into one place: to contain it, mould it, master it. Really, the term isn’t about data abundance at all – it reflects our grappling with a scarcity of attention.

The philosopher Luciano Floridi likens the “big data” question to being at a buffet where you can eat anything, but not everything. The challenge comes in the choosing. So how do you choose? Whether you’re a government, a corporation, or an individual, it’s your ultimate aims and values — your ethical priorities — that should ultimately guide your choosiness. In other words, the trick is to make sure you’re measuring what you value, rather than just valuing what you already measure.

James Williams is a doctoral student at the Oxford Internet Institute. He studies the ethical design of persuasive technology. His research explores the complex boundary between persuasive power and human freedom in environments of high technological persuasion.

James Williams was talking to blog editor Thain Simon.

Even if in most cases, this data is only likely to be used in an anonymised and aggregated form to identify trends in transport or shopping patterns, or to personalise the Internet services available to us, the fact that its collection is now so routine and so extensive should make us question whether the regulatory system governing data collection, storage and use is fit for purpose. A forthcoming OII policy forum on Tracing the Policy Implications of the Future Digital Economy (16 Feb) will consider this question, bringing together leading academics from across several disciplines with policy-makers and industry experts.

This is a topic which the OII is well-placed to address. Ian Brown’s Privacy Values Network project addresses a major knowledge gap, measuring the various costs and benefits to individuals of handing over data in different contexts, as without this we simply don’t know how much people value their privacy (or indeed understand its limits). The last Oxford Internet Survey (OxIS) rather surprisingly showed that in 2009 people were significantly less concerned about privacy online in the UK than in previous years (45% of all those surveyed in 2009 against 66% in 2007); we wait to see whether this finding is repeated when OxIS 2011 goes into the field next month.

Our faculty also have much to say about the adequacy (or otherwise) of the regulatory framework: a recent report by Ian Brown and Douwe Korff on New Challenges to Data Protection identified for the European Commission the scale of challenges presented to the current data protection regime, whilst Viktor-Mayer Schoenberger’s book Delete: The Virtue of Forgetting in the Digital Age has rightly raised the suggestion that personal information online should have an expiration date, to ensure it doesn’t hang around for years to embarrass us at a later date.

The forum will consider the way in which the market for information storage and collection is rapidly changing with the advent of new technologies, and on this point, one conclusion is clear: if we accept Helen Nissenbaum’s contention that personal information and data should be collected and protected according to the social norms governing different social contexts, then we need to get to grips pretty fast with the way in which these technologies are playing out in the way we work, play, learn and consume.