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?

Though there is nothing qualitatively new about involving more people in government and policy processes, digital technologies in principle make it possible to increase the quantity of such involvement dramatically, by lowering the costs of participation (Margetts et al., 2015) and making it possible to tap into people’s free time (Shirky, 2010). This difference in quantity is arguably great enough to obtain a quality of its own. We can thus be justified in using the term “crowdsourcing for public policy and government” to refer to new digitally enabled ways of involving people in any aspect of democratic politics and government, not replacing but rather augmenting more traditional participation routes such as elections and referendums.

In this editorial, we will briefly highlight some of the key emerging issues in research on crowdsourcing for public policy and government. Our entry point into the discussion is a collection of research papers first presented at the Internet, Politics & Policy 2014 (IPP2014) conference organized by the Oxford Internet Institute (University of Oxford) and the Policy & Internet journal. The theme of this very successful conference—our third since the founding of the journal—was “crowdsourcing for politics and policy.” Out of almost 80 papers presented at the conference in September last year, 14 of the best have now been published as peer-reviewed articles in this journal, including five in this issue. A further handful of papers from the conference focusing on labor issues will be published in the next issue, but we can already now take stock of all the articles focusing on government, politics, and policy.

The growing interest in crowdsourcing for government and public policy must be understood in the context of the contemporary malaise of politics, which is being felt across the democratic world, but most of all in Europe. The problems with democracy have a long history, from the declining powers of parliamentary bodies when compared to the executive; to declining turnouts in elections, declining participation in mass parties, and declining trust in democratic institutions and politicians. But these problems have gained a new salience in the last five years, as the ongoing financial crisis has contributed to the rise of a range of new populist forces all across Europe, and to a fragmentation of the center ground. Furthermore, poor accuracy of pre- election polls in recent elections in Israel and the UK have generated considerable debate over the usefulness and accuracy of the traditional way of knowing what the public is thinking: the sample survey.

Many place hopes on technological and institutional innovations such as crowdsourcing to show a way out of the brewing crisis of democratic politics and political science. One of the key attractions of crowdsourcing techniques to governments and grass roots movements alike is the legitimacy such techniques are expected to be able to generate. For example, crowdsourcing techniques have been applied to enable citizens to verify the legality and correctness of government decisions and outcomes. A well-known application is to ask citizens to audit large volumes of data on government spending, to uncover any malfeasance but also to increase citizens’ trust in the government (Maguire, 2011).

Articles emerging from the IPP2014 conference analyze other interesting and comparable applications. In an article titled “Population as Auditor of an Election Process in Honduras: The Case of the VotoSocial Crowdsourcing Platform,” Carlos Arias, Jorge Garcia and Alejandro Corpeño (2015) describe the use of crowdsourcing for auditing election results. Dieter Zinnbauer (2015) discusses the potentials and pitfalls of the use of crowdsourcing for some other types of auditing purposes, in “Crowdsourced Corruption Reporting: What Petrified Forests, Street Music, Bath Towels, and the Taxman Can Tell Us About the Prospects for Its Future.”

Besides allowing citizens to verify the outcome of a process, crowdsourcing can also be used to lend an air of inclusiveness and transparency to a process itself. This process legitimacy can then indirectly legitimate the outcome of the process as well. For example, crowdsourcing-style open processes have been used to collect policy ideas, gather support for difficult policy decisions, and even generate detailed spending plans through participatory budgeting (Wampler & Avritzer, 2004). Articles emerging from our conference further advance this line of research. Roxana Radu, Nicolo Zingales and Enrico Calandro (2015) examine the use of crowdsourcing to lend process legitimacy to Internet governance, in an article titled “Crowdsourcing Ideas as an Emerging Form of Multistakeholder Participation in Internet Governance.” Graham Smith, Robert C. Richards Jr. and John Gastil (2015) write about “The Potential of Participedia as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations.”

An interesting cautionary tale is presented by Henrik Serup Christensen, Maija Karjalainen and Laura Nurminen (2015) in “Does Crowdsourcing Legislation Increase Political Legitimacy? The Case of Avoin Ministeriö in Finland.” They show how a citizen initiative process ended up decreasing government legitimacy, after the government failed to implement the outcome of an initiative process that was perceived as highly legitimate by its supporters. Taneli Heikka (2015) further examines the implications of citizen initiative processes to the state–citizen relationship in “The Rise of the Mediating Citizen: Time, Space and Citizenship in the Crowdsourcing of Finnish Legislation.”

In many of the contributions that touch on the legitimating effects of crowdsourcing, one can sense a third, latent theme. Besides allowing outcomes to be audited and processes to be potentially more inclusive, crowdsourcing can also increase the perceived legitimacy of a government or policy process by lending an air of innovation and technological progress to the endeavor and those involved in it. This is most explicitly stated by Simo Hosio, Jorge Goncalves, Vassilis Kostakos and Jukka Riekki (2015) in “Crowdsourcing Public Opinion Using Urban Pervasive Technologies: Lessons From Real-Life Experiments in Oulu.” They describe how local government officials collaborating with the research team to test a new public screen based polling system “expressed that the PR value boosted their public perception as a modern organization.” That some government crowdsourcing initatives are at least in part motivated by such “crowdwashing” is hardly surprising, but it encourages us to retain a critical approach and analyze actual outcomes instead of accepting dominant discourses about the nature and effects of crowdsourcing at face value.

For instance, we must continue to examine the actual size, composition, internal structures and motivations of the supposed “crowds” that make use of online platforms. Articles emerging from our conference that contributed towards this aim include “Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data” by Benedikt Boecking, Margeret Hall and Jeff Schneider (2015) and “Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making” by Pete Burnap and Matthew L. Williams (2015). Anatoliy Gruzd and Ksenia Tsyganova won a best paper award at the IPP2014 conference for an article published in this journal as “Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro- and Anti-Maidan Groups.” These articles can be used to challenge the notion that crowdsourcing contributors are simply sets of independent individuals who are neatly representative of a larger population, and instead highlight the clusters, networks, and power structures inherent within them. This has implications to the democratic legitimacy of some of the more naive crowdsourcing initiatives.

One of the most original articles to emerge out of IPP2014 turns the concept of crowdsourcing for public policy and government on its head. While most research has focused on crowdsourcing’s empowering effects (or lack thereof), Gregory Asmolov (2015) analyses crowdsourcing as a form of social control. In an article titled “Vertical Crowdsourcing in Russia: Balancing Governance of Crowds and State–Citizen Partnership in Emergency Situations,” Asmolov draws on empirical evidence and theorists such as Foucault to show how crowdsourcing platforms can be used to institutionalize volunteer resources in order to align them with state objectives and prevent independent collective action. An article by Jorge Goncalves, Yong Liu, Bin Xiao, Saad Chaudhry, Simo Hosio and Vassilis Kostakos (2015) provides a less nefarious example of strategic use of online platforms to further government objectives, under the title “Increasing the Reach of Government Social Media: A Case Study in Modeling Government–Citizen Interaction on Facebook.”

Articles emerging from the conference also include two review articles that provide useful overviews of the field from different perspectives. “A Systematic Review of Online Deliberation Research” by Dennis Friess and Christiane Eilders (2015) takes stock of the use of digital technologies as public spheres. “The Fundamentals of Policy Crowdsourcing” by John Prpić, Araz Taeihagh and James Melton (2015) situates a broad variety of crowdsourcing literature into the context of a public policy cycle framework.

It has been extremely satisfying to follow the progress of these papers from initial conference submissions to high-quality journal articles, and to see that the final product not only advances the state of the art, but also provides certain new and critical perspectives on crowdsourcing. These perspectives will no doubt provoke responses, and Policy & Internet continues to welcome high-quality submissions dealing with crowdsourcing for public policy, government, and beyond.

Read the full editorial: Vili Lehdonvirta andJonathan Bright (2015) Crowdsourcing for Public Policy and Government. Editorial. Volume 7, Issue 3, pages 263–267.

References

Arias, C.R., Garcia, J. and Corpeño, A. (2015) Population as Auditor of an Election Process in Honduras: The Case of the VotoSocial Crowdsourcing Platform. Policy & Internet 7 (2) 185–202.

Asmolov, G. (2105) Vertical Crowdsourcing in Russia: Balancing Governance of Crowds and State–Citizen Partnership in Emergency Situations. Policy & Internet 7 (3).

Brabham, D. C. (2013). Citizen E-Participation in Urban Governance: Crowdsourcing and Collaborative Creativity: Crowdsourcing and Collaborative Creativity. IGI Global.

Boecking, B., Hall, M. and Schneider, J. (2015) Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data. Policy & Internet 7 (2) 159–184.

Burnap P. and Williams, M.L. (2015) Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making. Policy & Internet 7 (2) 223–242.

Christensen, H.S., Karjalainen, M. and Nurminen, L. (2015) Does Crowdsourcing Legislation Increase Political Legitimacy? The Case of Avoin Ministeriö in Finland. Policy & Internet 7 (1) 25-45.

Friess, D. and Eilders, C. (2015) A Systematic Review of Online Deliberation Research. Policy & Internet 7 (3).

Goncalves, J., Liu, Y., Xiao, B., Chaudhry, S., Hosio, S. and Kostakos, V. (2015) Increasing the Reach of Government Social Media: A Case Study in Modeling Government–Citizen Interaction on Facebook. Policy & Internet 7 (1) 80-102.

Gruzd, A. and Tsyganova, K. (2015) Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro- and Anti-Maidan Groups. Policy & Internet 7 (2) 121–158.

Heikka, T. (2015) The Rise of the Mediating Citizen: Time, Space and Citizenship in the Crowdsourcing of Finnish Legislation. Policy & Internet 7 (3).

Hosio, S., Goncalves, J., Kostakos, V. and Riekki, J. (2015) Crowdsourcing Public Opinion Using Urban Pervasive Technologies: Lessons From Real-Life Experiments in Oulu. Policy & Internet 7 (2) 203–222.

Howe, J. (2006). The Rise of Crowdsourcing by Jeff Howe | Byliner. Retrieved from

Maguire, S. (2011). Can Data Deliver Better Government? Political Quarterly, 82(4), 522–525.

Margetts, H., John, P., Hale, S., & Yasseri, T. (2015): Political Turbulence: How Social Media Shape Collective Action. Princeton University Press.

Prpić, J., Taeihagh, A. and Melton, J. (2015) The Fundamentals of Policy Crowdsourcing. Policy & Internet 7 (3).

Radu, R., Zingales, N. and Calandro, E. (2015) Crowdsourcing Ideas as an Emerging Form of Multistakeholder Participation in Internet Governance. Policy & Internet 7 (3).

Shirky, C. (2010). Cognitive Surplus: How Technology Makes Consumers into Collaborators. Penguin Publishing Group.

Smith, G., Richards R.C. Jr. and Gastil, J. (2015) The Potential of Participedia as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations. Policy & Internet 7 (2) 243–262.

Wampler, B., & Avritzer, L. (2004). Participatory publics: civil society and new institutions in democratic Brazil. Comparative Politics, 36(3), 291–312.

Zinnbauer, D. (2015) Crowdsourced Corruption Reporting: What Petrified Forests, Street Music, Bath Towels, and the Taxman Can Tell Us About the Prospects for Its Future. Policy & Internet 7 (1) 1–24.

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.

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.

Today’s parliamentary decision is also a milestone for another reason: it is the first piece of “crowdsourced” legislation on its way to becoming law in Finland. A 2012 constitutional change made it possible for 50,000 citizens or more to propose a bill to the parliament, through a mechanism known as the citizen initiative. Citizens can develop bills on a website maintained by the Open Ministry, a government-supported citizen association. The Open Ministry aims to be the deliberative version of government ministries that do the background work for government bills. Once the text of a citizen bill is finalised, citizens can also endorse it on a website maintained by the Ministry of Justice. If a bill attracts more than 50,000 endorsements within six months, it is delivered to the parliament.

A significant reason behind the creation of the citien initiative system was to increase citizen involvement in decision making and thus enhance the legitimacy of Finland’s political system: to make people feel that they can make a difference. Finland, like most Western democracies, is suffering from dwindling voter turnout rates (though in the last parliamentary elections, domestic voter turnout was a healthy 70.5 percent). However, here lies one of the potential pitfalls of the citizen initiative system. Of the six citizen bills delivered to the parliament so far, parliamentarians have outright rejected most proposals. According to research presented by Christensen and his colleagues at our Internet, Politics & Policy conference in Oxford in September (and to be published in issue 7:1 of Policy and Internet, March 2015), there is a risk that the citizen iniative system ends up having an effect that is opposite from what was intended:

“[T]hose who supported [a crowdsourced bill rejected by the parliament] experienced a drop in political trust as a result of not achieving this outcome. This shows that political legitimacy may well decline when participants do not get the intended result (cf. Budge, 2012). Hence, if crowdsourcing legislation in Finland is to have a positive impact on political legitimacy, it is important that it can help produce popular Citizens’ initiatives that are subsequently adopted by Parliament.”

One reason why citizen initiatives have faced a rough time in the parliament is that they are a somewhat odd addition to the parliament’s existing ways of working. The Finnish parliament, like most parliaments in representative democracies, is used to working in a government-opposition arrangement, where the government proposes bills, and parliamentarians belonging to government parties are expected to support those bills and resist bills originating from the opposition. Conversely, opposition leaders expect their members to be loyal to their own initiatives. In this arrangement, citizen initiatives have fallen into a no-man’s land where they are endorsed by neither government nor opposition members. Thanks to the party whip system, their only hope of passing has been in being adopted by the government. But the whole point of citizen initiatives is that they would allow bills not proposed by the government to reach parliament, making the exercise rather pointless.

The marriage equality citizen initiative was able to break this pattern not only because it enjoyed immense popular support, but also because many parliamentarians saw marriage equality as a matter of conscience, where the party whip system wouldn’t apply. Parliamentarians across party lines voted in support and against the initiative, in many cases ignoring their party leaders’ instructions.

Prime Minister Alexander Stubb commented immediately after the vote that the outcome “shows the power of citizen initiatives”, “citizen democracy and direct democracy”. Now that a precedent has been set, it is possible that subsequent citizen initiatives, too, get judged more on their merits than on who proposed them. Today’s decision on marriage equality may thus turn out to be historic not only for advancing equality and fairness, but also for helping to define crowdsourcing’s role in Finnish parliamentary decision making.

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.

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.

In the journal’s inaugural issue, founding Editor-in-Chief Helen Margetts outlined what are essentially two central premises behind Policy & Internet’s launch. The first is that “we cannot understand, analyze or make public policy without understanding the technological, social and economic shifts associated with the Internet” (Margetts 2009, 1). It is simply not possible to consider public policy today without some regard for the intertwining of information technologies with everyday life and society. The second premise is that the rise of the Internet is associated with shifts in how policy itself is made. In particular, she proposed that impacts of Internet adoption would be felt in the tools through which policies are effected, and the values that policy processes embody.

The purpose of the Policy and Internet journal was to take up these two challenges: the public policy implications of Internet-related social change, and Internet-related changes in policy processes themselves. In recognition of the inherently multi-disciplinary nature of policy research, the journal is designed to act as a meeting place for all kinds of disciplinary and methodological approaches. Helen predicted that methodological approaches based on large-scale transactional data, network analysis, and experimentation would turn out to be particularly important for policy and Internet studies. Driving the advancement of these methods was therefore the journal’s third purpose. Today, the journal has reached a significant milestone: over one hundred high-quality peer-reviewed articles published. This seems an opportune moment to take stock of what kind of research we have published in practice, and see how it stacks up against the original vision.

At the most general level, the journal’s articles fall into three broad categories: the Internet and public policy (48 articles), the Internet and policy processes (51 articles), and discussion of novel methodologies (10 articles). The first of these categories, “the Internet and public policy,” can be further broken down into a number of subcategories. One of the most prominent of these streams is fundamental rights in a mediated society (11 articles), which focuses particularly on privacy and freedom of expression. Related streams are children and child protection (six articles), copyright and piracy (five articles), and general e-commerce regulation (six articles), including taxation. A recently emerged stream in the journal is hate speech and cybersecurity (four articles). Of course, an enduring research stream is Internet governance, or the regulation of technical infrastructures and economic institutions that constitute the material basis of the Internet (seven articles). In recent years, the research agenda in this stream has been influenced by national policy debates around broadband market competition and network neutrality (Hahn and Singer 2013). Another enduring stream deals with the Internet and public health (eight articles).

Looking specifically at “the Internet and policy processes” category, the largest stream is e-participation, or the role of the Internet in engaging citizens in national and local government policy processes, through methods such as online deliberation, petition platforms, and voting advice applications (18 articles). Two other streams are e-government, or the use of Internet technologies for government service provision (seven articles), and e-politics, or the use of the Internet in mainstream politics, such as election campaigning and communications of the political elite (nine articles). Another stream that has gained pace during recent years, is online collective action, or the role of the Internet in activism, ‘clicktivism,’ and protest campaigns (16 articles). Last year the journal published a special issue on online collective action (Calderaro and Kavada 2013), and the next forthcoming issue includes an invited article on digital civics by Ethan Zuckerman, director of MIT’s Center for Civic Media, with commentary from prominent scholars of Internet activism. A trajectory discernible in this stream over the years is a movement from discussing mere potentials towards analyzing real impacts—including critical analyses of the sometimes inflated expectations and “democracy bubbles” created by digital media (Shulman 2009; Karpf 2012; Bryer 2012).

The final category, discussion of novel methodologies, consists of articles that develop, analyze, and reflect critically on methodological innovations in policy and Internet studies. Empirical articles published in the journal have made use of a wide range of conventional and novel research methods, from interviews and surveys to automated content analysis and advanced network analysis methods. But of those articles where methodology is the topic rather than merely the tool, the majority deal with so-called “big data,” or the use of large-scale transactional data sources in research, commerce, and evidence-based public policy (nine articles). The journal recently devoted a special issue to the potentials and pitfalls of big data for public policy (Margetts and Sutcliffe 2013), based on selected contributions to the journal’s 2012 big data conference: Big Data, Big Challenges? In general, the notion of data science and public policy is a growing research theme.

This brief analysis suggests that research published in the journal over the last five years has indeed followed the broad contours of the original vision. The two challenges, namely policy implications of Internet-related social change and Internet-related changes in policy processes, have both been addressed. In particular, research has addressed the implications of the Internet’s increasing role in social and political life. The journal has also furthered the development of new methodologies, especially the use of online network analysis techniques and large-scale transactional data sources (aka ‘big data’).

As expected, authors from a wide range of disciplines have contributed their perspectives to the journal, and engaged with other disciplines, while retaining the rigor of their own specialisms. The geographic scope of the contributions has been truly global, with authors and research contexts from six continents. I am also pleased to note that a characteristic common to all the published articles is polish; this is no doubt in part due to the high level of editorial support that the journal is able to afford to authors, including copyediting. The justifications for the journal’s establishment five years ago have clearly been borne out, so that the journal now performs an important function in fostering and bringing together research on the public policy implications of an increasingly Internet-mediated society.

And what of my own research interests as an editor? In the inaugural editorial, Helen Margetts highlighted work, finance, exchange, and economic themes in general as being among the prominent areas of Internet-related social change that are likely to have significant future policy implications. I think for the most part, these implications remain to be addressed, and this is an area that the journal can encourage authors to tackle better. As an editor, I will work to direct attention to this opportunity, and welcome manuscript submissions on all aspects of Internet-enabled economic change and its policy implications. This work will be kickstarted by the journal’s 2014 conference (26-27 September), which this year focuses on crowdsourcing and online labor.

Our published articles will continue to be highlighted here in the journal’s blog. Launched last year, we believe this blog will help to expand the reach and impact of research published in Policy and Internet to the wider academic and practitioner communities, promote discussion, and increase authors’ citations. After all, publication is only the start of an article’s public life: we want people reading, debating, citing, and offering responses to the research that we, and our excellent reviewers, feel is important, and worth publishing.

Read the full editorial:  Lehdonvirta, V. (2014) Past and Emerging Themes in Policy and Internet Studies. Policy & Internet 6(2): 109-114.

References

Bryer, T.A. (2011) Online Public Engagement in the Obama Administration: Building a Democracy Bubble? Policy & Internet 3 (4).

Calderaro, A. and Kavada, A. (2013) Challenges and Opportunities of Online Collective Action for Policy Change. Policy & Internet (5) 1.

Hahn, R. and Singer, H. (2013) Is the U.S. Government’s Internet Policy Broken? Policy & Internet 5 (3) 340-363.

Karpf, D. (2012) Online Political Mobilization from the Advocacy Group’s Perspective: Looking Beyond Clicktivism. Policy & Internet 2 (4) 7-41.

Margetts, H. (2009) The Internet and Public Policy. Policy and Internet 1 (1).

Margetts, H. and Sutcliffe, D. (2013) Addressing the Policy Challenges and Opportunities of ‘Big Data.’ Policy & Internet 5 (2) 139-146.

Shulman, S.W. (2009) The Case Against Mass E-mails: Perverse Incentives and Low Quality Public Participation in U.S. Federal Rulemaking. Policy & Internet 1 (1) 23-53.