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.

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

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

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

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

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

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

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

Vili Lehdonvirta is a Research Fellow at the OII. He is an economic sociologist who studies the design and socioeconomic implications of digital marketplaces and platforms, using conventional social research methods as well as novel data science approaches. Read Vili’s other Policy & Internet Blog posts on Uber and Airbnb:

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

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

Should we love Uber and Airbnb or protest against them?

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

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

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

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

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

Natural selection

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

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

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

Downside or upside?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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