Data in society – Page 3 – Data Big and Small

Data and theory: substitutes or complements? Lessons from history of economics

Today, my chapter on “Formalization and mathematical modelling” is published in a new series of three reference books on History of Economic Analysis (edited by G. Faccarello and H. Kurz, Edward Elgar). The chapter draws heavily on key ideas I developed as part of my thesis on the origins of mathematical economics. But this was a long time ago and reading it again today, I see it in a different light. I notice in particular that economics developed its distinctive mathematical flavour, which makes it neatly stand out relative to the other social sciences, at times in which social research was data-poor – and it did so not despite data paucity, but precisely because of it. William S. Jevons, a 19th-century forefather of the discipline who was clearly aware of the relevance of maths, wrote in 1871:

“The data are almost wholly deficient for the complete solution of any one problem”

yet:

“we have mathematical theory without the data requisite for precise calculation”

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First steps toward “Data Inclusion”

The concept of “data inclusion” is new and still slowly seeking its way in our linguistical habits, but it is gaining ground in the minds of those who care for disadvantaged, low-income, or otherwise underserved segments of society. A recent report of the US Federal Trade Commission (FTC) does precisely this. Looking at the commercial use of big data analytics, it considers cases in which big data analytics lead companies to make choices that are detrimental to the most vulnerable segments of society, for example by excluding them from credit or from employment opportunities. Instead, it asks how big data may be used in inclusive ways.

A first set of recommendations they make is for companies to be well aware of the regulations: on financial and credit reporting, equal opportunities, consumer protection. The second set of recommendations, though specifically aimed at research done in (or for) companies, is of relevance for public research as well, and consists in asking key questions about the quality of data and models, and about the reliability and validity of results:

How representative is your data set? In popular discourse, big data carry a promise of exhaustivity, which however is rarely fulfilled in practice (see this great FT article by Tim Hartford). In fact, big data sets are not necessarily statistically representative of the population they refer to, and information may be disproportionately missing about specific, possibly disadvantaged, populations.
Does your data model account for biases? Selection effects, which occur whenever some members of the population are less likely to be included in the sample than others, must be controlled for in order for results to be generalizable.
How accurate are your predictions based on big data? The issue is that most research with big data is predictive without being able to uncover the social or economic mechanisms underlying observed correlations, so that interpretation of results is potentially misleading. The report does not say, though, that recent developments in machine learning that support causality reasoning may alleviate this problem in the not-so-far future.
Does your reliance on big data raise ethical or fairness concerns? In all honesty, this is not specifically a question for research on big data, but for research in general. If a company’s analysis of employees’ behavior lead to solutions that involve forms of, say, racial or gender-based behavior, then that analysis shouldn’t be used – whether it’s done with “big” or “small” data.

It is important that major regulators like the FTC are taking notice. Big data open the way to major improvements in our life conditions, but not because data-driven analysis will take the lead over current best practices in research. Regulations, awareness of statistical issues and potential pitfalls, and ethics are ever more necessary for big data to fulfill their potential.

Hierarchy, market or network? The disruptive world of the digital platform

Economics traditionally considered firms and markets as two alternative ways of coordinating economic activities. Nobel prize winner Ronald H. Coase (1937) demonstrated that it all hinges on “transaction costs”, such as the need to search for a trade partner, the time needed to negotiate a contract, the legal expenses to draw it up and if necessary, to enforce it. When these costs are high, then hiring people in a firm is the right solution. When they are low, then a harmonious state will emerge spontaneously from the choices of independent, self-employed individuals. The difference, further emphasized by the work of Oliver Williamson, another Nobel, is between the world of bureaucracy, hierarchy and salaried work, and the world of the market and myriad micro-entrepreneurs.

This dichotomous description seemed reductive to economic sociologists, and Mark Granovetter (1985) pointed to social networks as coordination devices. Networks enable circulation of knowledge, formation of trust, emergence of shared norms in informal ways, thereby lowering costs and smoothing economic transactions. Walter W. Powell (1990) saw networks as an alternative to market and hierarchy, while others thought of it as a complement rather than a substitute. In some cases, the relevance of networks is flagrant: think of “collegial“, horizontal organizations such as legal partnerships, which are clearly not markets, and which have no vertical hierarchy either.

The rise of online platforms challenges these older views today. Powered by digital data and matching algorithms, platforms are meeting places for actors on the two sides of a market: riders and drivers (Uber, Lyft, BlaBlaCar), guests and hosts (Airbnb), buyers and sellers (eBay), and so on. Officially, platforms are intermediaries only, able to put in touch, say, those who need a lift and those who have a car, so that they can share the ride. Platforms don’t employ drivers and don’t own cars.

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Discussing platform cooperativism

On Monday, 7 December 2015 at Telecom ParisTech, I was discussant at a seminar by New School scholar Trebor Scholz on “Unpacking Platform Cooperativism“.

Internet platforms carry an unprecedented potential of value creation, exploiting the extraordinary power of data and algorithms to extract and distribute information to an extent never seen before. Information, we know since Hayek’s times, is the fuel that keeps markets going, that eliminates “lemons” and ensures an ever-better coordination between buyers and sellers, borrowers and lenders, or landlords and tenants. At the same time, the internet has channeled the dream of a viable non-market society, since Rheingold’s 1993 revival of the “community” and Barbrook’s 1998 “hi-tech gift economy“. So, can we put this informational efficiency to the service of a more humane economy, based on relationships, solidarity and reciprocation, rather than on the sheer market system?

The so-called “sharing economy” suggests answers, but also displays a tension: the efforts of myriad grassroots associations to develop collaboration as a value and a practice, sharply contrasts the spectacular growth of firms like Airbnb and Uber, now large multinationals, and their alleged cavalier attitude to anti-trust regulations and workers’ rights. If some say Uber is not really about sharing and collaboration, it is difficult to draw the line.

This ambiguity is fostered by a public discourse that focuses on the sharing of assets – the spare room in your home, or a sit in your car – that digital platforms enable. Asset-sharing has economic and social appeal: it increases efficiency by preventing assets from lying idle, while reducing waste, shifting emphasis away from consumerist values (“access is better than ownership“), and facilitating sociality beyond mere consumption.

But it is often forgotten that asset-sharing does not produce value by itself: it involves extra labour. In economic jargon, capital and labour and complementary production factors. In practice, if you want to put your spare room on Airbnb, you must produce an ad, monitor your message inbox and reply swiftly. You must clean the room and do the laundry before and after a guest’s visit. You must show your guests around when they arrive.

More importantly, the very opportunity of asset-sharing changes the incentives that shape labour supply – people’s willingness to sell their time and effort against a payment. Because of the expected compensation, some people will renounce use of a (non-spare) room to accommodate visitors instead, and others will do more journeys to drive passengers around – so it’s not really about sharing unused assets, it is about self-employment and starting a micro-business. A work opportunity as a complement to (and sometimes a substitute for) a main job.

This is where debates on internet platforms and the sharing economy rejoin the growing literature on digital labour — and where the contribution of Trebor Scholz is illuminating. Where others see assets (ie, capital), he sees labour. He shows us that the bottlenecks here are about labour, not capital, and that the success — be it economic or social– of the sharing economy is closely tied to the destiny of labour. Whether it appears on the surface as self-employment, micro-entrepreneurship or salaried work, doesn’t really matter. Trebor reminds us of Marx’s fundamental principle that production relations are central to our (capitalist) society, and value generation rests ultimately on labor. If this very crucial part of the human experience goes wrong, even the best side of the sharing economy – the one that endorses trust, reciprocity, and zero-waste – may fail to perform any transformative effects on society.

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International Program in Survey and Data Science

A new, master’s level programme of study in Survey and Data Science is to be offered jointly by the University of Mannheim, the University of Maryland, the University of Michigan, and Westat. Applications for the first delivery are accepted until 3 January, for a start in Spring 2016. Prospective students are professionals with a first degree, at least one year of work experience, and some background in statistics or applied mathematics. All courses are delivered in English, fully online, to small classes (it’s not a MOOC!). Tuition is free, thank to support from German public funds at least for the first few cohorts.

What is most interesting about this master is its twofold core, involving both more classical survey methodology and today’s trendy data science. Fundamental changes in the nature of data, their availability, the way in which they are collected, integrated, and disseminated, have found many professionals unprepared. These changes are partly due to “big” data from the internet and digital devices becoming increasingly predominant relative to “small” data from surveys. Big data offer the benefit of fast, low-cost access to an unprecedented wealth of informational resources, but also bring challenges as these are “found” rather than “designed” data: less structured, less representative, less well documented (if at all…). In part, these changes are also due to the world of surveys changing internally, with new technical challenges (regarding for example data preservation, in a world of pre-programmed digital obsolescence), legislative issues (such as those triggered by greater awareness of privacy protection), increased demand by multiple users, and a growing need to merge surveys and data from other (such as business and administrative) sources. It is therefore necessary, as the promoters of this new study programme rightly recognize, to prepare students for the challenges of working both with designed data from surveys and with big data.

It will be interesting to see how data science, statistics, and social science / survey methodology feed into each other and support each other (or fail to do so…). There is still work to be done to develop techniques for analyzing data that allow us to gain insights more thoroughly, not just more quickly, and help us develop solid theories, rather than just uncovering new relationships that might eventually turn out to be spurious.

archive_pic — *A paper archive – more and more often replaced by digitised versions today.*

Yesterday at Biblithèque Nationale de France, I took part in a panel discussion on longue durée in history, organised by the Revue Annales – Histoire et Sciences Sociales. Of course I am not a historian, and I wouldn’t be able to tell whether one interpretation of longue durée is better than another. But historians are now raising questions that are common to the social sciences and humanities more generally: how to benefit from big data and how to re-think the political engagement of the researcher. So I was there to talk about big data and how they change not just research practices and methods, but also researchers’ position relative to power, politics, and industry. This questions cross disciplinary boundaries, and all may benefit from dialogue.

BellinghamCB001 — *Collection of older sources is now often online and enables application of new methods.*

What ignited the historians’ debate was an attempt by two leading scholars, David Armitage and Jo Guldi, to restore history’s place as a critical social science, based on (among other things) increased availability of large amounts of historical data and the digital tools necessary to analyze them. Before their article in Annales, they published a full book in open access, the History Manifesto, where they develop their argument in more detail. Their writing is deliberately provocative, and indeed triggered strong (and sometimes very negative) reactions. Yet the sheer fact that so many people took the trouble to reply, proves that they stroke a chord.

What do they say about big data? They highlight the opportunity of accessing large and rich archives and to expand research beyond any previous limitations. Their enthusiasm may seem excessive but it is entirely understandable insofar as their goal is to shake up their colleagues. My approach was to take their suggestion seriously and ask: what opportunities and challenges do data bring about? How would they affect research, especially for historians?

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“Data for Humanity”: a simple message, but so necessary

The recent VW emissions scandal says it all: even a large company can’t get away with behaviours that disrespect key societal values. Protection of the environment is among these values today, so much so that not only public authorities step in to defend it, but even markets punish the transgressors.

Data protection is not (yet) such a value. Admittedly, some associations, individuals, and government officials fight for it, but the larger public is still unsure. It’s not that people don’t care, but that uncertainty as to what data are actually collected, for what usages, and by whom, is overwhelming; and it becomes difficult to identify the best course of action.

In this context, a new initiative is most welcome: an open letter on “Data for Humanity“, initiated by two scholars of the University of Frankfurt, pleads for a more responsible use of data. The message is simple: Do no harm. And if you can, on top of it, do something good. It’s so simple, and so necessary.

Sure, the world won’t change after this letter, but it will be a first step. Even the promotion of environmental protection started with simple, basic declarations, 30-40 years ago; and it was by insisting and perseverating, that it finally gained the conscience of everybody.

New publications on big data and official statistics

National Statistical Institutes (NSIs) have long been the recognised repositories of all socio-economic information, mandated by governments to collect and analyse data on their behalf. The development of big data is shaking this world. New actors are coming in and commercially-oriented, privately-produced information challenges the monopoly of NSIs. At the same time, NSIs themselves can tap into digital technologies and produce “big” data. More generally, these new sources offer a range of opportunities, challenges and risks to the work of NSIs.

The Statistical Journal of the IAOS, the flagship journal of the International Association for Official Statistics, has published a special section on big data – of particular interest to the extent that it is free of charge!

Fride Eeg-Henriksen and Peter Hackl introduce this special section by defining big data and emphasising its interest for official statistics. But it is crucial, albeit admittedly not easy, to separate the hype around big data from its actual importance.

The other papers are concrete examples of how big data may be integrated into official statistics:

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The data of my friend are my data

The rise of digital data, particularly data from the internet, is to be understood in social relational perspective. Online interactions – from email exchanges to use of VOIP services and participation in social media such as Facebook, Twitter and LinkedIn – make people’s social connections explicit and visible. The “social network”, once a metaphor used only in a small sub-field within sociology, is now familiar to everybody as the archetype of computer-mediated social interaction. Digital devices systematically record network structures, so that social ties become an essential part of every individual profile, and users are more and more aware of them.

One consequence of this is the booming popularity of network analysis concepts, which support the algorithms that handle digital data: for example, centrality measures are at the heart of search engine functionalities, and transitivity measures found “friend-of-a-friend” algorithms in social media. In passing, social network analysis itself which had been originally developed for small-sized, non-digital datasets (like surveys about friendship in schools) has undergone a major upgrade to account for social data from the web.

More importantly, the relational nature of digital data and the underlying possibilities to use social network analysis, open up new avenues for data collection. If user B publishes a post on, say, their Facebook wall, comments and “likes” received from their friends A, D and E will be connected to the profile of B, accessible and visible from it; in other words, it is possible to retrieve information on A, D or E through the profile of just B. In general social networks, a friend of my friend is my friend; in digital networks, the data of my friends are my data.

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