Transgene

Content

Kean Birch, University of York at Toronto

'Assetization as a techno-economic mode of governance: unpacking the transformation of digital data into an asset’

Biography

Kean Birch is an Associate Professor in the in the Faculty of Environmental and Urban Change at York University, Toronto. He is particularly interested in understanding technoscientific capitalism and draws on a range of perspectives from science & technology studies, economic geography, and economic sociology to study it. More specifically, his research focuses on the restructuring and transformation of the economy & financial knowledges, technoscience & technoscientific innovation, and the relationship between markets & natural environments. Currently, he is researching how different things (e.g. knowledge, personality, loyalty, etc.) are turned into 'assets' & how economic rents are then captured from those assets - basically, in processes of assetization and rentiership.

Abstract

Technoscientific capitalism is based on the asset form. And almost anything nowadays can be turned into an asset – that is, something that can be controlled, traded, and capitalized as a revenue stream, discounting future revenues in the present. Objects, experiences, life forms, bodily functions, intellectual outputs, and much more can be an object of this transformation, or assetization. Although assets can be bought and sold, the point is to get a durable economic rent from them rather than sell them in a market. Rent derived from ownership and/or control of asset. Analysing assetization helps us to unpack and understand the contingent nature of this process, thereby providing us with insights into how to intervene and at what points this intervention might be most helpful. As such, assetization can be considered as a mode of techno-economic governance, as much as an analytical tool for unpacking contemporary capitalism.

Discussion summary

The discussion focused on the assetization of data and clarified that assetization can provide an analytical framework for more than just the use of data by Big Tech companies, as in Birch’s work. The use of health data as a resource could be an example of this. However, in each specific case the processes of assetization could work differently and need to be examined in their specifics; a comparison of such cases, however, could lead to the identification of more generic processes.

Following this, it was argued that assetization in Big Tech companies works by enclosing the data and only giving selective access. The Big Tech companies do not trade their data. They mainly create value by restricting access to the data. Processes of valuing data can work differently, though, in companies that trade data instead of focusing on restricting access. Another aspect of data assets that was discussed is that they do not depreciate as quickly as other assets. Instead, the continuous production of data can refresh or increase the value of the dataset.

A final issue that came up is that of the imagined user of the data. The user is a techno-economic object that is constructed by the company as part of their business model. This user does not have to correspond with reality though. An example given was targeted ads in which the ad may target someone who has already bought a train ticket instead of targeting someone who can be convinced of buying a ticket.

Liz McFall, University of Edinburgh

‘Zooming in on risk? Insurtech, big data and the business of disruption’

Biography

Liz McFall is a Chancellor's Fellow at the University of Edinburgh. Her research focuses on how consumer markets are made, especially for dull, difficult and politically challenging products including life and health insurance and payday loans. In the past, this has involved research on the historical practices of advertising and marketing, particularly of doorstep financial products targeted at the poor including industrial life insurance and credit vouchers. More recently, her research has focused on big data-driven innovations in insurance and insurtech on the one hand, and on cities and civic planning, on the other. She is currently exploring the impact of data-driven innovations in insurtech and the arts, heritage and visitor economies. She has been involved in establishing the field of market studies through organising events, collective publications and conference streams at EGOS and the ISA for many years.

Abstract

Lemonade, the property and pet Insurtech company, based much of their successful IPO (Initial Public Offering) in July 2020, around the claim that they could offer a 100x zoom that would granularize risk assessment. Fellow property insurtech, Hippo, are about to follow suit with a $5billion IPO facilitated by a SPAC (Special Purpose Acquisition Company) merger. Neither company has made a profit yet but their status as tech fuelled ‘disruptors’ who can leverage BDA (Big Data Analytics) and AI to transform risk assessment has driven their valuations. This paper will aim to place these claims in the broader context of the business, regulatory and knowledge practices of insurance.

Discussion summary

The questions mainly focused on how different the insurtech companies (technologically innovative firms in the insurance industry) are from traditional insurers and the future development of this sector. Additionally, the question of what the increasing individualisation of risk means for insurance was raised. Concerning the trajectory of insurtech companies, it was observed that they try to copy the wider tech industry and are mainly trying to rebrand the insurance field by working from a blank slate and presenting a friendly image of themselves. However, their claims about novel use of data and algorithms are more of a narrative to sell themselves than something that is actually making them different from other insurance companies.

Furthermore, it was suggested that looking at different insurance sectors is important, as they operate in different ways. Life insurance, for example, is experiencing problems  because people do not see the need for it anymore, while other insurance industries are more stable and captive because having a policy has been made mandatory (such as car insurance). In the general insurance markets, insurance has become more commodified and consumers have become more promiscuous. It is in the general insurance markets that the insurtech companies mainly operate.

Related to the issue of commodification, the more personalised and individualised nature of insurance was posed, and the consequent demutualisation this has wrought. A consequence of this, it was noted, was that insurers are now effectively insuring individuals instead of groups, which raised the question if they are actually insuring people or selling them a financial product. The rise of insurtech and issues of individualisation therefore pose new epistemological questions for scholars addressing the insurance industry and the data this industry produces.

Karen Gregory, University of Edinburgh

‘Digital labour and the rise of the platform society’

Biography

Karen Gregory is a digital sociologist, ethnographer, and Senior Lecturer in the department of sociology at the University of Edinburgh, Programme Co-Director of the MSc in Digital Sociology, and she currently co-lead the Digital Social Science Research Cluster at the Center for Data, Culture and Society. She is currently working on a research project that examines the possibilities for solidarity in a digital economy, conducting interviews among Deliveroo riders in Scotland. She is particularly interested in the notion of “resilience” and the ways in which everyday working people navigate shifting economies and technological terrains. She is also interested in new and emerging digital research methods and research ethics in digital scholarship. Additionally, she works on issues of digital labour in higher education.

Abstract

The phrase “digital labour” is often associated with forms of unrecognized labour that take place on the Internet, but it is by no means limited to such activities, particularly as mobile devices and apps, sensors, and platforms continue to alter the nature, place, and measurement of labour and value. This talk will argue there have been three distinct “phases” of digital labour, from Web 2.0 business models and debates about the blurring of life and labour, to the rise of the sharing and gig economies, to current theorization of platforms as infrastructure. With each phase, we see new debates about the value and role of data, the nature of exploitation, and the need for legal frameworks and regulation that can attend to the global/local duality of the digital platform economy. This talk, therefore, takes up what is a relatively recent history of digital labour to trace these phases and debates. The talk will illustrate how we have arrived at a “platform society” and map out various strategies for how work and labour can be centered in our analysis of such a society.

Discussion summary

One of the first issues brought up in the discussion was the rules of the game for defining the value of data. Different groups are competing to legitimate these rules and this is connected to a broader history of labour struggles and social movements. It was indicated that what is needed is for data rights to be more transparent, and to enable people to make genuinely informed decisions about what platform they want to use.

Related to the problematics of platform companies such as Deliveroo, it was suggested that the one thing these platforms do is make labour invisible. They make activities look seamless and encourage a departure from the language of labour and work. Because of this, feminist and Marxist approaches to unpaid labour are especially suitable to inform studies of data platforms. These approaches have focused on other forms of unpaid (or undervalued) labour such as domestic labour and their insights apply to issues in data platforms.

A final point raised pertained to the similarities between the individualisation of risk in insurance and in these data platforms. It seems that in the case of Uber and Deliveroo, an individualisation of risk happens as well, in which these companies offload risk on to individual drivers.

Edward Nik-Khah, Roanoke College, Virginia

‘Commentary on Panel 2: Finance and global markets (Chaired by Ben Collier, University of Edinburgh)’

Biography

Edward Nik-Khah is Professor of Economics at Roanoke College. His research interest include the History of Information and Knowledge in Economics, Neoliberalism Studies, and Science and Technology Studies. He has completed research on interactions between the Chicago School of Economics, the pharmaceutical industry, and pharmaceutical science; the neoliberal origins of economics imperialism; and the tensions emerging from economists' assumption of a professional identity as designers of markets. He is the author, with Philip Mirowski, of The Knowledge We Have Lost in Information: The History of Information in Modern Economics, which examines the role of information in modern economics and how it influences policy and politics.

Discussion summary

A main topic in the discussion was the issue that many of the platform companies are not profitable at all (similar for many biotechnology companies). The question was raised that if they make no profit, why are they valued so highly? They often promise a future monopoly and the profits that come with that; these promises are accompanied by narratives of disruption and innovation. The value of these companies is thus mainly based on future potential profits.

Regarding the issue of developing a more general history of data platforms, it was suggested that we start by looking at specific datasets and platforms first and then zoom out to look at the webs of reference (a term used in Lowe’s presentation) between the multiple platforms and datasets. Such a process could potentially lead to capturing data journeys and including all relevant actors - such as consumers, workers, think-tanks, and consultancy groups - that were identified as often missing in prior discussions.

However, it was additionally suggested that such an endeavour start by focusing on the data and what happens with it and to it and what money is generated using the data. This would enable us to avoid relying too heavily on actor’s categories such as the language of platforms and the argot of companies and consultants, which we need to overcome.