WCF in discussion with Daniela Gabor

03/07/19 • 61 min

A decade after the 2008 financial crisis, new political economic imaginaries have emerged to make sense of our financialised world. Critical macro-finance is one of the most important of these trends. It has shed light on the infrastructure of contemporary global finance, the links between shadow banking, money markets and monetary policy, and the evolving governance architecture established in the wreckage of the crash.
To discuss critical macro-finance, WCF is excited to get into conversation with Daniela Gabor, Professor of Economics and Macro-Finance at UWE Bristol. Daniela is working on the intersections of economics, finance and political economy, researching a variety of areas such as shadow banking activities, especially repo markets, and their implications for monetary theory, central banking, sovereign bond markets and regulatory activities.

Previous Episode

WCF Dialogue: Operationalisations of Financialisation

If 'globalisation' was the dominating concept of the 1990s and 'neoliberalisation' followed in the 2000s, then 'financialisation' is arguably one of the hottest candidate for succession in the 2010s. Its inflationary use has been debated for some time now, without actually diminishing its appeal in various contexts.
In order to think through the stakes attendant to the continued research of financialisation and its multiple operationalisations, we invited two active protagonists in these debates. Ève Chiapello and Pauline Gleadle discussed each other's work and critically assessed the ways in which the study of financialisation continues to shape the current critical debates about global finance and global capitalism more generally.

Next Episode

Big-data credit scoring: risk management in Chinese social credit programmes

In this talk, Ruowen Xu examines the organisation process by which Big Data credit scoring models are produced, investigating the analytical work of data scientists who continuously maintain and improve their models to keep the results predictive. Big Data algorithmic technology is having a profound impact on our social, organisational, and public life and it permits large tech companies to perform analytics for consumer credit-risk assessments and to determine credit risk.
Based on ethnographic fieldwork in a credit score modelling team of a large tech company, Ruowen's research studies the development of an emerging Big Data algorithmic credit-scoring technology alongside the government’s programme for building a social credit system in China. Her findings show that data scientist work is a continuous, repetitive, and a pre-prescribed process of developing and updating models that are complemented with machine learning-generated results, and that the way that data scientist work is organised has a direct impact on the produced model. This research expands the perimeter of how we look at algorithms and, broadly, other data-driven computing devices by looking at the organisational setting through which they are produced.
For more information, please visit: https://warwick.ac.uk/fac/soc/pais/currentstudents/phd/resources/wcf/upcomingevents/ruowen-xu/