The Alan Turing Institute is pleased to announce nine new projects which have been awarded funding as part of The Turing-HSBC-ONS Economic Data Science Awards 2018, an innovative collaboration between the Turing, HSBC and the Office for National Statistics (ONS) which aims to advance ground-breaking research in economic data science.
Economic data science is an emerging new field at the boundary of economics and data science that combines techniques from both disciplines to harness the scale and scope of economic data now available to us. The projects, which were chosen through an open call, have been awarded a total of £0.75m to undertake research which aims to improve understanding of how the economy works. These projects aim to combine world-leading science with the potential for high impact outside academia – for example, in policy or business.
The projects include research into the effects of peer influence on social networks and its impact on financial decisions, exploring if positive and negative sentiment in the news has an effect on actual economic growth, and using machine learning to understand the needs of the gig economy workforce.
Research proposals were reviewed by an independent expert panel of academics and policy-makers.
The research will take place within the Turing’s Finance and Economics research programme, which applies data science and AI techniques to how the financial sector and the economy work, and uses these insights to address challenges of national and international importance.
The panel selected projects that can generate results in the first 6-9 months. The principal investigators involved are all researchers based at one of the Turing’s partner universities. Their collaborators are from UK and overseas universities and research organisations and institutions including think tanks.
Those expected to benefit from the research include businesses and industry, government and policymakers and those affected by the UK economy.
Lukasz Szpruch, The Alan Turing Institute’s new Programme Director for Finance and Economics will oversee the projects as they progress for the next 1-2 years. Lukasz is a Turing Fellow and a Reader (Associate Professor) at the School of Mathematics, University of Edinburgh.
Lukasz Szpruch, Programme Director for Finance and Economics at The Alan Turing Institute, said:
“AI and machine learning solutions are branching out to almost every corner of the financial industry. This creates fantastic opportunities but also carries out new challenges. The newly-funded research will focus on three key challenges within this space; how to analyse new data on economic activity; how to measure and account for the changing nature of work; and how machine learning can improve economic models. I am delighted to join the Turing as these new projects are launched, and as we enter an exciting time when the remit of the programme is expanded to include finance (both investment and retail), risk, cybersecurity and blockchain technology. I look forward to working with the Turing’s partners in industry, academia and the third sector, and researchers and organisations across the UK and beyond to bring the Turing’s research to address crucial challenges within the economy and financial sector.”
Rakshit Kapoor, Group Chief Data Officer at HSBC, said:
“As part of our five year partnership with The Alan Turing Institute, we are delighted to be an active partner in the Turing-HSBC-ONS Economic Data Science Awards 2018. This cutting edge research will help us all to better understand how our economies and societies are changing.”
Tom Smith, Managing Director of the ONS Data Science Campus, said:
“ONS is taking advantage of the data revolution to better understand the economy and our society. New sources of data, and techniques such as AI and machine learning are available for us to work with to deliver better statistics, and therefore better decisions for the UK.
The role of the Campus is to actively explore these novel data sources, to provide richer, more informed measurement and analyses on the economy and society for public benefit.
The projects have the potential to deliver high impact in this area, and we are really excited to be collaborating with the Institute and HSBC on such a strong programme of projects.”
The funded projects are:
Machine learning tools will be used to better understand the effects peers have on each other in social networks. Researchers will study one person in a social network being given information about micro-financing services—loans, savings, insurance and other financial services available for entrepreneurs, small businesses and individuals who lack access to traditional banking services—and the effect this has on other people's subsequent actions.
Researchers: Mingli Chen (University of Warwick), Chenlei Leng (University of Warwick)
This project will involve developing tools to identify abnormal data events (and fraudulent behaviour) in the VAT transactions network. This will inform tax administrations of transactions that merit investigation and will advise tax authorities on how they can enhance their policies and administrative practices to combat fraudulent transactions. The project will use data provided from the Bulgarian National Tax Authority.
Researchers: Christos Kotsogiannis (University of Exeter), Petros Dellaportas (UCL), Stanley Gyoshev (University of Exeter)
Forecasting of economic and financial variables is crucial for decision-making by central banks, monetary authorities, financial institutions, policy makers and international economic organisations. Many of these forecasts tend to do poorly when faced with volatile changes over time, such as those caused by Brexit. This project aims to use state-of-the-art computational techniques to improve these models and produce freely available software to implement these improvements, leading to significant impact through improved forecasting performance.
Researchers: Petros Dellaportas (UCL), Katerina Petrova (University of St Andrews)
Policy makers need to respond appropriately to changing, flexible, 'non-standard' work arrangements to ensure labour market policy effectively balances flexibility and security both for workers and employers. However, despite its growing importance, relatively little is known about workers in the gig economy. This project aims to use machine learning techniques to help discover more about the constraints and preferences of gig workers, in order to develop and disseminate a set of policy recommendations.
Researchers: Abigail Adams (University of Oxford), Stephen Machin (LSE)
There is a gap in knowledge about the interconnection between the determinants of land use, economic value and current environmental concerns, for example the decline in biodiversity in the UK. This project aims to fill this gap by establishing a better understanding of the interplay between land use, climatic and biophysical factors, and agricultural prices. The research will aim provide more accurate forecasts of land use in the UK, enabling more effective decision-making by government and business.
Researchers: Ian J. Bateman (University of Exeter), Namhyun Kim (University of Exeter), James Davidson (University of Exeter), Yingcun Xia (NUS), Carlo Fezzi (Trento), Patrick Wongsaart (Cardiff University).
Unstructured text presents a vast resource of data for economists. This project will develop a general purpose approach for connecting text-based information to traditional economic measurement systems, expanding the availability of 'real-time' information on economic activity.
Researchers: Thiemo Fetzer (University of Warwick), Elliott Ash (University of Warwick)
The UK government is encouraging cities to prepare for the economy of the future through investing in skills, amongst other things. Policy makers, however, face a 'data deficit' on skills, which impedes strategic planning and decision-making. Through a novel data modelling approach, this project aims to shed light on the industrial diversification potential of UK cities from a skills-based perspective.
Researchers: Neave O'Clery (University of Oxford), Cosmina Dorobantu (Turing), Francesca Froy, (UCL)
Is it possible to make real-time forecasts about the UK’s real economic growth by analysing the positive or negative sentiment of the words used in business news? The researchers have arranged access to Thomson-Reuters' news archive and the forecasts will help assess the risk of vulnerability in the UK’s economic activity over the business cycle.
Researchers: Shaun Vahey (University of Warwick), Craig Thamotheram (NIESR)
Recent technological advances are changing the world of work, expanding opportunities of some workers but potentially limiting those of others. Technological change may have also enabled firms to specialise in certain tasks to a greater extent than in the past. This project investigates how the pay and career prospects of different types of workers have been affected by changes in firm specialisation. This work will provide new evidence on the structure of the labour market to help inform significant policy- making decisions.
Researchers: Richard Blundell (UCL), Agnes Norris Keiller (IFS), Monica Costa Dias (IFS), Robert Joyce (IFS)
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