Arthur Turrell

Physics, economics, data science, & writing

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Dr Arthur Turrell is Deputy Director for Research and Capability at the ONS Data Science Campus. He is also a visitor to the Bank of England, the plasma physics group at Imperial College London, and the Data Analytics for Finance and Macro Research Centre at King's College London. His research combines methods from data science, economics, and physics.

As well as his research, Arthur is passionate about making science and economics more accessible - mostly because scientists shouldn't be allowed to have all the fun. You can find more information about his outreach activities in the media section and about his writing in the section on The Star Builders.

This website represents Arthur's personal views.

On the web


Twitter feed


On GitHub


On Google scholar

Temperature equilibration in degenerate plasmas

With Steve Rose and Mark Sherlock.
A procedure for performing Monte Carlo calculations of plasmas with an arbitrary level of degeneracy is outlined. It has possible applications in inertial confinement fusion and astrophysics. Degenerate particles are initialised according to the Fermi–Dirac distribution function, and scattering is via a Pauli blocked binary collision approximation. The algorithm is tested against degenerate electron–ion equilibration, and the degenerate resistivity transport coefficient from unmagnetised first order transport theory. The code is applied to the cold fuel shell and alpha particle equilibration problem of inertial confinement fusion.

Large-angle Coulomb collisions in plasmas

With Steve Rose and Mark Sherlock.
Large-angle Coulomb collisions allow for the exchange of a significant proportion of the energy of a particle in a single collision, but are not included in models of plasmas based on fluids, the Vlasov–Fokker–Planck equation, or currently available plasma Monte Carlo techniques. Their unique effects include the creation of fast ‘knock-on’ ions, which may be more likely to undergo certain reactions, and distortions to ion distribution functions relative to what is predicted by small-angle collision only theories. We present a computational method which uses Monte Carlo techniques to include the effects of large-angle Coulomb collisions in plasmas and which self-consistently evolves distribution functions according to the creation of knock-on ions of any generation. The method is used to demonstrate ion distribution function distortions in an inertial confinement fusion (ICF) relevant scenario of the slowing of fusion products.

Ultrafast collisional ion heating by electrostatic shocks

With Steve Rose and Mark Sherlock.
High-intensity lasers can be used to generate shockwaves, which have found applications in nuclear fusion, proton imaging, cancer therapies and materials science. Collisionless electrostatic shocks are one type of shockwave widely studied for applications involving ion acceleration. Here we show a novel mechanism for collisionless electrostatic shocks to heat small amounts of solid density matter to temperatures of ∼keV in tens of femtoseconds. Unusually, electrons play no direct role in the heating and it is the ions that determine the heating rate. Ions are heated due to an interplay between the electric field of the shock, the local density increase during the passage of the shock and collisions between different species of ion. In simulations, these factors combine to produce rapid, localized heating of the lighter ion species. Although the heated volume is modest, this would be one of the fastest heating mechanisms discovered if demonstrated in the laboratory.

Collisional energy transfer terms in plasmas

With Steve Rose and Mark Sherlock.
Particle-based simulations, such as in particle-in-cell (PIC) codes, are widely used in plasma physics research. The analysis of particle energy transfers, as described by the second moment of the Boltzmann equation, is often necessary within these simulations. We present computationally efficient, analytically derived equations for evaluating collisional energy transfer terms from simulations using discrete particles. The equations are expressed as a sum over the properties of the discrete particles.

Agent-based dynamics in corporate bond trading

With Karen Braun-Munzinger and Zijun Liu.
We construct an heterogeneous agent-based model of the corporate bond market and calibrate it against US data. The model includes the interactions between a market maker, three types of fund, and cash investors. In general, the sensitivity of the market maker to demand and the degree to which momentum traders are active strongly influence the over- and under-shooting of yields in response to shocks, while investor behaviour plays a comparatively smaller role. Using the model, we simulate experiments of relevance to two topical issues in this market. Firstly, we show that measures to reduce the speed with which investors can redeem investments can reduce the extent of yield dislocation. Secondly, we find the unexpected result that a larger fraction of funds using passive investment strategies increases the tail risk of large yield dislocations after shocks.

Making text count: economic forecasting using newspaper text

With Eleni Kalamara, Chris Redl, George Kapetanios, and Sujit Kapadia.
We consider the best way to extract timely signals from newspaper text and use them to forecast macroeconomic variables using three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. We find that newspaper text can improve economic forecasts both in absolute and marginal terms. We introduce a powerful new method of incorporating text information into forecasts that combines counts of terms with supervised machine learning techniques. This method improves forecasts of macroeconomic variables including GDP, inflation, and unemployment, including relative to existing text-based methods. Forecast improvements occur when it matters most, during stressed periods.

Interdisciplinary approaches to macroeconomics

With Andy Haldane.
Macroeconomic modelling has been under intense scrutiny since the Great Financial Crisis, when serious shortcomings were exposed in the methodology used to understand the economy as a whole. Criticism has been levelled at the assumptions employed in the dominant models, particularly that economic agents are homogeneous and optimizing and that the economy is equilibrating. This paper seeks to explore an interdisciplinary approach to macroeconomic modelling, with techniques drawn from other (natural and social) sciences. Specifically, it discusses agent-based modelling, which is used across a wide range of disciplines, as an example of such a technique. Agent-based models are complementary to existing approaches and are suited to answering macroeconomic questions where complexity, heterogeneity, networks, and heuristics play an important role.

Transforming naturally occurring text data into economic statistics

With Bradley Speigner, Jyl Djumalieva, David Copple, and James Thurgood.
Using a dataset of 15 million UK job adverts from a recruitment website, we construct new economic statistics measuring labour market demand. These data are ‘naturally occurring’, having originally been posted online by firms. They offer information on two dimensions of vacancies—region and occupation—that firm-based surveys do not usually, and cannot easily, collect. These data do not come with official classification labels so we develop an algorithm which maps the free form text of job descriptions into standard occupational classification codes. The created vacancy statistics give a granular picture of UK labour demand at the occupational level.

Using machine learning to create bottom-up job classifications

With Bradley Speigner, Jyl Djumalieva, David Copple, and James Thurgood.
What type of disaggregation should be used to analyse heterogeneous labour markets? How granular should that disaggregation be? Economic theory does not currently tell us; perhaps data can. Analyses typically split labour markets according to top-down classification schema such as sector or occupation. But these may be slow-moving or inaccurate relative to the structure of the labour market as perceived by firms and workers. Using a dataset of 15 million job adverts, we create an empirically driven, ‘bottom-up’ segmentation of the labour market. Our methodology demonstrates the potential of unsupervised machine learning in economics.

Pay Transparency and Cracks in the Glass Ceiling

With Emma Duchini and Stefania Simion.
This paper studies firms and employees’ responses to pay transparency requirements. Each year since 2018, more than 10,000 UK firms have been required to publicly disclose their gender pay gap and gender composition along the wage distribution. Theoretically, pay transparency represents an information shock that alters the bargaining power of male and female employees vis-à-vis the firm in opposite ways. As women are currently underpaid, this shock may improve their relative occupational and pay outcomes. Reputational concerns before employees, consumers and investors may also be key to understand firms’ response. We test these theoretical predictions using a difference-in-difference strategy that exploits variation in the UK mandate across firm size and time. This analysis delivers four main findings. First, pay transparency increases women’s probability of being hired in above-median-wage occupations by 5 percent compared to the pre-policy mean. Second, while this effect has not yet translated into a significant rise in women’s pay, the policy leads to a 2.8 percent decrease in men’s hourly pay, reducing the pre-policy gender pay gap by 15 percent. Third, combining the difference-in-difference strategy with a text analysis of job listings, we find suggestive evidence that treated firms adopt female-friendly hiring practices in ads for high-gender-pay-gap occupations. Fourth, a reputation motive seems to drive employers’ reaction, as firms publishing worse gender equality indicators score lower in YouGov Women’s Rankings. Moreover, publicly listed firms targeted by the policy experience a 35-basis-point average fall in stock prices in the days following the publication of their indicators.

Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning

With Ed Hill and Marco Bardoscia.
General equilibrium macroeconomic models are a core tool used by policymakers to understand a nation's economy. They represent the economy as a collection of forward-looking actors whose behaviours combine, possibly with stochastic effects, to determine global variables (such as prices) in a dynamic equilibrium. However, standard semi-analytical techniques for solving these models make it difficult to include the important effects of heterogeneous economic actors. The COVID-19 pandemic has further highlighted the importance of heterogeneity, for example in age and sector of employment, in macroeconomic outcomes and the need for models that can more easily incorporate it. We use techniques from reinforcement learning to solve such models incorporating heterogeneous agents in a way that is simple, extensible, and computationally efficient. We demonstrate the method's accuracy and stability on a toy problem for which there is a known analytical solution, its versatility by solving a general equilibrium problem that includes global stochasticity, and its flexibility by solving a combined macroeconomic and epidemiological model to explore the economic and health implications of a pandemic. The latter successfully captures plausible economic behaviours induced by differential health risks by age.


Public talks and articles.

ESCoE Workshop: Job vacancy data for the public good

A session I chaired at the Economic Statistics Centre of Excellence workshop on job vacancy data; the session had four excellent presentations on job vacancy data for the public good.

Q&A for Nature

An interview on how Bank of England research has changed due to coronavirus.

Webinar on NLP

A webinar discussing the application of natural language processing to economics and finance hosted by Dow Jones.

Machine learning the news

A Bank Underground post about our research paper "Making text count: economic forecasting with newspaper text".

Podcast interview for

Part of their series on rewiring macroeconomics. Check out previous episodes to hear from John Muellbauer, David Hendry, and David Vines.

Using machine learning to understand the mix of jobs in the economy in real-time

Bank Underground blog post on capturing changes in the types of job available in the economy using unsupervised machine learning. Original research paper here.

What’s in the news? Text-based confidence indices and growth forecasts

Bank Underground blog post on using newspaper text as an input to nowcasts.

Making big data work for economics

Bank Underground blog post on using 'big data' to develop new measures of job vacancies in the UK. Full paper here. We posted the code we developed on the Bank's github here.

"Adopting Agent-based models for public policy"

Lecture given at the US Treasury during a conference on Heterogeneous Agents and Agent-based Modelling

Why I left physics for economics

An article in The Guardian about why I chose to leave physics.

Interdisciplinarity for macro

Coverage on Central

Agent-based economic models offer more realism

Coverage in the Financial Times (£) of my work on agent-based models. For context and more, see the Martin Wolf story on rebuilding macroeconomics and the FT's collection on rethinking macroeconomics.

Power and progress

Power and progress - a short post on the Bank Underground blog showing the correlation between GDP per capita and electricity generation per capita.

Forming strong bonds

Forming strong bonds: dynamics in corporate bond markets. A post on the Bank Underground blog.

Quarterly Bulletin

Agent-based models: understanding the economy from the bottom up. An article in the 2016Q4 Bank of England Quarterly Bulletin.

Pint of Science

Talks in London and Cambridge on nuclear fusion for the Pint of Science festival. Interviewed on BBC Breakfast about the festival.

Laser Quest

A night celebrating the uses of lasers held at the Ace Hotel in Shoreditch by Super/Collider. There were also talks by Lian Han and Ceri Brenner, as well as some very interesting tea from Bompas & Parr.

Reach out

A video about the uses of light in science for the continuing professional development of primary school teachers.

Science in Parliament

An article in the parliamentary science magazine, aimed at policymakers.

Business green interview

Interviewed about Lockheed Martin's new fusion scheme.

Science Museum Late on Energy

Building a Star on Earth... with lasers! Part of the Science Museum's excellent Lates series.

Royal Society Summer Science Exhibition

Lead scientist of an exhibit called "Set the controls for the heart of the Sun" at the ever-fantastic RSSSE. For this exhibition, an ebook was created which is still available to download here for ipad and Mac (warning: it is a 400mb file). There was widespread, if sometimes odd, coverage of the exhibit by the British Council, The Telegraph, and Imperial College London, as well as a Q&A Twitter session still available on storify. I was also interviewed by the Royal Society for the event.

Plasma: The mysterious fourth state of matter

Plasma: The mysterious fourth state of matter, a talk at the 2011 British Science Festival.

Specification Curve

A Python package for performing specification curve analysis. You can see the source code here, use the button to go to the docs, or simply pip install specification_curve

Cookiecutter LaTeX book manuscript

This is an example repo for a LaTeX manuscript for a book, designed to be simple enough to easily export to Microsoft Word with Chapters (including hyperlinks), citations, and figures.

Cookiecutter research project

This is an example repository for a research project. git clone the project and use it as a skeleton for your own research project. A full explanation may be found in this accompanying blog post.

Occupation coder

Given a job title, job description, and job sector this algorithm assigns a 3-digit standard occupational classification (SOC) code to a job using the SOC 2010 standard.

Library of Statistical Techniques

I am a contributor to the Library of Statistical Techniques (LOST), a Rosetta stone for analysis with software packages. You can see the source code here or click the button for the website.

Get in touch

General enquiries:

For TV, media, and literary enquiries related to plasma physics:

Northbank Talent Management


+44 (0)20 7766 5220