Dr Arthur Turrell
Researcher in physics and economics

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About me

Hi! I am a research economist at the Bank of England, a visiting scientist to the plasma physics group at Imperial College London, and a visitor at the Data Analytics for Finance and Macro Research Centre at King's College London.

I am passionate about making science and economics more accessible and more easily understandable. That passion has led me to write a book about nuclear fusion titled
"How to Build a Star: The Science of Nuclear Fusion and the Quest to Harness Its Power". The book will be published by Weidenfeld & Nicolson in the Commonwealth (including the UK), by LEDA in the Czech Republic, and by Scribner in the US and rest of the world.

The book will be filled with the remarkable stories of the scientists who have dedicated their lives to achieving a seemingly impossible dream: to build a machine on Earth that recreates the source of the Sun's energy here on Earth. I'll be delving into the history of nuclear innovation, from splitting the atom to the rise of deadly nuclear weapons, and I'll be explaining how mastery of star power could provide essentially carbon free power for the planet for millions, or even billions, of years.

I am thrilled to be bringing this astonishing story to a wider audience. The quest for fusion is fascinating – there are mysterious types of matter, giant lasers, secretive start-ups in warehouses, and the most powerful forces in the Universe. It’s a story well worth telling.

If you're interested in my outreach activities or my writing, please scroll down to the media section.

You can also find links to my research papers below. All views are my own.

On the web

Bank of England

Bank research


Twitter feed


On Google scholar

Imperial plasma physics

Learn more about high energy density physics

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 in macroeconomic forecasting

With Eleni Kalamara, Chris Redl, George Kapetanios, and Sujit Kapadia.
Text has shown promise as a contemporaneous or forward-looking indicator of economic activity. But there are many different ways to employ text for this purpose. Using three popular daily newspapers we ask which methods best extract information about economic activity from text, and what variables newspaper text best predicts. We use different methods of creating features from text, including pre-defined algorithms, and different models, including machine learning. We find that a range of text-based indices can act as forward-looking proxies for other frequently used indicators of uncertainty and sentiment, and that text can improve forecasts of real economy variables, especially unemployment and CPI, at the 3 to 9 month horizon. We discuss which methods are best to use in practice, recognising the trade-off between predictive power and simplicity.

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 plausible, granular picture of UK labour demand and permit the analysis of Beveridge curves and mismatch unemployment 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 posted online between 2008 and 2016, we create an empirically driven, ‘bottom-up’ segmentation of the labour market which cuts across wage, sector, and occupation. Our segmentation is based upon applying machine learning techniques to the demand expressed in the text of job descriptions. This segmentation automatically identifies traditional job roles but also surfaces sub-markets not apparent in current classifications. We show that the segmentation has explanatory power for offered wages. The methodology developed could be deployed to create data-driven taxonomies in conditions of rapidly changing labour markets and demonstrates the potential of unsupervised machine learning in economics.

Pay Transparency Policies, Firms’ Hiring Strategies and the Gender Pay Gap. Evidence from the United Kingdom

With Emma Duchini and Stefania Simion.
This paper studies the impact of the UK 2017 pay transparency policy on firm and employees’ outcomes. To tackle the persistence of the glass ceiling phenomenon, many governments are promoting pay transparency policies. Yet, it is a priori unclear whether these initiatives can be effective at reducing gender gaps in the labour market. In particular, the mere act of publishing these figures may fail to trigger any further response, especially if there is no sanction on non-compliance or bad performance. In addition, pay transparency policies may have knock-on effects on firm productivity and the direction of this effect is also ambiguous. Disclosing gender pay gap figures is likely to impose bureaucratic costs on targeted firms, may discourage female employees, and be perceived as a threat by male workers. As such, it could weaken employees’ satisfaction and hurt firm productivity. Conversely, if firms respond by promoting gender equality, they may be able to recruit and retain high-talented women, and a more egalitarian environment may boost employees’ effort and productivity. This paper aims to shed light on these mechanisms by exploiting the UK setting and high-quality administrative data.


Public talks and articles.

Podcast interview for centralbanking.com

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 Banking.com.

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.

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.

Get in touch

General enquiries:


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

Northbank Talent Management


+44 (0)20 7766 5220