Tyler Curtis, from Hall Cross Academy, Doncaster is the winner of the Bank of England/Financial Times schools blogging competition. In his winning post, he looks at how artificial meat could reshape the economy and our environment…
Food, glorious food! But how glorious is it, especially meat, when its production is reminiscent of Mary Shelley’s Frankenstein? Traditionally, a significant portion of the world’s workforce has been employed in agriculture throughout history, forcing us to allocate massive amounts of scarce resources to the sector. Today, nearly 27 per cent of people work in agriculture worldwide, according to the World Bank (the figure is just 1 per cent in the UK). However, the industry is on the verge of a new revolution.
UK productivity growth has been puzzlingly slow since the crisis. After averaging 2% every year in the pre-crisis decade, growth in labour productivity (output per hour worked) has slowed to an average of only 0.5%. Extensive research and commentary on the productivity puzzles has suggested myriad causes for the malaise – including ‘zombie’ firms hoarding resources, sluggish investment in the face of uncertainty, mismeasurement and more – and have dismissed others that no longer seem plausible – including temporary labour hoarding. Using firm-level data, I show that slower aggregate growth is entirely driven by the more productive firms in the economy.
Aakash Mankodi and Tim Pike
Tetlock and Gardner’s acclaimed work on Superforecasting provides a compelling case for seeing forecasting as a skill that can be improved, and one that is related to the behavioural traits of the forecaster. These so-called Superforecasters have in recent years been pitted against experts ranging from U.S intelligence analysts to participants in the World Economic Forum, and have performed on par or better by accurately predicting the outcomes of a broad range of questions. Sounds like music to a central banker’s ears? In this post, we examine the traits of these individuals, compare them with economic forecasting and draw some related lessons. We conclude that considering the principles and applications of Superforecasting can enhance the work of central bank forecasting.
Joseph Noss, Liam Crowley-Reidy and Lucas Pedace
Dan Georgescu and Nicholas J. Higham
Correlation matrices arise in many applications to model the dependence between variables. Where there is incomplete or missing information for the variables, this may lead to missing values in the correlation matrix itself, and the problem of how to complete the matrix. We show that some of these practical problems can be solved explicitly, via simple formulae, and we explain how to use mathematical tools to solve the more general problem where explicit solutions may not exist. “Simple” is, of course, a relative term, and the underlying matrix algebra and optimization necessarily makes this article more mathematically sophisticated than the typical Bank Underground post.
Jeremy Franklin, Scott Woldum, Oliver Wood and Alex Parsons
How do markets react to the release of economic data? We use a set of machine learning and statistical algorithms to try to find out. In the period since the EU referendum, we find that UK data outturns have generally been more positive than market expectations immediately prior to their release. At the same time, the responsiveness of market interest rates to those data surprises fell below historic averages. The sensitivity of market rates has also been below historic averages in the US and Euro area, suggesting international factors may also have played a role. But there are some signs that the sensitivity has increased over the past year in the UK.
Smartphone apps and newsfeeds are designed to constantly grab our attention. And research suggests we’re distracted nearly 50% of the time. Could this be weighing down on productivity? And why is the crisis of attention particularly concerning in the context of the rise of AI and the need, therefore, to cultivate distinctively human qualities?
Aidan Saggers and Chiranjit Chakraborty
Investment in the Financial Technology (FinTech) industry has increased rapidly post crisis and globalisation is apparent with many investors funding companies far from their own physical locations. From Crunchbase data we gathered all the venture capital investments in FinTech start-up firms from 2010 to 2014 and created network diagrams for each year.
Chiranjit Chakraborty and Andreas Joseph
Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking. We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models. However, it struggles to cope with the unseen post-crises situation which highlights the care needed when considering new modelling approaches.