Arthur Turrell, Bradley Speigner, James Thurgood, Jyldyz Djumalieva, and David Copple
‘Big Data’ present big opportunities for understanding the economy. They can be cheaper and more detailed than traditional data sources, and on scales undreamt of by survey designers. But they can be challenging to use because they rarely adhere to the nice neat classifications used in surveys. We faced just this challenge when trying to understand the relationship between the efficiency with which job vacancies are filled and output and productivity growth in the UK. In this post, we describe how we analysed text from 15 million job adverts to glean insights into the UK labour market.
Ann Owen and Judit Temesvary
Earlier this year the Bank hosted a joint conference with the ECB and the Federal Reserve Board on Gender and Career Progression. In this guest post one of the speakers, Ann Owen, discusses her work with Judit Temesvary on how the composition of boards affects decision making and ultimately performance in the banking sector.
The representation of women on boards of US bank holding companies has increased (chart 1), but nevertheless remains well below the share of women in the overall employee base (chart 2). While this also raises questions of equity, our research asks if a lack of gender diversity on bank boards has an economic impact on their performance. We find that it does, and that this effect depends on 1) the existing level of gender diversity on the board, and 2) the level of bank capitalization. If risk-weighted capital ratios are a proxy for the quality of bank management, our findings suggest that at well-managed banks, gender diversity has a positive impact on bank performance- but only once a threshold level of diversity is reached.
Arthur Turrell, Bradley Speigner, James Thurgood, Jyldyz Djumalieva and David Copple
Recently, economists have been discussing, on the one hand, how artificial intelligence (AI) powered by machine learning might increase unemployment, and, on the other, how AI might create new jobs. Either way, the future of work is set to change. We show in recent research how unsupervised machine learning, driven by data, can capture changes in the type of work demanded.
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.