Arthur Turrell, Nikoleta Anesti and Silvia Miranda-Agrippino.
As the American playwright Arthur Miller wrote, “A good newspaper, I suppose, is a nation talking to itself.” Using text analysis and machine learning, we decided to put this to test – to find out whether newspaper copy could tell us about the national economy, and in particular, whether it can help us predict GDP growth.
Qun Harris, Analise Mercieca, Emma Soane and Misa Tanaka.
The bonus regulations were introduced based on the consensus amongst financial regulators that compensation practices were a contributing factor to the 2008-9 financial crisis. But little is known about how they affect behaviour in practice. So we conducted a lab experiment to examine how different bonus structures affect individuals’ risk and effort choices. We find that restrictions on bonuses, such as a bonus cap, can incentivise people to take less risk. But their risk-mitigating effects weaken or disappear once bonus payment is made conditional on hitting a high performance target. We also find some evidence that bonus cap discourages effort to search for better projects.
UK household debt is high relative to income. But is it “unsustainable”? Some commentators say “it is”; others say “there is no reason to worry”. To investigate, we build a simple model of the economic relationships between household debt, house prices and real interest rates which we believe must hold in the long run. In our model there is no single threshold beyond which debt suddenly becomes unsustainable, but we argue that household debt should be broadly sustainable under any rise in real interest rates of up to about 2 percentage points (pp) from current levels. We also show that falling real interest rates may have contributed around 20-25pp to the rise in the household debt-to-GDP ratio since the 1980s.
David Bholat, Nida Broughton, Janna Ter Meer and Eryk Walczak
Clear communications are important for central banks at a time when their responsibilities have increased but trust in public institutions has declined. Using an online experiment with a representative sample of the UK population, our recent paper measured how differently styled summaries of the Inflation Report impacted public comprehension and trust in its policy messages. We find that a new ‘Visual Summary’ of the Inflation Report, which makes use of graphics and simpler language, increases understanding of policy messages. And making more changes using insights from behavioural science can further increase public understanding. These changes also somewhat increase people’s trust in the information. Continue reading “Simply is best: enhancing trust and understanding of central banks through better communications”→
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 puzzlinglyslow 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.
In a recent research paper, we show that the way supervisors write to banks and building societies (hereafter ‘banks’) has changed since the financial crisis. Supervisors now adopt a more directive, forward-looking, complex and formal style than they did before the financial crisis. We also show that their language and linguistic style is related to the nature of the bank. For instance, banks that are closest to failure get letters that have a lot of risk-related language in them. In this blog, we discuss the linguistic features that most sharply distinguish different types of letters, and the machine learning algorithm we used to arrive at our conclusions.