Zahid Amadxarif, James Brookes, Nicola Garbarino, Rajan Patel and Eryk Walczak
The banking reforms that followed the financial crisis of 2007-08 led to an increase in UK banking regulation from almost 400,000 to over 720,000 words. Did the increase in the length of regulation lead to an increase in complexity?
Cristiano Cantore, Federico Di Pace, Riccardo M Masolo, Silvia Miranda-Agrippino and Arthur Turrell
The Covid-19 crisis has led to a swift shift in the emphasis of macroeconomic research. At the centre of this is a new field of inquiry called ‘epi-macro’ that combines epidemiological models with macroeconomic models. In this post, we give a brief introduction to some of the earliest papers in this fast-growing literature.
Zahid Amadxarif,Paula Gallego Marquez and Nic Garbarino
“We’ve done a lot to lower prudential barriers to entry into the banking sector […] but have we done enough to lower the equivalent barriers to growth?” asked PRA CEO Sam Woods in a recent speech. To make regulation proportionate, policymakers adapt regulatory requirements to the risks posed by each firm. But regulators face a trade-off between addressing systemic risks in a proportionate way and limiting regulatory complexity. New thresholds can create complexity and cliff-edge effects that can discourage healthy firms from growing. We identify regulatory thresholds for UK banks and building societies using textual analysis on a new dataset that contains the universe of prudential rules.
Central banks want to learn from history. They can do so by drawing on decades of work by economic historians, as well as their own archives which manifest layers of institutional memory. But the path from page to policy can be difficult to find. Central banks need therefore to invest in the capacity of their own staff to think historically. This will help them use evidence from the past to make better decisions in the future. In practice, this means producing historical research as well as consuming it. Institutions like central banks need to be fluent participants in the conversations which bridge the distance between past and present.
In the wake of Covid-19 lockdown, macroeconomic policymakers have to deal not only with the immediate contraction in the economy, but also with the medium and longer term macro-consequences. Over the past four months, the macroeconomic literature on these topics has expanded rapidly. This post reviews the literature that considers the channels via which the shock affects the economy, and the macroeconomic policy options for dealing with the aftermath, taking as given the shock caused by the virus and the lockdown.
India Loader, from South Wilts Grammar School, is the winner of the third Bank of England/Financial Times schools blog competition. The competition invited students across the UK to write a post on the theme: the economy and climate change.
To help save the planet and gain a competitive edge, cafes should obey a basic rule of behavioural economics by switching from offering discounts for customers who bring their own cups in favour of charging more for disposable ones.
Marco Minasi-Smith, from Fortismere School, London, is the runner-up of the third Bank of England/Financial Times schools blog competition. The competition invited students across the UK to write a post on the theme: the economy and climate change.
While Australia mourns the human and ecological cost of its ‘black summer’ of fires, the tragedy poses a question for economic policy-makers everywhere: how do we prevent climate crises becoming economic ones?
The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.
Whether in case of a breakup (Backstreet Boys), wondering why a relationship isn’t working (Mary J. Blige) or bad weather (Travis) – humans really care about explanations. The same holds in the world of finance, where firms increasingly deploy artificial intelligence (AI) software. But AI is often so complex that it becomes hard to explain why exactly it made a decision in a certain way. This issue isn’t purely hypothetical. Our recent survey found that AI already impacts customers – whether it’s calculating the price of an insurance policy or assessing a borrower’s credit-worthiness. In our new paper, we argue that so-called ‘explainability methods’ can help address this problem. But we also caution that, perhaps as with humans, gaining a deeper understanding of such models remains very hard.