Policymaking is invariably uncertain. I created a new index of ‘policymaker’s uncertainty’ based on a textual search of the minutes of the MPC meetings since 1997. The index is constructed by simply calculating the number of references to the word ‘uncertainty’ (and its derivatives, including ‘not certain’ and ‘far from certain’) as a share of the total word count. To avoid double-counting, it also excludes the Monetary Policy Summary that was introduced in 2015. One caveat of this approach is that it doesn’t distinguish instances of low or falling uncertainty from those where uncertainty was high. That aside, this measure can offer a new insight into uncertainty compared to indicators based on media references or business surveys.
When moving house, people often don’t move too far away. Many will be commuting to the same job or don’t want their kids to move school. But many people move long-distance when they sell one house and buy another.
When choosing a mortgage, a key question is whether to choose a fixed or variable-rate contract. By choosing the former, households are unaffected by official interest-rate decisions for the length of the fixation period. We can use transaction data on residential mortgages to get a sense of how long it takes interest-rate decisions to filter through to people’s finances.
Machine learning models are at the forefront of current advances in artificial intelligence (AI) and automation. However, they are routinely, and rightly, criticised for being black boxes. In this post, I present a novel approach to evaluate machine learning models similar to a linear regression – one of the most transparent and widely used modelling techniques. The framework rests on an analogy between game theory and statistical models. A machine learning model is rewritten as a regression model using its Shapley values, a payoff concept for cooperative games. The model output can then be conveniently communicated, eg using a standard regression table. This strengthens the case for the use of machine learning to inform decisions where accuracy and transparency are crucial.
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.