Who’s paying attention? How firms form policy rate expectations

Lea Havemeister, Nicholas Bloom, Philip Bunn, Paul Mizen, Gregory Thwaites and Ivan Yotzov

Monetary policymakers carefully craft their policy decisions and communication, and financial markets respond quickly. Yet the effect of policy on the economy ultimately depends on how firms perceive and anticipate monetary policy. We present new data from an economy-wide UK business survey on Bank Rate perceptions and expectations. The data provide direct evidence on monetary policy transmission, specifically on how firms form and update policy rate expectations. Firms’ perceptions of current policy rates are precise, and expectations adjust rapidly to policy decisions within days. Moreover, more productive firms and those with higher levels of borrowing forecast policy rates more accurately. CEOs and CFOs also link policy rate expectations to inflation expectations in ways consistent with standard macroeconomic models.

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Does higher productivity create inflationary or disinflationary pressure?

Ludovica Ambrosino, Jenny Chan and Silvana Tenreyro

Recent technological advances raise an important question for policymakers: will higher productivity lead to disinflationary or inflationary pressure? A coming wave of AI-driven productivity growth is often described as a disinflationary tailwind that would allow central banks to hold interest rates lower without reigniting inflationary pressures. Yet faster productivity growth can just as plausibly call for higher, not lower, interest rates. By raising expected future income and the returns to investment, it stimulates consumption and investment today, pushing up the natural rate of interest. Neither view is entirely wrong and our model reconciles the two by showing that the answer depends on the timing, permanence, and sectoral origin of the productivity shock.

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If AI disappoints? The transmission of US big-tech earnings news

Daniel Ostry, Roger Vicquéry and Emilio Zaratiegui

There is growing concern among policymakers, international organisations, and even big-tech Chief Executive Officers (exhibits I, II and III) that the current artificial intelligence (AI) boom features valuations increasingly detached from fundamentals. The Bank’s February 2026 Monetary Policy Report noted that an asset price correction is a key risk to the global economy, while the Bank’s July 2026 Financial Stability Report presented a scenario for how an AI correction could unfold. In this post, we study how negative big-tech earnings news transmits to global markets, which informed discussions around this scenario. We find that the effects ripple far beyond tech: equity indices decline, credit spreads widen and the US dollar depreciates. This last result, together with the limited response of Treasury yields, suggests muted flight-to-safety dynamics, unlike other financial stress episodes.

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Is artificial intelligence making us more productive? What the UK industry data show

Sandra Batten

Unlike previous waves of automation, machine learning and generative AI (Gen AI) technologies can perform non-routine cognitive tasks, such as those involving written or spoken language, and have the potential to affect a wider range of occupations. By augmenting or replacing workers in these tasks, these technologies promise to deliver significant productivity gains. AI adoption, while still limited, seems to be linked to productivity gains across industries in the US, although it can only explain a small fraction of the aggregate pick up in US productivity. This post examines the emerging evidence from UK industry data and finds some indication that AI is contributing to productivity growth following a similar pattern to previous key technologies.

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Canaries in the column? AI exposure and the UK’s hiring slowdown

Haley Schlicht

From Silicon Valley executives promising to automate white-collar work to headlines claiming AI is foreclosing the graduate entry market, the strained ‘low fire, low hire’ environment has increasingly been ascribed to technological transformation. UK vacancies nearly halved since their 2022 peak – a contraction so sustained it has reshaped the British hiring market for the better part of three years. This post examines how evidence of AI-driven transformation at the hiring margin is proving considerably more tenuous than the headlines suggest.

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Demographics, deposits and data: using machine learning to segment UK mortgages

Joe Grimshaw

Who are the UK’s mortgage borrowers, and how do their characteristics differ? Despite extensive literature on mortgage profiles, loan-level segmentation remains limited, existing work relies on aggregates or predefined categories. I address this gap by applying unsupervised machine learning to 20 years of data, allowing the model determine segments without prior assumptions. Three clusters emerge: one with low leverage, and two with high leverage but notably different income profiles. Lending composition has shifted gradually. High leverage, high-income borrowers now account for a larger market share, and first-time buyers increasingly fall into more leveraged segments. Machine learning is crucial for financial stability, revealing concentrations of characteristics, and trends, that aggregates and simple splits cannot, offering richer and earlier indications of potential vulnerabilities.

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Credit constraints and housing market access

Belinda Tracey and Neeltje van Horen

The Help-to-Buy (HTB) programme introduced in 2013 reopened the 95% loan to value (LTV) segment of the UK mortgage market, thereby reducing the minimum deposit requirement for many first-time buyers (FTBs) from 10% to 5% (Chart 1). That policy change offers a useful natural experiment to study how deposit constraints shape access to homeownership. We previously demonstrated that this easing of deposit constraints generated a clear increase in local spending. In a recent paper, we show that lowering this constraint increases FTB home purchases, particularly among households without access to external financial support for their deposit.

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When travel restrictions became trade frictions: evidence from Covid-era border closures

John Lewis

Covid travel restrictions limited movement of people but also made cross-border goods trade more difficult. Did this contribute to the fall in global goods trade during the pandemic, and if so by how much? In a recent paper using a structural gravity model on global trade flows with domestic trade, I show that a full closure reduced trade for a typical country pair by around 19%, implying a peak hit to global trade of about 23% in 2020 Q2. Hits were larger for nearby partners, and were concentrated in road and air freight, with seaborne trade unaffected. These differences explain why some countries could close borders with smaller trade hits than others. Trade rebounded as restrictions eased, suggesting no lasting scarring.

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Explainability in machine learning: do popular methods deliver on their promises?

Ivona Cickovic and Andrea Serafino

Machine learning models are increasingly used in organisational decision-making, yet their inner workings often remain opaque. When these systems influence real world outcomes, knowing what they predict is not enough – we also need to understand why. Explainability methods aim to illuminate this ‘black box,’ and feature attribution tools that link predictions to individual inputs are especially popular. They feel intuitive but rely on strict data assumptions that rarely hold, making their outputs unreliable. The 2019 Apple Card case illustrates why this matters: despite gender not being an explicit input, women appeared to receive lower credit limits than men with similar profiles – an outcome attribution methods struggle to explain. This post examines a key assumption underpinning these tools and how it distorts explanations.

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A ‘group washing machine’ and ‘tangled skein’: the failure of Slater Walker

David Rule

In August 1977, the Bank of England purchased the bank Slater Walker Limited, completing its rescue. The bank had been a subsidiary of Slater Walker Securities, controlled by Jim Slater, which also owned an insurer. This post describes how Slater misused depositors’ and policyholders’ funds to finance his wider business interests. The Bank of England sought to protect depositors by supporting the wider group rather than putting the bank into liquidation. The case remains relevant today when banks and insurers continue to be owned by financial and industrial groups, including private equity sponsors, and supervisors must consider how to address conflicts of interest and how far to insulate the bank or insurer from the rest of the group.

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