Targeted Financial Conditions Indices (TFCI): why average financial conditions miss what matters most

Miguel Herculano, Santiago Montoya-Blandon and Jorge Pinheiro

Financial conditions indices are widely used by central banks and policymakers, including in the UK, to summarise and monitor in real-time the state of financial conditions in an economy. But most indices focus on what happens to average conditions, rather than the risks policymakers are often most concerned about – such as sharp downturns in economic activity or surges in inflation. We develop novel targeted financial conditions indices (TFCIs), using US data, that focus directly on measuring and forecasting these risks. Instead of asking which financial variables move together on average, the approach identifies which ones matter for specific outcomes. We show that different risks are linked to different financial factors – and that focusing on these can improve the forecasting performance of these key macroeconomic targets.

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Monetary policy transmission: it’s all in the curve

Sofia Carollo and Natalie Burr

While monetary policy sets short-term policy rates, households and firms borrow at varying time horizons. How a policy decision reshapes the whole yield curve therefore matters. We trace the reactions of yields in narrow windows around UK monetary policy announcements across two dimensions: a ‘level’ surprise that shifts the entire curve and a ‘slope’ surprise that changes its steepness. We find that a level surprise affects CPI inflation more than a slope surprise does; this result is difficult to recover from surprises that conflate the two dimensions. So, the policy rate tells only part of the story: two curves considered equivalent from a stance perspective can lead to different inflation outcomes. Policymakers must be attuned to these differing effects.

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Has UK food inflation been under the weather?

India Rimmer, Hannah Copeland and Boromeus Wanengkirtyo

Global extreme weather events may feel far away, but they leave behind a trail of higher prices in our shopping baskets. As outlined in past Monetary Policy Reports, droughts, flooding and heatwaves occurring overseas often impact UK food inflation, which averaged 4.2% in 2025. But how much of the rise in food inflation last year can we blame on the weather? By constructing a new proxy for global weather shocks, we find that they increase UK food prices with a peak impact after one year. In the latest period, our model suggests that weather shocks contributed 0.8 percentage points to food inflation at peak in May 2025. Weather continues to matter for inflation amidst the current El Niño phenomenon.

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Is UK productivity growth low? A historical and cross-country perspective

Sophie Piton and Fabrizio Cadamagnani

A lot has been written about UK productivity and how weak it’s been in recent years. This post assesses UK productivity trends in a historical and cross-country perspective. Productivity growth has been weak across G7 economies over the past two decades, reflecting the end of the information and communications technology (ICT) revolution and the flattening gains from globalisation. The slowdown was particularly large in the UK, mainly because it experienced a larger decline in the share of manufacturing than peers and then because of the impact of Brexit. In recent years, US productivity growth has been accelerating thanks to tech, offering some optimism for the future of UK productivity.

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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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