Dragos Gorduza, Livia Silva Paranhos and Ivan Yotzov

Monetary policy decisions reflect incoming data, forecasts, and policymaker preferences. In this post, we use large language models (LLMs) to analyse Bank of England Monetary Policy Committee (MPC) transcripts from 2015 to 2017. We extract structured measures of individual views on inflation and growth, and the narratives justifying policy decisions. This gives a new perspective on UK monetary policy makers’ reasoning, using recently published transcripts. We show that concerns about economic growth relative to its potential weakened and later strengthened over the sample, while concerns about above-target inflation increased. We link these measures to voting behaviour and show that, over the sample period, both growth and inflation measures predict votes for higher rates, but only growth measures explain votes to cut Bank Rate.

Central bank deliberations are nuanced and cover a wide range of topics. Policymakers speak amid considerable uncertainty, and evidence from Federal Open Market Committee (FOMC) transcripts (Bennani and Romelli (2024); Aruoba and Drechsel (2026)) highlights that their reasoning is not always fully captured by voting records alone. The same vote can reflect very different concerns about inflation, growth, risks, and their balance. Our approach recovers part of that structure from MPC discussion transcripts using LLMs. In addition, the transcripts provide an opportunity to study how policymakers’ narratives and concerns evolved around the EU referendum in June 2016. This period was characterised by a relatively stable pre-referendum environment in 2015, increased economic uncertainty following the Brexit vote, and the start of a shift towards policy normalisation during 2017 as inflation rose and the economy proved more resilient than initially predicted.

Newly released Bank of England internal policy transcripts and extracted views from MPC members

Our main data set comprises 258 publicly available statements from individual MPC members covering 29 meeting transcripts between 2015 and 2017. These statements cover the meetings when members provide their indicative policy decisions. They are released with an eight-year lag, and 2017 is the last available year currently. We use generative AI tools to extract several dimensions of policymakers’ views from the text. To the best of our knowledge, we are the first to apply frontier natural language processing to these recently published transcripts.

First, we extract statement-level information for all speakers in the transcripts. The MPC consists of nine members and due to several tenures expiring during our sample, we have a total sample of 14 speakers (seven internal and seven external MPC members). Three meetings in 2017 only consisted of eight MPC members. We focus on each speaker’s view of UK inflation relative to target and output growth relative to potential over a one-year horizon. These measures relate closely to the weights policymakers implicitly place on inflation and output stabilisation in their decisions (eg Carney (2017)). We test how these text-based measures relate to voting decisions, including differences across speakers and over time.

In the LLM prompt, ‘inflation versus target’ scores how the text portrays the level of UK inflation relative to the Bank of England’s 2% CPI inflation target over the year ahead: -5 means inflation is judged very far below target, 0 close to target, and +5 very far above target. ‘Growth versus potential’ is the analogous measure for UK output growth relative to potential output over the same one-year ahead horizon. Each quantitative measure is produced by a separate call to a LLM (OpenAI gpt-5-mini), so that every variable maps to one explicit instruction. The output is constrained to a single integer on -5 to +5 scale via a structured response format.

Second, we identify the main arguments underpinning policy deliberation in each statement. Using LLMs, we rewrite these arguments as concise causal statements, classified by topic, type, and tense. We classify arguments into 14 topics (an argument can cover multiple topics), such as inflation, labour market, output, exchange rates, and political events. The topics are drawn from existing analyses of monetary policymaking transcripts (eg Prat et al (2018)) looking at the FOMC transcripts) and central banker speeches. These topics are selected to exhaustively cover the various issues policymakers may raise during their discussions; we focus the discussion on six out of the total 14 topics in this post for conciseness. Figure 1 below shows an example passage from Andy Haldane in February 2016, from which we extract the underlying causal argument and topics.

Figure 1: Extracting causal arguments and topics from MPC transcripts

Growth sentiment weakened and later recovered over the sample, while concerns about above-target inflation increased

We first show that these narrative-based indicators capture economically meaningful variation. In Chart 1, we plot our two text-based metrics of ‘inflation versus target’ and ‘growth versus potential’, alongside annual CPI inflation and GDP growth over the period. Growth sentiment deteriorated in the months prior to the referendum, and sharply following the referendum, before improving in 2017. This partly reflects lagged movements in GDP growth and the exceptional uncertainty about the near-term outlook for the UK economy. Financial markets reacted sharply following the referendum vote (as highlighted by MPC members themselves in the June and July 2016 meetings), business surveys deteriorated markedly, and a range of contemporaneous forecasts, including official assessments, warned of a substantial slowdown and potentially severe recessionary risks. As the economy proved more resilient than initially anticipated, growth sentiment recovered through 2017. This pattern partly reflects lagged movements in realised GDP growth. Meanwhile, perceived inflation relative to target closely tracks CPI inflation trends over this period (correlation of 0.75). Further analysis shows that the relationship between perceived growth and inflation becomes more positive and more statistically significant in the period after the referendum. This is consistent with a stronger ‘demand-side’ interpretation of the economic trends during this part of the sample.

Chart 1: Trends in text-based inflation and growth metrics over 2015–17

Narrative topics and differences across members

We next study how the content of the transcripts changes over time and across MPC members. This analysis is conducted at the statement level, with the language model identifying explicit causal statements and assigning them to one or more topics. Across 2015–17, the most discussed topics are output, inflation, and monetary policy rate setting, accounting for 19%, 15%, and 14% of statements, respectively. The rate-setting category is deliberately narrow: it captures only phrases referring directly to the policy-rate decision, while statements about considerations that may bear indirectly on that decision are classified by their immediate subject. Following the referendum, discussions became more focused on monetary policy rate setting, inflation–output trade-offs, and political events, while references to inflation and financial markets declined. Chart 2 shows these shifts.

Chart 2: Distribution of topic shares in MPC transcripts before and after the Brexit referendum

Views on inflation and growth help predict voting behaviour

Finally, we analyse whether these new text-based metrics explain Bank Rate voting behaviour. We focus on Bank Rate voting specifically, and not on the asset purchase decisions taken over this period. We regress binary outcomes for the decision to vote for a rate increase, rate hold, and rate cut on the member-specific views regarding inflation relative to target and growth relative to potential. The regressions additionally control for speaker fixed effects, annual CPI inflation, annual GDP growth, and year-ahead Bank of England forecasts for inflation and growth. Therefore, we test whether these new metrics capture any variation in voting behaviour over and above the aggregate data and official forecasts.

Chart 3: Voting behaviour and views on inflation and growth

Chart 3 summarises the results over the full sample and all MPC members. The left panel shows that perceptions of above-target inflation and above-potential growth both push members towards voting for a hike. These estimates are statistically significant at the 5% level. The middle panel shows that stronger views about above-target inflation are associated with a lower likelihood of voting for a hold, whereas the relationship with ‘growth versus potential‘ is insignificant. In the right panel, the pattern reverses: views about above-potential growth significantly reduce the likelihood of voting for a rate cut, while the inflation coefficient is small and insignificant. Overall, the qualitative assessments members express translate systematically into their policy choices.

While Chart 3 pools across the whole committee, the same relationships also hold when we estimate member-specific reaction functions, running a separate regression for each MPC member (excluding those who voted in fewer than 10 meetings and those who never voted for a hike or cut). For the likelihood of voting to increase Bank Rate, the coefficients on inflation versus target and growth versus potential are positive for essentially every member, and for a number of them the relationship is strong and statistically significant. For the likelihood of voting to cut, the coefficients on growth versus potential are negative across the board, consistent with the pooled finding that weaker growth perceptions increase the likelihood of a cut, while the inflation coefficients are smaller and more dispersed around zero. Reassuringly, then, the aggregate result is not driven by a handful of members: the link between expressed views and votes is a common feature across the committee, even as its strength varies from member to member.

Conclusions

Historical MPC transcripts contain rich information beyond simple vote tallies. Using frontier LLMs and natural language processing, we convert policy discussions into structured sentiment and narrative measures. We highlight how these measures change over time, across MPC members, and how they relate to voting behaviour. We focus on Bank Rate votes because policymakers explicitly describe the policy rate as the main policy instrument over this period. Future work could compare narratives in publicly released historical transcripts with contemporaneous public communications, such as MPC speeches, to examine how policy reasoning is expressed across different forms of published communication.

Dragos Gorduza and Livia Silva Paranhos work in the Bank’s Advanced Analytics Division and Ivan Yotzov works in the Bank’s Structural Economics Division.

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