Category: The Conversation

Source: The Conversation – UK – By Danielle Turton, Senior Lecturer in Sociolinguistics, Lancaster University

Imagine time-travelling to Manchester, England in the late 1700s. What do you think people would sound like?

That’s the challenge facing Amanda Seyfried in The Testament of Ann Lee: portraying a working-class Mancunian accent from three centuries ago.

When historical linguists reconstruct past speech, it is an interpretative process. It relies on written evidence, including spelling, poetic rhymes, criticisms in old pronouncing dictionaries about how people ought to speak, and dialect descriptions. From these fragments, we can piece together a historically informed reconstruction.

In the late 18th century, English certainly sounded different, but not unrecognisable. Manchester would have been variably rhotic at this time. This is the pronunciation of the strong “r” sound in words like “star” or “bird”. Rhoticity is a feature shared with present-day American English.

In terms of vowels, the northern pattern in which words like “good” and “blood” are exact rhymes was present then as it is today. Both of these features are present in Seyfried’s portrayal.

Another feature Seyfried exhibits, but which is no longer typical of 21st-century Manchester accents, is her lack of what linguists call diphthongs, or gliding vowels. You can hear this in words she says like “great” and “clothed” where she uses vowel sounds that viewers might recognise from traditionally Lancashire or Yorkshire speech. These sounds were entirely consistent with 18th-century Mancunian accents but not today’s.

Seyfried has said she based her accent on actor Maxine Peake’s – although Peake is from Bolton, and not Manchester proper, this is not a bad decision. Bolton has its own distinct accent, but smaller towns often retain older features for longer while urban centres tend to experience accent changes more quickly.

In that sense, Peake’s accent may reflect features that Manchester has since moved away from, making her a more suitable reference point than a present-day speaker from the city.

Historic accents on screen

Seyfried’s performance sits within a broadly plausible northern English frame. Viewers online are divided: some praise the accent, others find it distracting. The difficulty is that without recordings we cannot know exactly how a Manchester accent sounded in the 18th century. It is though, entirely possible that her pronunciation is closer to historical reality than modern ears expect.

To this end, dialect coaches on historical films face a dilemma: do they recreate the speech of the time as faithfully as possible and risk losing the audience, or use something more contemporary? How far back could we go and still understand English?

We would manage 18th-century English reasonably well. For instance, it’s easier to understand Robinson Crusoe in 1719 than the 1500s English in Shakespeare or even the late 1300s and early 1400s middle English in Chaucer’s Canterbury Tales. But recognisable does not mean identical, and reproducing it, accent and all, too strictly could alienate viewers.

Most historical films don’t try to recreate how people actually sounded in the past. In Hamnet, which is set in the 1580s, the characters speak in modern received pronunciation instead of the kind of English spoken in Shakespeare’s time.

Even in stories set closer to the 18th century, such as The Favourite, Olivia Coleman’s Queen Anne still sounds distinctly modern – arguably, even more so than her Queen Elizabeth II in The Crown. Actors playing Tudor courtiers, medieval knights and even Shakespeare himself are routinely given modern accents on screen. Audiences rarely question it – or even notice.

Sociolinguistic research has long shown that southern and “prestige” accents, like that of royalty or the upper classes, are often treated as neutral and timeless while regional varieties are more readily linked to place and class. It is perhaps not surprising, then, that when Manchester appears on screen – especially in a historical setting – audiences listen more closely.

Part of that scrutiny might stem from its rarity. Working-class accents are under-represented in major films, and are even less often heard in leading roles. When they do appear, they carry the weight of representation. That scrutiny is understandable. Accent carries belonging, and carelessness can feel dismissive.

Amanda Seyfried seems aware of this sensitivity, noting in interviews that she originally suggested Olivia Cooke, who is from Oldham in Greater Manchester, for the role of Ann Lee. That comment, I think, shows that she recognises something important: these accents signal place, history and belonging and they matter to people.

So how authentic is the accent in The Testament of Ann Lee? In the absence of recordings from that time, certainty is impossible. But perhaps the more interesting question is not whether Seyfried’s accent is perfect, but what it means to hear a northern voice carry a feature film. It shifts our assumptions about what the past sounded like, and about who we imagine at its centre.

Danielle Turton has received funding from The Leverhulme Trust.

– ref. Amanda Seyfried nails bits of the 1700s Manchester accent in The Testament of Ann Lee – a linguist explains how we know – https://theconversation.com/amanda-seyfried-nails-bits-of-the-1700s-manchester-accent-in-the-testament-of-ann-lee-a-linguist-explains-how-we-know-276917

Source: The Conversation – UK – By Lee John Curley, Lecturer in Psychology, Glasgow Caledonian University

Are juries really impartial? Or is it the beliefs and attitudes they bring to trial that leads them to vote guilty or not? These questions are particularly important when it comes to the influence that rape myths may have on juror and judicial decision-making in sexual offence trials.

Rape myths are widely held but misleading ideas about sexual violence: who commits it, who experiences it and what it’s supposed to look like. Common rape myths include beliefs about what “real rape” looks like (that people are only raped by strangers), or blaming the victim for their rape based on their behaviour or what they were wearing. In England and Wales, the government recently announced reforms to counter these myths in court.

Most research on rape myths has focused on cases where the complainant is female. Rightly so, as women and girls are disproportionately affected in rape and sexual assault crimes.

Read more:
How rape myths and unconscious biases prejudice the judicial system against women – and rape survivors in particular

However, men and boys are also victims of sexual offences. The Crime Survey for England and Wales reported in 2022 that 275,000 men experienced sexual assault in that one-year period alone. This is likely to be an underestimate, because men often don’t report these crimes for a variety of reasons.

This is why our new study explores how rape myths influence verdicts in male-on-male rape trials.

Previous research has suggested that rape myths relating to male survivors often blame victims, minimise the harm or exonerate the accused. One example is the belief that “real men” are able to stop unwanted sexual assaults from happening. Or jurors might believe that there would be some sign of physical resistance in “real” rape trials.

A key difference between rape myths about men and women relates to masculine archetypes and men’s perceived ability to “fight off” sexual advances. These attitudes can mean that jurors might not believe survivors who have alleged they have been raped, for example, if they had previously consented to sex with the same man.

We presented 463 mock jurors with a mock trial, in which one man accused another of rape. There were six versions of this experiment, involving men of different ethnicities, and both straight and gay men. The same evidence was presented in each of these versions, however, and the ethnicity or sexual orientation of the men involved did not appear to influence the jurors’ decision.

Jurors first completed the male rape myth acceptance scale, a tool developed by researchers to measure how strongly someone believes in male rape myths. They then evaluated the evidence presented in our mock trial and reached a verdict of guilty or not guilty.

Our findings suggested that those more likely to believe male rape myths were more likely to believe the accused, less likely to believe the complainant, and thus more likely to reach a not guilty verdict.

The opposite pattern was true for those with low belief in rape myths, with guilty verdicts being more likely. Essentially, male rape myths influenced how jurors constructed stories surrounding the evidence and ultimately influenced verdict preferences. These findings align with what other research has found in male-on-female rape trials.

Juror decisions and implications

We also asked jurors to tell us how they made their decisions. This qualitative data helped to explore the findings in more detail.

People who strongly believed common myths about male rape were much more likely to doubt the evidence. Jurors in this group had doubts about the reliability, clarity or sufficiency of the evidence presented in the mock trial.

They also frequently drew upon rape myths to explain their not guilty verdict decisions. One juror said: “He didn’t at any point say no or stop him touching his penis. Removed his own clothes, rolled over. There’s not enough evidence to prove guilt.”

In comparison, those who had low acceptance of male rape myths were more likely to perceive that the intoxication mentioned in the same trial was a clear barrier to consent. They rejected victim-blaming narratives. For instance, one mock juror said: “The complainant was too drunk to consent, drunk enough to cause sickness and need support in moving to his bedroom, if he can’t move around by himself he cannot consent to sex.”

Other recent research has found that male rape myths were frequently discussed by jurors in their deliberations. The justice system therefore clearly needs to use different tools to counter male rape myths – similar to what has been suggested to combat female rape myths.

These could be in the form of judicial instructions, educational videos or expert witnesses who can direct jurors away from the myths and nudge them towards the facts. Similar recommendations have been made by the UK government to counter rape myths in trials where the complainant is female.

Another solution could be more scientific juror selection. In the US, attorneys and judges use a process called voir dire to deselect biased jurors.

This process varies widely by length, whether questionnaires (such as scales that measure biases, like the rape myths scale) are allowed and who conducts it (judge only or attorneys and judge). Nevertheless, measures such as the male rape myths scale could be used to move those with high belief in rape myths from male-on-male rape trials, to other trial types where this bias is unlikely to influence decision-making (such as white collar crime).

Whatever the method, tackling rape myths is necessary to ensure justice for those who suffer sexual violence, regardless of their gender.

Lee John Curley receives funding from Leverhulme/British Academy.

Dominic Willmott received funding from The British Academy (SRG2223231748) to carry out the research project to which this article relates.

Kennath Widanaralalage does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. How male rape myths stop some victims of sexual assault from getting justice – new study – https://theconversation.com/how-male-rape-myths-stop-some-victims-of-sexual-assault-from-getting-justice-new-study-274247

Source: The Conversation – UK – By Kirk Chang, Professor of Management and Technology, University of East London

These days, gen Z appears to be pivoting towards skilled trades, perhaps driven by a desire for “AI-proof” job security. Many young workers now view blue-collar careers as more stable than office jobs in the face of rapid change.

It’s not just the youngest workers. A growing sense of unease about AI is reshaping how many people think about work. Within younger groups, this shift is showing up in hard numbers. In the UK, hiring of gen Z workers (those born in or after 1997) in construction and trade roles rose by 16.8% in the year to January 2026. The result is what some are calling the “toolbelt generation”.

But elsewhere in the workforce, many professionals are taking a pragmatic approach. Instead of competing with automation, they are learning how to work alongside it. Building fluency with AI tools is increasingly seen as a form of career insurance.

The goal is to move into roles designing, managing or directing AI systems. In that model, technology becomes a force multiplier (that is, it increases productivity), rather than a threat.

This shift is also driven by economics. AI-related skills command a clear premium in the jobs market. Beyond pay, there are other benefits. AI systems are particularly effective at handling repetitive, process-heavy tasks. When those functions are automated, employees can redirect their energy towards strategy, creative problem-solving and higher-value decision-making.

Many find that this shift not only improves productivity but also makes their work more engaging and meaningful.

Importantly, entering the AI space does not always require a computer science degree. Through online learning, bootcamps or just practical experimentation, workers can gain expertise in areas such as prompt engineering, workflow automation or AI application. The barrier to entry is lower than many assume, especially for those who already understand a specific industry.

Industry knowledge is, in fact, a major advantage. Organisations increasingly want people who can bridge domain expertise with technical capability. A healthcare professional who knows what patients need as well as understanding AI tools; a finance specialist who can apply machine learning to risk analysis; or a tradesperson who uses smart systems for efficiency can all bring unique value.

These hybrid profiles are becoming central to how companies integrate AI, creating interdisciplinary roles that did not exist a few years ago.

The flip side: risks and challenges

AI is creating opportunity, but it also brings risks and trade-offs. One of the most immediate challenges is the pace of change. Keeping skills current can feel like trying to hit a moving target. Over time, constantly doing more can lead to fatigue and burnout, particularly in highly competitive environments where staying relevant is tied to job security.

There is also an upfront cost. Transitioning into AI, especially into more technical or advanced positions, can require an investment of time and money before any financial return materialises.

And AI is said to be contributing to a hollowing out of traditional career ladders. Many entry-level roles, once considered stepping stones into industries such as finance or marketing are being automated or cut back. As a result, entry pathways into certain professions may narrow before new ones are established.

Read more:
AI could mark the end of young people learning on the job – with terrible results

Finally, working in AI often means grappling with complex ethical and safety questions. Workers must consider issues such as data bias, privacy, transparency and accountability. Decisions made during system design and deployment can have wide-reaching consequences. Navigating these responsibilities requires sound judgement and a clear understanding of these consequences.

Looking ahead

In many sectors, AI is unlikely to eliminate entire professions. Instead, it will reshape them. Tasks will be automated, workflows will evolve and job descriptions will shift. For most professionals, the practical response is not to abandon their field, but to integrate AI into it.

At the same time, technical fluency alone will not be enough. As automation takes over routine and rules-based work, human skills become more important. Critical thinking, judgement, empathy, communication and complex problem-solving remain difficult to replicate with algorithms. The more advanced the technology becomes, the more valuable distinctly human strengths appear to be.

There is also a widening gap across industries. AI is generating new, high-paying roles in areas such as engineering, data science and AI strategy. However, in positions where automation only partially replaces tasks, productivity may increase while wages do not. In some cases, partial automation can stifle pay or reduce opportunities for promotion.

Retraining and career pivoting in the AI age is becoming a mainstream response to structural change. AI is reshaping how work is done across sectors, while opening up new roles that are centred on oversight, integration, strategy and innovation. For many professionals, the question is not whether change is coming but how proactively they choose to respond.

The most resilient path forward is rarely about abandoning your field entirely. More often, it involves layering AI fluency on top of existing expertise. A finance professional who understands automation tools, for example, is better positioned than someone relying on legacy skills alone. In this sense, the objective of retraining is to move closer to the decision-making layer of work.

Ultimately, the AI era is not about a binary choice between optimism and fear. It is about positioning. Retraining and career pivoting are becoming central strategies for navigating this shift with intention rather than reacting after the fact.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Time to retrain? How to future-proof your career in the AI age – https://theconversation.com/time-to-retrain-how-to-future-proof-your-career-in-the-ai-age-276694

Source: The Conversation – UK – By Dónal Mulligan, Lecturer, School of Communications, Dublin City University

For most of us, generative AI (GenAI) has moved from novelty to everyday infrastructure astonishingly fast. Many adults now use tools like chatbots at work or casually, and many children are already encountering them through homework “help”, entertainment, or social sharing.

Unsupervised use of generative AI can expose children and young people to confidently presented misinformation, manipulative “keep chatting” dynamics, and inappropriate or emotionally risky content. The tone and conversational dynamics of many chatbots can encourage secrecy and over-reliance, or mimic authority without real understanding or duty of care. In school contexts, GenAI can quietly undermine learning, turning homework and writing into shortcuts rather than skill-building.

I’ve helped create new school resources on GenAI, including guidance for parents. But the most effective safety measures still depend on adults setting boundaries, modelling critical thinking, and staying close enough to a child’s digital life to notice what’s changing in it. What follows are some practical ways to talk about, assess, and limit younger people’s GenAI use.

1. Begin with curiosity – not crackdowns

If you start by telling a child that they shouldn’t use GenAI, you may prompt secrecy about their current and further uses. A better opener could be a simple request to demonstrate to you the AI tools or uses they’re familiar with. Ask what they like about it, what it helps with, and what they’d never use it for. The initial aim should be to normalise discussing AI, though not to normalise unrestricted use.

From here it’s easier to acknowledge that these are powerful and intriguing tools, but not a person or an authority, and not without risks and necessary considerations.

2. Don’t treat stated age limits as optional

An awkward reality that parents may currently have missed is that many popular AI services set 13 as a minimum age (with parental permission under 18). OpenAI states that ChatGPT “is not meant for children under 13”, and still requires parental consent for ages 13 to 18. The AI chatbot ecosystem is inconsistent, however. Anthropic requires Claude users to be 18+, explicitly citing heightened risks for younger users. Google, meanwhile, allows supervised access to Gemini for under-13s via parent-enabled controls.

Your practical rule should be to treat age limits as a clear safety signal rather than a box-ticking exercise. If a service says “13+” or “18+”, that’s telling you something about risk, content exposure and the likelihood of harm from unsupervised use by young people.

3. Encourage fact-checking

Children (and indeed plenty of adults) can mistake confidence for correctness. When talking about GenAI with children, emphasise that AI chatbots can and regularly do “hallucinate”. They invent plausible-sounding details and mix fabrication with fact. Understanding that their speedy and well-stated responses come at a cost of large and small inaccuracies is key.

Encourage verifying anything important – news, health claims, law, school facts, statements that may be repeated as “true”.

4. Help them know when to stop

Large language models (LLMs) are designed to keep conversation flowing. They compliment, encourage, reassure and suggest what to do next. This may be helpful for brainstorming but it’s potentially dangerous for emotionally loaded topics where a young person is vulnerable, impressionable, or isolated.

Recent litigation around “companion” chatbots has alleged that vulnerable young users were pulled into harmful spirals, including self-harm risk and secrecy from parents. These are complex and unfolding cases, but they are serious enough to treat as a major warning sign about unsupervised, open-ended AI conversations for minors.

Parents and teachers should name a firm boundary: no chatbot is a counsellor, therapist, or trusted confidant. If a conversation becomes sexual, self-harm related, frightening, or intensely personal, the rule should be to stop and speak to a trusted adult.

5. Don’t feed the machine personal data

Young people often understand privacy better when it’s framed as something tangible. Some rules: don’t share a full name, address, school, phone number, or identifiable photos. Don’t upload private documents or screenshots. Don’t paste in other people’s personal information. If you wouldn’t post it on a public noticeboard, don’t paste it into a chatbot.

6. AI should support the work, not do the work

GenAI poses an educational risk that deserves far more attention: cognitive off-loading. This happens when the tool performs the thinking step – the learner may finish faster, but will learn less. Research is increasingly linking heavier AI reliance with reduced critical thinking and lower cognitive effort, with off-loading and automation bias proposed as mechanisms. A practical way to explain this to young people is that “AI can help you learn, but it can also help you avoid learning”.

Read more:
How generative AI is really changing education – by outsourcing the production of knowledge to big tech

If you’re helping with homework, allow the use of GenAI for asking for an explanation in simpler terms, or requesting feedback on a draft. Don’t allow writing the essay, answering the homework questions directly, or producing a solution that the student can’t explain.

7. Make AI use visible and social

Where AI use is permitted, aim to reduce secrecy. Use AI in shared spaces at home. Set agreed times, not late-night private use. Coordinate with other adults: parents should share their concerns and approaches with other parents and with school staff.

We should treat Generative AI as we wish we’d treated social media much earlier – not as just another app, but as a behavioural technology that shapes attention, learning, confidence and relationships. Being AI aware is not about panic, but about adults building enough knowledge and confidence to guide children toward safe, age-appropriate, genuinely educational use, while regulation and curriculum development catch up.

Dónal Mulligan has received research funding from the EU’s Erasmus+ program. He is affiliated with Webwise, the Irish Internet Safety Awareness Centre, and with Media Literacy Ireland.

– ref. Seven tips for talking to children and young people about generative AI – https://theconversation.com/seven-tips-for-talking-to-children-and-young-people-about-generative-ai-275718

Source: The Conversation – UK – By Federico Iannacci, Senior Lecturer in Management, University of Sussex Business School, University of Sussex

Police officers often work with partial information under severe time constraints in situations that can change in seconds. Whether investigating a crime or patrolling a neighbourhood, they regularly have to make predictions based on instinct.

This “gut policing” isn’t just guesswork – it’s fast pattern recognition. It comes from training and years of dealing with real incidents, learning from colleagues, and building an instinctive sense of what matters and what doesn’t.

But instincts are no longer the only way police connect the dots. Many police forces are investing in AI-enabled tools, including predictive policing algorithms that forecast crime hotspots and offender assessment systems designed to support decision-making.

Read more:
A ‘black box’ AI system has been influencing criminal justice decisions for over two decades – it’s time to open it up

This reflects a wider global trend: police forces are integrating AI into everyday policing. These AI-enabled tools draw on large volumes of data and patterns that would be impossible for any single officer to analyse in real time. The aim is straightforward: to help ensure decisions are based on strong evidence and reliable data, rather than relying solely on instinct or experience.

Many people appear to accept the use of AI technology by police forces – so long as there are clear guidelines in place first.

AI has long been discussed as a threat to jobs and livelihoods. But what’s the reality? In this series, we explore the impact AI is already having on specific occupations – and how people in these jobs feel about their new AI assistants.

In England, police forces are already using AI tools in day-to-day work. These include Untrite Thrive, which helps staff in police control rooms decide how to allocate resources. Another example is Qlik Sense, used by Avon and Somerset Police for monitoring the likelihood of reoffending or perpetrating a crime. These developments align with a broader government agenda focused on efficiency and cost reduction.

But once you swap human judgment for more automated predictions, the value of officers’ traditional connect-the-dots police logic can be lost. There have been plenty of examples where AI tools have flagged the wrong people, the wrong places, or the wrong risks.

Unverified information

A House of Commons select committee recently highlighted serious failings in West Midlands Police’s use of the AI assistant Microsoft Copilot in its decision to stop Israeli fans of Maccabi Tel Aviv football club from travelling to Birmingham for a Europa League match against Aston Villa last November.

Claims made by this force about alleged disorder involving Maccabi fans at past matches were based on inaccurate information generated by Copilot, including a supposed game between the Israeli club and West Ham United that never happened.

“Information that showed the Maccabi fans to be a high risk was trusted without proper scrutiny,” explained the committee’s chair Karen Bradley. “Shockingly, this included unverified information generated by AI.”

This inaccurate AI‑generated information was repeated by senior police officers in safety advisory group meetings and even in oral evidence to MPs, demonstrating a lack of due diligence and overreliance on unverified AI outputs. The case is now subject to an investigation by the Independent Office for Police Conduct.

And this was not an isolated incident. The Harm Assessment Risk Tool deployed by Durham Constabulary was found to have displayed many flaws, from overestimation of the likelihood of reoffending to discrimination in its datasets.

And the Metropolitan Police’s now-discontinued Gang Matrix, a database that recorded intelligence related to alleged gang members, was heavily criticised by the Information Commissioner’s Office for unfairly labelling young black men as high‑risk based on flawed scoring.

Relying on AI-driven tools can be a double-edged sword in policing. They can improve decisions, but can also reinforce bias and amplify mistakes. In our experience of working with police forces in England, AI‑supported decision‑making works best when police officers combine their operational experience with data‑driven insights.

Reinforcing biases

Our ongoing study of AI use in policing shows that uncritical reliance on AI risks reinforcing existing biases, disproportionately affecting the poorest and most marginalised communities.

Our research, which is yet to be published, suggests that effective use of AI requires a difficult balance: officers must both trust and mistrust AI recommendations at the same time, maintaining a vigilant mindset.

To prevent biases creeping into AI‑supported decisions, police forces should invest in bias‑awareness training that prepares officers to question AI outputs regularly and constructively.

The National Police Chiefs’ Council covenant mandated that AI should support rather than replace human judgment. This is a step in the right direction. Yet even this principle can backfire if police officers treat AI recommendations as objective truth, rather than guidance that requires careful scrutiny.

These concerns take on renewed urgency in light of the government’s introduction of a national predictive policing prototype, announced in August 2025. The system, scheduled for nationwide deployment by 2030, combines AI‑powered crimemapping with behavioural‑pattern analysis, supported by a £4 million initial investment.

It draws on data from police forces, local councils and social services, and builds directly on the expanding fleet of live facial recognition vans now operating across seven forces across England and Wales.

Read more:
Facial recognition technology used by police is now very accurate – but public understanding lags behind

At the same time, developments inside policing organisations highlight the limits of technological oversight. The Met was recently reported to have begun using AI tools to flag potential officer misconduct by analysing internal data such as sickness records, absences and overtime patterns.

While the Met argues that such systems help raise standards and rebuild public trust, critics warn that such monitoring risks misclassifying workplace pressures as misconduct and eroding accountability rather than strengthening it.

Ultimately, whether AI technology improves policing outcomes depends on the governance surrounding it. Ensuring there is a vigilant human in every AI loop should be a non-negotiable safeguard.

Federico Iannacci has received funding from the British Academy for a small research grant entitled “Investigating the future of work in policing: a Qualitative Comparative Analysis of police forces in England and Wales.”

Stan Karanasios does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Will AI tools make better police officers? – https://theconversation.com/will-ai-tools-make-better-police-officers-277258

Source: The Conversation – UK – By Erhan Kilincarslan, Reader in Accounting and Finance, University of Huddersfield

The UK’s trade deficit of goods is the widest it has ever been. In 2025, the country spent £248.3 billion more on things than it sold to the rest of the world.

This is not just some abstract number, of interest only to markets and economists. The UK’s trade deficit has practical consequences which help to explain why global events show up so quickly in people’s food and energy bills.

Nor is this a new situation. While the UK runs a strong surplus in services such as finance and professional consulting, it consistently imports more goods than it exports.

On its own, that is not necessarily a problem. Many advanced economies run trade deficits of goods. The more important issue is what a country imports, and how essential those imports are to daily life.

For example, the UK relies heavily on imports for many things that households cannot easily live without, such as 40% of the food they consume.

It imports much of its energy too – and although the UK produces some domestic oil and gas, wholesale energy prices are strongly influenced by international markets.

Food and energy are not optional purchases. Households cannot simply stop eating or heating their homes when prices rise. Economists describe these goods as “inelastic”, meaning that demand does not tend to fall even when the price increases.

And this creates a direct link between global volatility and household vulnerability. When global supply chains are disrupted, whether it’s because of geopolitical tensions, extreme weather or commodity price spikes, any country which is dependent on imported essentials (Germany, Italy and Japan are other examples) feels the impact quickly.

The Bank of England has highlighted how global energy and food price shocks played a major role in the recent surge in UK inflation. International adjustments feed quickly into domestic cost-of-living pressures.

Currency changes

The UK’s trade deficits also mean it needs plenty of foreign currency to pay for all of the things it imports. When financial markets become volatile, the pound can weaken, increasing the cost of these imported goods – which leads to rising inflation.

For an economy that depends heavily on imported food, fuel and manufactured goods, currency movements can amplify inflationary pressure. Households may not follow exchange rate fluctuations, but they do notice higher supermarket prices and energy bills.

Not everything is in deficit, though. The UK runs a significant surplus in services, particularly in finance.

But this creates a disconnect between the UK’s overall national economic performance and household experience. While the export of services supports national income and employment, it does not directly reduce the prices people pay for imported food or energy.

This is why everyday price vulnerability can remain high even when overall trade figures appear manageable.

Also, import-driven price shocks do not affect all households equally. Lower-income households spend a larger share of their income on essentials such as food and energy. When prices rise, they have less flexibility to absorb the increase. Higher-income households may cut back elsewhere, but lower-income households often cannot.

When import costs rise, the financial strain is therefore more intense for those people with the least. The same global shock can be manageable for some households but seriously disruptive for others.

Part of the reason for this general situation is that since the early 1990s, global trade policy has prioritised efficiency through trade liberalisation and manufacturing processes being spread across multiple countries.

Importing goods from the most competitive global suppliers reduced prices in stable periods. But efficiency often comes at the expense of resilience. When supply chains are disrupted, countries that rely heavily on imports for essential goods have fewer domestic buffers. Politicians may then struggle to stabilise prices because the source of volatility lies abroad.

Trade off

The result is something many households recognise. Events far away can rapidly translate into higher bills at home.

But the issue is not trade itself. International trade brings clear benefits, including lower prices, greater choice and access to global goods and services.

The question is whether the UK’s balance between efficiency and resilience leaves households overly exposed to volatility. Recent cost of living pressures have demonstrated how quickly global shocks can reach household budgets.

Trade policy is therefore not just about competitiveness or GDP growth. It is also about economic resilience – how well households are protected from forces beyond their control. But this does not mean reversing global trade or pursuing full self-sufficiency, which would be likely to increase costs.

Instead, the government should be working on the UK’s resilience through things like diversified supply chains and stronger strategic reserves. Clearer contingency planning for essential goods would reduce the UK’s vulnerability to global shocks.

While the UK’s trade deficit is often treated as an abstract macroeconomic statistic, for many households its consequences are felt in something far more tangible – grocery and energy bills.

Erhan Kilincarslan does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Why the UK’s trade deficit makes household bills so vulnerable to global shocks – https://theconversation.com/why-the-uks-trade-deficit-makes-household-bills-so-vulnerable-to-global-shocks-276991