Summary

• There is a growing gap between transparent, expert-led policy analysis and the political objectives of government officials—especially when those objectives involve structural reforms aimed at long-term growth.
• The 2022 mini-budget crisis is a clear example of this trend: by sidestepping independent fiscal scrutiny, the government triggered market instability, which only eased after the OBR’s re-engagement helped restore credibility.
• Institutions tasked with evaluating government policy often face limitations. Their modelling tools are not well-suited for assessing the long-term impacts of reforms targeting productive capacity. This weakens trust in forecasts and frustrates policymakers, who feel constrained in their ability to pursue structural change.
• This is partly reflective of a widening divide between the needs of these organisations and cutting-edge macroeconomic research, which limits how much these forecasts reflect consensus in the discipline. As the evidence base evolves, traditional forecasting methods lag behind, making empirical research less useful in informing policy projections.
• We propose bridging the gap between academic research and public policy through the following steps:
• • Create a talent pipeline with academia
  • Collaborate with the UK’s macroeconomic centres of expertise
  • Reform institutional incentives to protect the OBR’s independence
• These targeted investments—costing approximately £2.9 million annually—would strengthen the UK’s capacity to evaluate bold policy reforms while preserving rigorous fiscal oversight.

The Challenge & Opportunity

In September 2022, as the bond market reacted negatively to the newly elected government’s proposed mini-budget and the pound sank to $1.03, a crisis quietly gripped the institutions tasked with macroeconomic policy and governance. Chancellor Kwasi Kwarteng introduced a sweeping £45 billion tax-cutting package, bypassing the customary forecasts from the Office for Budget Responsibility (OBR), despite the organisation’s offer to provide an impartial assessment.

Sidestepping the OBR marked a notable shift from the recently-established norm of consulting expert bodies for unbiased evaluations of fiscal policy changes. The government claimed forecasts were unnecessary for their “mini-budget,” yet the market’s swift reaction suggested otherwise. Despite its temporary snub, the OBR’s robust reputation remained, and they ultimately helped to stabilise the economy when asked to provide an assessment weeks later.

The episode illustrates how demands for transparent, expert-driven policy analysis can often conflict with the objectives of elected (and unelected) officials. Some elected officials might feel that current analytical tools fail to reflect the transformative potential of their plans – a concern gaining traction as governments begin to tackle barriers to expanding infrastructure and housing. Delivering on such commitments could have economic impacts that existing frameworks, developed at a time marked by a decline in the rates of public and private investment in physical infrastructure, can struggle to capture. 

This is important when it comes to the OBR. The modelling tools it uses are somewhat dated and aren’t designed to handle the longer-term implications of transformative proposals on infrastructure. Its existing framework can capture the effect of small changes in policy relatively well, but is not suited to estimating the effect of wholesale changes in policy. This could create a vulnerability that allows officials to cherry-pick forecasts that support greater fiscal flexibility, while downplaying less favorable projections—an approach many saw on display during the mini-budget episode. Worse still, it could bias policymakers away from truly transformative policies in favour of slight tinkering with the status quo.

There is also a growing divide between the practical needs of policy appraisal bodies like the OBR and the insights emerging from cutting-edge macroeconomic research. This divide matters because first, advanced macroeconomic models—like those with complex financial frictions or different types of agents—are better suited to assess structural reforms; second, much of the field's growing empirical evidence is based on these kinds of models. As the disparity widens, producing reliable short-term forecasts for policies with extended timelines becomes ever more difficult.

The OBR’s hard-earned fiscal credibility remains intact, yet it confronts challenges that could erode its future influence. It needs to deepen its connection to the research frontier to enhance its capacity to address officials’ priorities, evaluate ambitious supply-side reforms, and enable it to more directly speak to the growing body of empirical evidence on what matters in macroeconomics.

Why is Macroeconomic Forecasting Relevant?

The OBR provides two important types of economic forecast: projecting the impact of policy changes on key economic indicators, and estimating how these indicators might perform under current policies. These forecasts serve three critical functions:

• They assess whether policies will achieve their stated goals -, e.g., will a tax cut boost investment as stated?
• They gauge impacts on GDP, inflation, and employment
• They examine distributional effects across different groups and regions

The mini-budget crisis of 2022 provided showed why these forecasts matter. When the £45 billion package was unveiled without a customary assessment from the OBR, markets reacted by pushing up yields on government debt to levels not seen in over a decade. As yields rose, further amplified by the Liability Drive Investment (LDI) crisis, market instability was pushed to unsustainable levels, to the point of threatening the viability of some pension funds. This episode underscored that forecasting isn't just a dry technical exercise or pro-forma step of Westminster political choreography, but can have real economic implications.

This volatility was only tempered after the OBR was allowed to publish its own analysis a few weeks later. One major takeaway from this episode was the importance of the OBR as a trusted source of impartial economic analysis.

Why Should We Care About the OBR?

Of course, the OBR isn’t the only public institution that carries out the important work of generating these forecasts. For example, the Bank of England uses a "suite-of-models" approach. Its responsibilities focus primarily on setting the interest rate, and occasionally – especially in moments of crisis – quantitative easing and/or forward guidance. Because of its reduced toolkit, the Bank’s forecasting requirements can expand beyond a rigid modelling framework and use a wide range of approaches, including frontier macroeconomic models.

But the OBR faces a very different challenge. Unlike the Bank, it must track dozens of policy instruments and model the effects of a very wide range of potential policy interventions. Each Budget introduces multiple tax and spending changes that must be individually assessed and combined into a coherent forecast. As seen in the mini-budget episode, what the OBR judges to be the impact of policy interventions can have significant political consequences.

There have also been calls for the OBR to carry out more dynamic assessments of policy announcements, a practice described as dynamic scoring. Unlike the CBO in the US, the OBR never carries out exclusively static analysis of policy measures. That is to say, its modelling approach always includes some form of dynamic response. However, some of these effects might go unreported if they're considered to be small compared to the policy’s immediate impact without behavioural responses, or if there's significant uncertainty about the evidence behind the estimates. In some instances, the OBR’s model may itself be structurally unsuited to handle large reforms that could meaningfully impact the structure of the UK economy. This poses two important questions for how we think of its role:

• How strong is the evidence base for capturing the impact of a particular policy measure, particularly any dynamic behavioural response?
• How well can the modelling approach and associated inputs describe the potential changes to the British economy from a particular measure or set of measures?

Built for the Margins

At the heart of the OBR’s approach lies its large macro-econometric model. This system comprises hundreds of equations, capturing intricate relationships between a large number of economic variables. The substantial detail in the model detail allows the OBR to track myriad tax and spending instruments with remarkable specificity, ensuring that nearly every aspect of fiscal policy can be accurately reflected in forecasts that balance with national accounts. This is an important point to consider: any policy initiative must be able to show that the numbers add up when you total its potential impacts.The model ensures that is the case.

Although the OBR’s forecasts rely on much more than just one model, it’s worth noting that this particular model is especially good at producing short-term policy forecasts that align with the national accounts. While the OBR’s primary model is better suited for tracking targeted interventions because it leans heavily on historical patterns, this strength comes at a cost. Its detailed focus makes it harder to include more advanced economic mechanisms, especially during major shifts. It isn’t built to capture medium- or long-term effects, especially on the supply side. Instead, it uses a mix of other methods to estimate how the economy’s productive capacity might change. That makes the OBR’s model more dependent on the quality of those alternative inputs, and if these cannot capture those effects either, they could gradually impair the quality of the forecasts.

In practice, both approaches have their strengths. The OBR's existing framework is better aligned with routine budget planning needs. It ensures alignment with national accounts and allows tracking of the impacts of small changes to very specific policies. But when the economy undergoes significant changes and structural reforms are needed, it is not clear the same approach continues to deliver.

When the Model Breaks: Forecasting in a Changing Economy

Of course, the OBR recognises these limitations and complements its core framework with other models and considerable judgement for medium-term forecasts, allowing it to incorporate a greater range of economic insights without compromising the core modelling structure. Yet, this approach introduces its own challenges, as consistency between different sources of information becomes harder to maintain and its main model becomes relatively less important to the final result.

As a cross-party consensus emerges on the need to reform the planning system and address some of the barriers to both private and public investment in infrastructure, policy-makers begin to look beyond macroeconomic stabilisation policy and begin to focus on structural reforms. The global financial crisis, Brexit, the COVID-19 pandemic, and the energy shock after Russia’s invasion of Ukraine were all major disruptions to economic stability—but the policy debates they triggered were, in many ways, easier to follow. Questions like whether to spend more or cut back, and how that would affect public finances, were relatively familiar to both policymakers and analysts. The UK’s prolonged productivity stagnation since the financial crisis has shifted the focus toward more ambitious supply-side policies—policies that, in turn, require a forecasting framework capable of capturing their full impact.

In contrast, traditional forecasting models are usually calibrated to historical data. They do best when economic relationships remain predictable. When those relationships break down, models that rely heavily on them face particular challenges if they lack the theoretical foundations to explore how relationships may have changed. During periods of relative stability, these limitations may remain largely hidden. But when economic paradigms shift, the foundations of forecasting are subject to more stringent tests, and these events likely coincide with renewed interest from policy officials looking for assurance that their preferred interventions will have the desired effects.

Why Academic Macroeconomics Has Been Ignored

Practicality Matters

The OBR hasn’t updated its modelling framework—widely seen as outdated by many leading experts—even though central banks like the Bank of England and international institutions such as the World Bank and IMF regularly adopt more advanced models.

Why might that be? The simplest answer is that without a major effort to distill and adapt decades of cutting-edge research into a practical model, the change might not offer real benefits. For OBR economists, it’s not obvious that switching to a more uncertain approach would actually improve their short-term forecasting ability.

They might reasonably argue that the costs of adopting a new approach outweigh the benefits. In their view, the bigger barriers to producing more robust forecasts lie elsewhere: the lack of solid empirical evidence to refine model assumptions, and both implicit and explicit pressure to generate forecasts that skew toward more favourable outcomes or reflect only certain types of policy changes. From this perspective, launching a major overhaul of the OBR’s methods might seem, at best, unnecessary—and at worst, a threat to its core strength: delivering short-term forecasts that align with the national accounts.

But Practicality is Not “Sexy”

We could also flip the question on its head and ask: why hasn’t frontier macroeconomics, particularly computational macroeconomics, been able to deliver models that institutions like the OBR could more readily adopt and would better match its needs and requirements?

To answer that, we need to recognise that the goals of frontier macroeconomic research don’t always align with those of institutions tasked with forecasting the impacts of policy initiatives. As in the natural sciences, there’s often a disconnect between the needs of organisations producing practical outputs and the direction of cutting-edge academic work. Different tools serve different purposes, and pushing the research frontier doesn’t necessarily translate into better tools for real-world application.

This growing disconnect is driven by misaligned incentives, as frontier research increasingly generates empirical findings and mechanisms that are hard to integrate into the standard toolkit of policy institutions. Take 'uncertainty shocks' as an example—these operate mainly through changes in expectations. If no actual policy shift has occurred, models that don’t account for how expectations are formed can’t properly capture the effects, except by using ad hoc adjustments that risk undermining the model’s reliability.

It’s clear then that much of this divergence comes from the evolution of academic economics in the past several decades. Where once macroeconomic modelling with direct policy application formed a central pillar of the discipline, incentives have increasingly rewarded academics for making novel contributions to the field, uncovering new mechanisms and increasing our understanding of others in relative isolation to one another. That is to say, the practice of advancing knowledge often requires isolating one effect at a time, more precisely describing a very specific economic mechanism. This, however, makes the difficult task of adapting existing models to match the specific needs of an organisation like the OBR one that offers comparatively few rewards. While knowledge advances and the leading-edge of the field becomes increasingly sophisticated, its practical usefulness in assessing the impact of policy interventions doesn’t obviously follow suit.

Challenges On the Horizon

This growing divergence is poorly timed. Policymakers in countries like the UK have had to face the kinds of economic shocks many commentators believed to be consigned to history during the period of so-called Great Moderation, with financial crises, worldwide pandemics, or the re-emergence of both large commodity price shocks and war all emerging since the end of that period. More importantly for the issue at hand, policymakers also face major long-term challenges: persistently weak productivity and stagnant wages, the evolving nature of the UK’s relationship with the EU, and the growing threat of climate change.

While short-term shocks call for stabilisation policies—where tax and spending decisions have limited long-term effects and forecasts mainly assess whether the economy stays on course—long-term challenges demand a different approach. Policies to boost productivity or cut carbon emissions often take years to bear fruit, yet politicians must make costly decisions now. In these cases, forecasts like the OBR’s take on greater economic and political significance, as projections may be the only tool the public has to evaluate performance before real-world outcomes appear.

Unjustified Criticisms Stifle Debate

The gap between fundamental and applied research has been further amplified by a continued demand to advance the frontier of the discipline to respond to the various shocks experienced by developed economies. For example, during the COVID-19 pandemic, an almost entirely new subfield of macro-epidemiology modelling materialised practically overnight, as researchers scrambled to produce tools that could provide a better understanding of how the public’s behaviour in the face of that threat would affect economic activity.

On the other hand, while the financial crisis saw an explosion in models with financial frictions, building on older research on banking crises, the discipline faced large amounts of criticism for failing to predict and remedy the impacts of that crisis. While these criticisms were almost entirely unfair and grounded in a lack of awareness of what was happening at the research frontier, they nevertheless made most practitioners more likely to choose academic debates with other researchers rather than try to force a greater awareness of how much the discipline has evolved, even if some continued trying to move the public debate further. At the very least, many policymakers saw heterodox critiques of macroeconomics as offering a clearer and more accessible response to the challenges they faced.

Both the pull of novel research problems and push of uninformed public criticism reduced the reward for established macroeconomists engaging in public debate, and amplified the existing incentive structure of contemporary academia to advance the frontier without paying attention to how ideas are usefully deployed in policy. With careers tied to top journal publications and a sense that their expertise held little sway with policymakers, academic macroeconomists naturally turned their focus to advancing the research frontier—and made significant progress in doing so.

The Absence of a Unifying Theory

In practice, a common feature of recent advances in macroeconomic modelling is that they often don’t translate well to large-scale models of the whole economy. Take heterogeneous agent macroeconomics, for example—a field that has recently made impressive progress, both in computational power and in capturing richer individual-level behaviours. Yet, by focusing on individual heterogeneity, this work paradoxically drifts even further from the needs of institutions like the OBR, which primarily aim to forecast the impact of a wide range of policies on aggregate outcomes.

While these innovations may eventually improve forecasting, in the near term they’re difficult to integrate into the kind of framework the OBR relies on. These models increasingly operate in entirely different conceptual and technical languages—and translating between them offers little professional reward.

The issue isn’t a lack of progress in the field, but rather that progress has taken the form of a wide array of specialised models, each focused on a particular mechanism. Building a unified framework from these fragments is beyond the scope of individual researchers, especially when doing so offers limited academic payoff—while policymakers need practical answers on tight timelines.

The complexity, computational intensity, and abstract nature of these models make them poorly suited to the OBR’s day-to-day needs. A model designed to explore the deep drivers of productivity may offer valuable insights, but it’s unlikely to help forecast the fiscal impact of a targeted policy like changes to capital allowances or R&D tax credits.

Why Should We Care?

Is this a problem even worth solving? Why should we expend resources in trying to bridge this gap between practice and theory, which carries substantial translational costs? The OBR faces two distinct challenges that could increase pressure on the organisation:

• Policymakers are increasingly shifting their focus to long-term growth, aiming to boost the economy’s productive capacity. This shift is already evident in planning system reforms and efforts to lower the cost of infrastructure and energy.
• As the gap between the OBR’s modelling approach and frontier academic research continues to widen, it becomes harder to settle the question of which empirical findings should inform policy forecasts. By its nature, frontier research often produces insights that are difficult to apply directly—making them less useful for the practical demands of institutions like the OBR.

In short, OBR forecasts risk becoming less informative over time, particularly when it comes to assessing the medium-term impact of structural reforms. The strengths of its current approach—so effective for short-term fiscal analysis—are less useful in this context. At the same time, the evidence base it depends on is shrinking, as its models rely on empirical work that is increasingly disconnected from the research frontier. Key parameters in the OBR’s primary model no longer have clear equivalents in modern macroeconomic theory, making it harder to find high-quality data to update them.

Governments—now and in the future—are likely to focus more on structural interventions and supply-side reforms. Institutions tasked with assessing these policies will need tools capable of answering the specific questions they raise. In this more complex environment, producing forecasts becomes less about plugging numbers into models and more about weighing competing judgements. That kind of expert judgement is vital—but it also raises questions: whose judgement counts, and how legitimate is it when based on tools increasingly out of step with the discipline itself?

These tensions were already visible during the mini-budget episode, when efforts to sideline impartial analysis were widely seen as political overreach—and quickly punished by the markets. Even if future governments allow the OBR to operate with its usual independence, differing views about the long-term effects of structural reform will likely make economic forecasting more contested—and more politically charged.

How to Strengthen the OBR

A Translational Gap

The OBR provides an important public service. It generates detailed, credible forecasts of government spending plans and their impact on economic activity and public finances. But there's a risk the OBR’s toolkit will be much less well-equipped to evaluate these longer-term, supply-side policies.

On the other hand, simply adopting the latest macroeconomic models would be counterproductive. These models, while theoretical and computationally sophisticated, lack the granularity needed for the OBR's fiscal forecasting. What's needed is targeted investment in translational research - taking frontier research and consolidating and adapting it to meet the needs of detailed policy work and impact assessment.

This translational work would require creating stronger links between policy institutions and academia. Currently, these operate in parallel universes: published research is aimed at an audience of other leading researchers, and published models typically require substantial work to become useful for active policy analysis. On the other hand, the work developed in organisations like the OBR is largely disseminated for public awareness, rather than as contributions to the scientific frontier. 

Academic economists and the OBR both face significant practical obstacles to collaboration. For academics, career advancement depends overwhelmingly on publications in prestigious journals, which reward frontier innovations over practical application. Models that elegantly demonstrate a particular economic mechanism offer greater rewards than those solving implementation details. Meanwhile, OBR analysts operate under intense time pressures, delivering forecasts on tight deadlines that leave little room for experiments to incorporate novel methods, which often require substantial testing and debugging.

The OBR’s greatest strength lies in the granularity of its forecasts—detailed projections for a wide range of tax and spending measures. These well-defined, policy-specific outputs are especially useful in the short term, offering reliable insights into immediate impacts. But as the forecast horizon lengthens, the limitations of the underlying model become more apparent. It becomes less suited to capturing evolving economic relationships and structural changes, leading to greater reliance on analyst judgement to fill the gaps.

Making Research Work for Policy

This proposal isn’t about changing the OBR for its own sake. The OBR has developed a robust framework focused on carefully assessing government spending plans—and that should remain its core mission. Keeping pace with academic research only matters if it helps deliver more useful insights for policymakers and markets. While frontier models aren't ready for immediate deployment, they offer valuable methodological advances that could strengthen the OBR’s ability to assess supply-side reforms.

One key benefit of this proposal would be to ensure OBR analysts have better access to high-quality empirical estimates of key parameters. Some of this can be achieved by creating stronger incentives for researchers to produce meta-analyses and empirical reviews. But much of it will require updating the OBR’s underlying modelling infrastructure to be more compatible with recent advances.

Bridging this gap—between frontier research and applied policy forecasting—will take effort from both sides. We propose a few concrete steps that could better connect the OBR’s modelling needs with the academic research community. This will require buy-in not just from the OBR and universities, but also from senior policymakers, and it will mean committing additional resources. The case for that investment is strong. The OBR’s budget is just ~£4.8 million—smaller than some individual research grants—and even modest improvements in its capacity to assess policy could deliver outsized gains for government decision-making. As governments place increasing emphasis on structural reforms in areas like housing, infrastructure, and energy, the OBR’s models must be equipped to evaluate these long-term interventions. 

Fortunately, the tight academic job market presents an opportunity. Talented economists often earn modest salaries in academia despite years of training, while government roles like GES fast stream positions can offer more competitive early-career paths. Still, policy roles often require economists to step away from frontier research—reducing the appeal to candidates with cutting-edge training. Building teams that combine academic depth with applied experience will be key to expanding the OBR’s capabilities without losing its core strengths.

The Plan of Action

To strengthen the Office for Budget Responsibility (OBR), two key things must happen:

1. Its budget must reflect the scale and importance of its evolving role.
2. Academic economists working at the research frontier need clear incentives to engage in translational work. Without this, they are unlikely to contribute their expertise without compensation or career recognition.

Below are three areas of reform that would help bridge these gaps:

Create a Talent Pipeline into the OBR

• Visiting Researcher Program: By 2026, bring 5-10 academics or PhD students for 3-6 month stints at £500,000-£1 million with additional funding for the OBR. Visiting positions would allow researchers to stay connected with frontier research while gaining relevant practical experience that could shape future research projects. Better pay than alternative roles in academia would make it attractive for economists with closer ties to frontier research, increasing the OBR’s current capacity.
• Doctoral Training Partnership: By 2027, fund 10-20 PhD students a year at £250,000-£500,000, jointly funded by the OBR and UKRI (and/or, possibly, the Institute for Fiscal Studies). In addition to having economists at the research frontier establish links to the organisation through the visiting programme, it would fund research programmes with the specific aim of focusing research on problems that are relevant to the organisation. 
• Economist Development Pathway: By 2028, recruit 5–10 early-career economists annually into two-year, in-house roles (£200,000–£400,000 per hire). Allocate an additional £100,000 to support joint Treasury–Bank of England workshops. As the OBR takes on responsibilities like long-term forecasting and empirical evidence reviews, it will need this independent, in-house capacity.

Collaborate with Macroeconomic Expertise

• Lean on the UK expertise: Establish a £500,000–£700,000 annual budget to commission work from UK universities in areas like financial frictions, labour markets, or heterogeneous agent modelling. The UK has world-class expertise in macroeconomic modelling—this programme would bring that knowledge directly into the OBR.
• Empirical research program: In addition to targeted commissions, fund a recurring £1 million annual programme to review and integrate empirical findings into OBR models. This would ensure legacy assumptions are regularly updated, and new parameters from cutting-edge research are adopted where relevant.

Institutional Incentives and Oversight

• Trim the REF for Purpose: Current REF (Research Excellence Framework) guidance often fails to reward work that translates academic insights into usable tools and models. It is overly bureaucratic and prioritises “impact” as defined by outcomes, not process improvements. Until broader reform is possible, technical citations by public bodies like the OBR should count as impact under REF to incentivise this work.
• Hold OBR Steady: By 2026, create a 7-member independent board—composed of macroeconomists, econometricians, ex-OBR staff, and policy experts—to provide ongoing guidance on modelling decisions and methodological changes. Funded through the expanded OBR budget, this board would help protect the organisation from political interference and ensure rigorous standards are maintained.

These proposals deliver on three fronts:

• They create a revolving door between academia and the OBR, enabling two-way knowledge exchange and widening the scope of real-world problems researchers engage with.
• They connect the OBR to leading macroeconomic expertise within UK universities through strategic commissioning and collaborative programmes.
• They realign institutional incentives, ensuring that researchers are recognised and rewarded for work that improves the quality and relevance of economic models.

If implemented, these reforms would significantly improve the OBR’s ability to respond to shifting government priorities and adapt to a rapidly changing economic landscape.

FAQs

Why does the OBR use such intricate models rather than simpler, more theoretical approaches?

The OBR plays a vital public role: producing detailed, credible forecasts of how government spending and tax decisions affect the economy and public finances. To do this well, it needs models that reflect the complexity of the real fiscal system. These models allow the OBR to track specific tax and spending measures—everything from tweaks to VAT thresholds to major welfare reforms—with a level of detail that aligns closely with national accounts.

This granularity makes the models extremely useful for short-term forecasting and policy assessment. But it also comes with trade-offs. Because the models rely heavily on historical relationships, they can struggle when economic conditions shift in ways not captured by past data. The OBR is aware of these limits and supplements its core framework with alternative models and expert judgement—especially when making medium- to long-term projections.

Can't academia help develop better forecasting models for institutions like the OBR?

In theory, yes. In practice, it’s much harder. While academic economics has made huge strides, its incentives don’t naturally support the kind of translational work needed to adapt complex, frontier research into models usable by public bodies. Today, academic success is often driven by publishing novel insights, not by making existing models more applicable to real-world forecasting.

At the same time, OBR analysts operate under tight deadlines and face strong pressures for accuracy and transparency. That leaves little room for experimenting with newer, untested modelling approaches. The challenge isn't a lack of progress in economics—it’s that progress has produced a variety of specialised models, each illuminating one part of the economy, rather than a single unified approach ready to drop into practice. Making these tools work together—or usable by institutions like the OBR—requires time, funding, and structural incentives that currently don’t exist.

Was the OBR on the right track during the Truss mini-budget crisis, or did it overstate the risks?

The 2022 mini-budget crisis highlighted just how important independent fiscal oversight is. When the government unveiled £45 billion in unfunded tax cuts without an accompanying OBR forecast, markets reacted sharply—government borrowing costs surged to levels not seen in over a decade.

The absence of an independent assessment created uncertainty. Once the OBR was allowed to publish its analysis, that clarity helped restore market confidence and bring stability back. The episode wasn’t just about one forecast—it showed how expert, transparent scrutiny of fiscal policy underpins economic credibility.

Far from overstating the risks, the OBR’s sidelining during the mini-budget made the risks harder to assess—leading to greater volatility. Its return helped steady the ship. The takeaway: the OBR is not just a background institution, but a cornerstone of the UK’s economic stability. Strengthening its capacity will only become more important in the face of future shocks.