On the 29 November, the Bank of England published the results of its latest stress test of the UK financial system. Annual stress testing was introduced in the wake of the 2008 financial crisis. It models the ability of the financial system to withstand severe macroeconomic and financial market conditions. Typically, the focus has been on testing the resilience of the banking system.
This year’s was the first system-wide exploratory scenario (SWES). This recognises the growing significance of ‘shadow banking’. Shadow banking involves borrowing and lending involving non-bank financial institutions (NBFIs). Such institutions sit outside the regulatory cordons around banking but have become significant actors in the financial system.
However, this obscure part of the financial system poses systemic risks which are not clearly understood and from time to time require costly interventions. Examples include: problems in liability-driven investments (LDIs) for pension funds in September 2022; the money market liquidity crisis involving hedge funds in March 2020; the collapse of Long-term Capital Management (LTCM) in 1998 following the Russian Federation’s default (LTCM had significant holdings of Russian government bonds – see linked article on LTCM below).
The growing significance of shadow banking means that regulators have become increasingly concerned about the vulnerabilities in the financial system which arise from outside the traditional banking system.
In this blog we will explain stress-testing of the financial system and trace the rise in shadow banking which motivated the recent system-wide exploratory scenario (SWES). We will discuss the findings of the stress test, highlighting the systemic risks of shadow banking. Finally, we will discuss the implications for the regulation and supervision of the financial system.
What is stress testing?
Stress testing was introduced by the Bank of England after the financial crisis to assess the ability of the financial system to withstand severe economic and market scenarios.
In the run-up to the 2008 financial crisis, the liquidity and capital buffers of many banks had been extremely thin. These were only able to withstand moderate economic shocks and moderate conditions and buckled under the stresses of the crisis.
Regulators argued that the buffers needed to become much more robust and be able to withstand rare but severe economic and market conditions. The stress testing analogy was derived from engineering, where parts are expected to work not just in benign conditions but also in extreme, hostile environments.
Since 2014, the Bank of England has conducted annual stress testing. Stress testing models the impact of adverse economic conditions on banks’ liquidity, profitability and capital. The results are used to set policy for individual banks (microprudential) and for the system as a whole (macroprudential). Stress test results have allowed the Bank to adjust the loss-absorbing capital that banks must hold to reduce their likelihood of failure.
The scope of the testing has expanded over time to incorporate insurers, central counterparties (financial institutions that provide clearing and settlement services between financial traders) and cyber security. The most recent scenario recognised the increasing significance of non-deposit taking financial institutions in channelling credit. Fifty City of London institutions modelled how a period of intense stress would ripple through the shadow banking sector.
The arcane world of shadow banking
Shadow banking refers to borrowing and lending which occurs outside the banking sector. Traditionally banking involves taking deposits and using these to finance lending.
Shadow banking involves non-deposit taking financial institutions (NBFIs) such as hedge funds, insurance companies, pension funds, private equity funds, as well as some activities of investment banks. These institutions channel funds in different ways from lenders to borrowers. Typically, they use funds from investors to buy securities through financial markets. The emergence and growth of shadow banking has been explained by changing regulation and innovation.
Its first significant period of expansion in the late 1980s was driven by financial innovation. Increased use of ‘disintermediation’ – the replacement of credit channels through banks with ones through markets – meant an increase in the assets invested through NBFIs.
Despite this process playing a major role in the expansion of housing credit in the run-up to the 2008 financial crisis, it was the significant bailouts that banks received that drew the attention of regulators, not the role of shadow banking. This led to more stringent liquidity and capital requirements for banks under the BASEL III international regulations.
This regulatory tightening limited banks’ ability to offer credit, which meant that much of this activity migrated to the shadow banking sector.
Data from the Bank of England show that the percentage of total assets held by NBFIs rose from 41% in 2007 to 49% in 2020. The chart illustrates the total financial assets held by non-bank financial institutions in the UK between 2019 Q4 and 2023 Q3 (click here for a PowerPoint). The amount held has growth by approximately a third in that time, from £4321bn to £6069bn, peaking at £6670bn in 2022 Q3.
The lack of regulatory oversight stems from the nature of the activities in the shadow banking sector. While NBFIs conduct maturity transformation, provide liquidity and help manage risk, unlike banks, they do not accept deposits and are not part of the payments system involving the general public.
Consequently, the consensus among regulators has been that their activities do not pose the same systemic risks as banking of the breakdown of the payments mechanism and associated collapse in business and consumer confidence. Therefore, NBFIs are not subject to conventional regulation and supervision involving liquidity and capital requirements.
However, as the scale of borrowing and lending running through the sector has grown, this argument has become less difficult to justify. There is a concern that ‘regulatory arbitrage’ is happening and that the systemic risks associated with shadow banking are being underestimated.
The familiar risks of shadow banking
The systemic consequences of liquidity and solvency problems in the shadow banking sector may not seem obvious. Much of their activities are arcane and technical. However, there are plenty of examples of instances where the problems of hedge funds or pension funds have caused systemic issues.
While the consequences are not the same as those involving banks, in that the payments mechanism is not directly affected, the risks are. Just like banks, these institutions are exposed to liquidity risks, credit default risks and counterparty risks. The concern is that they do not have the same levels of liquidity or capital buffers as banks to insulate them from the consequences of such risks. Therefore, it might not take much economic stress for one or more of these institutions to fail and, given the increasing significance and interconnectedness of these activities, impose significant costs on the rest of the financial system.
It was for this reason that the Bank of England conducted its first system-wide exploratory scenario to analyse the impact of economic and market stress on these institutions and assess the nature and extent of systemic risks which resulted. Fifty City of London institutions modelled how a period of intense stress would ripple through the non-bank sector.
The scenario involved rising geopolitical tensions which caused a sharp rise in risk aversion and a demand for higher expected rates of return as compensation. This produced sharp rises in both sovereign and corporate bond yields and matching sharp declines in asset prices (remember bond yield and prices have a negative relationship).
The scenario found that the position and behaviour of NBFIs amplified the shock. These institutions invest significantly in marketable financial securities and their liquidity and solvency are susceptible to such falling prices.
The sharp decline in asset prices triggered margin calls – payments to cover open loss positions in financial securities. In response to these demands, while some NBFIs’ internal risk and leverage measures were breached, others illustrated greater risk-aversion and took precautionary action. These institutions acted to deleverage, derisk and recapitalise. Given the interconnectedness of financial markets, the individual actions of institutions rippled across financial markets, causing problems in other segments.
The significant decline in asset prices led insurance companies and pension funds to seek to improve their liquidity and solvency position by liquidating positions in money market funds and hedge funds. This, in turn, required these funds to seek liquidity. Such institutions tend to rely a lot on the repo market (involving short-term sale and repurchase credit agreements) to provide liquidity to investors. This avoids them having to sell assets. This practice has echoes of the banking sectors use of the short-term wholesale markets in the run-up to the 2008 financial crisis.
However, the SWES found that while banks were willing to take on some of the risk, their own concerns about liquidity and counterparty credit risk meant they did not offer sufficient short-term liquidity through the repo markets. If such funding dried up because of a higher risk perception, it could compromise the hedge funds’ ability to raise funds, requiring asset sales. This would amplify the shock to financial markets, driving prices of financial securities even lower.
The scenario concluded that the resulting heavy selling could seize up financial markets, particularly the UK sovereign and corporate bond markets, reducing the ability of companies to finance investment. This is a different type of credit crunch from 2008, which was restricted to banks – but a credit crunch, nonetheless.
At the same time, funds may make capital losses as they sell securities in the downturn. This creates solvency problems and the potential for failure.
In the SWES the institutions were often not able to anticipate how their counterparties, investors, or markets they operate in would behave in the stressed scenario, which echoes the experience of banks in 2007 and 2008 – a significant reason for the ‘crunch’ in banking credit was uncertainty about the creditworthiness of counterparties, meaning that banks were not prepared to lend to anybody.
Conclusion
Since the 2008 financial crisis, there has been a tightening of the regulation and supervision of banks which has limited their ability to channel credit. This has produced an expansion in the shadow banking sector.
However, while the shadow banking sector has not been subject to the same regulation and supervision as banks, there are still potential systemic risks associated with its operations. There have been several examples of such risks in the shadow banking sector which have led regulators to pay more attention. These underpinned the 2024 system-wide exploratory scenario (SWES) conducted by the Bank of England.
The scenario showed the possible transmission mechanism through which problems for NBFIs can have broader consequences. The report nevertheless concluded that:
…the UK financial system was well-capitalised, maintained high levels of liquidity and that asset quality remained strong.
Therefore, the UK financial system was resilient enough to withstand problems in shadow banking.
Although the results of the exercise provide a ‘framework of future system-wide analysis which can be embedded in future market-wide surveillance,’ history indicates that risks tend to exist in obscure and arcane parts of the financial system and that these never tend to be fully appreciated until a crisis occurs. This then tends to involve significant costs for taxpayers.
Articles
- Bank of England warns of risks from non-banks in future markets crisis
Financial Times from MSN, Martin Arnold (29/11/24)
- BoE finds non-bank financial firms pose wider risks in crisis periods
Reuters, Lawrence White (2/12/24)
- Finance Firms Beyond Banks Not Ready For Crisis, BOE Warns
Bloomberg from Yahoo finance, Laura Noonan and Greg Ritchie (29/11/24)
- Shadow Banking System: Definition, Examples, and How It Works
Investopedia, Michael Bromberg (18/10/24)
- US Treasuries: the lessons from March’s market meltdown
Financial Times, Colby Smith and Robin Wigglesworth (29/7/20)
- LDI: the better mousetrap that almost broke the UK
FT Alphaville, Alexandra Scaggs and Louis Ashworth (29/9/22)
- Long-Term Capital Management
CFA Institute, Ron Rimkus (18/4/16)
- Neil Woodford: the continuing fallout of a scandal
Financial Times, Owen Walker (19/3/21)
Bank of England documents and reports
Data
Questions
- Explain stress testing.
- What is shadow banking? Explain the factors driving the growth of credit in this part of the financial system.
- Compare and contrast the liquidity problems of banks with those of non-bank financial institutions (NBFIs).
- Analyse how financial crises can heighten problems of asymmetric information in financial markets.
Artificial Intelligence (AI) is transforming the way we live and work, with many of us knowingly or unknowingly using some form of AI daily. Businesses are also adopting AI in increasingly innovative ways. One example of this is the use of pricing algorithms, which use large datasets on market conditions to set prices.
While these tools can drive innovation and efficiency, they can also raise significant competition concerns. Subsequently, competition authorities around the world are dedicating efforts to understanding how businesses are using AI and, importantly, the potential risks its use may pose to competition.
How AI pricing tools can enhance competition
The use of AI pricing tools offers some clear potential efficiencies for firms, with the potential to reduce costs that can potentially translate into lower prices for consumers.
Take, for instance, industries with highly fluctuating demand, such as airlines or hotels. Algorithms can enable businesses to monitor demand and supply in real time and respond more quickly, which could help firms to respond more effectively to changing consumer preferences. Similarly, in industries which have extensive product ranges, like supermarkets, algorithms can significantly reduce costs and save resources that are usually required to manage pricing strategies across a large range of products.
Furthermore, as pricing algorithms can monitor competitors’ prices, firms can more quickly respond to their rivals. This could promote competition by helping prices to reach the competitive level more quickly, to the benefit of consumers.
How AI pricing tools can undermine competition
However, some of the very features that make algorithms effective can also facilitate anti-competitive behaviour that can harm consumers. In economic terms, collusion occurs when firms co-ordinate their actions to reduce competition, often leading to higher prices. This can happen both explicitly or implicitly. Explicit collusion, commonly referred to as illegal cartels, involves firms agreeing to co-ordinate their prices instead of competing. On the other hand, tacit collusion occurs when firms’ pricing strategies are aligned without a formal agreement.
The ability for these algorithms to monitor competitors’ prices and react to changes quickly could work to facilitate collusion, by learning to avoid price wars to maximise long-term profits. This could result in harm to consumers through sustained higher prices.
Furthermore, there may be additional risks if competitors use the same algorithmic software to set prices. This can facilitate the sharing of confidential information (such as pricing strategies) and, as the algorithms may be able to predict the response of their competitors, can facilitate co-ordination to achieve higher prices to the detriment of consumers.
This situation may resemble what is known as a ‘hub and spoke’ cartel, in which competing firms (the ‘spokes’) use the assistance of another firm at a different level of the supply chain (e.g. a buyer or supplier that acts as a ‘hub’) to help them co-ordinate their actions. In this case, a shared artificial pricing tool can act as the ‘hub’ to enable co-ordination amongst the firms, even without any direct communication between the firms.
In 2015 the CMA investigated a cartel involving two companies, Trod Limited and GB Eye Limited, which were selling posters and frames through Amazon (see linked CMA Press release below). These firms used pricing algorithms, similar to those described above, to monitor and adjust their prices, ensuring that neither undercut the other. In this case, there was also an explicit agreement between the two firms to carry out this strategy.
What does this mean for competition policy?
Detecting collusion has always been a significant challenge for the competition authorities, especially when no formal agreement exists between firms. The adoption of algorithmic pricing adds another layer of complexity to detection of cartels and could raise questions about accountability when algorithms inadvertently facilitate collusion.
In the posters and frames case, the CMA was able to act because one of the firms involved reported the cartel itself. Authorities like the CMA depend heavily on the firms involved to ‘whistle blow’ and report cartel involvement. They incentivise firms to do this through leniency policies that can offer firms reduced penalties or even complete immunity if they provide evidence and co-operate with the investigation. For example, GB eye reported the cartel to the CMA and therefore, under the CMA’s leniency policy, was not fined.
But it’s not all doom and gloom for competition authorities. Developments in Artificial Intelligence could also open doors to improved detection tools, which may have come a long way since the discussion in a blog on this topic several years ago. Competition Authorities around the world are working diligently to expand their understanding of AI and develop effective regulations for these rapidly evolving markets.
Articles
Questions
- In what types of markets might it be more likely that artificial intelligence can facilitate collusion?
- How could AI pricing tools impact the factors that make collusion more or less sustainable in a market?
- What can competition authorities do to prevent AI-assisted collusion taking place?
In this blog we show how we can apply fiscal metrics to assess the UK government’s fiscal stance. This captures the extent to which fiscal policy contributes to the level of economic activity in the economy.
Changes in the fiscal stance can then be used to estimate the extent to which discretionary fiscal policy measures represent a tightening or loosening of policy. We can measure the size and direction of fiscal impulses arising from changes in the government’s budgetary position.
Such an analysis is timely given the Autumn Budget presented by Rachel Reeves on 30 October 2024. This was the first Labour budget in 14 years and the first ever to be presented by a female Chancellor of the Exchequer.
We conclude by considering the forecast profile of expenditures and revenues for the next few years and the new fiscal rules announced by the Chancellor.
The fiscal stance
At its most simple, the fiscal stance measures the extent to which fiscal policy increases or decreases demand, thereby influencing growth and inflation (see Box 1.F, page 28, Autumn Budget 2024: see link below).
The fiscal stance is commonly estimated by measures of pubic-sector borrowing. To understand this, we can refer to the circular flow of income model. In this model, excesses of government spending (an injection) over taxation receipts (a withdrawal or leakage) represent a net injection into the circular flow and hence positively affect the level of aggregate demand for national output, all other things being equal.
A commonly used measure of borrowing in assessing the fiscal stance of the is the primary deficit. Unlike public-sector net borrowing, which is simply the excess of the sector’s spending over its receipts (largely taxation), the primary deficit subtracts net interest costs. It therefore excludes the interest payments on outstanding public-sector debts (and interest income earned on financial assets). The primary deficit can therefore be written as public-sector borrowing less net interest payments.
As discussed in our blog Fiscal impulses in November 2023, the primary deficit captures whether the public sector is able to afford its present fiscal choices by abstracting from debt-serving costs that reflect past fiscal choices. In this way, the primary deficit is a preferable measure to net borrowing both in assessing the impact on economic activity, i.e. the fiscal stance, and in assessing whether today’s fiscal choices will require government to issue additional debt.
Chart 1 shows public-sector net borrowing and the primary balance as shares of GDP for the UK since financial year 1975/76 (click here for a PowerPoint). The data are from the latest Public Finances Databank published by the Office for Budget Responsibility, published on the day of the Autumn Budget in October (see Data links below).
Over the period 1975/6 to 2023/24, public-sector net borrowing and the primary deficit had averaged 3.8% and 1.3% of GDP respectively. In the financial year 2023/24, they were 4.5% and 1.5% (they had been as high as 15.1% and 14.1% in 2020/21 as a result of COVID support measures). In 2024/25 net borrowing and the primary deficit are forecast to be 4.5% and 1.6% respectively. By 2027/28, while net borrowing is forecast to be 2.3% of GDP, there is forecast to be a primary surplus of 0.7% of GDP.
The Autumn Budget lays out plans for higher tax revenues to contribute two-thirds of the overall reduction in the primary deficit over the forecast period (up to 2029/30), while spending decisions contribute the remaining third.
The largest tax-raising measure is an increase in the employer rate of National Insurance Contributions (NICs) by 1.2 percentage points to 15% from April 2025. This will be levied on employee wages above a Secondary Threshold of £5000, reduced from £9100, which will increase in line with CPI inflation each year from April 2028. (See John’s blog, Raising the minimum wage: its effects on poverty and employment, for an analysis on the effects of this change.) This measure, allowing for other changes to the operation of employer NICs, is expected to raise £122 billion over the forecast period. This amounts to over two-thirds of the additional tax take from the taxation measures taken in the Budget.
Chart 2 shows both net borrowing and the primary deficit after being cyclically-adjusted (click here for a PowerPoint). This process adjusts these fiscal indicators to account for those parts of spending and taxation that are affected by the position of the economy in the business cycle. These are those parts that act as automatic stabilisers helping, as the name suggests, to stabilise the economy.
The process of cyclical adjustment leads to estimates of receipts and expenditures as if the economy were operating at its potential output level and hence with no output gap. The act of cyclically adjusting the primary deficit, which is our preferred measure of the fiscal stance, allows us to assess better the public sector’s fiscal stance.
Over the period from 1975/6 up to and including 2023/24, the cyclically-adjusted primary deficit (CAPD) averaged 1.1% of potential GDP. In 2024/25 the CAPD is forecast to be 1.5% of potential GDP. It then moves to a surplus of 0.5% by 2027/28. It therefore mirrors the path of the unadjusted primary deficit.
Measuring the fiscal impulse
To assess even more clearly the extent to which the fiscal stance is changing, we can use the cyclically-adjusted primary deficit to measure a fiscal impulse. This captures the magnitude of change in discretionary fiscal policy.
The term should not be confused with fiscal multipliers which measure the impact of fiscal changes on outcomes, such as real GDP and employment. Instead, we are interested in the size of the impulse that the economy is being subject to. Specifically, we are measuring discretionary fiscal policy changes that result in structural changes in the government budget and which, therefore, allow an assessment of how much, if at all, a country’s fiscal stance has tightened or loosened.
The size of the fiscal impulse is measured by the year-on-year percentage point change in the cyclically-adjusted public-sector primary deficit (CAPD) as a percentage of potential GDP. A larger deficit or a smaller surplus indicates a fiscal loosening. This is consistent with a positive fiscal impulse. On the other hand, a smaller deficit or a larger surplus indicates a fiscal tightening. This is consistent with a negative fiscal impulse.
Chart 3 shows the magnitude of UK fiscal impulses since the mid-1970s (Click here for a PowerPoint file). The scale of the fiscal interventions in response to the COVID-19 pandemic, which included the COVID-19 Business Interruption Loan Scheme (CBILS) and Job Retention Scheme (‘furlough’), stand out sharply. In 2020 the CAPD to potential output ratio rose from 1.7 to 14.4%. This represents a positive fiscal impulse of 12.4% of GDP.
This was followed in 2021 by a tightening of the fiscal stance, with a negative fiscal impulse of 10.1% of GDP as the CAPD to potential output fell back to 4.0%. Subsequent tightening was tempered by policy measures to limit the impact on the private sector of the cost-of-living crisis, including the Energy Price Guarantee and Energy Bills Support Scheme.
For comparison, the fiscal response to the global financial crisis from 2007 to 2009 saw a cumulative positive fiscal impulse of 5.6% of GDP. While smaller in comparison to the discretionary fiscal responses to the COVID-19 pandemic, it nonetheless represented a sizeable loosening of the fiscal stance.
Chart 4 focuses on the implied fiscal impulse for the forecast period up to 2029/30 (click here for a PowerPoint). The period is notable for a negative fiscal impulse each year. Across the period as a whole, this there is a cumulative negative fiscal impulse of 2.6% of GDP. Most of the ‘heavy-lifting’ of the fiscal consolidation occurs in the three financial years from 2025/26 during which there is a cumulative negative impulse of 2.0% of GDP.
Looking forward
To conclude, we consider the implications for the projected profiles of public-sector spending, receipts and liabilities over the forecast period up to 2029/30.
Chart 5 plots data since the mid-1950s (click here for a PowerPoint). It shows the size of total public-sector spending (also known as ‘total managed expenditures’), taxation receipts (sometimes referred as the ‘tax burden’) and total public-sector receipts as shares of GDP. This last one includes additional receipts, such as interest payments on financial assets and income generated by public corporations, as well as taxation receipts.
The OBR forecasts that in real terms (i.e. after adjustment for inflation), public-sector spending will increase on average over the period from 2025/26 to 2029/30 by 1.4% per year, but with total receipts due to rise more quickly at 2.5% per year and taxation receipts by 2.8% per year. The implications of this, as discussed in the OBR’s October 1014 Economic and Fiscal Outlook (see link below), are that:
the size of the state is forecast to settle at 44% of GDP by the end of the decade, almost 5 percentage points higher than before the pandemic” while additional tax revenues will “push the tax take to a historic high of 38% of GDP by 2029-30
Finally, the government has committed to two key rules: a stability rule and an investment rule.
The stability rule. This states that the current budget must be in surplus by 2029/30 or, once 2029/30 becomes the third year of the forecast period, it will be in balance or surplus every third year of the rolling forecast period thereafter. The current budget refers to the difference between receipts and expenditures other than capital expenditures. In effect, it captures the ability of government to meet day-to-day spending and is intended to ensure that over the medium term any borrowing is solely for investment. It is important to note that ‘balance’ is defined in a range of between a deficit and surplus of no more than 0.5% of GDP.
The stability rule replaces the borrowing rule of the previous government that public net borrowing, therefore inclusive of investment expenditures, was not to exceed 3% of GDP by the fifth year of the rolling forecast period.
The investment rule. The government is planning to increase investment. In order to do this in a financially sustainable way, the investment rule states that public-sector net financial liabilities (PSNFL) or net financial debt for short, is falling as a share GDP by 2029/30, until 2029/30 becomes the third year of the forecast period. PSNFL should then fall by the third year of the rolling forecast period. PSNFL is a broader measure of the sector’s balance sheet than public-sector net debt (PSND), which was targeted under the previous government and which was required to fall by the fifth year of the rolling forecast period.
The new target, as well as now extending to the Bank of England, ‘nets off’ not just liquid liabilities (i.e. cash in the bank and foreign exchange reserves) but also financial assets such as shares and money owed to it, including expected student loan repayments. While liabilities are broader too, including for example, the local government pension scheme, the impact is expected to reduce the new liabilities target by £236 billion or 8.2 percentage points of GDP in 2024/25. The hope is that both rules can support what the Budget Report labels a ‘step change in investment’.
As Chart 6 shows, public investment as a share of GDP has not exceeded 6% this century and during the 2010s averaged only 4.4% (click here for a PowerPoint). The forecast has it rising above 5% for a time, but easing to 4.8% by end of the period.
This suggests more progress will be needed if the UK is to experience a significant and enduring increase in public investment. Of course, this needs to be set in the context of the wider public finances and is illustrative of the choices facing fiscal policymakers across the globe after the often violent shocks that have rocked economies and impacted on the state of the public finances in recent years.
Articles
Official documents
Data
Questions
- Explain what is meant by the following fiscal terms:
(a) Structural deficit,
(b) Automatic stabilisers,
(c) Discretionary fiscal policy,
(d) Public-sector net borrowing,
(e) Primary deficit,
(f) Current budget balance,
(g) Public-sector net financial liabilities (PSNFL).
- Explain the difference between a fiscal impulse and a fiscal multiplier.
- In designing fiscal rules what issues might policymakers need to consider?
- What are key differences between the fiscal rules of the previous Conservative government and the new Labour government in the UK? What economic arguments would you make for and against the ‘old’ and ‘new’ fiscal rules?
- What is meant by the ‘sustainability’ of the public finances? What factors might impact on their sustainability?
In an interview with Joe Rogan for his podcast, The Joe Rogan Experience, just before the US election, Donald Trump stated that, “To me, the most beautiful word – and I’ve said this for the last couple of weeks – in the dictionary today and any is the word ‘tariff’. It’s more beautiful than love; it’s more beautiful than anything. It’s the most beautiful word. This country can become rich with the use, the proper use of tariffs.”
President-elect Trump has stated that he will impose tariffs on imports of 10% or 20%, with 60% and 100% tariffs on imports from China and Mexico, respectively. This protection for US industries, combined with lighter regulation, will, he claims, provide a stimulus to the economy and help create jobs. The revenues will also help to reduce America’s budget deficit.
But it is not that straightforward.
Problems with tariffs for the USA
Imposing tariffs is likely to reduce international trade. But international trade brings net benefits, which are distributed between the participants according to the terms of trade. This is the law of comparative advantage.
In the simple two-country case, the law states that, provided the opportunity costs of producing various goods differ between the two countries, both of them can gain from mutual trade if they specialise in producing (and exporting) those goods that have relatively low opportunity costs compared with the other country. The total production and consumption of the two countries will be higher.
So if the USA has a comparative advantage in various manufactured products and a trading partner has a comparative advantage in tropical food products, such as coffee or bananas, both can gain by specialisation and trade.
If tariffs are imposed and trade is thereby reduced between the USA and its trading partners, there will be a net loss, as production will switch from lower-cost production to higher-cost production. The higher costs of less efficient production in the USA will lead to higher prices for those goods than if they were imported.
At the same time, goods that are still imported will be more expensive as the price will include the tariff. Some of this may be borne by the importer, meaning that only part of the tariff is passed on to the consumer. The incidence of the tariff between consumer and importer will depend on price elasticities of demand and supply. Nevertheless, imports will still be more expensive, allowing the domestically-produced substitutes to rise in price too, albeit probably by not so much. According to work by Kimberly Clausing and Mary E Lovely for the Peterson Institute (see link in Articles below), Trump’s proposals to raise tariffs would cost the typical American household over $2600 a year.
The net effect will be a rise in inflation – at least temporarily. Yet one of Donald Trump’s pledges is to reduce inflation. Higher inflation will, in turn, encourage the Fed to raise interest rates, which will dampen investment and economic growth.
Donald Trump tends to behave transactionally rather than ideologically. He is probably hoping that a rapid introduction of tariffs will then give the USA a strong bargaining position with foreign countries to trade more fairly. He is also hoping that protecting US industries by the use of tariffs, especially when coupled with deregulation, will encourage greater investment and thereby faster growth.
Much will depend on how other countries respond. If they respond by raising tariffs on US exports, any gain to industries from protection from imports will be offset by a loss to exporters.
A trade war, with higher tariffs, will lead to a net loss in global GDP. It is a negative sum game. In such a ‘game’, it is possible for one ‘player’ (country) to gain, but the loss to the other players (countries) will be greater than that gain.
Donald Trump is hoping that by ‘winning’ such a game, the USA could still come out better off. But the gain from higher investment, output and employment in the protected industries would have to outweigh the losses to exporting industries and from higher import prices.
The first Trump administration (2017–21), as part of its ‘America First’ programme, imposed large-scale tariffs on Chinese imports and on steel and aluminium from across the world. There was wide-scale retaliation by other countries with tariffs imposed on a range of US exports. There was a net loss to world income, including US GDP.
Problems with US tariffs for the rest of the world
The imposition of tariffs by the USA will have considerable effects on other countries. The higher the tariffs and the more that countries rely on exports to the USA, the bigger will the effect be. China and Mexico are likely to be the biggest losers as they face the highest tariffs and the USA is a major customer. In 2023, US imports from China were worth $427bn, while US exports to China were worth just $148bn – only 34.6% of the value of imports. The percentage is estimated to be even lower for 2024 at around 32%. In 2023, China’s exports to the USA accounted for 12.6% of its total exports; Mexico’s exports to the USA accounted for 82.7% of its total exports.
It is possible that higher tariffs could be extended beyond China to other Asian countries, such as Vietnam, South Korea, Taiwan, India and Indonesia. These countries typically run trade surpluses with the USA. Also, many of the products from these countries include Chinese components.
As far as the UK is concerned, the proposed tariffs would cause significant falls in trade. According to research by Nicolò Tamberi at the University of Sussex (see link below in Articles):
The UK’s exports to the world could fall by £22 billion (–2.6%) and imports by £1.4 (–0.16%), with significant variations across sectors. Some sectors, like fishing and petroleum, are particularly hard-hit due to their high sensitivity to tariff changes, while others, such as textiles, benefit from trade diversion as the US shifts demand away from China.
Other badly affected sectors would include mining, pharmaceuticals, finance and insurance, and business services. The overall effect, according to the research, would be to reduce UK output by just under 1%.
Countries are likely to respond to US tariffs by imposing their own tariffs on US imports. World Trade Organization rules permit the use of retaliatory tariffs equivalent to those imposed by the USA. The more aggressive the resulting trade war, the bigger would be the fall in world trade and GDP.
The EU is planning to negotiate with Trump to avoid a trade war, but officials are preparing the details of retaliatory measures should the future Trump administration impose the threatened tariffs. The EU response is likely to be strong.
Articles
The Most Beautiful Word In The Dictionary: TariffsYouTube, Joe Rogan and Donald Trump
- The exact thing that helped Trump win could become a big problem for his presidency
CNN, Matt Egan (7/11/24)
- Trump’s New Trade War With China Is Coming
Newsweek, Micah McCartney (9/11/24)
- Trump tariff threat looms large on several Asian countries – not just China – says Goldman Sachs
CNBC, Lee Ying Shan (11/11/24)
- Trump’s bigger tariff proposals would cost the typical American household over $2,600 a year
Peterson Institute for International Economics, Kimberly Clausing and Mary E Lovely (21/8/24)
- More tariffs, less red tape: what Trump will mean for key global industries
The Guardian, Jasper Jolly, Dan Milmo, Jillian Ambrose and Jack Simpson (7/11/24)
- Trump tariffs would halve UK growth and push up prices, says thinktank
The Guardian, Larry Elliott (6/11/24)
- China is trying to fix its economy – Trump could derail those plans
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Questions
- Explain why, according to the law of comparative advantage, all countries can gain from trade.
- In what ways may the imposition of tariffs benefit particular sections of an economy?
- Is it in countries’ interests to retaliate if the USA imposes tariffs on their exports to the USA?
- Why is a trade war a ‘negative sum game’?
- Should the UK align with the EU in resisting President-elect Trump’s trade policy or should it seek independently to make a free-trade deal with the USA? is it possible to do both?
- What should China do in response to US threats to impose tariffs of 60% or more on Chinese imports to the USA?
On 25 October 2024, Moody’s, one of the major credit ratings agencies, announced that it was downgrading France’s economic outlook to negative. This was its first downgrading of France since 2012. It followed a similar revision by Fitch’s, another ratings agency, on 11 October.
While Fitch’s announcement did not have a significant impact on the yields of French government bonds, expectations around Moody’s did. In the week preceding the announcement, the net increases in the yield on generic 10-year government debt was approximately 9 basis points (0.09 percentage points). On the day itself, the yield rose by approximately 5.6 basis points (0.056 percentage points).
The yield rose further throughout the rest of October, finishing nearly 0.25 percentage points above its level at the start of the month. However, as Figure 1 illustrates, these increases are part of a longer-term trend of rising yields for French government debt (click here for a PowerPoint).
The yield on 10-year French government debt began 2024 at 2.56% and had an upward trend for the first half of the year. The yield peaked at 3.34% on 1 July. It then fell back below 3% for a while. The negative economic outlook then pushed yields back above 3% and they finished October at 3.12%, half a percentage point above the level at the start of the year. This represents a significant increase in borrowing costs for the French government.
In this blog, we will explain why the changes in France’s economic outlook translate into increases in yields for French government bonds. We will also analyse why yields have increased and examine the prospects for the markets in French government bonds.
Pricing signals of bond yields
A bond is a tradable debt instrument issued by governments to finance budget deficits – the difference between tax receipts and spending. Like any financial instruments, investment in bonds involves a commitment of funds today in anticipation of interest payments through time as compensation, with a repayment of its redemption value on the date the bond matures.
Since the cash flows associated with holding a bond occur at different points in time, discounted cash flow analysis is used to determine its value. This gives the present value of the cash flows discounted at the appropriate expected rate of return. In equilibrium this will be equal to the bond’s market price, as the following equation shows.
Where:
P = the equilibrium price of the bond
C = cash coupon payments
M = redemption value at maturity
r = yield (expected rate of return in equilibrium.
Interest payments tend to be fixed at the time a bond is issued and reflect investors’ expected rate of return, expressed as the yield in bond markets. This is determined by prevailing interest rates and perceived risk. Over time, changes in interest rates and perceptions of risk will change the expected rate of return (yield), which will, in turn, change the present value of the cash flows, and hence fundamental value.
Prices move in response to changes in fundamental value and since this happens frequently, this means that prices change a lot. For bonds, as the coupon payments (C each year and the redemption price () are fixed, the only factor that can change is the expected rate of return (yield). This is reflected in the observed yield at each price.
If the expected rate of return rises, this increases the discount rate applied to future cash flows and reduces their present value. At the current price, the fixed coupon is not sufficient to compensate investors. So, investors sell the bonds and price falls until it reaches a point where the yield offered is equal to that required. The reverse happens if the expected rate of return falls.
The significant risk associated with bonds is credit default risk – the risk that the debt will not be repaid. The potential for credit default is a significant influence of the compensation investors require for holding debt instruments like bonds (ceteris paribus). An increase in expected credit default risk will increase the expected return (compensation). This will be reflected in a lower price and higher yield.
Normally, with the bonds issued by high-income countries, such as those in Europe and North America, the risk of default is extremely low. However, if a country’s annual deficits or accumulated debt increase to what markets consider to be unsustainable levels, the perceived risk of default may rise. Countries’ levels of risk are rated by international ratings agencies, such as Moody’s and Fitch. Investors pay a lot of attention to the information provided by such agencies.
Moody’s downgrade in its economic outlook for France from ‘stable’ to ‘negative’ indicated weak economic performance and higher credit default risk. This revision rippled through bond markets as investors adjusted their views of the country’s economic risk. The rise in yields observed is a signal that bond investors perceive higher credit default risk associated with French government debt and are demanding a higher rates of return as compensation.
Why has France’s credit default risk premium risen now?
As we have seen, credit default risk is not normally considered a significant issue for sovereign borrowers like France. Some of the issue around perceived credit default risk for the French government relate to the size of the French government’s deficit and the projections for it. Following a spike in borrowing associated with the COVID-19 pandemic in 2020, the annual government budget deficit and the overall level of debt as percentages of GDP have remained high. The annual deficit is projected to be 6% for 2024 and still 5% for 2025. The ratio of outstanding French government debt to Gross Domestic Product (GDP) ballooned to 123% in 2020 and is still expected to be 115% by the end of 2025. France has been put on notice to reduce its debt towards the Eurozone limit of 60% of GDP.
Governments in France last achieved a balanced budget in 1974. They have run deficits ever since. Figure 2 illustrates the French government budget deficits from 1990 to 2023 (click here for a PowerPoint). The figure shows that France experienced deficits in the past similar to today’s. These, however, did not tend to worry bond markets too much.
So why are investors currently worried? This stems from France’s debt mountain and from concerns that the government will not be able to deal with it. Investors are concerned that both weak growth and increasingly volatile politics will thwart efforts to reduce debt levels.
Let’s take growth. Even by contemporary European standards, France’s growth prospects are anaemic. GDP is expected to grow by just 1.1% for 2024 and 1% for 2025. Both consumer and business confidence are low. None of this suggests a growth spurt soon which will boost the tax revenues of the French government sufficiently to address the deficit.
Further, political instability has grown due to the inconclusive parliamentary elections which Emmanuel Macron surprisingly called in July. No single political grouping has a majority and the President has appointed a Centrist Prime Minister, Michel Barnier (the former EU Brexit negotiator). His government is trying to pass a budget through the Assemblée Nationale involving a mixture of spending cuts and tax hikes which amount to savings of €60 billion ($66 billion). This is equivalent to 2% of GDP.
The parliamentary path of the budget bill is set to be torturous with both the left and right wing blocs in the Assemblée opposing most of the provisions. Debate in the Assemblée Nationale and Senate are expected to drag on into December, with the real prospect that the government may have to use presidential decree to pass the budget. Commentators argue that this will fuel further political chaos.
France looks more like Southern Europe
In the past, bond investors were more tolerant of France’s budget deficits. French government bonds were attractive options for investors wanting to hold euro-denominated bonds while avoiding riskier Southern European countries such as Greece, Italy, Portugal and Spain. Since France has run persistent government deficits for a long time, it offered bond investors a more liquid market than more fiscally-parsimonious Northern European neighbours, such as Germany and the Netherlands. Consequently, France’s debt instruments offered a slight risk premium on the yields for those countries.
However, that has changed. France’s credit default risk premium is rising to levels comparable to its Southern neighbours. On 26 September 2024, the yield on generic French government 10-year debt rose above its Spanish equivalent for the first time since 2008.
As Figure 3 illustrates, this was the culmination of a trend evident throughout 2024, with the difference in yields between the two declining steadily (click here for a PowerPoint). At the start of the year, the yield on Spanish debt offered a 40 basis points premium over the French equivalent. By October, the yield on Spanish debt was consistently below that of French debt. All of this is due to bond investors’ rising expectations about France’s credit default risk. Now, France’s borrowing costs are not only above Spain, but also closer to those of Greece and Italy than of Germany.
Strikingly, Spain’s budget deficit was 3.5% in 2023 and is expected to narrow to 2.6% by 2025. The percentage of total debt to GDP is 104% and falling. Moreover, following Spain’s inconclusive election in 2023, the caretaker government put forward budgetary plans involving fiscal tightening without the need for legislation. This avoided the political wrangling France is facing.
For France, these developments raise the prospect of yields rising further as bond investors now see alternatives to French government debt in the form of Spain’s. This country have already undertaken the painful fiscal adjustments that France seems incapable of completing.
Articles
Data
Questions
- What is credit default risk?
- Explain why higher credit default risk is associated with higher yields on France’s government debt.
- Why would low economic growth worsen the government’s budget deficit?
- Why would political instability increase credit default risk?
- What has happened to investors’ perceptions of the risk associated with French government debt relative to Spain’s?
- How has this manifested itself in the relative yields of the two countries’ government debt?