Category: Economics: Ch 05

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Artificial intelligence in pricing: a double-edged sword?

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

  1. In what types of markets might it be more likely that artificial intelligence can facilitate collusion?
  2. How could AI pricing tools impact the factors that make collusion more or less sustainable in a market?
  3. What can competition authorities do to prevent AI-assisted collusion taking place?
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World Uncertainty Index

We continue to live through incredibly turbulent times. In the past decade or so we have experienced a global financial crisis, a global health emergency, seen the UK’s departure from the European Union, and witnessed increasing levels of geopolitical tension and conflict. Add to this the effects from the climate emergency and it easy to see why the issue of economic uncertainty is so important when thinking about a country’s economic prospects.

In this blog we consider how we can capture this uncertainty through a World Uncertainty Index and the ways by which economic uncertainty impacts on the macroeconomic environment.

World Uncertainty Index

Hites Ahir, Nicholas Bloom and Davide Furceri have constructed a measure of uncertainty known as the World Uncertainty Index (WUI). This tracks uncertainty around the world using the process of ‘text mining’ the country reports produced by the Economist Intelligence Unit. The words searched for are ‘uncertain’, ‘uncertainty’ and ‘uncertainties’ and a tally is recorded based on the number of times they occur per 1000 words of text. To produce the index this figure is then multiplied up by 100 000. A higher number therefore indicates a greater level of uncertainty. For more information on the construction of the index see the 2022 article by Ahir, Bloom and Furceri linked below.

Figure 1 (click here for a PowerPoint) shows the WUI both globally and in the UK quarterly since 1991. The global index covers 143 countries and is presented as both a simple average and a GDP weighted average. The UK WUI is also shown. This is a three-quarter weighted average, the authors’ preferred measure for individual countries, where increasing weights of 0.1, 0.3 and 0.6 are used for the three most recent quarters.

From Figure 1 we can see how the level of uncertainty has been particularly volatile over the past decade or more. Events such as the sovereign debt crisis in parts of Europe in the early 2010s, the Brexit referendum in 2016, the COVID-pandemic in 2020–21 and the invasion of Ukraine in 2022 all played their part in affecting uncertainty domestically and internationally.

Uncertainty, risk-aversion and aggregate demand

Now the question turns to how uncertainty affects economies. One way of addressing this is to think about ways in which uncertainty affects the choices that people and businesses make. In doing so, we could think about the impact of uncertainty on components of aggregate demand, such as household consumption and investment, or capital expenditures by firms.

As Figure 2 shows (click here for a PowerPoint), investment is particularly volatile, and much more so than household spending. Some of this can be attributed to the ‘lumpiness’ of investment decisions since these expenditures tend to be characterised by indivisibility and irreversibility. This means that they are often relatively costly to finance and are ‘all or nothing’ decisions. In the context of uncertainty, it can make sense therefore for firms to wait for news that makes the future clearer. In this sense, we can think of uncertainty rather like a fog that firms are peering through. The thicker the fog, the more uncertain the future and the more cautious firms are likely to be.

The greater caution that many firms are likely to adopt in more uncertain times is consistent with the property of risk-aversion that we often attribute to a range of economic agents. When applied to household spending decisions, risk-aversion is often used to explain why households are willing to hold a buffer stock of savings to self-insure against unforeseen events and their future financial outcomes being worse than expected. Hence, in more uncertain times households are likely to want to increase this buffer further.

The theory of buffer-stock saving was popularised by Christopher Carroll in 1992 (see link below). It implies that in the presence of uncertainty, people are prepared to consume less today in order to increase levels of saving, pay off existing debts, or borrow less relative to that in the absence of uncertainty. The extent of the buffer of financial wealth that people want to hold will depend on their own appetite for risk, the level of uncertainty, and the moderating effect from their own impatience and, hence, present bias for consuming today.

Risk aversion is consistent with the property of diminishing marginal utility of income or consumption. In other words, as people’s total spending volumes increase, their levels of utility or satisfaction increase but at an increasingly slower rate. It is this which explains why individuals are willing to engage with the financial system to reallocate their expected life-time earnings and have a smoother consumption profile than would otherwise be the case from their fluctuating incomes.

Yet diminishing marginal utility not only explains consumption smoothing, but also why people are willing to engage with the financial system to have financial buffers as self-insurance. It explains why people save more or borrow less today than suggested by our base-line consumption smoothing model. It is the result of people’s greater dislike (and loss of utility) from their financial affairs being worse than expected than their like (and additional utility) from them being better than expected. This tendency is only likely to increase the more uncertain times are. The result is that uncertainty tends to lower household consumption with perhaps ‘big-ticket items’, such as cars, furniture, and expensive electronic goods, being particularly sensitive to uncertainty.

Uncertainty and confidence

Uncertainty does not just affect risk; it also affects confidence. Risk and confidence are often considered together, not least because their effects in generating and transmitting shocks can be difficult to disentangle.

We can think of confidence as capturing our mood or sentiment, particularly with respect to future economic developments. Figure 3 plots the Uncertainty Index for the UK alongside the OECD’s composite consumer and business confidence indicators. Values above 100 for the confidence indicators indicate greater confidence about the future economic situation and near-term business environment, while values below 100 indicate pessimism towards the future economic and business environments.

Figure 3 suggests that the relationship between confidence and uncertainty is rather more complex than perhaps is generally understood (click here for a PowerPoint). Haddow, Hare, Hooley and Shakir (see link below) argue that the evidence tends to point to changes in uncertainty affecting confidence, but with less evidence that changes in confidence affect uncertainty.

To illustrate this, consider the global financial crisis of the late 2000s. The argument can be made that the heightened uncertainty about future prospects for households and businesses helped to erode their confidence in the future. The result was that people and businesses revised down their expectations of the future (pessimism). However, although people were more pessimistic about the future, this was more likely to have been the result of uncertainty rather than the cause of further uncertainty.

Conclusion

For economists and policymakers alike, indicators of uncertainty, such as the Ahir, Bloom and Furceri World Uncertainty Index, are invaluable tools in understanding and forecasting behaviour and the likely economic outcomes that follow. Some uncertainty is inevitable, but the persistence of greater uncertainty since the global financial crisis of the late 2000s compares quite starkly with the relatively lower and more stable levels of uncertainty seen from the mid-1990s up to the crisis. Hence the recent frequency and size of changes in uncertainty show how important it to understand how uncertainty effects transmit through economies.

Academic papers

Articles

Data

Questions

  1. (a) Explain what is meant by the concept of diminishing marginal utility of consumption.
    (b) Explain how this concept helps us to understand both consumption smoothing and the motivation to engage in buffer-stock saving.
  2. Explain the distinction between confidence and uncertainty when analysing macroeconomic shocks.
  3. Discuss which types of expenditures you think are likely to be most susceptible to uncertainty shocks.
  4. Discuss how economic uncertainty might affect productivity and the growth of potential output.
  5. How might the interconnectedness of economies affect the transmission of uncertainty effects through economies?
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Risk aversion in late-cycle slowing economies: the US experience

In recent months there has been growing uncertainty across the global economy as to whether the US economy was going to experience a ‘hard’ or ‘soft landing’ in the current business cycle – the repeated sequences of expansion and contraction in economic activity over time. Announcements of macroeconomic indicators have been keenly anticipated for signals about how quickly the US economy is slowing.

Such heightened uncertainty is a common feature of late-cycle slowing economies, but uncertainty now has been exacerbated because it has been a while since developed economies have experienced a business cycle like the current one. The 21st century has been characterised by low inflation, low interest rates and recessions caused by various types of crises – a stock market crisis (2001), a banking crisis (2008) and a global pandemic (2020). In contrast, the current cycle is a throwback to the 20th century. The high inflation and the ensuing increases in interest rates have produced a business cycle which echoes the 1970s. Therefore, few investors have experience of such economic conditions.

The focus for investors during this stage of the cycle is when the slowing economy will reach the minimum. They will also be concerned with the depth of the slowdown: will there still be some growth in income, albeit low; or will the trough be severe enough to produce a recession, and, if so, how deep? Given uncertainty around the length and magnitude of business cycles, this leads to greater risk aversion among investors. This affects reactions to announcements of leading and lagging macroeconomic indicators.

This blog examines what sort of economic conditions we should expect in a late-cycle economy. It analyses the impact this has had on investor behaviour and the ensuing dynamics observed in financial markets in the USA.

The Business Cycle


The business cycle refers to repeated sequences of expansion and contraction (or slowdown) in economic activity over time. Figure 1 illustrates a typical cycle. Typically, these sequences include four main stages. In each one there are different effects on consumer and business confidence:

  • Expansion: During this stage, the economy experiences growth in GDP, with incomes and consumption spending rising. Business and consumer confidence are high. Unemployment is falling.
  • Peak: This is the point at which the economy reaches its maximum output, but growth has ceased (or slowed). At this stage, inflationary pressures peak as the economy presses against potential output. This tends to result in tighter monetary policy (higher interest rates).
  • Slowdown: The higher interest rates raise the cost of borrowing and reduce consumption and investment spending. Consumption and incomes both slow or fall. (Figure 1 illustrates the severe case of falling GDP (negative growth) in this stage.) Unemployment starts rising.
  • Trough: This is the lowest point of the cycle, where economic activity bottoms out and the economy begins to recover. This can be associated with slow but still rising national income (a soft landing) or national income that has fallen (a hard landing, as shown in Figure 1).

While business cycles are common enough to enable such characterisation of their temporal pattern, their length and magnitude are variable and this produces great uncertainty, particularly when cycles approach peaks and troughs.

As an economy’s cycle approaches a trough, such as US economy’s over the past few months, uncertainty is exacerbated. The high interest rates used to tackle inflation will have increased borrowing costs for businesses and consumers. Access to credit may have become more restricted. Profit margins are reduced, especially for industrial sectors sensitive to the business cycle, reducing expected cash flows.

The combination of these factors can increase the risk of a recession, producing greater volatility in financial markets. This manifests itself in increased risk aversion among investors.

Utility theory suggests that, in general, investors will exhibit loss aversion. This means that they do not like bearing risk, fearing that the return from an investment may be less than expected. In such circumstances, investors need to be compensated for bearing risk. This is normally expressed in terms of expected financial return. To bear more risk, investors require higher levels of return as compensation.

As perceptions of risk change through the business cycle, so this will change the return investors will require from the financial instruments they hold. Perceived higher risk raises the return investors will require as compensation. Conversely, lower perceived risk decreases the return investors expect as compensation.

Investors’ expected rate of return is manifested in the discount rate that they use to value the anticipated cash flows from financial instruments in discounted cash flow (DCF) analysis. Equation 1 is the algebraic expression of the present-value discounted series of cash flows for financial instruments:

 
 
Where:
V = present value
C = anticipated cash flows in each of time periods 1, 2, 3, etc.
r = expected rate of return

For fixed-income debt securities, the cash flow is constant, while for equity securities (shares), expectations regarding cash flows can change.

Slowing economies and risk aversion

In a slowing economy, with great uncertainty about the scale and timing of the bottom of the cycle, investors become more risk averse about the prospects of firms. This this leads to higher risk premia for financial instruments sensitive to a slowdown in economic activity.

This translates into a higher expected return and higher discount rate used in the valuation of these instruments (r in equation 1). This produces decreases in perceived value, decreased demand and decreased prices for these financial instruments. This can be observed in the market dynamics for these instruments.

First, there may be a ‘flight to safety’. Investors attach a higher risk premium to risker financial instruments, such as equities, and seek a ‘safe-haven’ for their wealth. Therefore, we should observe a reorientation from more risky to less risky assets. Demand for equities falls, while demand for safer assets, such as government bonds and gold, rises.

There is some evidence for this behaviour as uncertainty about the US economic outlook has increased. Gold, long seen as a hedge against market decline, is at record highs. US Government bond prices have risen too.

To analyse whether this may be a flight to safety, I analysed the correlation between the daily US government bond price (5-year Treasury Bill) and share prices represented by the two more significant stock market indices in the USA: the S&P 500 and the Nasdaq Composite. I did this for two different time periods. Table 1 shows the results. Panel (a) shows the correlation coefficients for the period between 1 May 2024 and 31 July 2024; Panel (b) shows the correlation coefficients for the period between 1 August 2024 and 9 September 2024.

In the period between May and July 2024, the 5-year Treasury Bill and share price indices had significantly positive correlations. When share prices rose, the Treasury Bill’s price rose; when share prices fell, the bill’s price fell. During that period, expectations about falling interest rates dominated valuations and that effected the valuations of all financial instruments in the same way – lower expected interest rates reduce the opportunity cost of holding instruments and reduces the expected rates of return. Hence, the discount rate applied to cash flows is reduced, and present value rises. The opposite happens when macroeconomic indicators suggest that interest rates will stay high (ceteris paribus).

As the summer proceeded, worries about a ‘hard landing’ began to concern investors. A weak jobs report in early August particularly exercised markets, producing a ‘flight to safety’. Greater risk aversion among investors meant that they expect a higher return from equities. This reduced perceived value, reducing demand and price (ceteris paribus). To insulate themselves from higher risk, investors bought safer assets, like government bonds, thereby pushing up their prices. This behaviour was consistent with the significant negative correlation observed between US government debt prices and the S&P 500 and Nasdaq indices in Panel (b).

Another signal of increased risk aversion among investors is ‘sector rotation’ in their equity portfolios. Increased risk aversion among investors will lead them to divest from ‘cyclical’ companies. Such companies are in industrial sectors which are more sensitive to the changing economic conditions across the business cycle – consumer discretionary and communication services sectors, for example. To reduce their exposure to risk, investors will switch to ‘defensive’ sectors – those less sensitive to the business cycle. Examples include consumer staples and utility sectors.

Cyclical sectors will suffer a greater adverse impact on their cash flows and risk in a slowing economy. Consequently, investors expect higher return as compensation. This reduces the value of those shares. Demand for them falls, depressing their price. In contrast, defensive sectors will be valued more. They will see an increase in demand and price. This sector rotation seems to have happened in August (2024). Figure 2 shows the percentage change between 1 August and 9 September 2024 in the S&P 500 index and four sector indices, comprising companies from the communication services, consumer discretionary, consumer staples and utilities sectors.


Overall, the S&P 500 index was slightly higher, as shown by the first bar in the chart. However, while the cyclical sectors experienced decreases in their share prices, particularly communication services, the defensive companies experienced large price increases – nearly 3% for utilities and over 6% for consumer staples.

Conclusion

Economies experience repeated sequences of expansion and contraction in economic activity over time. At the moment, the US economy is approaching the end of its current slowing phase. Increased uncertainty is a common feature of late-cycle economies and this manifests itself in heightened risk aversion among investors. This produces certain dynamics which have been observable in US debt and equity markets. This includes a ‘flight to safety’, with investors divesting risky financial instruments in favour of safer ones, such as US government debt securities and gold. Also, investors have been reorientating their equity portfolios away from cyclicals and towards defensive securities.

Articles

Data

Questions

  1. What is risk aversion? Sketch an indifference curve for a risk-averse investor, treating expected return and risk as two-characteristics of a financial instrument.
  2. Show what happens to the slope of the indifference curve if the investor becomes more risk averse.
  3. Using demand and supply analysis, illustrate and explain the impact of a flight to safety on the market for (i) company shares and (ii) US government Treasury Bills.
  4. Use economic theory to explain why the consumer discretionary sector may be more sensitive than the consumer staples sector to varying incomes across the economic cycle.
  5. Research the point of the economic cycle that the US economy has reached as you read this blog. What is the relationship between bond and equity prices? Which sectors have performed best in the stock market?
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The economics of ‘greeniums’ in sustainable finance: doing good has a price

Sustainability has become one of the most pressing issues facing society. Patterns of human production and consumption have become unsustainable. On the environmental front, climate change, land-use change, biodiversity loss and depletion of natural resource are destabilising the Earth’s eco-system.

Furthermore, data on poverty, hunger and lack of healthcare show that many people live below minimum social standards. This has led to greater emphasis being placed on sustainable development: ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (The Brundtland Report, 1987: Ch.2, para. 1).

The financial system has an important role to play in channelling capital in a more sustainable way. Since current models of finance do not consider the welfare of future generations in investment decisions, sustainable finance has been developed to analyse how investment and lending decisions can manage the trade-off inherent in sustainable development: sacrificing return today to enhance the welfare of future generations.

However, some commentators argue that such trade-offs are not required. They suggest that investors can ‘do well by doing good’. In this blog, I will use ‘green’ bonds (debt instruments which finance projects or activities with positive environmental and social impacts) to explain the economics underpinning sustainable finance and show that doing good has a price that sustainable investors need to be prepared to pay.

I will analyse why investors might not be doing so and point to changes which may be required to ensure financial markets channel capital in a way consistent with sustainable development.

The growth of sustainable finance

Sustainable finance has grown rapidly over the past decade as concerns about climate change have intensified. A significant element of this growth has been in global debt markets.

Figure 1 illustrates the rapid growth in the issuance of sustainability-linked debt instruments since 2012. While issuance fell in 2022 due to concerns about rising inflation and interest rates reducing the real return of fixed-income debt securities, it rebounded in 2023 and is on course for record levels in 2024. (Click here for a PowerPoint.)

Green bonds are an asset class within sustainability-linked debt. Such bonds focus on financing projects or activities with positive environmental and social impacts. They are typically classified as ‘use-of-proceeds’ or asset-linked bonds, meaning that the proceeds raised from their issuance are earmarked for green projects, such as renewable energy, clean transportation, and sustainable agriculture. Such bonds should be attractive to investors who want a financial return but also want to finance investments with a positive environmental and/or social impact.

One common complaint from commentators and investors is the ‘greenium’ – the price premium investors pay for green bonds over conventional ones. This premium reduces the borrowing costs of the issuers (the ‘counterparties’) compared to those of conventional counterparties. This produces a yield advantage for issuers of green bonds (price and yield have a negative relationship), reducing their borrowing costs compared to issuers of conventional bonds.

An analysis by Amundi in 2023 using data from Bloomberg estimated that the average difference in yield in developed markets was –2.2 basis points (–0.022 percentage points) and the average in emerging markets was –5.6 basis points (–0.056 percentage points). Commentators and investors suggest that the premium is a scarcity issue and once there are sufficient green bonds, the premium over non-sustainable bonds should disappear.

However, from an economics perspective, such interpretations of the greenium ignore some fundamentals of economic valuation and the incentives and penalties through which financial markets will help facilitate more sustainable development. Without the price premium, investors could buy sustainable debt at the same price as unsustainable debt, earn the same financial return (yield) but also achieve environmental and social benefits for future generations too. Re-read that sentence and if it sounds too good to be true, it’s because it is too good to be true.

‘There is no such thing as a free lunch’

In theory, markets are institutional arrangements where demand and supply decisions produce price signals which show where resources are used most productively. Financial markets involve the allocation of financial capital. Traditional economic models of finance ignore sustainability when appraising investment decisions around the allocation of capital. Consequently, such allocations do not tend to be consistent with sustainable development.

In contrast, economic models of sustainable finance do incorporate such impacts of investment decisions and they will be reflected in the valuation, and hence pricing, of financial instruments. Investors, responding to the pricing signals will reallocate capital in a more sustainable manner.

Let’s trace the process. In models of sustainable finance, financial instruments such as green bonds funding investments with positive environmental impacts (such as renewable energy) should be valued more, while instruments funding investments with negative environmental impacts (such as fossil fuels) should be valued less. The prices of the green bonds financing renewable energy projects should rise while the prices of conventional bonds financing fossil-fuel companies should fall.

As this happens, the yield on the green bonds falls, lowering the cost of capital for renewable-energy projects, while yields on the bonds financing fossil-fuel projects rise, ceteris paribus. As with any market, these differential prices act as signals as to where resources should be allocated. In this case, the signals should result in an allocation consistent with sustainable development.

The fundamental point in this economic valuation is that sustainable investors should accept a trade-off. They should pay a premium and receive a lower rate of financial return (yield) for green bonds compared to conventional ones. The difference in price (the greenium), and hence yield, represents the return investors are prepared to sacrifice to improve future generations’ welfare. Investors cannot expect to have the additional welfare benefit for future generations reflected in the return they receive today. That would be double counting. The benefit will accrue to future generations.

A neat way to trace the sacrifice sustainable investors are prepared to make in order to enhance the welfare of future generations is to plot the differences in yields between green bonds and their comparable conventional counterparts. The German government has issued a series of ‘twin’ bonds in recent years. These twins are identical in every respect (coupon, face value, credit risk) except that the proceeds from one will be used for ‘green’ projects only.

Figure 2 shows the difference in yields on a ‘green bond’ and its conventional counterpart, both maturing on 15/8/2050, between June 2021 and July 2024. The yield on the green bond is lower – on average about 2.2 basis points (0.022 percentage points) over the period. This represents the sacrifice in financial return that investors are prepared to trade off for higher environmental and social welfare in the future. (Click here for a PowerPoint.)

The yield spread fluctuates through time, reflecting changing perceptions of environmental concerns and hence the changing value that sustainable investors attach to future generations. The spread tends to widen when there are heightened environmental concerns and to narrow when such concerns are not in the news. For example, the spread on the twin German bonds reached a maximum of 0.045 percentage points in November 2021. This coincided with the 26th UN Climate Change Conference of the Parties (COP26) in Glasgow. The spread has narrowed significantly since early 2022 as rising interest rates and falling real rates of return on bonds in the near-term seem to have dominated investors’ concerns.

These data suggest that, rather than being too large, the greeniums are too small. The spreads suggest that markets in debt instruments do not seem to attach much value to future generations. The valuation, price and yield of green bonds are not significantly different from their conventional counterparts. This narrow gap indicates insufficient reward for better sustainability impact and little penalty for worse sustainability impact.

This pattern is repeated across financial markets and does not seem to be stimulating the necessary investment to achieve sustainable development. An estimate of the scale of the deficit in green financing is provided by Bloomberg NEF (2024). While global spending on the green energy transition reached $1.8 trillion in 2023, Bloomberg estimates that $4.8 trillion needs to be invested every year for the remainder of this decade if the world is to remain on track under the ‘net zero’ scenario. Investors do not seem to be prepared to accept the trade-off needed to provide the necessary funds.

Can financial markets deliver sustainable development?

Ultimately, the hope is that all financial instruments will be sustainable. In order to achieve that, access to finance would require all investors to incorporate the welfare of future generations in their investment decisions and accept sacrificing sufficient short-term financial return to ensure long-term sustainable development. Unfortunately, the pricing of green bonds suggests that investors are not prepared to accept the trade-off. This restricts the ability of financial markets to deliver an allocation of resources consistent with sustainable development.

There are several reasons why financial markets may not be valuing the welfare of future generations fully.

  • Bounded rationality means that it is difficult for sustainable investors to assign precise values to future and distant benefits.
  • There are no standardised sustainability metrics available. This produces great uncertainty in the valuation of future welfare.
  • Investors also exhibit cognitive biases, which means they may not value the welfare of future generations properly. These include present bias (favouring immediate rewards) and hyperbolic discounting (valuing the near future more than the distant future).
  • Economic models of financial valuation use discount rates to assess the value of future benefits. Higher discount rates reduce the perceived value of benefits occurring in the distant future. As a result, long-term impacts (such as environmental conservation) may be undervalued.
  • There may be large numbers of investors who are only interested in financial returns and so do not consider the welfare of future generations in their investment decisions.

Consequently, investors need to be educated about the extent of trade-offs required to achieve the necessary investments in sustainable development. Furthermore, practical models which better reflect the welfare of future generations in investment decisions need to be employed. However, challenges persist in fully accounting for future generations and it may need regulatory frameworks to provide appropriate incentives for effective sustainable investment.

Articles

Report

Data

Questions

  1. Using demand and supply analysis, illustrate and explain the impact of sustainable investing on the markets for (i) green bonds and (ii) conventional bonds. Highlight how this should produce an allocation of finance capital consistent with sustainable development.
  2. Research the yields on the twin bonds issued by Germany since this blog was published. Can you identify any association between heightened environmental concerns and the spread between the ‘green’ and conventional bond?
  3. Analyse the issues which prevent financial markets from producing the pricing signals which produce an allocation of resources consistent with sustainable development.
  4. Research some potential regulatory policies which may provide appropriate incentives for sustainable investment.
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Vetting vets

The Competition and Markets Authority (CMA) is proposing to launch a formal Market Investigation into anti-competitive practices in the UK’s £2bn veterinary industry (for pets rather than farm animals or horses). This follows a preliminary investigation which received 56 000 responses from pet owners and vet professionals. These responses reported huge rises in bills for treatment and medicines and corresponding rises in the cost of pet insurance.

At the same time there has been a large increase in concentration in the industry. In 2013, independent vet practices accounted for 89% of the market; today, they account for only around 40%. Over the past 10 years, some 1500 of the UK’s 5000 vet practices had been acquired by six of the largest corporate groups. In many parts of the country, competition is weak; in others, it is non-existent, with just one of these large companies having a monopoly of veterinary services.

This market power has given rise to a number of issues. The CMA identifies the following:

  • Of those practices checked, over 80% had no pricing information online, even for the most basic services. This makes is hard for pet owners to make decisions on treatment.
  • Pet owners potentially overpay for medicines, many of which can be bought online or over the counter in pharmacies at much lower prices, with the pet owners merely needing to know the correct dosage. When medicines require a prescription, often it is not made clear to the owners that they can take a prescription elsewhere, and owners end up paying high prices to buy medicines directly from the vet practice.
  • Even when there are several vet practices in a local area, they are often owned by the same company and hence there is no price competition. The corporate group often retains the original independent name when it acquires the practice and thus is is not clear to pet owners that ownership has changed. They may think there is local competition when there is not.
  • Often the corporate group provides the out-of-hours service, which tends to charge very high prices for emergency services. If there is initially an independent out-of-hours service provider, it may be driven out of business by the corporate owner of day-time services only referring pet owners to its own out-of-hours service.
  • The corporate owners may similarly provide other services, such as specialist referral centres, diagnostic labs, animal hospitals and crematoria. By referring pets only to those services owned by itself, this crowds out independents and provides a barrier to the entry of new independents into these parts of the industry.
  • Large corporate groups have the incentive to act in ways which may further reduce competition and choice and drive up their profits. They may, for example, invest in advanced equipment, allowing them to provide more sophisticated but high-cost treatment. Simpler, lower-cost treatments may not be offered to pet owners.
  • The higher prices in the industry have led to large rises in the cost of pet insurance. These higher insurance costs are made worse by vets steering owners with pet insurance to choosing more expensive treatments for their pets than those without insurance. The Association of British Insurers notes that there has been a large rise in claims attributable to an increasing provision of higher-cost treatments.
  • The industry suffers from acute staff shortages, which cuts down on the availability of services and allows practices to push up prices.
  • Regulation by the Royal College of Veterinary Surgeons (RCVS) is weak in the area of competition and pricing.

The CMA’s formal investigation will examine the structure of the veterinary industry and the behaviour of the firms in the industry. As the CMA states:

In a well-functioning market, we would expect a range of suppliers to be able to inform consumers of their services and, in turn, consumers would act on the information they receive.

Market failures in the veterinary industry

The CMA’s concerns suggest that the market is not sufficiently competitive, with vet companies holding significant market power. This leads to higher prices for a range of vet services. However, the CMA’s analysis suggests that market failures in the industry extend beyond the simple question of market power and lack of competition.

A crucial market failure is asymmetry of information. The veterinary companies have much better information than pet owners. This is a classic principal–agent problem. The agent, in this case the vet (or vet company), has much better information than the principal, in this case the pet owner. This information can be used to the interests of the vet company, with pet owners being persuaded to purchase more extensive and expensive treatments than they might otherwise choose if they were better informed.

The principal–agent problem also arises in the context of the dependant nature of pets. They are the ones receiving the treatment and, in this context, are the principals. Their owners are the ones acquiring the treatment for them and hence are the pets’ agents. The question is whether the owners will always do the best thing for their pets. This raises philosophical questions of animal rights and whether owners should be required to protect the interests of their pets.

Another information issue is the short-term perspective of many pet owners. They may purchase a young and healthy pet and assume that it will remain so. However, as the pet gets older, it is likely to face increasing health issues, with correspondingly increasing vet bills. But many owners do not consider such future bills when they purchase the pet. They suffer from what behavioural economists call ‘irrational exuberance’. Such exuberance may also occur when the owner of a sick pet is offered expensive treatment. They may over-optimistically assume that the treatment will be totally successful and that their pet will not need further treatment.

Vets cite another information asymmetry. This concerns the costs they face in providing treatment. Many owners are unaware of these costs – costs that include rent, business rates, heating and lighting, staff costs, equipment costs, consumables (such as syringes, dressings, surgical gowns, antiseptic and gloves), VAT, and so on. Many of these costs have risen substantially in recent months and are reflected in the prices pet owners are charged. With people experiencing free health care for themselves from the NHS (or other national provider), this may make them feel that the price of pet health care is excessive.

Then there is the issue of inequality. Pets provide great benefits to many owners and contribute to owners’ well-being. If people on low incomes cannot afford high vet bills, they may either have to forgo having a pet, with the benefits it brings, or incur high vet bills that they ill afford or simply go without treatment for their pets.

Finally, there are the external costs that arise when people abandon their pets with various health conditions. This has been a growing problem, with many people buying pets during lockdown when they worked from home, only to abandon them later when they have had to go back to the office or other workplace. The costs of treating or putting down such pets are born by charities or local authorities.

The CMA is consulting on its proposal to begin a formal Market Investigation. This closes on 11 April. If, in the light of its consultation, the Market Investigation goes ahead, the CMA will later report on its findings and may require the veterinary industry to adopt various measures. These could require vet groups to provide better information to owners, including what lower-cost treatments are available. But given the oligopolistic nature of the industry, it is unlikely to lead to significant reductions in vets bills.

Articles

CMA documents

Questions

  1. How would you establish whether there is an abuse of market power in the veterinary industry?
  2. Explain what is meant by the principal–agent problem. Give some other examples both in economic and non-economic relationships.
  3. What market advantages do large vet companies have over independent vet practices?
  4. How might pet insurance lead to (a) adverse selection; (b) moral hazard? Explain. How might (i) insurance companies and (ii) vets help to tackle adverse selection and moral hazard?
  5. Find out what powers the CMA has to enforce its rulings.
  6. Search for vet prices and compare the prices charged by at least three vet practices. How would you account for the differences or similarities in prices?