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?
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
- The World Uncertainty Index
National Bureau of Economic Research, Working Paper 29763, Hites Ahir, Nicholas Bloom and Davide Furceri (February 2022)
- The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence
Brookings Papers on Economic Activity, Christopher D Carroll (Vol 2, 1992)
- Macroeconomic uncertainty: what is it, how can we measure it and why does it matter?
Bank of England Quarterly Bulletin, 2013 Q2, Abigail Haddow, Chris Hare, John Hooley and Tamarah Shakir (13/6/13)
Articles
Data
Questions
- (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.
- Explain the distinction between confidence and uncertainty when analysing macroeconomic shocks.
- Discuss which types of expenditures you think are likely to be most susceptible to uncertainty shocks.
- Discuss how economic uncertainty might affect productivity and the growth of potential output.
- How might the interconnectedness of economies affect the transmission of uncertainty effects through economies?
On Saturday 31 August, tickets for the much-heralded Oasis reunion tour went on sale through the official retailer, Ticketmaster. When the company sells tickets, the acts or their promoters can choose whether to use a static pricing system, where each type of ticket is sold at a set price until they have all been sold. Or they can use a dynamic pricing system (‘in-demand’ or ‘platinum’ tickets, as Ticketmaster calls them), where there is a starting price quoted, but where prices then rise according to demand. The higher the demand, the more the price is driven up. Acts or their promoters have the option of choosing an upper limit to the price.
Dynamic pricing
The Oasis tickets were sold under the dynamic pricing system, a system previously used for Harry Styles, Bruce Springsteen, Coldplay and Blackpink concerts, but one rejected by Taylor Swift for her recent Eras tour. Standing tickets for the Oasis concert with a face value of around £135 were quickly being sold for over £350. There were long online queues, with the prices rising as people slowly moved up the queue. When they reached the front, they had to decide quickly whether to pay the much higher price. Some people later suffered from buyer’s remorse, when they realised that in the pressure of the moment, they had paid more than they could afford.
Dynamic pricing is when prices change with market conditions: rising at times when demand exceeds supply and falling when supply exceeds demand. It is sometimes referred to as ‘surge pricing’ to reflect situations when price surges in times of excess demand.
Dynamic pricing is a form of price discrimination. It is an imperfect form of first-degree price discrimination, which is defined as people being charged the maximum price they are willing to pay for a product. Pricing in an eBay auction comes close to first-degree price discrimination. With dynamic pricing in the ticket market, some people may indeed pay the maximum, but others earlier in the queue will be lucky and pay less than their maximum.
Ticketmaster justifies the system of dynamic pricing, saying that it gives ‘fans fair and safe access to the tickets, while enabling artists and other people involved in staging live events to price tickets closer to their true market value’. The company argues that if the price is below the market value, a secondary market will then drive ticket prices up. Ticket touts will purchase large amounts of tickets, often using bots to access the official site and then resell them at highly inflated prices on sites such as Viagogo and Stubhub, where ticket prices for popular acts can sell for well over £1000. The day after Oasis tickets went on sale, Viagogo had seats priced at up to £26 000 each!
Oasis and Ticketmaster have tried to stamp out the unofficial secondary market by stating that only tickets bought through the official retailers (Ticketmaster, Gigsandtours and SeeTickets) will be valid. If fans want to resell a ticket – perhaps because they find they can no longer go – they can resell them on the official secondary market though Ticketmaster’s Fan-to-Fan site or Twickets. These official secondary sites allow holders of unwanted tickets to sell them for anything up to the original face value, but no more. Buyers pay a 12% handling fee. It remains to be seen whether this can be enforced with genuine tickets resold on the secondary market.
Examples of dynamic pricing
Dynamic pricing is not a new pricing strategy. It has been used for many years in the transport, e-commerce and hospitality sectors. Airlines, for example, have a pricing model whereby as a flight fills up, so the prices of the seats rise. If you book a seat on a budget airline a long time in advance, you may be able to get it at a very low price. If, on the other hand, you want a seat at the last minute, you may well have to pay a very high price. The price reflects the strength of demand and its price elasticity. The business traveller who needs to travel the next day for a meeting will have a very low price sensitivity and may well be prepared to pay a very high price indeed. Airlines also learn from past behaviour and so some popular routes will start at a higher price. A similar system of dynamic pricing is used with advance train tickets, with the price rising as trains get booked up.
The dynamic pricing system used by airlines and train companies is similar, but not identical, to first-degree price discrimination. The figure below illustrates first-degree price discrimination by showing a company setting the price for a particular product.
Assume initially that it sets a single profit-maximising price. This would be a price of P1, at an output of Q1, where marginal revenue (MR) equals marginal cost (MC). (We assume for simplicity that average and marginal costs are constant.) Total profit will be area 1: i.e. the blue area ((P1 – AC) × Q1). Area 2 represents consumer surplus, with all those consumers who would have been prepared to pay a price above P1, only having to pay P1.
Now assume that the firm uses first-degree price discrimination, selling each unit of the product at the maximum price each consumer is willing to pay. Starting with the consumer only willing to pay a price of P2, the price will go on rising up along the demand with each additional consumer being charged a higher price up to the price where the demand curve meets the vertical axis. In such a case, the firm’s profit would be not just the blue area, but also the green areas 2 and 3. Note that there is no consumer surplus as area 2 is now part of the additional profit to the firm.
Although dynamic pricing by airlines is similar to this model of first-degree price discrimination, in practice some people will be paying less than they would be willing to pay and the price goes up in stages, not continuously with each new sale of a ticket. Thus, compared with a fixed price per seat, the additional profit will be less than areas 2 + 3, but total profit will still be considerably greater than area 1 alone. Note also that there is a maximum quantity of seats (Qmax), represented by a full flight. The airline would hope that demand and its pricing model are such that Qmax is less than Q2.
Dynamic pricing also applies in the hospitality sector, as hotels raise the prices for rooms according to demand, with prices at peak times often being considerably higher than off-season prices. Rather then pre-setting prices for particular seasons, dates or weekends/weekdays, many hotels, especially chains and booking agents, adjust prices dynamically as demand changes. Airbnb offers property owners what it calls ‘Smart Pricing’, where nightly prices change automatically with demand.
Another example is Uber, which uses dynamic pricing to balance demand and supply location by location. In times of peak demand on any route, the company’s algorithm will raise the price. This will encourage people to delay travelling if they can or use alternative means of transport. It will also encourage more Uber drivers to come to that area. In times of low demand, the price will fall. This will encourage more people to use the service (rather than regular taxis or buses) and discourage drivers from working in that area.
Where dynamic pricing varies with the time or date when the purchase is made, it is sometimes referred to as inter-temporal pricing. It is a form of second-degree price discrimination, which is where a firm offers consumers a range of different pricing options for the same or similar products.
Another example of dynamic pricing, which is closer to first-degree price discrimination is the use of sophisticated algorithms and AI by Amazon, allowing it to update the prices of millions of products many times a day according to market conditions. Another is eBay auctions, where the price rises as the end date is reached, according to the willingness to pay of the bidders.
Attitudes to dynamic pricing
Consumers have grown accustomed to dynamic pricing in many industries. People generally accept the pricing model of budget airlines, for example. What makes it acceptable is that most people feel that they can take advantage of early low-priced seats and can compare the current prices on different flights and airlines when making their travel plans. Pricing is transparent. With the Oasis concert, however, there wasn’t the same degree of price transparency. Many people were surprised and dismayed to find that when they got to the front of the online queue, the price had risen dramatically.
People are familiar of dynamic pricing in the context of price cuts to shift unsold stock. Supermarkets putting stickers on products saying ‘reduced for quick sale’ is an example. Another is seasonal sales. What is less acceptable to many consumers is firms putting up prices when demand is high. They see it a profiteering. Many supermarkets are introducing electronic shelf labels (ESLs), where prices can be changed remotely as demand changes. Consumers may react badly to this if they see the prices going up. The supermarket, however, may find it a very convenient way of reducing prices to shift stock – something consumers are hardly likely to complain about.
Returning to the Oasis tour, the UK government responded to the outrage of fans as ticket prices soared. Culture Secretary, Lisa Nandy, announced that the government will investigate how surge pricing for concert tickets is used by official retailers, such as Ticketmaster. This will be part of a planned review of ticket sales that seeks to establish a fairer and more transparent system of pricing.
The problem is that, with some fans being prepared to pay very high prices indeed to see particular acts and with demand considerably exceeding supply at prices that fans would consider reasonable, some way needs to be found of rationing demand. If it is not price, then it will inevitably involve some form of queuing or rationing system, with the danger that this encourages touts and vastly inflated prices on the secondary market.
Perhaps a lesson can be drawn from the Glastonbury Festival, where prices are fixed, people queue online and where security systems are in place to prevent secondary sales by ticket touts. The 2024 price was set at £355 + a £5 booking fee and purchasers were required to register with personal details and a photo, which was checked on admission.
Update
On 5 September, the CMA announced that it was launching an investigation into Ticketmaster over the Oasis concert sales. Its concerns centred on ‘whether buyers were given clear and timely information, and whether consumer protection law was breached’. This followed complaints by fans that (i) they were not given clear and timely information beforehand that the tickets involved dynamic pricing and warned about the possible prices they might have to pay and (ii) on reaching the front of the queue they were put under pressure to buy tickets within a short period of time.
Meanwhile, band member stated that they were unaware that dynamic pricing would be used and that the decision to use the system was made by their management.
Videos
Articles
- Oasis ticket sales – everything you need to know about reunion
BBC News (27/8/24)
- Ticketmaster demand-based pricing system criticised
BBC News, Annabel Rackham (10/10/22)
- I loved Oasis – until I saw Ticketmaster’s dynamic pricing
Metro, Issy Packer (2/9/24)
- A supersonic swindle: my £1,423 Oasis Ticketmaster hell
The Guardian, Josh Halliday (1/9/24)
- Bands urged to oppose dynamic pricing of concert tickets after Oasis ‘fiasco’
The Guardian, Josh Halliday and Rob Davies (1/9/24)
- Oasis tickets: what is dynamic pricing and why is it used for live music?
The Guardian, Rob Davies (1/9/24)
- Viagogo defends reselling Oasis tickets for thousands of pounds as ‘legal’
City A.M. (31/8/24)
- Oasis fans face ‘eye-watering’ resale ticket prices
i News, Adam Sherwin (30/8/240
- Would you really pay €500 for an Oasis ticket?
RTE Brainstorm, Emma Howard (3/9/24)
- When ‘dynamic pricing’ works – and when it doesn’t
Investors’ Chronicle, Hermione Taylor (11/12/23)
- The pros, cons and misconceptions of dynamic pricing for retailers
Computer Weekly, Glynn Davis (20/6/24)
- Dynamic Pricing (Taylor’s Version)
Linkedin, Economic Insight (10/7/23)
- Dynamic pricing: successful companies that use this pricing strategy
PriceTweakers, Simon Gomez (25/5/24)
- Five lessons for businesses investigating dynamic pricing
FT Strategies
- Inflated Oasis ticket prices ‘depressing’ – government promises review of dynamic pricing
Sky News (2/9/24)
- Lisa Nandy hits out at ‘incredibly depressing’ Oasis ticket sale and orders probe into surge pricing
Independent, Archie Mitchell (2/9/24)
- Oasis ticket row: How Ticketmaster’s owner has grip on UK live music scene
BBC News, Chi Chi Izundu and James Stewart (4/9/24)
- CMA launches investigation into Ticketmaster over Oasis concert sales
CMA Press Release (5/9/24)
- Revealed: the touts offering Oasis tickets for thousands on resale sites
The Guardian, Rob Davies, Hannah Al-Othman and Tiago Rogero (7/9/24)
Questions
- What is the difference between dynamic pricing and surge pricing?
- What is buyer’s remorse? How could dynamic pricing be used while minimising the likelihood of buyer’s remorse?
- Distinguish between first-degree, second-degree and third-degree price discrimination. Do the various forms of dynamic pricing correspond to one or more of these three types?
- Distinguish between consumer and producer surplus. How may dynamic pricing lead to a reduction in consumer surplus and an increase in producer surplus?
- Should Ticketmaster sell tickets on the same basis as tickets for the Glastonbury Festival?
- Is Oasis a monopoly? What are the ticket pricing implications?
- Are there any industries where firms would not benefit from dynamic pricing? Explain.
- What are the arguments for and against allowing tickets to be sold on the secondary market for whatever price they will fetch?
- How powerful is Ticketmaster in the primary and secondary ticket markets?
Global long-term economic growth has slowed dramatically since the financial crisis of 2007–8. This can be illustrated by comparing the two 20-year periods 1988 to 2007 and 2009 to 2028 (where IMF forecasts are used for 2024 to 2028: see WEO Database under the Data link below). Over the two periods, average annual world growth fell from 3.8% to 3.1%. In advanced countries it fell from 2.9% to 1.6% and in developing countries from 4.8% to 4.3%. In the UK it fell from 2.4% to 1.2%, in the USA from 3.1% to 1.8% and in Japan from 1.9% to 0.5%.
In the UK, labour productivity growth in the production industries was 6.85% per annum from 1998 to 2006. If this growth rate had been maintained, productivity would have been 204% higher by the end of 2023 than it actually was. This is shown in the chart (click here for a PowerPoint).
The key driver of long-term economic growth is labour productivity, which can best be measured by real GDP per hour worked. This depends on three things: the amount of capital per worker, the productivity of this capital and the efficiency of workers themselves – the latter two giving total factor productivity (TFP). Productivity growth has slowed, and with it the long-term rate of economic growth.
If we are measuring growth in output per head of the population, as opposed to simple growth in output, then another important factor is the proportion of the population that works. With ageing populations, many countries are facing an increase in the proportion of people not working. In most countries, these demographic pressures are likely to increase.
A major determinant of long-term economic growth and productivity is investment. Investment has been badly affected by crises, such as the financial crisis and COVID, and by geopolitical tensions, such as the war in Ukraine and tensions between the USA and China and potential trade wars. It has also been adversely affected by government attempts to deal with rising debt caused by interventions following the financial crisis and COVID. The fiscal squeeze and, more recently higher interest rates, have dampened short-term growth and discouraged investment, thereby dampening long-term growth.
Another factor adversely affecting productivity has been a lower growth of allocative efficiency. Competition in many industries has declined as the rate of new firms entering and exiting markets has slowed. The result has been an increase in concentration and a growth in supernormal profits.
In the UK’s case, growth prospects have also been damaged by Brexit. According to Bank of England and OBR estimates, Brexit has reduced productivity by around 4% (see the blog: The costs of Brexit: a clearer picture). For many companies in the UK, Brexit has hugely increased the administrative burdens of trading with the EU. It has also reduced investment and led to a slower growth in the capital stock.
The UK’s poor productivity growth over many yeas is examined in the blog The UK’s poor productivity record.
Boosting productivity
So, how could productivity be increased and what policies could help the process?
Artificial intelligence. One important driver of productivity growth is technological advance. The rapid advance in AI and its adoption across much of industry is likely to have a dramatic effect on working practices and output. Estimates by the IMF suggest that some 40% of jobs globally and 60% in advanced countries could be affected – some replaced and others complemented and enhanced by AI. The opportunities for raising incomes are huge, but so too are the dangers of displacing workers and deepening inequality, as some higher-paid jobs are enhanced by AI, while many lower paid jobs are little affected and other jobs disappear.
AI is also likely to increase returns to capital. This may help to drive investment and further boost economic growth. However, the increased returns to capital are also likely to exacerbate inequality.
To guard against the growth of market power and its abuse, competition policies may need strengthening to ensure that the benefits of AI are widely spread and that new entrants are encouraged. Also training and retraining opportunities to allow workers to embrace AI and increase their mobility will need to be provided.
Training. And it is not just training in the use of AI that is important. Training generally is a key ingredient in encouraging productivity growth. In the UK, there has been a decline in investment in adult education and training, with a 70% reduction since the early 2000s in the number of adults undertaking publicly-funded training, and with average spending on training by employers decreasing by 27% per trainee since 2011. The Institute for Fiscal Studies identifies five main policy levers to address this: “public funding of qualifications and skills programmes, loans to learners, training subsidies, taxation of training and the regulation of training” (see link in articles below).
Competition. Another factor likely to enhance productivity is competition, both internationally and within countries. Removing trade restrictions could boost productivity growth; erecting barriers to protect inefficient domestic industry would reduce it.
Investment. Policies to encourage investment are also key to productivity growth. Private-sector investment can be encouraged by tax incentives. For example, in the UK the Annual Investment Allowance allows businesses to claim 100% of the cost of plant and machinery up to £1m in the year it is incurred. However, for tax relief to produce significant effects on investment, companies need to believe that the policy will stay and not be changed as economic circumstances or governments change.
Public-sector investment is also key. Good road and rail infrastructure and public transport are vital in encouraging private investment and labour mobility. And investment in health, education and training are a key part in encouraging the development of human capital. Many countries, the UK included, cut back on public-sector capital investment after the financial crisis and this has had a dampening effect on economic growth.
Regional policy. External economies of scale could be encouraged by setting up development areas in various regions. Particular industries could be attracted to specific areas, where local skilled workers, managerial expertise and shared infrastructure can benefit all the firms in the industry. These ‘agglomeration economies’ have been very limited in the UK compared with many other countries with much stronger regional economies.
Changing the aims and governance of firms. A change in corporate structure and governance could also help to drive investment and productivity. According to research by the think tank, Demos (see the B Lab UK article and the second report below), if legislation required companies to consider the social, economic and environmental impact of their business alongside profitability, this could have a dramatic effect on productivity. If businesses were required to be ‘purpose-led’, considering the interests of all their stakeholders, this supply-side reform could dramatically increase growth and well-being.
Such stakeholder-governed businesses currently outperform their peers with higher levels of investment, innovation, product development and output. They also have higher levels of staff engagement and satisfaction.
Articles
- World Must Prioritize Productivity Reforms to Revive Medium-Term Growth
IMF Blog, Nan Li and Diaa Noureldin (10/4/24)
- Why has productivity slowed down?
Oxford Martin School News, Ian Goldin, Pantelis Koutroumpis, François Lafond and Julian Winkler (18/3/24)
- How can the UK revive its ailing productivity?
Economics Observatory, Michelle Kilfoyle (14/3/24)
- With the UK creeping out of recession, here’s an economist’s brief guide to improving productivity
The Conversation, Nigel Driffield (13/3/24)
- UK economy nearly a third smaller thanks to ‘catastrophically bad’ productivity slowdown
City A.M., Chris Dorrell (12/3/24)
- Can AI help solve the UK’s public sector productivity puzzle?
City A.M., Chris Dorrell (11/3/24)
- AI Will Transform the Global Economy. Let’s Make Sure it Benefits Humanity
IMF Blog, Kristalina Georgieva (14/1/24)
- Productivity and Investment: Time to Manage the Project of Renewal
NIESR, Paul Fisher (12/3/24)
- Productivity trends using key national accounts indicators
Eurostat (15/3/24)
- New report says change to company law could add £149bn to the UK economy
B Lab UK (28/11/23)
- Investment in training and skills: Green Budget Chapter 9
Institute for Fiscal Studies, Imran Tahir (12/10/23)
Reports
Data
Questions
- Why has global productivity growth been lower since 2008 than before 2008?
- Why has the UK’s productivity growth been lower than many other advanced economies?
- How does the short-run macroeconomic environment affect long-term growth?
- Find out why Japan’s productivity growth has been so poor compared with other countries.
- What are likely to be the most effective means of increasing productivity growth?
- How may demand management policies affect the supply side of the economy?
- How may the adoption of an ESG framework by companies for setting objectives affect productivity growth?
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
- UK competition watchdog plans probe into veterinary market
Financial Times, Suzi Ring and Oliver Ralph (12/3/24)
Vet prices: Investigation over concerns pet owners are being overchargedSky News (12/3/24)
- UK watchdog plans formal investigation into vet pricing
The Guardian, Kalyeena Makortoff (12/3/24)
- ‘Eye-watering’ vet bills at chain-owned surgeries prompt UK watchdog review
The Guardian, Kalyeena Makortoff (7/9/23)
- Warning pet owners could be overpaying for medicine
BBC News, Lora Jones & Jim Connolly (12/3/24)
- I own a vet practice, owners complain about the spiralling costs of treatments, but I only make 5 -10% profit – here’s our expenditure breakdown
Mail Online, Alanah Khosla (14/3/24)
- Vets bills around the world: As big-name veterinary practices come under pressure for charging pet owners ‘eyewatering’ care costs, how do fees in Britain compare to other countries?
Mail Online, Rory Tingle, Dan Grennan and Katherine Lawton (13/3/24)
CMA documents
Questions
- How would you establish whether there is an abuse of market power in the veterinary industry?
- Explain what is meant by the principal–agent problem. Give some other examples both in economic and non-economic relationships.
- What market advantages do large vet companies have over independent vet practices?
- 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?
- Find out what powers the CMA has to enforce its rulings.
- 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?