Category: Economics for Business: Ch 21

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Will AI make the world less equal?

With businesses increasing their use of AI, this is likely to have significant effects on employment. But how will this affect the distribution of income, both within countries and between countries?

In some ways, AI is likely to increase inequality within countries as it displaces low-skilled workers and enhances the productivity of higher-skilled workers. In other ways, it could reduce inequality by allowing lower-skilled workers to increase their productivity, while displacing some higher-skilled workers and managers through the increased adoption of automated processes.

The effect of AI on the distribution of income between countries will depend crucially on its accessibility. If it is widely available to low-income countries, it could significantly enhance the productivity of small businesses and workers in such countries and help to reduce the income gap with the richer world. If the gains in such countries, however, are largely experienced by multinational companies, whether in mines and plantations, or in labour-intensive industries, such as garment production, few of the gains may accrue to workers and global inequality may increase.

Redistribution within a country

The deployment of AI may result in labour displacement. AI is likely to replace both manual and white-collar jobs that involve straightforward and repetitive tasks. These include: routine clerical work, such as data entry, filing and scheduling; paralegal work, contract drafting and legal research; consulting, business research and market analysis; accounting and bookkeeping; financial trading; proofreading, copy mark-up and translation; graphic design; machine operation; warehouse work, where AI-enabled warehouse robots do many receiving, sorting, stacking, retrieval, carrying and loading tasks (e.g. Amazon’s Sequoia robotic system); basic coding or document sifting; market research and advertising design; call-centre work, such as enquiry handling, sales, telemarketing and customer service; hospitality reception; sales cashiers in supermarkets and stores; analysis of health data and diagnosis. Such jobs can all be performed by AI assistants, AI assisted robots or chat bots.

Women are likely to be disproportionately affected because they perform a higher share of the administrative and service roles most exposed to AI.

Workers displaced by AI may find that they can find employment only in lower-paid jobs. Examples include direct customer-facing roles, such as bar staff, shop assistants, hairdressers and nail and beauty consultants.

Such job displacement by AI is likely to redistribute income from relatively low-skilled labour to capital: a redistribution from wages to profits. This will tend to lead to greater inequality.

AI is also likely to lead to a redistribution of income towards certain types of high-skilled labour that are difficult to replace with AI but which could be enhanced by it. Take the case of skilled traders, such as plumbers, electricians and carpenters. They might be able to use AI in their work to enhance their productivity, through diagnosis, planning, problem-solving, measurement, etc. but the AI would not displace them. Instead, it could increase their incomes by allowing them to do their work more efficiently or effectively and thus increase their output per hour and enhance their hourly reward. Another example is architecture, where AI can automate repetitive tasks and open up new design possibilities, allowing architects to focus on creativity, flexibility, aesthetics, empathy with clients and ethical decision-making.

An important distinction is between disembodied and embodied AI investment. Disembodied AI investment could include AI ‘assistants’, such as ChatGPT and other software that can be used in existing jobs to enhance productivity. Such investment can usually be rolled out relatively quickly. Although the extra productivity may allow some reduction in the number of workers, disembodied AI investment is likely to be less disruptive than embodied AI investment. The latter includes robotics and automation, where workers are replaced by machines. This would require more investment and may be slower to be adopted.

Then there are jobs that will be created by AI. These include prompt engineers, who develop questions and prompt techniques to optimise AI output; health tech experts, who help organisations implement new medical AI products; AI educators, who train people in the uses of AI in the workplace; ethics advisors, who help companies ensure that their uses of AI are aligned with their values, responsibilities and goals; and cybersecurity experts who put systems in place to prevent AI stealing sensitive information. Such jobs may be relatively highly paid.

In other cases, the gains from AI in employment are likely to accrue mainly to the consumer, with probably little change in the incomes of the workers themselves. This is particularly the case in parts of the public sector where wages/salaries are only very loosely related to productivity and where a large part of the work involves providing a personal service. For example, health professionals’ productivity could be enhanced by AI, which could allow faster and more accurate diagnosis, more efficient monitoring and greater accuracy in surgery. The main gainers would be the patients, with probably little change in the incomes of the health professionals themselves. Teachers’ productivity could be improved by allowing more rapid and efficient marking, preparation of materials and record keeping, allowing more time to be spent with students. Again, the main gainers would be the students, with little change in teachers’ incomes. Other jobs in this category include social workers, therapists, solicitors and barristers, HR specialists, senior managers and musicians.

Thus there is likely to be a distribution away from lower-skilled workers to both capital and higher-skilled workers who can use AI, to people who work in new jobs created by AI and to the consumers of certain services.

AI will accelerate productivity growth and, with it, GDP growth, but will probably displace workers faster than new roles emerge. This is likely to increase inequality and be a major challenge for society. Can the labour market adapt? Could the effects be modified if people moved to a four- or three-day week? Will governments introduce statutory limits to weekly working hours? Will training and education adapt to the new demands of employers?

Redistribution between countries

AI threatens to widen the global rich–poor divide. It will give wealthier nations a productivity and innovation edge, which could displace low-skilled jobs in low-income nations. Labour-intensive production could be replaced by automated production, with the capital owned by the multinational companies of just a few countries, such as the USA and China, which between them account for 40% of global corporate AI R&D spending. For some companies, it would make sense to relocate production to rich countries, or certain wealthier developing countries, with better digital infrastructure, advanced data systems and more reliable power supply.

For other companies, however, production might still be based in low-income countries to take advantage of low-cost local materials. But there would still be a redistribution from wages in such countries to the profits of multinationals.

But it is not just in manufacturing where low-income countries are vulnerable to the integration of AI. Several countries, such as India, the Philippines, Mexico and Egypt have seen considerable investment in call centres and IT services for business process outsourcing and customer services. AI now poses a threat to employment in this industry as it has the potential to replace large numbers of workers.

AI-related job losses could exacerbate unemployment and deepen poverty in poorer countries, which, with limited resources, limited training and underdeveloped social protection systems, are less equipped to absorb economic and social shocks. This will further widen the global divide. In the case of embodied AI investment, it may only be possible in low-income countries through multinational investment and could displace many traditional jobs, with much of the benefit going in additional multinational profit.

But it is not all bad news for low-income countries. AI-driven innovations in healthcare, education, and agriculture, if adopted in poor countries, can make a significant contribution to raising living standards and can slow, or even reverse, the widening gap between rich and poor nations. Some of the greatest potential is in small-scale agriculture. Smallholders can boost crop yields though precision farming powered by AI; AI tools can help farmers buy seeds, fertilisers and animals and sell their produce at optimum times and prices; AI-enabled education tools can help farmers learn new techniques.

Articles

Questions

  1. What types of job are most vulnerable to AI?
  2. How will AI change the comparative advantage of low-income countries and what effect will it be likely to have on the pattern of global trade?
  3. Assess alternative policies that governments in high-income countries can adopt to offset the growth in inequality caused by the increasing use of AI.
  4. What policies can governments in low-income countries or aid agencies adopt to offset the growth in inequality within low-income countries and between high- and low-income countries?
  5. How might the growth of AI affect your own approach to career development?
  6. Is AI likely to increase or decrease economic power? Explain.
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The 4Ps approach to merger policy in the UK

The approach towards mergers remains the most controversial area of competition policy. Some argue that policy makers in both the UK and EU have been too easily persuaded by the arguments put forward by firms and so have allowed too many mergers to proceed. Others claim that the opposite is true and that merger policy has prohibited mergers that should have been allowed to proceed. This, then, has a negative impact on investment, innovation, productivity and growth.

In recent years there has been more specific criticism of merger policy in the UK. The government has indicated that it wants the Competition and Markets Authority (CMA) to be less interventionist and take a more pro-growth approach.

In February 2025, in response to this criticism, the CMA launched its new ‘4 Ps’ approach to merger policy: Pace, Predictability, Proportionality and Process. Various changes to the investigation process have been proposed in the past 12 months using this framework.

Pace. The time taken by the CMA to initially assess a merger before deciding whether a Phase 1 investigation is necessary (i.e. the pre-notification procedure) was reduced from 65 to 40 working days. Also, the target to complete straightforward Phase 1 investigations was reduced from 35 to 25 days.

Predictability. The proposed merger guidelines, published in October 2025, provide more detail on (a) what criteria will be used to measure market shares when applying the ‘share of supply test’ (this is where the combined UK market share of two merging businesses is at least 25%, provided one business has a UK turnover of at least £10 million), and (b) the factors that are likely to lead to the competition authorities concluding that one business has gained ‘material influence over another’. Businesses had complained that there was too much uncertainty about the way the share of supply test and material influence were applied. The CMA is also considering greater alignment with other international regulators over decision making rather than its previous policy of acting independently. All these measures should increase the predictability of the investigation process.

Proportionality. Proportionality refers to the objective of addressing any competition issues in merger cases in a way that places the minimum burden on the businesses involved. To improve proportionality, the CMA has indicated that in future cases it will be more willing to use behavioural remedies – requiring firms to take or desist from certain actions. New draft guidelines identify more situations where the use of behavioural remedies may be appropriate. However, they also show that the CMA still views structural remedies (e.g. preventing the merger or requiring firms to demerge or to sell certain assets) as more effective in many situations. Another important measure to improve proportionality is the introduction of a new ‘wait and see’ approach to global mergers. The CMA will now wait to see if the actions taken by other competition authorities in global cases address any concerns in the UK market before deciding whether to launch a review.

Process. To improve the process, the CMA has announced plans to engage with businesses at a much earlier point in the process. For example, it has pledged to share its provisional thinking in the early stages of an investigation by implementing new ‘teach-in’ sessions and having more regular update meetings. Much earlier meetings that focus on possible remedies will also take place. This may make it possible for the CMA to assess the suitability of more complex remedies during a Phase 1 investigation rather than having to wait for a longer and more costly Phase 2 review. Phase 2 reviews will also no longer be managed by panels of independent experts. This role will now be carried out by the internal CMA board.

Some critics argue that the CMA has not fully considered the potential benefits of mergers in many cases. For example, a merger could (a) have procompetitive effects, known as rivalry enhancing efficiencies (REEs) and/or (b) benefits for consumers outside of the relevant market, known as relevant customer benefits (RCBs). In response to this criticism, the CMA is currently reassessing its approach to including evidence on REEs and RCBs.

The CMA is still currently consulting with interested parties about many of these proposed changes. It will be interesting to see what final decisions are made in the next couple of years.

Articles

CMA documentation

Questions

  1. Of all the mergers considered by the CMA in 2024/25, find out what percentage were formally investigated. How many were blocked from taking place? Do you believe that this indicates that merger policy is too weak or too strong?
  2. What three criteria must be met for a business arrangement to be classed as a ‘relevant merger situation’ by the CMA?
  3. Identify some different methods that one business could use to gain material influence over the way another company operates.
  4. Outline the ‘turnover test’, the ‘share of supply test’ and the ‘hybrid test’.
  5. Discuss the potential advantages of using behavioural remedies as opposed to structural remedies in merger cases. Why has the CMA still preferred the use of structural remedies in most situations?
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Age-old problems but new technology: developments in collusion in the algorithmic era

In my previous blog post on this site, I examined how AI-powered pricing tools can act as a ‘double-edged sword’: offering efficiency gains, while also creating opportunities for collusion. I referred to one of the early examples of this, which was the case involving Trod Ltd and GB Eye, where two online poster and frame sellers on Amazon used pricing algorithms to monitor and adjust their prices. However, in this instance there was also an explicit agreement between the firms. As some commentators put it, it was ‘old wine in new bottles‘, meaning a fairly conventional cartel that was simply facilitated through digital tools.

Since then, algorithms have increasingly become part of everyday life and are now embedded in routine business practice.

Some of the effects may have a positive effect on competition. For example, algorithms can help to lower barriers to entry. In some markets, incumbents benefit from long-standing experience, while new firms face significant learning costs and are at a disadvantage. By reducing these learning costs and supporting entry, algorithms could contribute to making collusion harder to sustain.

On the other hand, algorithms could increase the likelihood of collusion. For example, individual algorithms used by competing firms may respond to market conditions in predictable ways, making it easier for firms to collude tacitly over time.

Algorithms can also improve the ability of firms to monitor each other’s prices. This is particularly relevant for multi-product firms. Traditionally, we might expect these markets to be less prone to collusion because co-ordinating across many products is complex. AI can overcome this complexity. In the Sainsbury’s/Asda merger case, for example, the Competition and Markets Authority suggested that the main barrier to reaching and monitoring a pricing agreement was the complexity of pricing across such a wide range of products. However, the CMA also suggested that technological advances could increase its ability to do so in the future.

The ‘hub-and-spoke’ model

One of the other growing concerns is the ability of AI pricing algorithms to facilitate collusion by acting as a ‘hub’ in a ‘hub-and-spoke’ arrangement. In this type of collusion, competing firms (the ‘spokes’) need not communicate directly with one another. Instead, the ‘hub’ helps them to co-ordinate their actions.

While there have been only limited examples of an AI pricing algorithm acting as a hub in practice, what once seemed to be a largely theoretical concern has now become a live enforcement issue.

A very recent example is the RealPage case in the United States. The Department of Justice (DOJ) filed an antitrust lawsuit against RealPage Inc. in August 2024, alleging that RealPage, acting as the ‘hub’, facilitated collusion between landlords (the ‘spokes’).

RealPage provided pricing software to numerous landlords, including the largest landlord in the USA, which manages around 950 000 rental units across the country. These landlords would normally compete independently in setting rental prices, discounts and lease terms to win consumers. However, by feeding competitively sensitive information that would not usually be shared between rivals into RealPage’s system, the software generated pricing recommendations that, according to the DOJ, led to co-ordinated rent increases across competing apartment complexes.

In the RealPage case, the authorities reported that they had access to internal documents and statements from the parties involved, which helped support their allegations. These included references within RealPage to helping landlords ‘avoid the race to the bottom’ and comments from a landlord describing the software as ‘classic price fixing’.

Evidence in these cases really matters because the standard of proof required to establish a hub-and-spoke arrangement is much higher than for traditional cases of explicit collusion. This is because it can be difficult to distinguish between legitimate and anti-competitive communication between retailers and suppliers. Also, proving ‘anti-competitive intent’ is inherently challenging.

Other competition authorities around the world are also turning their attention to these issues. For example, the European Commission recently announced that a number of investigations into algorithmic pricing are underway, signalling a clear shift toward more active scrutiny. As technology continues to advance, it is clear that algorithmic pricing will remain an area where both firms and authorities must move and adapt quickly.

Articles

Questions

  1. In what ways does the RealPage case differ from the earlier Trod Ltd and GB Eye Ltd case? Consider the roles played by the firms, the nature of the alleged co-ordination, and the extent to which pricing algorithms were used to facilitate the conduct.
  2. How might the use of pricing algorithms affect the likelihood of firms colluding, either explicitly or tacitly? Consider ways that algorithms may make collusion easier to sustain but also ways in which they may reduce its likelihood.
  3. Should firms be held liable for anti-competitive outcomes produced by algorithms that ‘self-learn’, even if they did not intend those outcomes? Explain why or why not.
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How to increase productivity – the key to economic growth

The productivity gap between the UK and its main competitors is significant. In 2024, compared to the UK, output per hour worked was 10.0% higher in France, 19.8% higher in Germany and 41.1% higher in the USA. These percentages are in purchasing-power parity terms: in other words, they reflect the purchasing power of the respective currencies – the pound, the euro and the US dollar.

GDP per hour worked (in PPP terms) is normally regarded as the best measure of labour productivity. An alternative measure is GDP per worker, but this does not take into account the length of the working year. Using this measure, the gap with the USA is even higher as workers in the USA work longer hours and have fewer days holiday per year than in the UK.

The productivity gap is not a new phenomenon. It has been substantial and growing over the past 20 years. (The exception was in 2020 during lockdowns when many of the least productive sectors, such as hospitality, were forced to close temporarily.)

The productivity gap is shown in the two figures. Both figures show labour productivity for the UK, France, Germany and the USA from 1995 to 2024.

Figure 1 shows output (GDP) per hour, measured in US dollars in PPP terms.

Figure 2 shows output (GDP) per hour relative to the UK, with the UK set at 100. The gap narrowed somewhat up to the early 2000s, but since then has widened.

Low UK productivity has been a source of concern for UK governments and business for many years. Not only does it constrain the growth in living standards, it also make the UK less attractive as a source of inward investment and less competitive internationally.

Part of the reason for low UK productivity compared to that in other countries is a low level of investment. As a proportion of GDP, the UK has persistently had the lowest, or almost the lowest, level of investment of its major competitors. This is illustrated in Table 1.

It is generally recognised by government, business and economists that if the economy is to be successful, the productivity gap must be closed. But there is no ‘quick fix’. The policies necessary to achieve increased productivity are long term. There is also a recognition that the productivity problem is a multi-faceted one and that to deal with it requires policy initiatives on a broad front: initiatives that encompass institutional changes as well as adjustments in policy.

So what can be done to improve productivity and how can this be achieved at the micro as well as the macro level?

Improving productivity: things that government can do

Encouraging investment. Over the years, UK governments have increased investment allowances, enabling firms to offset the cost of investment against pre-tax profit, thereby reducing their tax liability. For example, in the UK, companies can offset a multiple of research and development costs against corporation tax. The rate of relief for small and medium-sized enterprises (SMEs) allows companies that work in science and technology to deduct an extra 86% of their qualifying expenditure from their trading profit in addition to the normal 100% deduction: i.e. a total of 186% deduction. Meanwhile, since April 2016, larger companies have been able to claim a R&D expenditure credit, initially worth 11 per cent of R&D expenditures, then 12 per cent from 2018 and 13 per cent from 2020. This was then raised to 20 per cent from 2023.

Strengthening competition. A number of studies have revealed that, with increasing market share, business productivity growth slows. As a result, government policy sought to strengthen competition policy. The Competition Act 1998, which came into force in March 2000, and the Enterprise Act of 2002, enhanced the powers of the Office of Fair Trading (OFT) (a predecessor to the Competition and Markets Authority) in respect to dealing with anti-competitive practices. It was given the ability to impose large fines on firms which had been found guilty of exploiting a dominant market position. Today, one of the strategic goals of the Competition and Markets Authority (CMA) is the aim of ‘extending competition frontiers’ in order to improve the way competition works.

Encouraging an enterprise culture. The creation of an enterprise culture is seen as a crucial factor not only to encourage innovation but also to stimulate technological progress. Innovation and technological progress are crucial to sustaining growth and raising living standards. The UK government launched the Small Business Service in April 2000, later renamed Business and Industry. Its role is to co-ordinate small-business policy within government and liaise with business, providing advice and information. However, according to the OECD, there remains considerable scope for increasing the level of government support for entrepreneurship in the UK.

Improving productivity: things that organisations can do

In the podcast from the BBC’s The Bottom Line series, titled ‘Productivity: How Can British Business Work Smarter’ (see link below), Evan Davis and guests discuss what productivity really looks like in practice – from offices and factories, to call centres and operating theatres.’ The episode identifies a number of ways in which labour productivity can be improved. These include:

  • People could work harder;
  • Workers could be better trained and more skilled and thus able to produce more per hour;
  • Capital could be increased so that workers have more equipment or tools to enable them to produce more, or there could be greater automation, releasing labour to work on other tasks;
  • Workplaces could be arranged more efficiently so that less time is spent moving from task to task;
  • Systems could put in place to ensure that tasks are done correctly the first time and that time is not wasted having to repeat them or put them right;
  • Workers could be better incentivised to work efficiently, whether through direct pay or promotion prospects, or by increasing job satisfaction or by management being better attuned to what motivates workers and makes them feel valued;
  • Firms could move to higher-value products, so that workers produce a greater value of output per hour.

The three contributors to the programme discuss various initiatives in their organisations (an electronics manufacturer, NHS foundation trusts and a provider of office services to other organisations).

They also discuss the role that AI plays, or could play, in doing otherwise time-consuming tasks, such as recording and paying invoices and record keeping in offices; writing grants or producing policy documents; analysing X-ray results in hospitals and performing preliminary diagnoses when patients present with various symptoms; recording conversations/consultations and then sorting, summarising and transcribing them; building AI capabilities into machines or robots to enable them to respond to different specifications or circumstances; software development where AI writes the code. Often, there is a shortage of time for workers to do more creative things. AI can help release more time by doing a lot of the mundane tasks or allowing people to do them much quicker.

There are huge possibilities for increasing labour productivity at an organisational level. The successful organisations will be those that can grasp these possibilities – and in many cases they will be incentivised to so so as it will improve their profitability or other outcomes.

Podcast

Articles

Data

Questions

  1. In what different ways can productivity be measured? What is the most appropriate measure for assessing the effect of productivity on (a) GDP and (b) human welfare generally?
  2. Why has the UK had a lower level of labour productivity than France, Germany and the USA for many years? What can UK governments do to help close this gap?
  3. Find out how Japanese labour productivity has compared with that in the UK over the past 30 years and explain your findings.
  4. Research an organisation of your choice to find out ways in which labour productivity could be increased.
  5. Identify various ways in which AI can improve productivity. Will organisations be incentivised to adopt them?
  6. Has Brexit affected UK labour productivity and, if so, how and why?
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What do ticket resellers and parents have in common?

Examples of rent seeking in economic theory

In March 2024, two people were convicted of running a business that used dishonest and illegal methods to buy and sell tickets for popular live events such as Ed Sheeran, Lady Gaga and Little Mix concerts. Between June 2015 and December 2017, this business purchased 47 000 tickets using 127 names and 187 different e-mail addresses.

Economists refer to these actions as examples of rent seeking. However, many rent-seeking activities are not illegal.

What is rent seeking?

Rent seeking in economic theory refers to costly actions taken by people (i.e. they involve effort and expertise) to try to gain a greater share of a given level of profit /surplus. These actions do not generate any extra surplus or value for society and typically involve people trying to game or manipulate a situation or system for their own personal gain.

In many cases, the opportunity cost of these actions can be considerable. In this case, the opportunity cost is the surplus for society that could have been gained if this effort/expertise had been used to carry out more productive tasks.

A widely cited example of rent seeking is where firms exert time and effort to try to influence government policy through lobbying. Most lobbying activities in the UK are not illegal.

Non-price allocation

When prices are set below the market-clearing rate, by either the government or a private organisation, the quantity demanded of the good/service will exceed the quantity supplied. Therefore, non-price allocation must play a role. In other words, some method other than willingness to pay the price, must be used to determine which consumers receive the goods.

In some instances, such as visits to the GP or places at state schools, the good or service has a zero monetary price. In these cases. non-price allocation methods completely replace the role of the price in determining which consumers obtain the goods/services.

In other examples, a positive monetary price is set, but below the market-clearing rate. In these cases, the price partly determines who get the good/service (i.e. people must be willing to pay the non-market-clearing price), but non-price allocation also plays a role. The further below the market-clearing level the price is set, the greater the potential role for non-price methods.

Some common methods of non-price allocation include:

  • First-come first served. This typically results in some type of queueing, either in person or online (a virtual queue).
  • A random selection process. For example, some goods/services are allocated via a lottery, with names of consumers being randomly drawn.
  • The government or other public bodies in charge of allocating the good develop a set of rules to determine which consumers/people get the good. For example, when allocating places at popular state schools, priority is often given to children who live close to the school (i.e. in the catchment area) or who live in families with certain religious beliefs.

Examples of rent seeking

When non-price methods of allocation are implemented, can consumers engage in activities that increase their chances of getting hold of the good/service? Can they manipulate the system for their own advantage and gain a greater share of any surplus? This is rent seeking.

A survey carried out in January 2025 provides some interesting evidence of rent-seeking actions taken by parents to try to secure a place for their child at a popular school. Twenty-seven per cent of the respondents admitted they had tried to manipulate the system to get their child into their preferred school. Out of those who admitted attempting to manipulate the system:

  • 30 per cent registered a child at either another family member’s or friend’s address that was closer to a popular school.
  • 25 per cent exaggerated religious beliefs and attended church services to try to secure a school place.
  • 9 per cent temporarily rented a second home inside the catchment area for the school.
  • 7 per cent moved into the catchment area for the application, only to move out once their child’s place was secured.

Some of these actions may be dishonest but are not illegal.

Rent-seeking activities in the ticketing market for live events

In the primary market for tickets, prices for popular live events are often set below market-clearing levels. Therefore, non-price methods, such as first come, first served, are used to allocate the tickets. This typically results in some type of queueing. Rent-seeking activities include actions taken by consumers to increases their chances of getting nearer to the front of the queue.

If the tickets are being sold from a physical outlet (i.e. a sales kiosk), then some consumers may start queueing many hours before the kiosk opens – in some cases camping overnight. An example is the ‘The Queue’ for Wimbledon tennis matches. Rather than queueing themselves, some people might pay others to queue on their behalf.

People who are paid to queue are sometimes referred to as a ‘line stander’, ‘queue stander’, ‘line sitter’ or ‘queue professional’. Line standers offer their services via market platforms, such as TaskRabbit.

When tickets are sold online, non-market allocation includes both queuing and random selection. Typically, people have to create an account with the primary market ticketing website (Ticketmaster, See Tickets, Eventbrite or AXS) before the sale begins. Then, using this account, they can enter an online waiting room around 15 minutes before the tickets are available to purchase. There is thus an element of first come, first served. When the sale starts, people in the waiting room are randomly allocated a place in the online queue. Once they reach the front of the online queue, the event organiser normally places limits on the number of tickets they can purchase.

What can people do to manipulate this system and so increase their chances of purchasing tickets? In other words, what are the possible rent-seeking activities? One possibility is to create multiple accounts using the details of friends/family and then join the waiting room with each of these accounts using separate devices. Professional resellers often try to use specialist software, called bots, that can create thousands of fake accounts and so significantly increase the chances of getting to the front of the queue. Once they get to the front of the queue, an account created by a bot can proceed through the purchasing process much faster than a person can. The tickets can then be sold for a profit in the uncapped secondary market via websites such as Stubhub and Viagogo.

The UK government passed a law in 2017 that made the use of bots to circumvent ticket purchase limits an illegal activity. The use of ticket bots in the EU became illegal in 2022. Primary market ticketing websites have also invested in technology that tries to detect and block the use of this type of software.

Government policy in the resale of tickets

Should the government prohibit the resale of tickets or implement a resale price cap to try to deter this rent-seeking activity?

Many economists would oppose this policy because of the benefits of the secondary market. For example, resale helps to reallocate tickets to those consumers with the highest willingness to pay. Therefore, the secondary-ticketing market can have a positive impact on allocative efficiency, but it comes at a cost – rent-seeking activities.

Research by economists published more than ten years ago found that the positive impact of the resale market on allocative efficiency outweighed the rent-seeking costs. However, developments in technology have increased the level of rent seeking in recent years, making it easier and less costly for professional resellers to purchase large amounts of tickets in the primary market. Therefore, it is possible that the rent-seeking cost of the secondary market now exceeds its positive impact on allocative efficiency. A case can thus be made for greater intervention by the government.

Recent accusations have also been made about possible rent-seeking activities by sellers in the primary ticketing market too, adding to concerns.

Some of the problems of implementing a resale price cap were discussed in a previous post: Ticket resales – is it time to introduce a price cap?

Articles

Information

Questions

  1. Compare and contrast the meaning of the word ‘rent’ in everyday language with its use in economic theory.
  2. Give examples of some policies that a business might lobby the government to implement. What arguments might the business make to justify each of these policies?
  3. Outline some of the non-price methods that are used to allocate health care in the UK.
  4. Draw a demand and supply diagram to illustrate the incentives for rent-seeking activities when prices are set below market-clearing levels.
  5. Outline some potential rent-seeking activities by sellers in the primary ticketing market.
  6. Discuss some of the opportunity costs of rent-seeking activity in the market for tickets.
  7. Explain why the growing use of paid line standers might increase the demand for a good/service.
  8. Explain why the percentage of tickets for popular live events purchased by professional resellers has increased in the past 10 years.