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.
I
n 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
- 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.
- 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.
- 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.
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?
Like most other sectors of the economy, private schools have been significantly affected by the coronavirus pandemic. As with all schools, they have been restricted to providing their pupils with online instruction. In addition, some parents are likely to have seen their ability to pay the high fees private schools charge restricted. As a result of both of these factors, private schools have been forced to look into providing discounts or refunds on their fees. However, the UK competition authority have received evidence that these schools may have been communicating with each other over how they will set these fee reductions. The authority is concerned that this will allow the schools to restrict the discounts and keep their fees higher.
In other markets (see here and here) the competition authorities have been prepared to relax certain elements of competition law in light of the coronavirus situation. However, price fixing is the severest breach of competition law and the Competition and Markets Authority (CMA) has been clear that this continues to be the case in the current climate. A CMA spokesperson said:
Where cooperation amongst businesses or other organisations is necessary to protect consumers in the coronavirus outbreak, the CMA will not take enforcement action. But we will not tolerate organisations agreeing prices or exchanging commercially sensitive information on future pricing or business strategies with their competitors, where this is not necessary to meet the needs of the current situation.
Therefore, the CMA has written to the Independent Schools Council and other bodies representing the private school sector. This letter made clear that communicating over the fee reductions would be very likely to breach competition law and could result in fines being imposed.
This warning is important since the sector has a history of illegal communication between schools. In 2006 the Office of Fair Trading (OFT) (one of the predecessors to the CMA) imposed fines when it discovered that 50 of them, including Eton and Harrow, had for a number of years shared information on the fees they intended to charge. The OFT discovered that this had taken place following evidence obtained by a student who hacked into their school’s computer system. Here the student found information on the intended fees of competitor schools and leaked this information to the press. It is clear that the CMA will keep a close eye on private schools as they react to the ongoing pandemic.
Articles
Questions
- What are the key features of the private school sector? Is this a market where you would expect competition to be intense?
- Why is price fixing the severest breach of competition law?
- Assuming communication between the private schools is eradicated, how would you expect the sector to be affected by the coronavirus pandemic?
Price fixing agreements between firms are one of the most serious breaches of competition law. Therefore, if detected, the firms involved face substantial fines (see here for an example), plus there is also the potential for jail sentences and director disqualification for participants. However, due to their secretive nature and the need for hard evidence of communication between firms, it is difficult for competition authorities to detect cartel activity.
In order to assist detection, competition authorities offer leniency programmes that guarantee full immunity from fines to the first participant to come forward and blow the whistle on the cartel. This has become a key way in which competition authorities detect cartels. Recently, competition authorities have introduced a number of new tools to try to enhance cartel detection.
First, the European Commission launched an online tool to make it easier for cartels to be reported to them. This tool allows anonymous two-way communication in the form of text messages between a whistle blower and the Commission. The Commissioner in charge of competition policy, Margrethe Vestager, stated that:
If people are concerned by business practices that they think are wrong, they can help put things right. Inside knowledge can be a powerful tool to help the Commission uncover cartels and other anti-competitive practices. With our new tool it is possible to provide information, while maintaining anonymity. Information can contribute to the success of our investigations quickly and more efficiently to the benefit of consumers and the EU’s economy as a whole.
Second, the UK Competition and Markets Authority (CMA) has launched an online and social media campaign to raise awareness of what is illegal under competition law and to encourage illegal activity to be reported to them. The CMA stated that:
Cartels are both harmful and illegal, and the consequences of breaking the law are extremely serious. That is why we are launching this campaign – to help people understand what cartel activity looks like and how to report it so we can take action.
This campaign is on the back of the CMA’s own research which found that less that 25% of the businesses they surveyed believed that they knew competition law well. Furthermore, the CMA is now offering a reward of up to £100,000 and guaranteed anonymity to individuals who provide them with information.
It will be fascinating to see the extent to which these new tools are used and whether they aid the competition authorities in detecting and prosecuting cartel behaviour.
Articles
Questions
- Why do you think leniency programmes are a key way in which competition authorities detect cartels?
- Who do you think is most likely to blow the whistle on a cartel (see the article above by A.Stephan)?
- Why is it worrying that so few businesses appear to know competition law well?
- Which of the two tools do you think is most likely to enhance cartel detection? Explain why.
The European Commission has recently carried out a number of investigations into the various sectors of the industry that supplies parts to car manufacturers. Firms have been found guilty of engaging in anti-competitive practices in the supply of bearings, wire harnesses and the foam used in car seats. The latest completed case relates to firms that supply alternators and starters – both important components in a car engine.
On January 27th the European Commission announced that it was imposing fines on some Japanese manufacturing companies. Melco (Mitsubishi Electric), Hitachi and Denso were found guilty of participating in a cartel between September 2004 and February 2010 that restricted competition in the supply alternators and starters to car manufacturers.
The Commission gathered evidence showing that senior managers in the three businesses held discussions about how to implement various anti-competitive practices. These either took place on the phone or at meetings in offices/restaurants. In particular the firms agreed:
|
|
| • |
to co-ordinate their responses to tenders issued by car manufacturers. This involved them agreeing on the price each firm would bid. |
| • |
to exchange commercially sensitive information about pricing and marketing strategies. |
| • |
which of them would supply each car manufacturer with alternators and starters. |
These activities are in breach of Article 101 of the Treaty on the Functioning of the European Union (2009). The European Commissioner for Competition, Margrethe Vestager, stated that:
“Today’s decision sanctions three car part producers whose collusion affected component costs for a number of car manufacturers selling cars in Europe, and ultimately European consumers buying them. If European consumers are affected by a cartel, the Commission will investigate it even if the cartel meetings took place outside of Europe”
The fines imposed on the three businesses were as follows:
– Denso €0
– Hitachi €26 860 000
– Melco €110 929 000
How are these fines calculated? When calculating the size of the fine to impose on a firm the Commission takes into account a number of factors. These include:
|
|
| • |
the size of its annual sales affected by the anti-competitive activities. |
| • |
its market share. |
| • |
the geographical area of its sales. |
| • |
how long it had taken part in the cartel. |
| • |
whether it had previously been found guilty of engaging in anti-competitive practices. |
| • |
if it initiated the cartel in the first place i.e. was it the ring leader? |
In this particular case the size of the fine imposed on both Hitachi and Melco was increased because they had both previously been found guilty of breaking EU competition rules.
If a member of the cartel comes forward with information that helps the Commission with its investigation, a reduction in the size of the fine can be applied under a provision called a Leniency Notice (2006). Timing as well as the quality of the information provided influences the size of this reduction. For example, only the first firm to come forward with relevant information can receive a reduction of up to 100% i.e. obtain full immunity. This explains how Denso could be found guilty but not have to pay a fine. (This firm’s initial approach to the Commission actually triggered the investigation.) Any subsequent firms that come forward with information receive smaller fine reductions. Hitachi and Melco received reductions of 30% and 28% respectively.
If a firm accepts the Commission’s decision a further reduction of up to 10% can be applied. This is called a Settlement Notice (2008). All three firms were awarded the full 10% discount in this case.
The European Commission is currently investigating the behaviour of firms that supply car thermal systems, seatbelts and exhaust systems.
Articles
Car parts price-fixing fines for Hitachi and Mitsubishi Electric BBC News 27/01/16
EU antitrust regulators to fine Japanese car part makers: sources Tech News 26/01/16
Mitsubishi Electric and Hitachi get $150 EU cartel fine Bloomberg 27/01/16
EU fines Mitsubishi Electric, Hitachi for car part cartel Reuters 27/1/16
Questions
- What market conditions would make the formation of a cartel more likely?
- Draw a diagram to illustrate the impact of a profit maximising cartel agreement on the price, output and profit in an industry.
- Draw a diagram to illustrate the incentive that each firm has to cheat on an agreed cartel price and output.
- Why did the European Commission introduce Settlement Notices?