Category: Essential Economics for Business: Ch 06

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The merger of Vodafone and Three: competition implications

In September 2023, UK mobile phone network operators Vodafone and Three (owned by CK Hutchinson) announced their intention to merge. At the time, in terms of total revenue from the supply of mobile phone services to consumers, Vodafone and Three had market shares of 23% and 12%, respectively.

In addition to Vodaphone and Three, there are two other major network operators – the BT Group (BT & EE) and Virgin-media 02, with market shares of around 31% and 23%, respectively, with other operators having a combined market share of 12%. As we shall see below, these other operators use one of the four major networks. Therefore, the merged entity of Vodafone-Three would become the market leader with a share of around 35% and there would only be three major network operators competing in the UK.

Not surprisingly, the UK competition agency, the Competition and Markets Authority (CMA), decided to conduct a detailed investigation into whether the merger would harm competition. However, in early December 2024 the CMA announced its decision to allow the merger to go ahead, subject to several important commitments by the merging parties.

CMA’s phase 1 findings

The CMAs phase 1 investigation raised several concerns with the merger (see fifth CMA link below).

First, it was worried that retail and business customers would have to pay higher prices for mobile services after the merger.

Second, in addition to the four mobile network operators, the UK market is served by a number of mobile ‘virtual’ network operators (MVNOs), for example Sky Mobile and Lyca Mobile. As we saw above, these suppliers account for around 12% of the consumer retail market. The MVNOs do not own their own networks and instead agree wholesale terms with one of the network operators to access their network and supply their own retail mobile services. The CMA was concerned that since the merger would reduce the number of networks competing to host these MVNOs from four to three, it would result in MVNOs paying higher wholesale access prices.

Vodafone and Three did not offer any remedies to the CMA to address these competition concerns. Consequently, the CMA referred the case to phase 2 for a more thorough investigation.

CMA’s phase 2 findings

The CMA’s analysis in phase 2 confirmed its earlier concerns (see linked report below). It was still worried that because the merged entity would become the largest network operator, retail customers would face higher prices or get a poorer service – for example, a reduced data allowance in their contract. In addition, the CMA remained concerned that the MVNOs would be negatively impacted and that this would lessen their ability to offer the best deals to retail customers.

However, during the phase 2 investigation, the merging parties put forward various efficiency justifications for the merger. They argued that the merger would provide them with much needed scale and investment capacity to improve their network and roll-out 5G technology. The CMA recognised these claims but questioned the merging parties’ incentives to go through with the investment once the merger was approved. Furthermore, it was concerned that if they did invest, this would be funded by raising the prices charged to consumers.

As a result, the CMA only agreed to allow the merger once Vodafone and Three accepted remedies that would address these concerns.

The remedies necessary for the merger to proceed

First, the merged entity must cap a range of tariffs and data plans it offers in the retail market for three years.

Second, again for three years, it must commit to maintain the wholesale contract terms it offers to MNVOs.

Finally, over the next eight years, the merged entity must deliver the network upgrade plans that it claimed the merger would allow. The CMA believes that in the long run this network development would significantly boost competition between the three remaining mobile network operators.

The acceptance of remedies of this nature was unusual for the CMA. Typically, like other competition agencies, the CMA has favoured divestment remedies in which the merging parties are required to sell-off some of the assets or capacity acquired. In contrast, the remedies in the Vodafone-Three deal impact on the merging parties’ behaviour.

One clear disadvantage of such remedies is that they require the merged firm’s actions to be monitored, in this case for eight years, to make sure it adheres to the agreed behaviour. One reason why the CMA may have been willing to accept this is that the communications industries regulator, OFCOM, will be able to assist with this monitoring.

It was also surprising that the CMA was willing to allow the number of network operators to decrease to three. Previously, there had been a perception that it was important to maintain four networks. This was certainly the view in 2016 when Three’s attempted merger with O2 was prohibited. This decision was made by the European Commission (EC). However, the CMA raised serious concerns to the EC and when the merging parties offered behavioural remedies argued that these were:

materially deficient as they will not lead to the creation of a fourth Mobile Network Operator (MNO) capable of competing effectively and in the long-term with the remaining three MNOs such that it would stem the loss of competition caused by the merger.

Why has the authorities’ attitude towards the merger changed?

So why has there been a change of stance in this latest attempted merger in the mobile phone sector?

One explanation is that the market has fundamentally changed over time. The margins for network operators have declined, network usage has grown and there has been a lack of investment in expensive 5G technology. This would certainly fit with the CMA’s desire to use the remedies to facilitate network investment.

A second possible explanation is that the CMA has recently faced criticism from UK Prime Minister, Keir Starmer (see third Guardian article below). In a speech at the International Investment Summit in London in October 2024, he said that

We will rip out the bureaucracy that blocks investment and we will make sure that every regulator in this country take growth as seriously as this room does.

In response to this, the CMA has indicated that in 2025 it will review its approach to mergers, ensuring that only truly problematic mergers don’t proceed, and reconsider when behavioural remedies may be appropriate (see final CMA link below).

The CMA’s decision in the Vodafone-Three case certainly demonstrates that it is now willing to accept behavioural remedies when there is a regulator in place to support the subsequent monitoring.

It will be interesting to see how this merger affects competition in the mobile phone market and, more generally, whether the CMA starts to implement behavioural remedies more widely, especially in markets where it would have to do all the subsequent monitoring.

Articles

CMA reports, etc

Questions

  1. Why is it beneficial to have MVNOs in the market for mobile phone services?
  2. Why is it important that MVNOs have a choice of mobile networks to supply their retail mobile services?
  3. How do you think the other mobile network operators will react to the Vodafone-Three merger?
  4. Compare the relative benefits of blocking a merger with requiring merging companies to adopt certain remedies.
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Artificial intelligence in pricing: a double-edged sword?

Artificial Intelligence (AI) is transforming the way we live and work, with many of us knowingly or unknowingly using some form of AI daily. Businesses are also adopting AI in increasingly innovative ways. One example of this is the use of pricing algorithms, which use large datasets on market conditions to set prices.

While these tools can drive innovation and efficiency, they can also raise significant competition concerns. Subsequently, competition authorities around the world are dedicating efforts to understanding how businesses are using AI and, importantly, the potential risks its use may pose to competition.

How AI pricing tools can enhance competition

The use of AI pricing tools offers some clear potential efficiencies for firms, with the potential to reduce costs that can potentially translate into lower prices for consumers.

Take, for instance, industries with highly fluctuating demand, such as airlines or hotels. Algorithms can enable businesses to monitor demand and supply in real time and respond more quickly, which could help firms to respond more effectively to changing consumer preferences. Similarly, in industries which have extensive product ranges, like supermarkets, algorithms can significantly reduce costs and save resources that are usually required to manage pricing strategies across a large range of products.

Furthermore, as pricing algorithms can monitor competitors’ prices, firms can more quickly respond to their rivals. This could promote competition by helping prices to reach the competitive level more quickly, to the benefit of consumers.

How AI pricing tools can undermine competition

However, some of the very features that make algorithms effective can also facilitate anti-competitive behaviour that can harm consumers. In economic terms, collusion occurs when firms co-ordinate their actions to reduce competition, often leading to higher prices. This can happen both explicitly or implicitly. Explicit collusion, commonly referred to as illegal cartels, involves firms agreeing to co-ordinate their prices instead of competing. On the other hand, tacit collusion occurs when firms’ pricing strategies are aligned without a formal agreement.

The ability for these algorithms to monitor competitors’ prices and react to changes quickly could work to facilitate collusion, by learning to avoid price wars to maximise long-term profits. This could result in harm to consumers through sustained higher prices.

Furthermore, there may be additional risks if competitors use the same algorithmic software to set prices. This can facilitate the sharing of confidential information (such as pricing strategies) and, as the algorithms may be able to predict the response of their competitors, can facilitate co-ordination to achieve higher prices to the detriment of consumers.

This situation may resemble what is known as a ‘hub and spoke’ cartel, in which competing firms (the ‘spokes’) use the assistance of another firm at a different level of the supply chain (e.g. a buyer or supplier that acts as a ‘hub’) to help them co-ordinate their actions. In this case, a shared artificial pricing tool can act as the ‘hub’ to enable co-ordination amongst the firms, even without any direct communication between the firms.

In 2015 the CMA investigated a cartel involving two companies, Trod Limited and GB Eye Limited, which were selling posters and frames through Amazon (see linked CMA Press release below). These firms used pricing algorithms, similar to those described above, to monitor and adjust their prices, ensuring that neither undercut the other. In this case, there was also an explicit agreement between the two firms to carry out this strategy.

What does this mean for competition policy?

Detecting collusion has always been a significant challenge for the competition authorities, especially when no formal agreement exists between firms. The adoption of algorithmic pricing adds another layer of complexity to detection of cartels and could raise questions about accountability when algorithms inadvertently facilitate collusion.

In the posters and frames case, the CMA was able to act because one of the firms involved reported the cartel itself. Authorities like the CMA depend heavily on the firms involved to ‘whistle blow’ and report cartel involvement. They incentivise firms to do this through leniency policies that can offer firms reduced penalties or even complete immunity if they provide evidence and co-operate with the investigation. For example, GB eye reported the cartel to the CMA and therefore, under the CMA’s leniency policy, was not fined.

But it’s not all doom and gloom for competition authorities. Developments in Artificial Intelligence could also open doors to improved detection tools, which may have come a long way since the discussion in a blog on this topic several years ago. Competition Authorities around the world are working diligently to expand their understanding of AI and develop effective regulations for these rapidly evolving markets.

Articles

Questions

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

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

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

World Uncertainty Index

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

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

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

Uncertainty, risk-aversion and aggregate demand

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

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

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

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

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

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

Uncertainty and confidence

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

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

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

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

Conclusion

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

Academic papers

Articles

Data

Questions

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

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

Questions

  1. What is the difference between dynamic pricing and surge pricing?
  2. What is buyer’s remorse? How could dynamic pricing be used while minimising the likelihood of buyer’s remorse?
  3. 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?
  4. Distinguish between consumer and producer surplus. How may dynamic pricing lead to a reduction in consumer surplus and an increase in producer surplus?
  5. Should Ticketmaster sell tickets on the same basis as tickets for the Glastonbury Festival?
  6. Is Oasis a monopoly? What are the ticket pricing implications?
  7. Are there any industries where firms would not benefit from dynamic pricing? Explain.
  8. What are the arguments for and against allowing tickets to be sold on the secondary market for whatever price they will fetch?
  9. How powerful is Ticketmaster in the primary and secondary ticket markets?