Category: Essential Economics for Business: Ch 03

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From swiping right to swiping out: the economics of the dating app decline

According to Ofcom’s November 2024 Online Nation report (see report linked below), UK adults are falling out of love with dating apps. Use of the top three platforms in the UK (Tinder, Hinge, and Bumble) is declining, even though most users are juggling multiple apps at once. So, what’s going on? Economics may have some valuable insights to help explain the decline.

Too much choice

First, dating platforms don’t function like typical commodity markets, where prices adjust until supply and demand balance. Instead, dating can be seen as what economists call a ‘matching market’, where success depends on mutual interest, not on a specific price. So even with thousands of potential matches, forming actual connections remains difficult, and more choice doesn’t necessarily translate into better outcomes.

In fact, more choice can backfire. The paradox of choice, a behavioural economics concept, suggests that too many options can lead to choice paralysis. Instead of feeling empowered by an abundance of potential partners, users can feel overwhelmed, unsure, and often less satisfied with whatever choice they end up making (if they make one at all).

So, while we often think of dating apps, like many other platforms, benefiting from positive network effects, where more users increase the platform’s value by offering more potential matches, this can also have negative effects. Swiping through endless profiles and repeating the same small talk, can turn dating into a chore rather than an exciting opportunity.

Adverse selection

What makes this even harder is that users can’t easily distinguish between who’s genuinely looking for the same thing you are, and who’s just there to pass the time. This information asymmetry leads to the adverse selection problem – a concept famously explored by economist George Akerlof in his 1970 paper ‘The Market for Lemons’ (see link below). He showed how lack of information about product quality can cause high-quality sellers to exit, resulting in market failure where the market becomes dominated by low-quality goods (i.e. ‘lemons’).

A similar dynamic can play out on dating apps. If users believe most profiles are unserious or not genuine, they become less willing to engage, or even stay on the platform. Meanwhile, the most genuine users may give up altogether, worsening the quality of the pool and discouraging others.

In economics, there are some well-known ways in which the problem of adverse selection could be overcome. One such possibility is through signalling, where the more informed person tries to reveal important information to the uninformed person. Indeed, platforms have experimented with signalling mechanisms, like verification tools for example. Paid subscriptions have also been implemented, which could help to some extent (assuming that those who are willing to pay are those who are genuine and serious about finding a match). But these solutions only go so far, and with fewer users paying to signal intent, the problem persists.

Lack of innovation

This ties into the wider revenue model of dating apps. Unlike many apps that rely on revenue from advertising on one side of the market to offer the app free to consumers on the other side, dating platforms often rely more on revenue through monthly subscriptions and paid upgrades. But with fewer users willing to pay, these platforms may be under pressure. This financial pressure may also affect their ability to innovate or improve the service.

In fact, in the dating app world, there is another reason why platforms may not be innovating as much as they should, aside from simply trying to convince their users to pay for a better service. While it seems like there’s endless choice in the dating app world, much of the market is controlled by a single company, InterActiveCorp (IAC), which owns Tinder, Hinge, Match.com and more. With limited competition, there’s less incentive to compete on quality.

Worse still, dating apps face a unique business problem: if their service works too well, users leave and delete the app. So, there may be a built-in tension between helping users succeed and keeping them swiping.

The outlook for dating apps

So, is the decline in dating app use just temporary, or the start of something bigger? Time will tell. However, from an economics perspective, there is a noticeable shift in demand towards substitutes, such as organised in-person social events and activities, which encourages more and more of these opportunities to emerge. This shift may reflect changing preferences and the costs (in terms of time and emotional energy) that users are willing to invest in online dating.

At the same time, AI already plays a key role in dating apps, and new possibilities seem to be emerging. For example, we could see a bigger rollout of AI-driven chatbots that facilitate conversations or even interact on behalf of users. This could make it easier to connect with potential matches and might help in addressing some of the other issues discussed above.

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Questions

  1. How might ‘signalling’ and ‘screening’ be used to create new features or services that could help overcome the adverse selection problem in this market?
  2. Can you think of any other ways in which the adverse selection problem could be overcome in this context?
  3. Draw a diagram to illustrate the two-sided nature of the dating app market, making clear where there may be positive or negative network effects.
  4. How else might dating app platforms be making revenue that allows them to offer the app to users at no charge?
  5. Is the dating app market competitive? You might consider factors such as the availability of substitutes, barriers to entry and innovation.
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Why are Tesla sales plummeting?

Tesla sales have fallen dramatically recently. In Europe they were down 47.7% in January 2025 compared with January 2024. In Spain the figure was 75.4%, in France 63.4%, in Germany 59.5%, in Sweden 44.3%, in Norway 37.9%, in the UK 18.2% and in Italy 13.4%. And it was not just Europe. In Australia the figure was 33.2%, in China 15.5% and in California 11.6%. Meanwhile, Tesla’s share price has fallen from a peak of $480 on 17 December 2024 to $338 on 21 February 2025, although that compares with $192 in February 2024.

So why have Tesla sales fallen? It’s not because of a rise in price (a movement up the demand curve); indeed, Tesla cut its prices in 2024. Part of the reason is on the supply side. In several countries, stocks of Teslas are low. Some consumers who would have bought have had to wait. However, the main reason is that the demand curve has shifted to the left. So why has this happened?

A reaction to Elon Musk?

One explanation is a growing unpopularity of Elon Musk among many potential purchasers of electric vehicles (EVs). People are more likely to buy an EV if they are environmentally concerned and thus more likely to be Green voters or on the political left and centre. Elon Musk, by supporting Donald Trump and now a major player in the Trump administration, is seen as having a very different perspective. Trump’s mantra of ‘drill, baby drill’ and his announced withdrawal from the Paris agreement and the interventions of Trump, Vance and Musk in European politics have alienated many potential purchasers of new Teslas. Elon Musk has been a vocal supporter of the right-wing Alternative for Germany (AfD) party, describing the party as the ‘last spark of hope for this country’ (see BBC article linked below).

There has been outspoken criticism of Musk in the media and the Financial Times reports existing owners of Teslas, who are keen to distance themselves from Musk, ordering stickers for their cars which read ‘I bought this before Elon went crazy’. In a survey by Electrifying.com, 59% of UK potential EV buyers stated that Musk’s reputation put them off buying a Tesla.

Other reasons for a leftward shift in the demand for Teslas

But is it just the ‘Musk factor’ that has caused a fall in demand? It is useful to look at the general determinants of demand and see how each might have affected the demand for Teslas.

The price, number, quality and availability of substitutes  Tesla faces competition, not only from long-established car companies, such as Ford, VW, Volvo/Polestar, Seat/Cupra and Toyota, moving into the EV market, but also from Chinese companies, such as BYD and NIO. These are competing in all segments of the EV market and competition is constantly increasing. Some of these companies are competing strongly with Tesla in terms of price; others in terms of quality, style and imaginative features. The sheer number of competitor models has grown rapidly. For some consumers, Teslas now seem dated compared with competitors.

The price and availability of complements.  The most relevant complement here is electrical charging points. As Teslas can be charged using both Tesla and non-Tesla charging points, there is no problem of compatibility. The main issue is the general one for all EVs and that is how to achieve range conveniently. The fewer the charging points and more widely disbursed they are, the more people will be put off buying an EV, especially if they are not able to have a charging point at home. Clearly, the greater the range of a model (i.e. the distance that can be travelled on a full battery), the less the problem. Teslas have a relatively high range compared with most (but not all) other makes and so this is unlikely to account for the recent fall in demand, especially relative to other makes.

Expectations.  The current best-selling Tesla EV is the Model Y. This model is being relaunched in a very different version, as are other Tesla models. Consumers may prefer to wait until the new models become available. In the meantime, demand would be expected to fall.

Conclusions

As we have seen, there have been a number of factors adversely affecting Tesla sales. Growing competition is a major factor. Nevertheless, the increasing gap politically between Elon Musk and many EV consumers is a major factor – a factor that is likely to grow in significance if Musk’s role in the Trump administration continues to be one of hostility towards the liberal establishment and in favour of the hard right.

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Questions

  1. Why have BYD EV sales risen so rapidly?
  2. If people feel strongly about a product on political or ethical grounds, how is that likely to affect their price elasticity of demand for the product?
  3. Find out how Tesla shareholders are reacting to Elon Musk’s behaviour.
  4. Find out how Tesla sales have changed among (a) Democratic voters and (b) Republican voters in the USA. How would you explain these trends?
  5. Identify some products that you would or would not buy on ethical grounds. How carefully have you researched these products?
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Hybrid working: too much homework or the ‘new normal’?

During the pandemic, most people who were not furloughed were forced to work from home. After lockdown restrictions were lifted, many employers decided to continue with people working remotely, at least for some of the time.

Today, this hybrid model, whereby workers work partly from home or local workspaces and partly in the office/factory/warehouse etc., has become the ‘new normal’ for around 26% of the working population in Great Britain – up from around 10% at the end of the national lockdowns in the Spring of 2021.

Increasingly, however, employers who had introduced hybrid working are requiring their employees to return to the office, arguing that productivity and hence profits will rise as a result. Amazon is an example. Other employers, such as Asda, are increasing the time required in the office for hybrid workers.

Hybrid working had peaked at around 31% in November 2923 as the chart shows (click here for a PowerPoint). The chart is based on the December 20 database, Public opinions and social trends, Great Britain: working arrangements from the Office for National Statistics (see link under Data, below).

But why are some employers deciding that hybrid working is less profitable than working full time in the office. And does it apply to all employers and all employees or only certain types of firm and certain types of job?

The first thing to note is that hybrid work is more common among certain groups. These include older workers, parents, graduates and those with greater flexibility in scheduling their work, especially those in managerial or professional roles with greater flexibility. Certain types of work on the other hand do not lend themselves to hybrid work (or working completely from home, for that matter). Shop workers and those providing a direct service to customers, such as those working in the hospitality sector, cannot work remotely.

Benefits of hybrid working

For some employees and employers, hybrid working has brought significant benefits.

For employees, less time and money is spent on commuting, which accounts for nearly an hour’s worth of the average worker’s daily time. According to the ONS survey, respondents spent an additional 24 minutes per day on sleep and rest and 15 minutes on exercise, sports and other activities that improved well-being compared to those who worked on-site. Working at home can make juggling work and home life easier, especially when workers can work flexible hours during the day, allowing them to fit work around family commitments.

Employers benefit from a healthier and more motivated staff who are more productive and less likely to quit. Hybrid work, being attractive to many workers, could allow employers to attract and retain talented workers. Also, employees may work longer hours if they are keen to complete a task and are not ‘clocking off’ at a particular time. Working from home allows workers to concentrate (unless distracted by other family members!).

By contrast, office working can be very inefficient, especially in open offices, where chatty colleagues can be distracting and it is difficult to concentrate. What is more, employees who are slightly unwell may continue working at home but may feel unable to commute to the office. If they did, they could spread their illness to other colleagues. Not allowing people to work from home can create a problem of ‘presenteeism’, where people feeling under the weather turn up to work but are unproductive.

One of the biggest benefits to employers of hybrid work is that costs can be saved by having smaller offices and by spending less on heating, lighting and facilities.

With hybrid working, time spent on site can be devoted to collaborative tasks, such as meetings with colleagues and customers/suppliers and joint projects where face-to-face discussion is required, or at least desirable. Tasks can also be completed that required specialist equipment or software not available at home.

Problems of hybrid working

So, if hybrid working has benefits for both employers and employees, why are some employers moving back to a system where employees work entirely on site?

Some employers have found it hard to monitor and engage employees working from home. Workers may be easily distracted at home by other family members, especially if they don’t have a separate study/home office. People may feel detached from their co-workers on days they work from home. After a time, productivity may wane as workers find ways of minimising the amount of time actually working during declared work times.

Far from improving work-life balance, for some workers the boundaries between work and personal life can become blurred, which can erode the value of personal and family time. This can create a feeling of never escaping from work and be demotivating and reduce productivity. Employees may stay logged on longer and work evenings and weekends in order to complete tasks.

Unless carefully planned, on days when people do go into the office they might not work effectively. They may be less likely to have profitable ad hoc conversations with co-workers, and meetings may be harder to arrange. Misunderstandings and miscommunication can occur when some employees are in the office but others are at home.

Some employers have found that the problems of hybrid working in their organisations have outweighed the benefits and that productivity has fallen. In justifying its ending of hybrid working from 1 January 2025, Amazon CEO, Andy Jessy, wrote in a memo to staff in September 2024:

To address the … issue of being better set up to invent, collaborate, and be connected enough to each other and our culture to deliver the absolute best for customers and the business, we’ve decided that we’re going to return to being in the office the way we were before the onset of COVID. When we look back over the last five years, we continue to believe that the advantages of being together in the office are significant.1

But is the solution to do as Amazon is doing and to abandon hybrid working and have a mass ‘return to the office’?

Improving hybrid working

There are ways of making hybrid working more effective so that the benefits can be maximised and the costs minimised.

Given that there are specific benefits from home working and other specific benefits from working on-site, it would be efficient to allocate time between home and office to maximise these benefits. The optimum balance is likely to vary from employer to employer, job to job and individual to individual.

Where work needs to be done in teams and where team meetings are an important element of that work, it would generally make sense for such meetings to be held in person, especially when there needs to be a lot of discussion. If the team requires a brief catch up, however, this may be more efficiently done online via Teams or Zoom.

Individual tasks, on the other hand, which don’t require consultation with colleagues or the use of specific workplace facilities, are often carried out more efficiently when there is minimum chance of interruption. For many workers, this would be at home rather than in an office – especially an open-plan office. For others without a protected work space at home or nearby, it might be better to come into the office.

The conclusion is that managers need to think carefully about the optimum distribution between home and office working and accept that a one-size-fits-all model may not be optimum for all types of job and all workers. Recognising the relative benefits and costs of working in different venues and over different hours may help to achieve the best balance, both for employers and for workers. A crucial element here is the appropriate use of incentives. Workers need to be motivated. Sometimes this may require careful monitoring, but often a more hands-off approach by management, with the focus more on output and listening to the concerns of workers, rather than on time spent, may result in greater productivity.

1Message from CEO Andy Jassy: Strengthening our culture and teams, Amazon News (16/9/24)

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Questions

  1. Why may hybrid working be better for (a) employees and (b) employers than purely home working or purely working in the office?
  2. Why are many firms deciding that workers who were formerly employed on a hybrid basis should now work entirely from the office?
  3. What types of job are better performed on site, or with only a small amount of time working from home?
  4. What types of job are better performed by working at home with just occasional days in the office?
  5. Does the profile of workers (by age, qualifications, seniority, experience, family commitments, etc) affect the likelihood that they will work from home at least some of the time?
  6. How would you set about measuring the marginal productivity of a worker working from home? Is it harder than measuring the marginal productivity of the same worker doing the same job but working in the office?
  7. How may working in the office increase network effects?
  8. How may behavioural economics help managers to understand the optimum balance of home and on-site working?
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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.

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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.

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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?