As John reminds us in his blog A seven year emergency we have now seen the official Bank Rate at 0.5 per cent for the past seven years. Understandably many attribute the financial crisis that led to the easing of monetary policy to the lending practices of commercial banks. Consequently, it is important that we better understand (and monitor) banks’ behaviour. Some argue that these practices are affected by the macroeconomic environment, with credit conditions varying across the business cycle. We consider here what recent patterns in interest rates might tell us about credit conditions.
One way in the macroeconomic environment might affect commercial banks’ lending practices is through the difference between banks’ lending rates and the official Bank Rate. We can think of such interest rate differentials – or spreads – as a credit premium. In other words, the greater are commercial borrowing rates relative to the Bank Rate, the greater the credit premium being demanded by banks. On the other hand, the lower the interest rate on borrowing relative to the Bank Rate, the smaller the credit premium.
Some economists argue that interest-rate differentials will fall when the economy is doing well and increase when the economy is doing less well. This is because the probability of default by borrowers is seen as smaller when the macroeconomic environment improves. If this is the case, it will tend to amplify the business cycle, since economic shocks will have larger affects on economic activity.
Consider a positive demand-side shock, such as a rise in consumer confidence, which lowers the propensity of households to save. As the positive shock causes the economy’s aggregate demand to rise, the economy grows. This growth in economic activity might result in lower borrowing rates offered by commercial banks relative to the official Bank Rate. Since savings rates tend to be close to the official Bank Rate, this also means that the cost of borrowing falls relative to the interest rates on savings. This financial effect further stimulates the demand for credit and, as a consequence, aggregate demand and economic activity. It is an example of what economists called the financial accelerator.
Similarly, the financial accelerator means that negative shocks depress economic activity by more than would otherwise be the case. A fall in consumer confidence, for example, would cause economic activity to fall as aggregate demand weakens. This, in turn, causes banks to raise borrowing rates relative to the Bank Rate and savings rates. This further dampens economic activity.
The chart shows the Bank Rate along with the average unsecured borrowing rate on loans by Monetary Financial Institutions (MFIs) of £10 000. (Secured borrowing is that which is secured against property.) We use this borrowing rate to capture general trends in commercial borrowing rates.
As expected, we can see that the borrowing rate is greater than the Bank Rate. In other words, there is a positive interest-rate differential. However, this differential is seen to vary. It falls sharply in the period up to the financial crisis. In early 2002 it was running at 8 percentage points. By summer 2007 the differential had fallen to only 1.7 percentage points. (Click here to download a PowerPoint of the chart.)
The period from 2002 to 2007 was characterised by consistently robust growth. The UK economy grew over this period by about 2.7 per cent per annum. This would certainly fit with the story that economic growth may have contributed to an easing of credit conditions which, in turn, helped to induce growth. Regardless, the falling interest-rate differential points to credit conditions easing.
The story from 2008 changes very quickly as the interest-rate differential increases very sharply. In 2009, as the official Bank Rate was cut to 0.5 per cent, the unsecured borrowing rate climbed to close to 10.5 per cent. Consequently, the interest-rate differential rose to 10 percentage points. Inter-bank lending had dried up with banks concerned that banks would default on loans. The increase in interest rates on lending to the non-bank private sector was stark and evidence of a credit market disruption.
The interest-rate differential has steadily declined since its peak at the end of 2009 as the unsecured borrowing rate has fallen. Hence credit conditions have eased. In fact, in February 2016 our indicative interest rate differential stood at 3.8 percentage points, unchanged from its level in January. This is its lowest level since July 2008. Furthermore, today’s differential is lower than the 6.5 percentage point average over the period from 1997 to 2003, before the differential then went on its pre-crisis fall.
Given concerns about the impact of credit cycles on the macroeconomy we can expect the authorities to keep a very keen eye on credit conditions in the months ahead.
Articles
Bank holds UK interest rates at 0.5% BBC News (17/3/16)
UK’s record low interest rates to continue in 2016 The Guardian, Katie Allen (3/3/16)
Big rise in consumer credit in January BBC News, Brian Milligan (29/2/16)
Household debt binge has no end in sight, says OBR The Telegraph, Szu Ping Chan (17/3/16)
Data
Bankstats (Monetary and Financial Statistics) – Latest Tables Bank of England
Statistical Interactive Database – interest and exchange rates data Bank of England
Questions
- Why would we expect banks’ borrowing rates to be higher than the official Bank Rate?
- What factors might lead to a change in the interest-rate differential between banks’ borrowing rates and the official Bank Rate?
- How would we expect a credit market disruption to affect the interest-rate differential?
- Explain how the financial accelerator affects the change in the size of the economy following a positive demand shock.
- Explain how the financial accelerator affects the change in the size of the economy following a negative demand shock.
- What is the impact of the financial accelerator of the amplitude of the business cycle?
- How might banks’ credit criteria change as the macroeconomic environment changes?
- How might regulators intervene to minimise the effect of the financial accelerator?
When people think about healthcare in the UK they tend to associate it with the NHS. However, there is a £5 billion private healthcare market. Concerns have been expressed about the lack of effective competition in this sector and it has been investigated by the competition authorities over a 5-year period.
Approximately 4 million people in the UK have a private medical insurance policy. The majority of these are paid for by employers, although some people pay directly. Four companies dominate the health insurance market (AXA PPP, Bupa, Pru Health and Aviva) with a combined market share of over 90%.
Health insurance companies purchase healthcare services for their policy holders from private hospitals. The majority of private hospitals in the UK are owned by the following businesses – BMI, HCA, Nuffield, Ramsey and Spire. Some concerns have been expressed about the lack of competition between private hospitals in some areas of the country.
After its initial analysis into the sector, the Office of Fair Trading (OFT) referred the case to the Competition Commission (CC) in April 2012 to carry out a full market investigation. This process was then taken over by the Competition and Markets Authority (CMA) when it replaced the OFT and CC. The final report was published on April 2nd 2014.
One specific region that was identified in this report as having a lack of effective competition was central London for patients with health insurance. In particular it was concluded that:
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The market in central London was heavily concentrated and HCA had a dominant market position – its aggregated share of admissions across 16 specialities (e.g. Oncology, Cardiology, Neurology, Dermatology etc.) was 45% to 55%. |
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There were significant barriers to entry including substantial sunk costs. A particular issue for a new entrant or existing business was the problem of securing suitable sites in central London to build new hospitals and in obtaining planning permission. It was pointed out in the report that the market structure in central London had changed very little in the previous 10 years despite a rapidly growing demand for private healthcare. |
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HCA was charging insured patients higher prices for similar treatments than its leading rival – The London Clinic. HCA was also found to be making returns that were in excess of the cost of capital. |
One of the key recommendations of the report was that HCA should be forced to sell–off one or two of the hospitals that it owned in central London to increase the level of competition.
Unsurprisingly HCA was very unhappy with the decision and applied to the Competition Appeal Tribunal (CAT) for a review of the case. During this review, economists working for HCA found errors with the analysis carried out by the CMA into the pricing of health services for insured customers.
In January 2015 the CAT concluded that the findings and recommendations of the report on insured patients in central London should be overturned and the CMA should reconsider the case. In November 2015 the CMA announced that having reviewed the case it had come to a similar set of conclusions: i.e. there was a lack of effective competition and HCA should be forced to sell off two of its hospitals in London.
HCA still claimed that the pricing analysis was incorrect because it did not fully take into account that HCA treated patients with more complex conditions than TLC and that was why their prices were higher.
On March 22nd 2016 the CMA announced that it had reversed its ruling and HCA would no longer be expected to sell off any of its hospitals. The reason given for this change in recommendation was the appearance of new entrants into the market. For example, Cleveland Clinic a US-based private healthcare provider has purchased a long-term lease on a property in Belgravia, central London. It plans to convert the office space into a private hospital with 2015 beds.
A spokesperson for Bupa commented that:
“The CMA has confirmed again that there isn’t enough competition in central London, with HCA dominating the private hospital market and charging higher prices. We ask the CMA to act now to address this gap.”
It will be interesting to see the impact these new entrants have on the market in the future.
Articles
London develops as a global healthcare hub Financial Times Gill Plimmer (31/01/16)
Competition watchdog reverses ruling on private hospitals Financial Times Gill Plimmer, (22/03/16)
CMA’s private healthcare provisional decision on remedies CMA 22/03/16
Competition problems provisionally found in private healthcare CMA 10/11/15
CMA welcomes Court of Appeal verdict in private healthcare case CMA 21/05/15
Questions
- Define sunk costs using some real-world examples.
- Why might the existence of sunk costs create a barrier to entry?
- Draw a diagram to illustrate why a profit-maximising business with significant market power might charge higher prices than one in a very competitive environment.
- What is the cost of capital? Explain why returns that are greater than the cost of capital might be evidence that a firm is making excessive profits.
- Draw a diagram to illustrate the impact of new entrants in a market.
A number of famous Business Schools in the UK and US such as MIT Sloan, NYU Stern and Imperial College have launched new programmes in business analytics. These courses have been nicknamed ‘Big Data finishing school’. Why might qualifications in this area be highly valued by firms?
Employees who have the skills to collect and process Big Data might help firms to successfully implement a pricing strategy that approaches first-degree price discrimination.
First-degree price discrimination is where the seller of a product is able to charge each consumer the maximum price he or she is prepared to pay for each unit of the product. Successfully implementing this type of pricing strategy could enable a firm to make more revenue. It might also lead to an increase in economic efficiency. However, the strategy might be opposed on equity grounds.
In reality, perfect price discrimination is more of a theoretical benchmark than a viable pricing strategy. Discovering the maximum amount each of its customers is willing to pay is an impossible task for a firm.
It may be possible for some sellers to implement a person-specific pricing strategy that approaches first-degree price discrimination. Firms may not be able to charge each customer the maximum amount they are willing to pay but they may be able to charge different prices that reflect customers’ different valuations of the product.
How could a firm go about predicting how much each of its customers is willing to pay? Traditionally smaller sellers might try to ‘size up’ a customer through individual observation and negotiation. The clothes people wear, the cars they drive and their ethnicity/nationality might indicate something about their income. Second-hand car dealers and stall-holders often haggle with customers in an attempt to personalise pricing. The starting point of these negotiations will often be influenced by the visual observations made by the seller.
The problem with this approach is that observation and negotiation is a time-consuming process. The extra costs involved might be greater than the extra revenue generated. This might be especially true for firms that sell a large volume of products. Just imagine how long it would take to shop at a supermarket if each customer had to haggle with a member of staff over each item in their supermarket trolley!! There is also the problem of designing compensation contracts for sales staff that provide appropriate incentives.
However the rise of e-commerce may lead to a very different trading environment. Whenever people use their smart phones, laptops and tablets to purchase goods, they are providing huge amounts of information (perhaps unconsciously) to the seller. This is known as Big Data. If this information can be effectively collected and processed then it could be used by the seller to predict different customers’ willingness to pay.
Some of this Big Data provides information similar to that observed by sellers in traditional off-line transactions. However, instead of visual clues observed by a salesperson, the firm is able to collect and process far greater quantities of information from the devices that people use.
For example, the Internet Protocol (IP) address could be used to identify the geographical location of the customer: i.e. do they live in a relatively affluent or socially deprived area? The operating system and browser might also indicate something about a buyer’s income and willingness to pay. The travel website, Orbitz, found that Apple users were 40 per cent more likely to book four or five star hotel rooms than customers who used Windows.
Perhaps the most controversial element to Big Data is the large amount of individual-level information that exists about the behaviour of customers. In particular, browsing histories can be used to find out (a) what types of goods people have viewed (b) how long they typically spend on-line and (c) their previous purchase history. This behavioural information might accurately predict price sensitivity and was never available in off-line transactions.
Interestingly, there has been very little evidence to date that firms are implementing personalised pricing on the internet. One possible explanation is that effective techniques to process the mass of available information have not been fully developed. This would help to explain the growth in business analytics courses offered by universities. PricewaterhouseCoopers recently announced its aim to recruit one thousand more data scientists over the next two years.
Another possible explanation is that firms fear a backlash from customers who are deeply opposed to this type of pricing. In a widely cited survey of consumers, 91% of the respondents believed that first-degree price discrimination was unfair.
Articles
Big data is coming for your purchase history – to charge you more money The Guardian, Anna Bernasek and DT Mongan (29/5/15)
Big data is an economic justice issue, not just a Privacy Problem The Huffington Post, Nathan Newman (16/5/15)
MIT’s $75,000 Big Data finishing school (and its many rivals) Financial Times, Adam Jones (20/3/16)
The Government’s consumer data watchdog New York Times, Natasha Singer (23/5/2015)
The economics of big data and differential pricing The Whitehouse blog, Jason Furman, Tim Simcoe (6/2/2015)
Questions
- Explain the difference between first- and third-degree price discrimination.
- Using an appropriate diagram, explain why perfect price discrimination might result in an economically more efficient outcome than uniform pricing.
- Draw a diagram to illustrate how a policy of first-degree price discrimination could lead to greater revenue but lower profits for a firm.
- Why would it be so difficult for a firm to discover the maximum amount each of its customers was willing to pay?
- Explain how the large amount of information on the individual behaviour of customers (so-called Big Data) could be used to predict differences in their willingness to pay.
- What factors might prevent a firm from successfully implementing a policy of personalised pricing?
In many cases, we simply leave the market to do what it does best – equate demand with supply and from this we get an equilibrium price and the optimal quantity. But, what happens if either the price or quantity is ‘incorrect’? What happens if the market fails to deliver an efficient outcome? In this case, we look to governments to intervene and ‘correct’ the market and such intervention can take place on the demand and/or supply-side. One area where it is generally felt that government intervention is needed is drugs and the trafficking of them across borders.
There are many ways in which governments have tried to tackle the problem of drug usage. The issue is that drugs are bad for individuals, for the community, society and the economy. Too much is produced and consumed and hence we have a classic case of market failure and this justifies government intervention.
But, how should governments intervene? With a substance such as drugs, we have an inelastic demand with resepect to price – any increase in price leads to only a small decrease in quantity. So any policy implemented by governments that attempts to change the market price will have limited effect in restricting demand. With globalisation, drugs can be moved more easily across borders and hence global co-operation is needed to restrict the flow. The article below considers the area of drugs and drug trafficking and looks at some of the policy options open to government.
Narconomics: The business of drug trafficking Houston Chronicle (16/3/16)
Questions
- Why does the market fail in the case of drug trafficking?
- Draw the demand curve you would expect for drugs and use this to explain why an increase in price will have limited effect on demand.
- Is there an argument for making drugs legal as a means of raising tax revenue?
- If better educational programmes are introduced about the perils of drug usage, how would this affect the market? Use a demand and supply diagram to help explain your answer.
- Why does globalisation make the solutions to drug trafficking more difficult to implement?
- Could drug usage and drug trafficking and hence the need to invest more money in tackling the problem actually boost an economy’s rate of growth? If so, does this mean that we should encourage drug usage?
Back in October, we looked at the growing pressure in the UK for a sugar tax. The issue of childhood obesity was considered by the Parliamentary Health Select Committee and a sugar tax, either on sugar generally, or specifically on soft drinks, was one of the proposals being considered to tackle the problem. The committee studied a report by Public Health England, which stated that:
Research studies and impact data from countries that have already taken action suggest that price increases, such as by taxation, can influence purchasing of sugar sweetened drinks and other high sugar products at least in the short-term with the effect being larger at higher levels of taxation.
In his Budget on 16 March, the Chancellor announced that a tax would be imposed on manufacturers of soft drinks from April 2018. This will be at a rate of 18p per litre on drinks containing between 5g and 8g of sugar per 100ml, such as Dr Pepper, Fanta and Sprite, and 24p per litre for drinks with more than 8g per 100ml, such as Coca-Cola, Pepsi and Red Bull.
Whilst the tax has been welcomed by health campaigners, there are various questions about (a) how effective it is likely to be in reducing childhood obesity; (b) whether it will be enough or whether other measures will be needed; and (c) whether it is likely to raise the £520m in 2018/19, falling to £455m by 2020/21, as predicted by the Treasury: money the government will use for promoting school sport and breakfast clubs.
These questions are all linked. If demand for such drinks is relatively inelastic, the drinks manufacturers will find it easier to pass the tax on to consumers and the government will raise more revenue. However, it will be less effective in cutting sugar consumption and hence in tackling obesity. In other words, there is a trade off between raising revenue and cutting consumption.
This incidence of tax is not easy to predict. Part of the reason is that much of the market is a bilateral oligopoly, with giant drinks manufacturers selling to giant supermarket chains. In such circumstances, the degree to which the tax can be passed on depends on the bargaining strength and skill of both sides. Will the supermarkets be able to put pressure on the manufacturers to absorb the tax themselves and not pass it on in the wholesale price? Or will the demand be such, especially for major brands such as Coca-Cola, that the supermarkets will be willing to accept a higher price from the manufacturers and then pass it on to the consumer?
Then there is the question of the response of the manufacturers. How easy will it be for them to reformulate their drinks to reduce sugar content and yet still retain sales? For example, can they produce a product which tastes like a high sugar drink, 
but really contains a mix between sugar and artificial sweeteners – effectively a hybrid between a ‘normal’ and a low-cal version? How likely are they to reduce the size of cans, say from 330ml to 300ml, to avoid raising prices?
The success of the tax on soft drinks in cutting sugar consumption depends on whether it is backed up by other policies. The most obvious of these would be to impose a tax on sugar in other products, including cakes, biscuits, low-fat yoghurts, breakfast cereals and desserts, and also many savoury products, such as tinned soups, ready meals and sauces. But there are other policies too. The Public Health England report recommended a national programme to educate people on sugar in foods; reducing price promotions of sugary food and drink; removing confectionery or other sugary foods from end of aisles and till points in supermarkets; setting broader and deeper controls on advertising of high-sugar foods and drinks to children; and reducing the sugar content of the foods we buy through reformulation and portion size reduction.
Articles
- Sugar tax: How it will work?
BBC News, Nick Triggle (16/3/16)
- Will a sugar tax actually work?
The Guardian, Alberto Nardelli and George Arnett (16/3/16)
- Coca-Cola and other soft drinks firms hit back at sugar tax plan
The Guardian, Sarah Butler (17/3/16)
- Sugar tax could increase calories people consume, economic experts warn
The Telegraph, Kate McCann, and Steven Swinford (17/3/16)
- Nudge, nudge! How the sugar tax will help British diets
Financial Times, Anita Charlesworth (18/3/16)
- Is the sugar tax an example of the nanny state going too far?
Financial Times (19/3/16)
- Government’s £520m sugar tax target ‘highly dubious’, analysts warn
The Telegraph, Ben Martin (17/3/16)
- Sorry Jamie Oliver, I’d be surprised if sugar tax helped cut obesity
The Conversation, Isabelle Szmigin (17/3/16)
- Sugar sweetened beverage taxes
What Works for Health (17/12/15)
Questions
- What determines the price elasticity of demand for sugary drinks in general (as opposed to one particular brand)?
- How are drinks manufacturers likely to respond to the sugar tax?
- How are price elasticity of demand and supply relevant in determining the incidence of the sugar tax between manufacturers and consumers? How is the degree of competition in the market relevant here?
- What is meant by a socially optimal allocation of resources?
- If the current consumption of sugary drinks is not socially optimal, what categories of market failure are responsible for this?
- Will a sugar tax fully tackle these market failures? Explain.
- Is a sugar tax progressive, regressive or proportional? Explain.
- Assess the argument that the tax on sugar in soft drinks may actually increase the amount that people consume.
- The sugar tax can be described as a ‘hypothecated tax’. What does this mean and is it a good idea?
- Compare the advantages and disadvantages of a tax on sugar in soft drinks with (a) banning soft drinks with more than a certain amount of sugar per 100ml; (b) a tax on sugar; (c) a tax on sugar in all foods and drinks.
