The articles below examine the rise of the sharing economy and how technology might allow it to develop. A sharing economy is where owners of property, equipment, vehicles, tools, etc. rent them out for periods of time, perhaps very short periods. The point about such a system is that the renter deals directly with the property owner – although sometimes initially through an agency. Airbnb and Uber are two examples.
So far the sharing economy has not developed very far. But the development of smart technology will soon make a whole range of short-term renting contracts possible. It will allow the contracts to be enforced without the need for administrators, lawyers, accountants, bankers or the police. Payments will be made electronically and automatically, and penalties, too, could be applied automatically for not abiding by the contract.
One development that will aid this process is a secure electronic way of keeping records and processing payments without the need for a central authority, such as a government, a bank or a company. It involves the use of ‘blockchains‘ (see also). The technology, used in Bitcoin, involves storing data widely across networks, which allows the data to be shared. The data are secure and access is via individuals having a ‘private key’ to parts of the database relevant to them. The database builds in blocks, where each block records a set of transactions. The blocks build over time and are linked to each other in a logical order (i.e. in ‘chains’) to allow tracking back to previous blocks.
Blockchain technology could help the sharing economy to grow substantially. It could significantly cut down the cost of sharing information about possible rental opportunities and demands, and allow minimal-cost secure transactions between owner and renter. As the IBM developerWorks article states:
Rather than use Uber, Airbnb or eBay to connect with other people, blockchain services allow individuals to connect, share, and transact directly, ushering in the real sharing economy. Blockchain is the platform that enables real peer-to-peer transactions and a true ‘sharing economy’.
Article
New technology may soon resurrect the sharing economy in a very radical form The Guardian, Ben Tarnoff (17/10/16)
Blockchain and the sharing economy 2.0 IBM developerWorks, Lawrence Lundy (12/5/16)
2016 is set to become the most interesting year yet in the life story of the sharing economy Nesta, Helen Goulden (Dec 2015)
Blockchain Explained Business Insider, Tina Wadhwa and Dan Bobkoff (16/10/16)
A parliament without a parliamentarian Interfluidity, Steve Randy Waldman (19/6/16)
Blockchain and open innovation: What does the future hold Tech City News, Jamie QIU (17/10/16)
Banks will not adopt blockchain fast Financial Times, Oliver Bussmann (14/10/16)
Blockchain-based IoT project does drone deliveries using Ethereum International Business Times, Ian Allison (14/10/16)
Questions
- What do you understand by the ‘sharing economy’?
- Give some current examples of the sharing economy? What other goods or services might be suitable for sharing if the technology allowed?
- How could blockchain technology be used to cut out the co-ordinating role carried out by companies such as Uber, eBay and Airbnb and make their respective services a pure sharing economy?
- Where could blockchain technology be used other than in the sharing economy?
- How can blockchain technology not only record property rights but also enforce them?
- What are the implications of blockchain technology for employment and unemployment? Explain.
- How might attitudes towards using the sharing economy develop over time and why?
- Referring to the first article above, what do you think of Toyota’s use of blockchain to punish people who fall behind on their car payments? Explain your thinking.
- Would the use of blockchain technology in the sharing economy make markets more competitive? Could it make them perfectly competitive? Explain.
Can behavioural economics be applied to the case of Sweden? The Swedish government is trying this out by changing government policy in a way that may encourage its residents to change their behaviour.
People in many countries in the world live in what is often called a ‘throwaway society’. If something breaks, it’s often easier and cheaper simply to get rid of it and buy a new one. But with changes in government policy, including VAT cuts on repairs to white goods, the objective is to encourage consumers to repair their goods, rather than buying new ones. This is also contributing towards the wider objective of sustainable consumption, which is being promoted by the Swedish government.
Per Bolund, who is one of Sweden’s six Green Ministers, spoke about this policy commenting that:
“Consumers are quite active in changing both what they buy and how they buy in Sweden … We believe that getting lower costs for labour is a big part in making it more rational to repair rather than just to buy cheap and throw away …If we don’t change the economic incentives the change will never come.”
Whether or not this policy works will take some time to see, but it’s certainly an interesting test of how changing incentives affect consumer behaviour. You can read about other examples of nudging in the following blog A nudge in the right direction?.
Articles
Waste not want not: Sweden to give tax breaks for repairs The Guardian, Richard Orange (19/9/16)
Can Sweden tackle the throwaway society? BBC News (20/9/16)
Trendy now, trash tomorrow Huffington Post, Kirsten Brodde (29/9/16)
Hong Kong needs a strategy quickly for dealing with waste South China Morning Post (27/9/16)
Questions
- If VAT on repairs falls, how will this affect consumer behaviour?
- Do you think there would be an income and a substitution effect from this change in government policy? What would they be?
- How is the Swedish government using incentives to change consumer behaviour?
- If it is cheaper to buy a new white good, then is it rational to buy a new one rather than repair an existing one?
- How effective do you think this policy would be in encouraging consumers to change their behaviour?
- Find some other examples of how people might be nudged to behave in ways that are in their own interest or that of society.
Behavioural economists study how people’s buying, selling and other behaviour responds to various incentives and social situations. They don’t accept the simplistic notion that people are always rational maximisers. As the Livemint article below states, “According to behavioural economists, the human brain neither has the time nor the ability to process all the information involved in decision making, as assumed by the rational model.” Instead, rationality is bounded: people use simple rules of thumb in making decisions – rules they have developed over time in the light of experience.
So can people’s behaviour be altered by understanding their limited rationality? Advertisers are only too well aware of a number of psychological ‘tricks’ to change people’s purchasing behaviour. For example, wanting to be approved of by your friends is used by advertisers to sell various fashion products and toiletries. Often, people need only a relatively small ‘nudge’ to change the way they behave.
And it is not just advertisers who are using the insights of behavioural economics. Governments are increasingly trying to find ways of nudging people to behave in ways that are better for themselves or for society.
In 2010, David Cameron set up a ‘Nudge Unit’, formally know as ‘The Behavioural Insights Team‘. It has produced a number of academic papers on topics as diverse as tax compliance, incentives for university attendance, charitable giving in the workplace and using SMS reminders to reduce missed hospital appointments. The academic evidence can then be use as the basis for policy.
Another nudge unit has been set up in Australia (see second article below). The USA, Singapore and various other countries are increasingly using the insights of behavioural economics to devise policy to affect human behaviour.
Two recent pieces of work by the UK team concern ways of discouraging doctors from over-prescribing antibiotics and using encouraging text messages to FE students to reduce dropout rates. Another nudge has been used by the tax authorities (HMRC) who have been sending out texts to remind people to pay their taxes on time and to make them aware that they are being monitored. The message read, “Most people pay on time to avoid penalties”.
The articles below look at these recent initiatives and how human behaviour can be changed in a relatively low-cost way. In most cases this involves a simple nudge.
Articles
Nudge-unit trials reveal best ways to prod people Sydney Morning Herald, Nick Miller (29/8/15)
Government ‘nudge unit’ to attempt to change people’s behaviours Sydney Morning Herald, Nick Miller (15/9/16)
New frontiers of human behaviour Livemint, Biju Dominic (15/9/16)
Doctors ‘nudged’ into prescribing far fewer antibiotics New Scientist (15/9/16)
GPs handing out fewer antibiotics after warning of over-prescribing, says study BT (15/9/16)
Study of colleges shows ‘encouraging’ texts dramatically cut dropout rates FE Week, Paul Offord (22/7/15)
The text messages getting teenagers better grades BBC Today Programme, David Halpern and Fiona Morey (15/9/16)
Ping! Pay your tax now or face a penalty. HMRC sends out ‘threatening’ SMS texts to taxpayers The Telegraph, Christopher Hope (15/9/16)
Publications of Behavioural Insights Team
Publications list BIT
The Behavioural Insights Team’s Update Report: 2015–16: overview BIT (15/9/16)
The Behavioural Insights Team’s Update Report: 2015–16 BIT (15/9/16)
Blog BIT
Questions
- Explain what is meant by bounded rationality.
- Give some examples from your own behaviour of decisions made using rules of thumb.
- Should we abandon models based on the assumption of rational maximising behaviour (e.g. attempts to maximise consumer surplus or to maximise profit)?
- Find out some other examples of how people might be nudged to behave in ways that are in their own interest or that of society.
- How might people be nudged to eat more healthily or to give up smoking?
- To what extent can financial incentives, such as taxes, fines, grants or subsidies be regarded nudging? Explain.
- Why, do you think, the message by an Australian hospital, “if you attend, the hospital will not lose the $125 we lose when a patient does not turn up” was successful in reducing missed appointments by 20%, while the message, “if you do not attend, the hospital loses $125” was not as effective?
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?
The articles below examine the rise of the sharing economy and how technology might allow it to develop. A sharing economy is where owners of property, equipment, vehicles, tools, etc. rent them out for periods of time, perhaps very short periods. The point about such a system is that the renter deals directly with the property owner – although sometimes initially through an agency. Airbnb and Uber are two examples.
One development that will aid this process is a secure electronic way of keeping records and processing payments without the need for a central authority, such as a government, a bank or a company. It involves the use of ‘blockchains‘ (see also). The technology, used in Bitcoin, involves storing data widely across networks, which allows the data to be shared. The data are secure and access is via individuals having a ‘private key’ to parts of the database relevant to them. The database builds in blocks, where each block records a set of transactions. The blocks build over time and are linked to each other in a logical order (i.e. in ‘chains’) to allow tracking back to previous blocks.
