The Centre for Central Banking Studies recently hosted their annual Chief Economists Workshop, whose theme was “What can policymakers learn from other disciplines”. In this guest post, one of the keynote speakers at the event, David Halpern, CEO of the Behavioural Insights Team, argues that insights from behaviour science can improve the design and effectiveness of economic policy interventions.
Behaviour science has had major impacts on policy in recent years. Introducing a more realistic model of human behaviour – to replace the ‘rational’ utility-maximizer – has enabled policymakers to boost savings; increase tax payments; encourage healthier choices; reduce energy consumption; boost educational attendance; reduce crime; and increase charitable giving. But there remain important areas where its potential has yet to be realised, including macroeconomic policy and large areas of regulatory practice. Businesses, consumers, and even regulators are subject to similar systematic biases to other humans. These include overconfidence; being overly influenced by what others are doing; and being influenced by irrelevant information. The good news is that behavioural science offers the prospect of helping regulators address some of their most pressing issues. This includes: anticipating and addressing ‘animal spirits’ that drive bubbles or sentiment-driven slowdowns; reducing corrupt market practices; and encouraging financial products that are comprehensible to humans.
People aren’t ‘econs’
A basic tenet of classical economics is that any one pound (or dollar) is the same as any other. A large body of work, and not least that of the Nobel Prize winner Danny Kahneman, has shown that this isn’t true. In general, people behave as if a dollar lost is roughly twice as valuable as a dollar gained. Similarly, people will cross town to save $10 on a $50 shirt, but won’t bother for $10 on a $500 television.
These systematic ‘biases’ – or mental shortcuts – are often utilised by marketers, yet not by those who build econometric models or regulatory policy. Even where such biases have become well-known, economists have tended to assume that they will average out, and can be tucked away in random error terms. But there are many examples where these biases do not average out, and lead to serious consequences, most obviously in the form of ‘irrational exuberance’, or when market panic takes hold.
Biases affect key market players – including their predictions
Humans tend to be overly optimistic, particularly when it concerns our own behaviour. Smokers know that smoking is unhealthy, but think it will be other smokers who will die. Borrowers overestimate how fast they will pay back their loans or credit card bills. And business leaders overestimate their company’s future earnings and the chance that their new products will succeed.
We tend to be particularly overconfident about the level of error in our predictions. CFOs were asked to estimate the percentage return on next year’s S&P: to fill in the blanks in the sentences: “there is a 1-in-10 chance that the return is higher than ____” and “there is a 1-in-10 chance that the return is lower than ___”. In other words, they were asked to pick to range between which the market will fall 80% of the time. However, over a decade, returns only fell between these predictions about 33% of the time (see graph).
This overconfidence has significant implications for the economy. It helps explain why investors manage their portfolios too actively, believing they can outsmart the market when they can’t, or why policymakers may take excessive risks, rooted in overconfidence in their predictions.
But we can also use this example to see how behavioural science can help. If instead of asking CFOs to give a range between two numbers, they are asked to put a probability on a range of possibilities, they give estimates that prove much more realistic.
This is just one of many types of systematic bias which seem to affect ‘experts’ almost as much as the general public. For example, purchasing and selection choices are strongly affected by the introduction of otherwise irrelevant information. Would you rather have: A) a web subscription to the Economist magazine for $59, or B) a web subscription and print subscription for $125? Now imagine a third option was added to the mix: C) a print subscription alone for $125. Few choose C): why pay the same amount of money and get less? However, its addition does have an effect: adding option C makes people much more likely to choose the more expensive option B. It’s an example of many such effects – in this case a “decoy”- that shape our choices on a daily basis.
Behavioural insights are being used by policymakers
But it’s not all bad. There are now many examples showing how introducing a more realistic behavioural model can lead to dramatically better policy outcomes, and often at dramatically lower cost. Perhaps the most famous, and cross-nationally reproduced, has been the changing of defaults in pension saving. In the UK alone, more than 5 million extra people have started saving for pensions since the default changed from an opt-in, to an opt-out process, which started with large firms in 2012. Similarly in the USA, savings rates have typically leapt from around 50 to around 90 percent of eligible workers when 401k defaults change to an opt-out. This change has been popular too: even among the 1 in 10 who opt out, support for setting the default as an opt-out runs at around 75 percent. In contrast, the massive tax subsidies on which governments continue to spend tens of billions have strikingly modest effects: Raj Chetty has estimated that every $1 of tax subsidy only leads to around 1 cent of additional saving (making it a pretty good place to start if you have a large budget deficit to sort out).
The work of the UK’s Behavioural Insights Team (BIT), and more recently other government units such as the White House Social and Behavioral Sciences Team (SBST), has shown that such behaviourally based approaches can improve many outcomes. Informing late tax-payers about the fact that ‘most people pay on time’ substantially boosts payment rates – by around 15% in the UK, and much more in some parts of the world. Sending encouraging texts to further education students, especially at the end of holidays, cut drop-out rates by a third. Informing businesses that they ‘had been chosen’ to receive information about a government growth voucher increased click-through rates by more than 50 percent.
However, economic regulators, and central banks in particular, have been slower on the uptake. As Richard Thaler recently wrote:
If I were to pick the field of economics I am most anxious to see adopt behaviourally realistic approaches, it would, alas, be the field where behavioural approaches have had the least impact so far: macroeconomics. (Misbehaving, p. 349)
What would I do if I were a central banker or economic regulator?
Having decided that you should employ some behavioural scientists, what should you ask them to do? Here are ten suggestions.
- Unpack ‘animal spirits’. These arise in the minds of economic actors. We can unpack the biases behind them and do something about them – an indeed BIT is currently working on such a project.
- Design markets that work for humans. For example, add QR codes on bills so that consumers can easily extract their consumption data and make better choices.
- Shift focus from products to ‘choice engines’. Get Price Comparison sites to do the work – but make sure they stay clean.
- Trial light-touch ways to improve estimates by key economic actors. Encourage the use of estimation frames.
- Revisit corruption. Behavioural science has taught us a lot about deception – with many surprises.
- Think about ‘social trust’. It’s a better predictor of national economic growth rates than human capital, and varies greatly between countries.
- Be more aware of systematic (behavioural) bias in data. People misremember, as well as mispredict, and there’s evidence it’s getting worse in survey data.
- Promote rainy-day saving. Recent work suggests that the behavioural and economic effects of having even small amounts of saving are even larger than previously thought.
- Turn the ‘art’ of central banking into a science. We know markets respond not just to what is said, but how it is said.
- Finally, get experimental. There are many options, from designing fiscal interventions to the wording in letter. Wherever possible, we should test, learn and adapt.
Future generations may look back on current economic modelling rather like today’s medics look back on their medieval predecessors: practice based on plausible myths. The UK’s medium-term economic future, and perhaps much of the world’s, rests heavily on sentiment. This in turn rests on the mental shortcuts and social influences that affect so much of our behaviour.
Empirically based behavioural models are leading to real advances in other fields, and they need to be brought into economic regulation too.
David Halpern is the CEO of The Behavioural Insights Team.
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