Tag: cumulative causation

It is impossible to make both precise and accurate forecasts of a country’s rate of economic growth, even a year ahead. And the same goes for other macroeconomic variables, such as the rate of unemployment or the balance of trade. The reason is that there are so many determinants of these variables, such as political decisions or events, which themselves are unpredictable. Economics examines the effects of human interactions – it is a social science, not a natural science. And human behaviour is hard to forecast.

Leading indicators

Nevertheless, economists do make forecasts. These are best estimates, taking into account a number of determinants that can be currently measured, such as tax or interest rate changes. These determinants, or ‘leading indicators’, have been found to be related to future outcomes. For example, surveys of consumer and business confidence give a good indication of future consumer expenditure and investment – key components of GDP.

Leading indicators do not have to be directly causal. They could, instead, be a symptom of underlying changes that are themselves likely to affect the economy in the future. For example, changes in stock market prices may reflect changes in confidence or changes in liquidity. It is these changes that are likely to have a direct or indirect causal effect on future output, employment, prices, etc.

Macroeconomic models show the relationships between variables. They show how changes in one variable (e.g. increased investment) affect other variables (e.g. real GDP or productivity). So when an indicator changes, such as a rise in interest rates, economists use these models to estimate the likely effect, assuming other things remain constant (ceteris paribus). The problem is that other things don’t remain constant. The economy is buffeted around by a huge range of events that can affect the outcome of the change in the indicator or the variable(s) it reflects.

Forecasting can never therefore be 100% accurate (except by chance). Nevertheless, by carefully studying leading indicators, economists can get a good idea of the likely course of the economy.

Leading indicators of the US economy

At the start of 2019, several leading indicators are suggesting the US economy is likely to slow and might even go into recession. The following are some of the main examples.

Political events. This is the most obvious leading indicator. If decisions are made that are likely to have an adverse effect on growth, a recession may follow. For example, decisions in the UK Parliament over Brexit will directly impact on UK growth.

As far as the USA is concerned, President Trump’s decision to put tariffs on steel and aluminium imports from a range of countries, including China, the EU and Canada, led these countries to retaliate with tariffs on US imports. A tariff war has a negative effect on growth. It is a negative sum game. Of course, there may be a settlement, with countries agreeing to reduce or eliminate these new tariffs, but the danger is that the trade war may continue long enough to do serious damage to global economic growth.

But just how damaging it is likely to be is impossible to predict. That depends on future political decisions, not just those of the recent past. Will there be a global rise in protectionism or will countries pull back from such a destructive scenario? On 29 December, President Trump tweeted, ‘Just had a long and very good call with President Xi of China. Deal is moving along very well. If made, it will be very comprehensive, covering all subjects, areas and points of dispute. Big progress being made!’ China said that it was willing to work with the USA over reaching a consensus on trade.

Rises in interest rates. If these are in response to a situation of excess demand, they can be seen as a means of bringing inflation down to the target level or of closing a positive output gap, where real national income is above its potential level. They would not signify an impending recession. But many commentators have interpreted rises in interest rates in the USA as being different from this.

The Fed is keen to raise interest rates above the historic low rates that were seen as an ’emergency’ response to the financial crisis of 2007–8. It is also keen to reverse the policy of quantitative easing and has begun what might be described as ‘quantitative tightening’: not buying new bonds when existing ones that it purchased during rounds of QE mature. It refers to this interest rate and money supply policy as ‘policy normalization‘. The Fed maintains that such policy is ‘consistent with sustained expansion of economic activity, strong labor market conditions, and inflation near the Committee’s symmetric 2 percent objective over the medium term’.

However, many commentators, including President Trump, have accused the Fed of going too fast in this process and of excessively dampening the economy. It has already raised the Federal Funds Rate nine times by 0.25 percentage points each time since December 2015 (click here for a PowerPoint file of the chart). What is more, announcing that the policy will continue makes such announcements themselves a leading indicator of future rises in interest rates, which are a leading indicator of subsequent effects on aggregate demand. The Fed has stated that it expects to make two more 0.25 percentage point rises during 2019.

Surveys of consumer and business confidence. These are some of the most significant leading indicators as consumer confidence affects consumer spending and business confidence affects investment. According to the Duke CFO Global Business Outlook, an influential survey of Chief Financial Officers, ‘Nearly half (48.6 per cent) of US CFOs believe that the US will be in recession by the end of 2019, and 82 per cent believe that a recession will have begun by the end of 2020’. Such surveys can become self-fulfilling, as a reported decline in confidence can itself undermine confidence as both firms and consumers ‘catch’ the mood of pessimism.

Stock market volatility. When stock markets exhibit large falls and rises, this is often a symptom of uncertainty; and uncertainty can undermine investment. Stock market volatility can thus be a leading indicator of an impending recession. One indicator of such volatility is the VIX index. This is a measure of ’30-day expected volatility of the US stock market, derived from real-time, mid-quote prices of S&P 500® Index (SPXSM) call and put options. On a global basis, it is one of the most recognized measures of volatility – widely reported by financial media and closely followed by a variety of market participants as a daily market indicator.’ The higher the index, the greater the volatility. Since 2004, it has averaged 18.4; from 17 to 28 December 2018, it averaged 28.8. From 13 to 24 December, the DOW Jones Industrial Average share index fell by 11.4 per cent, only to rise by 6.2 per cent by 27 December. On 26 December, the S&P 500 index rallied 5 per cent, its best gain since March 2009.

Not all cases of market volatility, however, signify an impending recession, but high levels of volatility are one more sign of investor nervousness.

Oil prices. When oil prices fall, this can be explained by changes on the demand and/or supply side of the oil market. Oil prices have fallen significantly over the past two months. Until October 2018, oil prices had been rising, with Brent Crude reaching $86 per barrel by early October. By the end of the year the price had fallen to just over $50 per barrel – a fall of 41 per cent. (Click here for a PowerPoint file of the chart.) Part of the explanation is a rise in supply, with shale oil production increasing and also increased output from Russia and Saudi Arabia, despite a commitment by the two countries to reduce supply. But the main reason is a fall in demand. This reflects both a fall in current demand and in anticipated future demand, with fears of oversupply causing oil companies to run down stocks.

Falling oil prices resulting from falling demand are thus an indicator of lack of confidence in the growth of future demand – a leading indicator of a slowing economy.

The yield curve. This depicts the yields on government debt with different lengths to maturity at a given point in time. Generally, the curve slopes upwards, showing higher rates of return on bonds with longer to maturity. This is illustrated by the blue line in the chart. (Click here for a PowerPoint file of the chart.) This is as you would expect, with people requiring a higher rate of return on long-term lending, where there is normally greater uncertainty. But, as the Bloomberg article, ‘Don’t take your eyes off the yield curve‘ states:

Occasionally, the curve flips, with yields on short-term debt exceeding those on longer bonds. That’s normally a sign investors believe economic growth will slow and interest rates will eventually fall. Research by the Federal Reserve Bank of San Francisco has shown that an inversion has preceded every US recession for the past 60 years.
The US economy is 37 quarters into what may prove to be its longest expansion on record. Analysts surveyed by Bloomberg expect gross domestic product growth to come in at 2.9 percent this year, up from 2.2 percent last year. Wages are rising as unfilled vacancies hover near all-time highs.
With times this good, the biggest betting game on Wall Street is when they’ll go bad. Barclays Plc, Goldman Sachs Group Inc., and other banks are predicting inversion will happen sometime in 2019. The conventional wisdom: Afterward it’s only a matter of time – anywhere from 6 to 24 months – before a recession starts.

As you can see from the chart, the yield curve on 24 December 2018 was still slightly upward sloping (expect between 6-month and 1-year bonds) – but possibly ready to ‘flip’.

However, despite the power of an ‘inverted’ yield in predicting previous recessions, it may be less reliable now. The Fed, as we saw above, has already signalled that it expects to increase short-term rates in 2019, probably at least twice. That alone could make the yield curve flatter or even downward sloping. Nevertheless, it is still generally thought that a downward sloping yield curve would signal belief in a likely slowdown, if not outright recession.

So, is the USA heading for recession?

The trouble with indicators is that they suggest what is likely – not what will definitely happen. Governments and central banks are powerful agents. If they believed that a recession was likely, then fiscal and monetary policy could be adjusted. For example, the Fed could halt its interest rate rises and quantitative tightening, or even reverse them. Also, worries about protectionism may subside if the USA strikes new trade deals with various countries, as it did with Canada and Mexico in USMCA.


Surveys and Data


  1. Define the term ‘recession’.
  2. Are periods of above-trend expansion necessarily followed by a recession?
  3. Give some examples of leading indicators other than those given above and discuss their likely reliability in predicting a recession.
  4. Find out what has been happening to confidence levels in the EU over the past 12 months. Does this provide evidence of an impending recession in the EU?
  5. For what reasons may there be lags between a change in an indicator and a change in the variables for which it is an indicator?
  6. Why has the shape of the yield curve previously been a good predictor of the future course of the economy? Is it likely to be at present?
  7. What is the relationship between interest rates, government bond prices (‘Treasuries’ in the USA) and the yield on such bonds?

Where you live in Great Britain can have a profound effect on your earning potential. According to a report published by the Social Mobility Commission, there is a growing geographical divide, with more affluent areas getting relatively richer, while ‘many other parts of the country are being left behind economically and hollowed out socially’.

The Commission uses a Social Mobility Index to rank the 324 local authorities in England. The index is a measure of the social mobility prospects for people from disadvantaged backgrounds. It is ‘made up of 16 key performance indicators spanning each major life stage’.

The index shows that children from disadvantaged backgrounds have lower educational attainment, poorer initial jobs and poorer prospects for advancement in the labour market. Often they are stuck in low paid jobs with little chance of getting on the housing ladder and fewer chances of moving away from the area.

The problem is not simply one of a North-South divide or one of inner cities versus the suburbs. Many inner-city areas have been regenerated, with high incomes and high social and geographical mobility. Other inner-city areas remain deprived.

The worst performing areas are remote rural or coastal areas and former industrial areas, where industries have closed. As the author of the report states in the Guardian article linked below:

These areas have fewer specialist teachers, fewer good schools, fewer good jobs and worse transport links. … Many of these areas have suffered from a lack of regeneration: few high-paying industries are located there, and they often exhibit relatively limited job opportunities and clusters of low pay.

The problem often exists within areas, with some streets exhibiting growing affluence, where the residents have high levels of social mobility, while other streets have poor housing and considerable levels of poverty and deprivation. Average incomes for such areas thus mask this type of growing divide within areas. Indeed, some of the richest areas have worse outcomes for disadvantaged children than generally poorer areas.

There are various regional and local multiplier effects that worsen the situation. Where people from disadvantaged backgrounds are successful, they tend to move away from the deprived areas to more affluent ones, thereby boosting the local economy in such areas and providing no stimulus to the deprived areas. And so the divide grows.

Policies, according to the report, need to focus public investment, and incentives for private investment, in deprived areas. They should not focus simply on whole regions. You can read the specific policy recommendations in the articles below.


Social mobility is a stark postcode lottery. Too many in Britain are being left behind The Guardian, Alan Milburn (28/11/17)
State of the Nation – Sector Response FE News (28/11/17)
Social mobility: the worst places to grow up poor BBC News, Judith Burns and Adina Campbell (28/11/17)
How Britain’s richest regions offer worst prospects for poor young people Independent, May Bulman (28/11/17)
Small Towns Worst Places In Britain For Social Mobility, New ‘State Of The Nation’ Report Reveals Huffington Post, Paul Waugh (28/11/17)


Social mobility in Great Britain: fifth state of the nation report Social Mobility Commission, News (28/11/17)
Fifth State of the Nation Report Social Mobility Commission, News (28/11/17)


  1. Explain how local multipliers operate.
  2. What is the relationship between social immobility as identified in the report and the elasticity of supply of labour in specific jobs?
  3. What is the link between geographical, occupational and social mobility?
  4. Explain why, apart from London, English cities are ‘punching below their weight on social mobility outcomes’.
  5. Go through each of the key policy recommendations of the report and consider the feasibility of introducing them.
  6. What policies could be adopted to retain good teachers in schools in deprived areas?
  7. To what extent might an increased provision of training ease the problem of social mobility?
  8. Investigate policies adopted in other European countries to tackle local deprivation. Are there lessons that can be learned by the UK government, devolved governments, local authorities or other agencies?

Both the financial and goods markets are heavily influenced by sentiment. And such sentiment tends to be self-reinforcing. If consumers and investors are pessimistic, they will not spend and not invest. The economy declines and this further worsens sentiment and further discourages consumption and investment. Banks become less willing to lend and stock markets fall. The falling stock markets discourage people from buying shares and so share prices fall further. The despondency becomes irrational and greatly exaggerates economic fundamentals.

This same irrationality applies in a boom. Here it becomes irrational exuberance. A boom encourages confidence and stimulates consumer spending and investment. This further stimulates the boom via the multiplier and accelerator and further inspires confidence. Banks are more willing to lend, which further feeds the expansion. Stock markets soar and destabilising speculation further pushes up share prices. There is a stock market bubble.

But bubbles burst. The question is whether the current global stock market boom, with share prices reaching record levels, represents a bubble. One indicator is the price/earnings (PE) ratio of shares. This is the ratio of share prices to earnings per share. Currently the ratio for the US index, S&P 500, is just over 26. This compares with a mean over the past 147 years of 15.64. The current ratio is the third highest after the peaks of the early 2000s and 2008/9.

An alternative measure of the PE ratio is the Shiller PE ratio (see also). This is named after Robert Shiller, who wrote the book Irrational Exuberance. Unlike conventional PE ratios, which only look at average earnings over the past four quarters, the Shiller PE ratio uses average earnings over the past 10 years. “Because this factors in earnings from the previous ten years, it is less prone to wild swings in any one year.”

The current level of the Shiller PE ratio is 29.14, the third highest on record, this time after the period running up to the Wall Street crash of 1929 and the dot-com bubble of the late 1990s. The mean Shiller PE ratio over the past 147 years is 16.72.

So are we in a period of irrational exuberance? And are stock markets experiencing a bubble that sooner or later will burst? The following articles explore these questions.


2 Clear Instances of Irrational Exuberance Seeking Alpha, Jeffrey Himelson (12/2/17)
Promised land of Trumpflation-inspired global stimulus has been slow off the mark South China Morning Post, David Brown (20/2/17)
A stock market crash is a way off, but this boom will turn to bust The Guardian, Larry Elliott (16/2/17)
The “boring” bubble is close to bursting – the Unilever bid proves it MoneyWeek, John Stepek (20/2/17)


  1. Find out what is meant by Minksy’s ‘financial instability hypothesis’ and a ‘Minsky moment’. How might they explain irrational exuberance and the sudden turning point from a boom to a bust?
  2. Is it really irrational to buy shares with a very high PE ratio if everyone else is doing so?
  3. Why are people currently exuberant?
  4. What might cause the current exuberance to end?
  5. How does irrational exuberance affect the size of the multiplier?
  6. How might the behaviour of banks and other financial institutions contribute towards a boom fuelled by irrational exuberance?
  7. Compare the usefulness of a standard PE ratio with the Shiller PE ratio.
  8. Other than high PE ratios, what else might suggest that stock markets are overvalued?
  9. Why might a company’s PE ratio differ from its price/dividends ratio (see)? Which is a better measure of whether or not a share is overvalued?

Is too much expected of economists? When economic forecasts turn out to be wrong, as they often are, economists are criticised for having inaccurate or unrealistic models. But is this a fair criticism?

The following article by Richard Whittle from Manchester Metropolitan University looks at what economists can and cannot do. The article highlights two key problems for economic forecasting.

The first concerns human behaviour, which is influenced by a whole range of factors and can change very rapidly in response to changing circumstances. Moods of optimism or pessimism can quickly spread in response to a news item, such as measures announced by Donald Trump or latest data on growth or the housing market.

The second concerns the whole range of possible economic shocks. Such shocks, by their very nature, are hard to predict and can quickly make forecasts wrong. They could be a surprise election result, a surprise government policy change, a natural disaster, a war or a series of terrorist attacks. And these shocks, in turn, affect human behaviour. Consumption and investment may rise or fall as the events affect confidence and herd behaviour.

But is it a fair criticism of economics that it cannot foretell the future? Do economists, as the article says, throw up their hands and curse economics as a futile endeavour? Not surprisingly, the answer given is no! The author gives an analogy with medicine.

A doctor cannot definitely prevent illness, but can offer advice on prevention and hopefully offer a cure if you do get ill. This is the same for the work economists do.

Economists can offer advice on preventing crises or slowdowns but cannot definitively prevent them from happening. Economists can also offer robust advice on restoring growth, although when the advice is that the economy has grown too fast and should slow, it is often not welcomed by policy makers.

Helping understanding the various drivers in an economy and how humans are likely to respond to various incentives is a key part of what economists do. But making predictions with 100% certainty is asking too much of economists.

And just as medical professionals can predict that if you smoke, eat unhealthy food or take no exercise you are likely to be less healthy and die younger, but cannot say precisely when an individual will die, so too economists can predict that certain policy measures are likely to increase or decrease GDP or employment or inflation, but they cannot say precisely how much they will be affected.

As the article says, “the true value of the economist lies not in mystical fortune telling, but in achieving a better understanding of the nature of the economies in which we live and work.”


How to be an economist in 2017 The Conversation, Richard Whittle (24/1/17)


  1. For what reasons has economics been ‘in crisis’? What is the solution to this crisis?
  2. Look at some macroeconomic forecasts for a country of your choice made two years ago for today (see, for example, forecasts made by the IMF, OECD or a central bank). How accurate were they? Explain any inaccuracies.
  3. To what extent is economic forecasting like weather forecasting?
  4. What is meant by cumulative causation? Give some examples. Why does cumulative causation make economic forecasting difficult?
  5. How is the increased usage of contactless card payments likely to affect spending patterns? Explain why.
  6. Why is it difficult to forecast the effects of Brexit?
  7. How can economic advisors help governments in designing policy?
  8. Why do people tend to overweight high probabilities and underweight low ones?