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.
Are periods of above-trend expansion necessarily followed by a recession?
Give some examples of leading indicators other than those given above and discuss their likely reliability in predicting a recession.
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?
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?
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?
What is the relationship between interest rates, government bond prices (‘Treasuries’ in the USA) and the yield on such bonds?
The Christmas and new year period often draws attention to the financial well-being of households. An important determinant of this is the extent of their indebtedness. Rising levels of debt mean that increasing amounts of households’ incomes becomes prey to servicing debt through repayments and interest charges. They can also result in more people becoming credit constrained, unable to access further credit. Rising debt levels can therefore lead to a deterioration of financial well-being and to financial distress. This was illustrated starkly by events at the end of the 2000s.
The total amount of lending by monetary financial institutions to individuals outstanding at the end of October 2018 was estimated at £1.61 trillion. As Chart 1 shows, this has grown from £408 billion in 1994. Hence, indivduals in the UK have experience a four-fold increase in the levels of debt. (Click here to download a PowerPoint of the chart.)
The debt of individuals is either secured or unsecured. Secured debt is debt secured by property, which for individuals is more commonly referred to as mortgage debt. Unsecured debt, which is also known as consumer credit, includes outstanding debt on credit cards, overdrafts on current accounts and loans for luxury items such as cars and electrical goods. The composition of debt in 2018 is unchanged from that in 1994: 87 per cent is secured debt and 13 per cent unsecured debt.
The fourfold increase in debt is taken by some economists as evidence of financialisation. While this term is frequently defined in distinctive ways depending upon the content in which it is applied, when viewed in very general terms it describes a process by which financial institutions and markets become increasingly important in everyday lives and so in the production and consumption choices that economists study. An implication of this is that in understanding economic decisions, behaviour and outcomes it becomes increasingly important to think about the potential impact of the financial system. The financial crisis is testimony to this.
In thinking about financial well-being, at least at an aggregate level, we can look at the relative size of indebtedness. One way of doing this is to measure the stock of individual debt relative to the annual flow of GDP (national income). This is illustrated in Chart 2. (Click hereto download a PowerPoint of the chart.)
The growth in debt among individuals owed to financial institutions during the 2000s was significant. By the end of 2007, the debt-to-GDP ratio had reached 88 per cent. Decomposing this, the secured debt-to-GDP ratio had reached 75 per cent and the unsecured debt-to-GDP ratio 13 per cent. Compare this with the end of 1994 when secured debt was 46 per cent of GDP, unsecured debt 7 per cent and total debt 53 per cent. In other words, the period between 1994 and 2007 the UK saw a 25 percentage point increase in the debt-to-GDP ratio of individuals.
The early 2010s saw a consolidation in the size of the debt (see Chart 1) which meant that it was not until 2014 that debt levels rose above those of 2008. This led to the size of debt relative to GDP falling back by close to 10 percentage points (see Chart 2). Between 2014 and 2018 the stock of debt has increased from around £1.4 trillion to the current level of £1.61 trillion. This increase has been matched by a similar increase in (nominal) GDP so that the relative stock of debt remains little changed at present at around 76 per cent of GDP.
Chart 3 shows the annual growth rate of net lending (lending net of repayments) by monetary financial institutions to individuals. This essentially captures the growth rate in the stocks of debt, though changes in the actual stock of debt are also be affected by the writing-off of debts. (Click here to download a PowerPoint of the chart.)
We can see quite readily the pick up in lending from 2014. The average annual rate of growth in total net lending since 2014 has been just a little under 3½ per cent. This has been driven by unsecured lending whose growth rate has been close to 8½ per cent per annum, compared to just 2.7 per cent for secured lending. In 2016 the annual growth rate of unsecured lending was just shy of 11 per cent. This helped to fuel concerns about possible future financial distress. These concerns remain despite the annual rate of growth in unsecured debt having eased slightly to 7.5 per cent.
Despite the aggregate debt-to-GDP ratio having been relatively stable of late, the recent growth in debt levels is clearly not without concern. It has to be viewed in the context of two important developments. First, there remains a ‘debt hangover’ from the financial distress experienced by the private sector at the end of the 2000s, which itself contributed to a significant decline in economic activity (real GDP fell by 4 per cent in 2009). This subequently affected the financial well-being of the public sector following its interventions to cushion the economy from the full effects of the economic downturn as well as to help stabilise the financial system. Second, there is considerable uncertainty surrounding the UK’s exit from the European Union.
The financial resilience of all sectors of the economy is therefore of acute concern given the unprecedented uncertainty we are currently facing while, at the same time, we are still feeling the effects of the financial distress from the financial crisis of the late 2000s. It therefore seems timely indeed for individuals to take stock of their stocks of debt.
The latest consumer confidence figures from the European Commission point to consumer confidence in the UK remaining at around its long-term average. Despite this, confidence is markedly weaker than before the outcome of the EU referendum. Yet, the saving ratio, which captures the proportion of disposable income saved by the household sector, is close to its historic low. We consider this apparent puzzle and whether we can expect the saving ratio to rise.
The European Commission’s consumer confidence measure is a composite indicator based on the balance of responses to 4 forward-looking questions relating to the financial situation of households, the general economic situation, unemployment expectations and savings.
Chart 1 shows the consumer confidence indicator for the UK. The long-term average (median) of –6.25 shows that negative responses across the four questions typically outweigh positive responses. In October 2018 the confidence balance stood at –5.2, essentially unchanged from its September value of –5.8. While above the long-term average, recent values mark a weakening in confidence from levels before the EU referendum. At the beginning of 2016 the aggregate confidence score was running at around +4. (Click here to download a PowerPoint of the chart.)
Chart 1 shows two periods where consumer confidence fell markedly. The first was in the early 1990s. In 1990 the UK joined the Exchange Rate Mechanism (ERM). This was a semi-fixed exchange rate system whereby participating EU countries allowed fluctuations against each other’s currencies, but only within agreed bands, while being able to collectively float freely against all other currencies. In attempting to staying in the ERM, the UK was obliged to raise interest rates in order to protect the pound. The hikes to rates contributed to a significant dampening of aggregate demand and the economy slid into recession. Britain crashed out of the ERM in September 1992.
The second period of declining confidence was during the global financial crisis in the late 2000s. The retrenchment among financial institutions meant a significant tightening of credit conditions. This too contributed to a significant dampening of aggregate demand and the economy slid into recession. Whereas the 1992 recession saw the UK national output contract by 2.0 percent, this time national output fell by 6.3 per cent.
The collapses in confidence from 1992 and from 2007/08 are likely to have helped propagate the effects of the fall in aggregate demand that were already underway. The weakening of confidence in 2016 is perhaps a better example of a ‘confidence shock’, i.e. a change in aggregate demand originating from a change in confidence. Nonetheless, a fall in confidence, whether it amplifies existing shocks or is the source of the shock, is often taken as a signal of greater economic uncertainty. If we take this greater uncertainty to reflect a greater range of future income outcomes, including potential income losses, then households may look to insure themselves by increasing current saving.
It is usual to assume that people suffer from diminishing marginal utility of total consumption. This means that while total satisfaction increases as we consume more, the additional utility from consuming more (marginal utility) decreases. An implication of this is that a given loss of consumption reduces utility by more than an equivalent increase in consumption increases utility. This explains why people prefer more consistent consumption levels over time and so engage in consumption smoothing. The utility, for example, from an ‘average’ consumption level across two time periods, is higher, than the expected utility from a ‘low’ level of consumption in period 1 and a ‘high’ level of consumption in period 2. This is because the loss of utility from a ‘low’ level of consumption relative to the ‘average’ level is greater than the additional utility from the ‘high’ level relative to the ‘average’ level.
If greater uncertainty, such as that following the EU referendum, increases the range of possible ‘lower’ consumption values in the future even when matched by an increase in the equivalent range of possible ‘higher’ consumption values, then expected future utility falls. The incentive therefore is for people to build up a larger buffer stock of saving to minimise utility losses if the ‘bad state’ occurs. Hence, saving which acts as a from of self-insurance in the presence of uncertainty is known as buffer-stock saving or precautionary saving.
Chart 2 plots the paths of the UK household-sector saving ratio and consumer confidence. The saving ratio approximates the proportion of disposable income saved by the household sector. What we might expect to see if more uncertainty induces buffer-stock saving is for falls in confidence to lead to a rise in the saving ratio. Conversely, less uncertainty as proxied by a rise in confidence would lead to a fall in the saving ratio. (Click here to download a PowerPoint of the chart.)
The chart provides some evidence that of this. The early 1990s and late 2000s certainly coincided with both waning confidence and a rising saving ratio. The saving ratio rose to as high as 15.2 per cent in 1993 and 12.0 per cent in 2009. Meanwhile the rising confidence seen in the late 1990s coincided with a fall in the saving ratio to 4.7 per cent in 1999.
As Chart 2 shows, the easing of confidence since 2016 has coincided with a period where the saving ratio has been historically low. Across 2017 the saving ratio stood at just 4.5 per cent. In the first half of 2018 the ratio averaged just 4.2 per cent. While the release of the official figures for the saving ratio are less timely than those for confidence, the recent very low saving ratio may be seen to raise concerns. Can softer confidence data continue to co-exist with such a low saving ratio?
There are a series of possible explanations for the recent lows in the saving ratio. On one hand, the rate of price inflation has frequently exceeded wage inflation in recent years so eroding the real value of earnings. This has stretched household budgets and limited the amount of discretionary income available for saving. On the other hand, unemployment rates have fallen to historic lows. The rate of unemployment in the three months to August stood at 4 per cent, the lowest since 1975. Unemployment expectations are important in determining levels of buffer stock saving because of the impact of unemployment on household budgets.
Another factor that has fuelled the growth of spending relative to income, has been the growth of consumer credit. In the period since July 2016, the annual rate of growth of consumer credit, net of repayments, has averaged 9.7 per cent. Behavioural economists argue that foregoing spending can be emotionally painful. Hence, spending has the potential to exhibit more stickiness than might otherwise be predicted in a more uncertain environment or in the anticipation of income losses. Therefore, the reluctance or inability to wean ourselves off credit and spending might be a reason for the continuing low saving ratio.
We wait to see whether the saving ratio increases over the coming months. However, for now, the UK household sector appears to be characterised by low saving and fragile confidence. Whether or not this is a puzzle, is open to question. Nonetheless, it does appear to carry obvious risks should weaker income growth materialise.
Draw up a series of factors that you think might affect consumer confidence.
Which of the following statements is likely to be more accurate: (a) Consumer confidence drives economic activity or (b) Economic activity drives consumer confidence?
What macroeconomic indicators would those compiling the consumer confidence indicator expect the indicator to predict?
How does the diminishing marginal utility of consumption (or income) help explain why people engage in buffer stock saving (precautionary saving)?
How might uncertainty affect consumer confidence?
How does greater income uncertainty affect expected utility? What affect might this have on buffer stock saving?
Source: Wikimedia Commons: author, Today Testing (https://todaytesting.com)In his Budget on 29 October, the UK Chancellor, Philip Hammond, announced a new type of tax. This is a ‘digital services tax’, which, after consultation, he is planning to introduce in April 2020. The target of the tax is the profits made by major companies providing social media platforms (e.g. Facebook and Twitter), internet marketplaces (e.g. Amazon and eBay) or search engines (such as Alphabet’s Google).
The proposed digital services tax is a 2% tax on the revenues earned by such companies in the UK. It would only apply to large companies, defined as those whose global revenue is at least £500m a year. It is expected to raise around £400m per year.
The EU is considering a similar tax at a rate of 3%. India, Pakistan, South Korea and several other countries are considering introducing digital taxes. Indeed, many countries are arguing for a worldwide agreement on such a tax. The OECD is studying the implications of the possible use of such a tax by its 36 members. If an international agreement on such a tax can be reached, a separate UK tax may not go ahead. As the Chancellor stated in his Budget speech:
In the meantime we will continue to work at the OECD and G20 to seek a globally agreed solution. And if one emerges, we will consider adopting it in place of the UK Digital Services Tax.
The proposed UK tax is a hybrid between direct and indirect taxes. Like corporation tax, a direct tax, its aim is to tax companies’ profits. But, unlike corporation tax, it would be harder for such companies to avoid. Like VAT, an indirect tax, it would be a tax on revenue, but, unlike VAT, it would be an ‘end-stage’ tax rather than a tax on value added at each stage of production. Also, it would not be a simple sales tax on companies as it would be confined to revenue (such as advertising revenue) earned from the use in the UK of search engines, social media platforms and online marketplaces. As the Chancellor said in his speech.
It is important that I emphasise that this is not an online-sales tax on goods ordered over the internet: such a tax would fall on consumers of those goods – and that is not our intention.
There is, however, a political problem for the UK in introducing such a tax. The main companies it would affect are American. It is likely that President Trump would see such taxes as a direct assault on the USA and could well threaten retaliation. As the Accountancy Age article states, ‘Dragging the UK into an acrimonious quarrel with one of its largest trading partners is perhaps not what the Chancellor intends.’ This will be especially so as the UK seeks to build new trading relationships with the USA after Brexit. As the BBC article states, ‘The chancellor will be hoping that an international agreement rides to his rescue before the UK tax has to be imposed.’
Would you like to be a millionaire? Of course you would – who wouldn’t, right? Actually the answer to this question may be more complicated than you might think (see for instance Sgroi et al (2017) on the economics of happiness: see linked article below), but, generally speaking, most people would answer positively to this question.
What if I told you, however, that you could become a millionaire (actually, scratch that – think big – make that “trillionaire”) overnight and be deeply unhappy about it? If you don’t believe me see what happened to Zimbabwe 10 years ago, when irresponsible money printing and fiscal easing drove the country’s economy to staggering hyperinflation (see the blogs A remnant of hyperinflation in Zimbabwe and Fancy a hundred trillion dollar note?. At the peak of the crisis, prices were increasing by a factor of 130 each year. I have in my office a 100 trillion Zimbabwean dollar note (see below) which I show in my lectures when I talk about hyperinflation to my first year Economics for Business students (if you are one of them, make sure not to miss it next February at UEA!). How much is this 100 trillion note worth? Nothing (except, may be, for collectors). It has been withdrawn from circulation as it ended up not even being worth the cost of the paper on which it was printed.
The Zimbabwean economy managed to pull itself out of this spiral of economic death, partly by informally replacing its hyperinflationary currency with the US greenback, and partly by keeping its fiscal spending under control and reverting to more sane economic policy making. That lasted until 2013, after which the government launched a Zimbabwean digital currency (known as “Zollar”) that had a nominal value set equal to a US dollar; and forced its exporters to exchange their greenbacks for Zollars. It then started spending these USD to finance a very ambitious and unsustainable programme of fiscal expansion.
The Economist published yesterday a story that shows the results of this policy – wild price increases and empty supermarket shelves are both back. According to the newspaper’s report:
At a supermarket in Harare, Zimbabwe’s capital, the finance minister is staring aghast at a pack of nappies. ‘This is absolutely ridiculous!’, exclaims Mthuli Ncube. ‘$49!’ A manager says it cost $23 two weeks ago, before pointing out other eye-watering items such as $20 Coco Pops. […] Over the past two weeks zollars have been trading at as little as 17 cents to the dollar. The devaluation has led to a surge in prices—and not just in imported goods like nappies. Football fans attending the Zimbabwe v Democratic Republic of Congo game on October 16th were shocked to learn that ticket prices had doubled on match day.
How long will it take for the 100 trillion Zollar to make its appearance again? We shall find out. I am sure Zimbabweans will be less than thrilled!
Using an AS/AD diagram, explain the concept of hyperinflation. How can irresponsible fiscal policy-making lead to hyperinflation?
What are the effects of hyperinflation on the people who live in the affected countries? Search the web for examples and case studies, and use them to support your answer.
Once it has started, what policies can be used to fight hyperinflation? Use examples to support your answer.
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.
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. 
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.
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. 
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:
The Christmas and new year period often draws attention to the financial well-being of households. An important determinant of this is the extent of their indebtedness. Rising levels of debt mean that increasing amounts of households’ incomes becomes prey to servicing debt through repayments and interest charges. They can also result in more people becoming credit constrained, unable to access further credit. Rising debt levels can therefore lead to a deterioration of financial well-being and to financial distress. This was illustrated starkly by events at the end of the 2000s. 
The total amount of lending by monetary financial institutions to individuals outstanding at the end of October 2018 was estimated at £1.61 trillion. As Chart 1 shows, this has grown from £408 billion in 1994. Hence, indivduals in the UK have experience a four-fold increase in the levels of debt. (Click 
In thinking about financial well-being, at least at an aggregate level, we can look at the relative size of indebtedness. One way of doing this is to measure the stock of individual debt relative to the annual flow of GDP (national income). This is illustrated in Chart 2. (Click 
Chart 3 shows the annual growth rate of net lending (lending net of repayments) by monetary financial institutions to individuals. This essentially captures the growth rate in the stocks of debt, though changes in the actual stock of debt are also be affected by the writing-off of debts. (Click 
Chart 1 shows the consumer confidence indicator for the UK. The long-term average (median) of –6.25 shows that negative responses across the four questions typically outweigh positive responses. In October 2018 the confidence balance stood at –5.2, essentially unchanged from its September value of –5.8. While above the long-term average, recent values mark a weakening in confidence from levels before the EU referendum. At the beginning of 2016 the aggregate confidence score was running at around +4. (Click 
Chart 2 plots the paths of the UK household-sector saving ratio and consumer confidence. The saving ratio approximates the proportion of disposable income saved by the household sector. What we might expect to see if more uncertainty induces buffer-stock saving is for falls in confidence to lead to a rise in the saving ratio. Conversely, less uncertainty as proxied by a rise in confidence would lead to a fall in the saving ratio. (Click 
The proposed digital services tax is a 2% tax on the revenues earned by such companies in the UK. It would only apply to large companies, defined as those whose global revenue is at least £500m a year. It is expected to raise around £400m per year.
Would you like to be a millionaire? Of course you would – who wouldn’t, right? Actually the answer to this question may be more complicated than you might think (see for instance Sgroi et al (2017) on the economics of happiness: see linked article below), but, generally speaking, most people would answer positively to this question.
