Category: Economics 10e: Ch 15

The need to get real with GDP

The distinction between nominal and real values is an incredibly important one in economics. We apply the latest GDP numbers from the ONS to show how the inflation-adjusted numbers help to convey the twin characteristics of growth: positive longer-term growth but variable short-term rates of growth. It is real GDP numbers that help us to understand better the macroeconomic environment and, not least, its inherent volatility. To use nominal GDP numbers means painting a less than clear, if not inaccurate, picture of the macroeconomic environment.

The provisional estimate for GDP (the value of output) in the UK in 2018 is £2.115 trillion, up 3.2 per cent from £2.050 trillion in 2017. These are the actual numbers, or what are referred to as nominal values. They make no adjustment for inflation and reflect the prices of output that were prevailing at the time. Hence, the figures are also referred to as GDP at current prices.

The use of nominal GDP data can be something of a problem when we compare historical values. In 1950, for example, as we can see from Chart 1, nominal GDP in 1950 was a mere £12.926 billion. In other words, the nominal figures show that the value of the country’s output was 163.595 times greater in 2018 (or an increase of 162,595 per cent). However, if we want to make a more meaningful comparison of the country’s national income we need to adjust for inflation. (Click here to download a PowerPoint copy of the chart.)

If we measure GDP at constant prices we eliminate the effect of inflation. This allow us to make a more meaningful comparison of national income. Consider first the real GDP numbers for 1950 and 2018. GDP in 1950 at 2016 prices was £373.9 billion. This is higher than the nominal (current-price) value because prices in 2016 were higher than those in 1950. Meanwhile, GDP in 2018 when measured at 2016 prices was £2.034 trillion. This real value is smaller than the corresponding nominal value because prices in 2016 where lower than those in 2018.

Between 1950 and 2018 there was a proportionate increase in real GDP of 5.439 (or a 443.9 per cent increase). Because we have removed the effect of inflation the real growth figure is much lower than the nominal growth figure. Crucially, what we are left with is an indicator of the growth in the volume of output. Whereas nominal growth rates are affected both by changes in volumes and prices, real growth rates reflect only changes in volumes.

Consider now output growth between 2017 and 2018. As we saw earlier, the nominal figures suggest growth of 3.2 per cent. In fact, GDP at constant 2016 prices increased from £2005.4 trillion in 2017 to £2,033.6 trillion in 2018: an increase of 1.4 per cent. This was the lowest rate of growth in national output since 2012 when output also grew by 1.4 per cent. In 2017 national output had increased by 1.8 per cent, the same increase as in 2016.

To put the recent growth in national output into context, Chart 2 shows the annual rate of growth in real GDP each year since 1950. Across the period, the average annual rate of growth in real GDP and, hence, in the volume of national output was 2.5 per cent. In the current decade growth has averaged only 1.9 per cent. This followed falls of 0.3 per cent and 4.2 per cent in 2008 and 2009 respectively as the effects of the financial crisis on the economy were felt. (Click here to download a PowerPoint copy of the chart.)

By plotting the percentage changes in real GDP from year to year, we get a much clearer sense of the inherent instability that we identified at the outset as a characteristic of growth. This is true not only for the UK, but economies more generally. This instability is the key characteristic of the macroeconomic environment. It influences and informs much of what we study in economics.

The variability of growth rates that create the instability of economies again requires an understanding of the distinction between nominal and real GDP. Chart 3 illustrates the growth in GDP both in nominal and real terms. The average annual rate of growth of nominal GDP is 7.8 per cent, considerably higher than the average real growth rate of 2.5 per cent per year. The difference again reflects the effect of rising prices. (Click here to download a PowerPoint copy of the chart.

Chart 3 clearly shows the wrong conclusions that can be drawn if one was to focus on the growth in nominal GDP from year to year. Perhaps the best example is 1975. In this year nominal GDP grew by 24.2 per cent. However, the volume of national output contracted: real GDP fell by 1.5 per cent. The growth in nominal GDP reflects the rapid growth in prices seen in that year. The economy’s average price level (the GDP deflator) rose by 26.1 per cent. Hence, the growth in nominal GDP reflected not an increase in the volume of output – that fell – but instead a large increase in prices.

The importance of the distinction between nominal and real GDP is further demonstrated by the fact that since 1950 nominal GDP has fallen in only one year. In 2009 nominal GDP fell by 2.7 per cent. The 1.6 per cent rise in the economy’s average price level was not enough to offset the fall in the volume of output of just over 4.2 per cent. In other years when the volume of output (real GDP) fell, the effect of rising prices meant that the value of output (nominal GDP) nonetheless rose.

So to conclude, the distinction between nominal and real GDP is crucial when analysing economic growth. To understand the distinction gives you a truly real advantage in making sense of the macroeconomic environment.

Questions

1. What do you understand by the term ‘macroeconomic environment’? What data could be used to describe the macroeconomic environment?
2. When a country experiences positive rates of inflation, which is higher: nominal economic growth or real economic growth?
3. Does an increase in nominal GDP mean a country’s production has increased? Explain your answer.
4. Does a decrease in nominal GDP mean a country’s production has decreased? Explain your answer.
5. Why does a change in the growth of real GDP allow us to focus on what has happened to the volume of production?
6. What does the concept of the ‘business cycle’ have to do with real rates of economic growth?
7. When would falls in real GDP be classified as a recession?
8. Distinguish between the concepts of ‘short-term growth rates’ and ‘longer-term growth’.
9. Why might the distinction between nominal and real be important when analysing changes in people’s pay? What would be the significance of an increase in real pay?

Who Tweets Wins

Today’s title is inspired from the British Special Air Service (SAS) famous catchphrase, ‘Who Dares Wins’ – similar variations of which have been adopted by several elite army units around the world. The motto is often credited to the founder of the SAS, Sir David Stirling (although similar phrases can be traced back to ancient Rome – including ‘qui audet adipiscitur’, which is Latin for ‘who dares wins’). The motto was used to inspire and remind soldiers that to successfully accomplish difficult missions, one has to take risks (Geraghty, 1980).

In the world of economics and finance, the concept of risk is endemic to investments and to making decisions in an uncertain world. The ‘no free lunch’ principle in finance, for instance, asserts that it is not possible to achieve exceptional returns over the long term without accepting substantial risk (Schachermayer, 2008).

Undoubtedly, one of the riskiest investment instruments you can currently get your hands on is cryptocurrencies. The most well-known of them is Bitcoin (BTC), and its price has varied spectacularly over the past ten years – more than any other asset I have laid my eyes on in my lifetime.

The first published exchange rate of BTC against the US dollar dates back to 5 October 2009 and it shows \$1 to be exchangeable for 1309.03 BTC. On 15 December 2017, 1 BTC was traded for \$17,900. But then, a year later the exchange rate was down to just over \$1 = \$3,500. Now, if this is not volatility I don’t know what is!

In such a market, wouldn’t it be wonderful if you could somehow predict changes in market sentiment and volatility trends? In a hot-off-the press article, Shen et al (2019) assert that it may be possible to predict changes in trading volumes and realised volatility of BTC by using the number of BTC-related tweets as a measure of attention. The authors source Twitter data on Bitcoin from BitInfoCharts.com and tick data from Bitstamp, one of the most popular and liquid BTC exchanges, over the period 4/9/2014 to 31/8/2018.

According to the authors:

This measure of investor attention should be more informed than that of Google Trends and therefore may reflect the attention Bitcoin is receiving from more informed investors. We find that the volume of tweets are significant drivers of realised [price] volatility (RV) and trading volume, which is supported by linear and nonlinear Granger causality tests.

They find that, according to Granger causality tests, for the period from 4/9/2014 to 8/10/2017, past days’ tweeting activity influences (or at least forecasts) trading volume. While from 9/10/2017 to 31/8/2018, previous tweets are significant drivers/forecasters of not only trading volume but also realised price volatility.

And before you reach out for your smartphone, let me clarify that, although previous days’ tweets are found in this paper to be good predictors of realised price volatility and trading volume, they have no significant effect on the returns of Bitcoin.

Questions

1. Explain how the number of tweets can be used to gauge investors’ intentions and how it can be linked to changes in trading volume.
2. Using Google Scholar, make a list of articles that have used Twitter and Google Trends to predict returns, volatility and trading volume in financial markets. Present and discuss your findings.
3. Would you invest in Bitcoin? Why yes? Why no?

Let me Google it for you…

How would your life be without the internet? For many of you, this is a question that may be difficult to answer – as the internet has probably been an integral part of your life, probably since a very young age. We use internet infrastructure (broadband, 4G, 5G) to communicate, to shop, to educate ourselves, to keep in touch with each other, to buy and sell goods and services. We use it to seek and find new information, to learn how to cook, to download music, to watch movies. We also use the internet to make fast payments, transfer money between accounts, manage our ISA or our pension fund, set up direct debits and pay our credit-card bills.

Martin Jenkins, a former homeless man, now entrepreneur, thinks that the magnitude of this effect is rather significant. In fact, he is so convinced about the importance of bringing the internet to poorer households, that he recently founded a company, Neptune, offering low-income households in the Bronx district of New York free access to online education, healthcare and finance portals. His venture was mentioned in a recent (and very interesting) BBC article – a link to which can be found at the end of this blog. But is internet connectivity really that important when it comes to economic and labour market outcomes? And is there a systematic link between economic growth and internet penetration rates?

These are all questions that have been the subject of intensive debate over the last few years, in the context of both developed and developing economies. Indeed, the ‘digital divide’ as it is known (the economic gap between the internet haves and have nots) is not something that concerns only developing countries. According to a recent policy brief published by the New York City Comptroller:

More than one-third (34 percent) of households in the Bronx lack broadband at home, compared to 30 percent in Brooklyn, 26 percent in Queens, 22 percent in Staten Island, and 21 percent in Manhattan.

The report goes on to present data on the percentage of households with internet connection at home by NYC district, and it does not take advanced econometric skills for one to notice that there is a clear link between median district income and broadband access. Wealthier districts (e.g. Manhattan Community District 1 & 2 – Battery Park City, Greenwich Village & Soho PUMA), tend to have a significantly higher share of households with broadband access, than less affluent ones (e.g. NYC-Brooklyn Community District 13 – Brighton Beach & Coney Island PUMA) – 88% of total households compared with 58%.

But, do these large variations in internet connectivity matter? The evidence is mixed. On the one hand, there are several studies that find a clear, strong link between internet penetration and economic growth. Czernich et al (2011), for instance, using data on OECD countries over the period 1996–2007, find that “a 10 percentage point increase in broadband penetration raised annual per capita growth by 0.9–1.5 percentage points”.

Another study by Koutroumpis (2018) examined the effect of rolling out broadband in the UK.

For the UK, the speed increase contributed 1.71% to GDP in total and 0.12% annually. Combining the effect of the adoption and speed changes increased UK GDP by 6.99% cumulatively and 0.49% annually on average”. (pp.10–11)

The evidence is less clear, however, when one tries to estimate the benefits between different types of workers – low and high skilled. In a recent paper, Atasoy (2013) finds that:

gaining access to broadband services in a county is associated with approximately a 1.8 percentage point increase in the employment rate, with larger effects in rural and isolated areas.

most of the employment gains result from existing firms increasing the scale of their labor demand and from growth in the labor force. These results are consistent with a theoretical model in which broadband technology is complementary to skilled workers, with larger effects among college-educated workers and in industries and occupations that employ more college-educated workers.

Similarly, Forman et al (2009) analyse the effect of business use of advanced internet technology and local variation in US wage growth, over the period 1995–2000. Their findings show that:

Advanced internet technology is associated with larger wage growth in places that were already well off. These are places with highly educated and large urban populations, and concentration of IT-intensive industry. Overall, advanced internet explains over half of the difference in wage growth between these counties and all others.

How important then is internet access as a determinant of growth and economic activity and what role does it have in bridging economic disparities between communities? The answer to this question is most likely ‘very important’ – but less straightforward than one might have assumed.

Questions

1. Is there a link between economic growth and internet access? Discuss, using examples.
2. Explain the arguments for and against government intervention to subsidise internet access of poorer households.
3. How important is the internet to you and your day to day life? Take a day offline (yes, really – a whole day). Then come back and write about it.

Desperately seeking confidence

Consumer and business confidence reflect the sentiment, emotion, or anxiety of consumers and businesses. Confidence surveys therefore try to capture these feelings of optimism or pessimism. They aim to shed light on spending intentions and hence the short-term prospects for private-sector spending. For example, a fall in confidence would be expected to lead to a fall in consumption and investment spending. This is particularly relevant in the UK with the ongoing uncertainty around Brexit. We briefly summarise here current patterns in confidence.

Through the use of surveys attempts are made to measure confidence. One long-standing survey is that conducted for the European Commission. Each month consumers and firms across the European Union are asked a series of questions, the answers to which are used to compile indicators of consumer and business confidence. For instance, consumers are asked about how they expect their financial position to change. They are offered various options such as ‘get a lot better, ‘get a lot worse’ and balances are then calculated on the basis of positive and negative replies.

The chart plots confidence in the UK for consumers and different sectors of business since the mid 1990s. The chart captures the volatility of confidence. This volatility is generally greater amongst businesses than consumers, and especially so in the construction sector. (Click here to download a PowerPoint copy of the chart.)

The chart nicely captures the collapse in confidence during the global financial crisis in the late 2000s. The significant tightening of credit conditions contributed to a significant dampening of aggregate demand which was further propagated (amplified) by the collapse in confidence. Consequently, the economy slid in to recession with national output contracting by 6.3 per cent during the 5 consecutive quarters during which output fell.

To this point, the current weakening of confidence is not of the same magnitude as that of the late 2000s. In January 2009 consumer confidence had fallen to an historic low of -35. Nonetheless, the December 2018 figure for consumer confidence was -9, the lowest figure since July 2016 the month following the EU referendum, and markedly lower than the +8 seen as recently as 2014. The long-term (median) average for the consumer confidence balance is -6.

The weakening in consumer confidence is mirrored by a weakening in confidence in the retail and service sectors. The confidence balances in December 2018 in these two sector both stood at -8 which compares to their longer-term averages of around +5. In contrast, confidence in industry and construction has so far held fairly steady with confidence levels in December 2018 at +8 in industry and at 0 in construction compared to their long-term averages of -4 and -10 respectively.

It will be interesting to see how confidence has been affected by recent events. The glut of stories suggesting that trading conditions were especially difficult for retailers over the Christmas and New Year period is consistent with the weakening confidence already observed amongst consumers and retailers. However, it is unlikely that recent events will have done anything other than to exacerbate the trend for a weakening of confidence of domestic consumers and retailers. Hence, the likelihood is an intensification of caution and prudence.

Questions

1. Draw up a series of factors that you think might affect both consumer and business confidence. How similar are both these lists?
2. Which of the following statements is likely to be more accurate: (a) Confidence drives economic activity or (b) Economic activity drives confidence?
3. What macroeconomic indicators would those compiling the consumer and business confidence indicators expect each indicator to predict?
4. What is meant by the concept of ‘prudence’ in the context of spending? What factors might determine the level of prudence
5. How might prudence be expected to affect spending behaviour?
6. How might we distinguish between confidence shocks’ and confidence as a ‘propagator’ of shocks?

Is the USA heading for recession?

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.

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.

Questions

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?