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.
I could spend hours writing about all the things that we do over the internet these days, and I would probably never manage to come up with a complete list. Just think about how many hours you spend online every day. Most likely, much of your waking time is spent using internet-based services one way or another (including apps on your phone, streaming on your phone, tablet or your smart TV and similar). If your access to the internet was disrupted, you would certainly feel the difference. What if you just couldn’t afford to have computer or internet access? What effect would that have on your education, your ability to find a job, and your income?
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.
But then he adds:
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.
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.
With the UK parliament in Brexit gridlock, the Labour opposition is calling for a general election. Although its policy over Brexit and a second referendum is causing splits in the party, the Labour party is generally agreed that pubic expenditure on health, education and transport infrastructure needs to increase – that there needs to be an end to fiscal austerity. However, to fund extra public expenditure would require an increase in taxes and/or an increase in government borrowing.
One of the arguments against increasing government borrowing is that it will increase public-sector debt. The desire to get public-sector debt down as a percentage of GDP has been central to both the Coalition and Conservative governments’ economic strategy. Austerity policies have been based on this desire.
But, in the annual presidential address to the American Economics Association, former chief economist at the IMF, Olivier Blanchard, criticised this position. He has argued for several years that cutting government deficits may weaken already weak economies and that this may significantly reduce tax revenues and potential national income, thereby harming recovery and doing long-term economic damage. Indeed, the IMF has criticised excessively tight fiscal policies for this reason.
In his presidential address, he expanded the argument to consider whether an increase in government borrowing will necessarily increase the cost of servicing government debt. When the (nominal) interest rate (r) on government borrowing is below the nominal rate of economic growth (gn), (r < gn), then even if total debt is not reduced, it is likely that the growth in tax revenues will exceed the growth in the cost of servicing the debt. Debt as a proportion of GDP will fall. The forecast nominal growth rate exceeds the 10-year nominal rate on government bonds by 1.3% in the USA, 2.2% in the UK and 1.8% in the eurozone. In fact, with the exception of a short period in the 1980s, nominal growth (gn) has typically exceeded the nominal interest rate on government borrowing (r) for decades.
When r < gn, this then gives scope for increasing government borrowing to fund additional government spending without increasing the debt/GDP ratio. Indeed, if that fiscal expansion increases both actual and potential income, then growth over time could increase, giving even more scope for public investment.
Back in October, we examined the rise in oil prices. We said that, ‘With Brent crude currently at around $85 per barrel, some commentators are predicting the price could reach $100. At the beginning of the year, the price was $67 per barrel; in June last year it was $44. In January 2016, it reached a low of $26.’ In that blog we looked at the causes on both the demand and supply sides of the oil market. On the demand side, the world economy had been growing relatively strongly. On the supply side there had been increasing constraints, such as sanctions on Iran, the turmoil in Venezuela and the failure of shale oil output to expand as much as had been anticipated.
But what a difference a few weeks can make!
Brent crude prices have fallen from $86 per barrel in early October to just over $50 by the end of the year – a fall of 41 per cent. (Click here for a PowerPoint of the chart.) Explanations can again be found on both the demand and supply sides.
On the demand side, global growth is falling and there is concern about a possible recession (see the blog: Is the USA heading for recession?). The Bloomberg article below reports that all three main agencies concerned with the oil market – the U.S. Energy Information Administration, the Paris-based International Energy Agency and OPEC – have trimmed their oil demand growth forecasts for 2019. With lower expected demand, oil companies are beginning to run down stocks and thus require to purchase less crude oil.
On the supply side, US shale output has grown rapidly in recent weeks and US output has now reached a record level of 11.7 million barrels per day (mbpd), up from 10.0 mbpd in January 2018, 8.8 mbpd in January 2017 and 5.4 mbpd in January 2010. The USA is now the world’s biggest oil producer, with Russia producing around 11.4 mpbd and Saudi Arabia around 11.1 mpbd.
Total world supply by the end of 2018 of around 102 mbpd is some 2.5 mbpd higher than expected at the beginning of 2018 and around 0.5 mbpd greater than consumption at current prices (the remainder going into storage).
So will oil prices continue to fall? Most analysts expect them to rise somewhat in the near future. Markets may have overcorrected to the gloomy news about global growth. On the supply side, global oil production fell in December by 0.53 mbpd. In addition OPEC and Russia have signed an accord to reduce their joint production by 1.2 mbpd starting this month (January). What is more, US sanctions on Iran have continued to curb its oil exports.
But whatever happens to global growth and oil production, the future price will continue to reflect demand and supply. The difficulty for forecasters is in predicting just what the levels of demand and supply will be in these uncertain times.
Workers in the UK and USA work much longer hours per year than those in France and Germany. This has partly to do with the number of days paid holiday per year, partly with the number of hours worked per day and partly with the number of days worked per week.
According to the latest OECD figures, in 2017 average hours worked per year ranged from 2257 in Mexico (the OECD’s highest) to 1780 in the USA, 1710 in Japan, 1681 in the UK, 1514 in France, 1408 in Denmark and 1356 in Germany (the OECD’s lowest). Annual working hours have been falling in most countries across the decades, as the chart shows. However, in most countries the process has slowed in recent years and in the UK, the USA and France working hours have begun to rise. (Click here for a PowerPoint of the chart.)
But why do working hours differ so much from country to country? How do they relate to productivity? How do they relate to human happiness and welfare more generally?
Causes of the differences
There are various reasons for the differences in hours worked between countries.
In a situation where individual workers can choose how many hours to work, they have to decide the best trade off for them between income and leisure. As wages rise over time, there will be substitution and income effects of these extra hourly wages. Higher wages make work more valuable in terms of what people can buy from an extra hour’s work. There is thus an incentive to substitute work for leisure and hence work longer. This is the substitution effect. On the other hand, higher wages allow people to work fewer hours for a given income. This is the income effect.
As incomes rise, generally the substitution effect will tend to decline relative to the income effect. This is because of the diminishing marginal utility of income. Richer people will tend to value a given rise in income less than poorer people and therefore will value the income from extra work less than poorer people. Richer people will prefer to work fewer hours than poorer people. Generally workers in richer OECD countries work fewer hours than those in poorer OECD countries.
But this does not explain why people in the USA, Canada, Japan and the UK work longer hours than people in Germany, Denmark, Norway, The Netherlands and France.
One possible explanation for these differences is the role of trade unions. These tend to be stronger in countries with lower working hours. Reducing the working week or obtaining longer holidays is one of the key objectives of unions.
Another is income distribution. The USA, despite its high average (mean) income, has a relatively unequal distribution of income compared with Germany or France. The post-tax-and-benefits Gini coefficient in the USA is around 0.39, whereas in Germany it is 0.29, meaning that Germany has a more equal distribution of disposable income than the USA. In fact, rises in real incomes in the USA over the past 10 years have gone almost exclusively to the top 10 per cent of earners, leaving the median income little changed. In fact median household income only rose above its 2007 (pre-recession) level in 2016.
Social and cultural explanations may also be important. People in countries with higher working hours relative to hourly wages may put a greater store on consumption relative to leisure. The desire to shop may be very strong. The ‘Anglo-Saxon’ economic model pursued by right-of-centre governments in English-speaking countries, such as the USA, Canada, Australia and the UK puts emphasis on low taxes, low regulation, low public expenditure and self-advancement. Such a model encourages a more individualistic approach to work, with more emphasis on earning money.
Then there is the attitude to hours worked generally. There is a saying that in the UK the last one to leave the office is seen as the hardest working, whereas in Germany the last one to leave is seen as the least efficient. Social pressures, from colleagues, family, friends and society more generally can have a major effect on people’s choices between work and leisure.
Productivity, in terms of output per hour worked, tends to decline as workers work longer hours. People get tired and possibly bored and demotivated towards the end of a long day or week. If workers are paid by the output they produce and if productivity declines towards the end of the day, then the hourly wage would fall as the day progresses. This would act as a disincentive to work long hours. In practice, most workers are normally paid a constant rate per hour for normal-time working. For overtime, they may even be paid a higher rate, despite their likely lower productivity. This encourages them to work longer hours than if they were paid according to their marginal productivity.
Linking pay more closely to productivity could encourage people to opt for fewer hours (if they had the choice). Indeed some companies are now encouraging workers to choose their hours – which may mean fewer hours as people seek a better work–life balance. (See the BBC article below about PwC’s employment strategy.) Alternatively, some other employers adopt the system of giving workers a set amount of work to do and then they can leave work when it is finished. This acts as an incentive to work more efficiently.
It is interesting that countries where workers work more hours per year tend to have a lower output per hour worked relative to output per worker than countries where workers work fewer hours. This is illustrated in the chart opposite. The USA, with its longer working hours, has higher output per person employed than France and Germany but very similar output per hour worked.
Hours and happiness
So are people who choose to work longer hours and take home more money likely to be happier than those who choose to work fewer hours and take home less money? If people were rational and had perfect knowledge, then they would choose the balance between work and leisure that best suited them.
In practice, labour markets are highly imperfect. People often do not have choices about the amount they work; they work the hours they are told. Even if they do have a choice, they are unlikely to have perfect knowledge about the impact of long hours on their health and happiness over their lifetime. They may not even be good judges of the shorter-term effects of more work and more pay. They may believe that more money will buy them more happiness only to find soon afterwards that they are wrong.