Author: Nicholas Vasilakos

You’ve had a busy day at work. You check your watch; it’s almost 5pm. You should be packing soon – except, your boss is still in their office. You shouldn’t really be seen leaving before your boss, should you? You don’t want to be branded as ‘that guy’ – the one who is ‘not committed’, ‘not willing to go the extra mile’, ‘not flexible enough’, first out of the door’ – you don’t want to have that label pinned on your performance appraisal. After all, your boss is still hard at work, and so are your other colleagues.

So you wait, pretending to work – although you do not really do much – perhaps you’re checking Facebook, reading the news or similar. And so does your boss, not wanting to be seen leaving before anyone else. But what example is this going to set for you and your other colleagues. You all wait for someone to make the first move – a prisoner’s dilemma situation. The only difference is that it’s you who is the prisoner in this situation, also known as ‘presenteeism’.

What is presenteeism? If you search the term on Google Scholar or Scopus, you will come across a number of articles in the fields of health and labour economics that define presenteeism as a phenomenon in which employees who feel physically unwell choose to go to work, or stay on at work, rather than asking for time off to get better (see, for instance, Hansen and Andersen, 2008 and several others). This is also known as ‘sickness presenteeism’.

According to Cooper and Lu (2016), however, the use of the term can be extended to describe a wider situation in which a worker is physically present at their workplace but not functioning (by reason of tiredness, physical illness, mental ill-health, peer pressure or whatever else). As explained in Biron and Saksvik (2009):

Cooper’s conceptualisation of presenteeism implied that presenteeism was a behaviour determined by specific determinants (i.e. long working hours and a context of uncertainty). This tendency to stay at work longer than required to display a visible commitment is what Simpson (1998) calls ‘competitive presenteeism’ where people compete on who will stay in the office the longest.

Unsurprisingly, the effect of presenteeism on the wellbeing of workers and the economic performance of firms has been looked at extensively from different angles and disciplines – including health economists, organisational behaviour and labour economists – for a recent and comprehensive review of the literature on this topic see Lohaus and Habermann (2019). Most of these studies agree that the effects of presenteeism are negative; in particular, they identify significant negative effects on the physical health of workers (Skagen and Collins, 2016); emotional exhaustion and mental health issues (Demerouti et al, 2009); persistent productivity loss (Warren et al, 2011); lower work engagement and negative feelings (Asfaw et al, 2017) – among several others. There seems, therefore, to be plenty of convincing evidence that presenteeism is bad for everyone – business owners, managers and staff.

So next time that you find yourself stuck at work working silly hours, feeling totally unproductive and just staying to be seen, email this blog to your boss and other colleagues – and ask them if they wish to join you for a drink or a walk.

You’re welcome!

(By the way, there’s 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 the office is seen as the least efficient!)

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References

Questions

  1. ‘Presenteeism leads to lower productivity and firm performance and should be discouraged by business owners and managers’. Discuss.
  2. In a recent interview given to Reuters, Jack Ma, the Chinese billionaire and owner of Ali Baba, defended his ‘996 work model’ (working 9am to 9pm for 6 days a week) as a ‘huge blessing’. Find and review some articles on this topic, and use them to write a response. Your response should be substantiated using relevant economic theory and empirical research.
  3. Have you or anyone you know found yourself guilty of presenteeism? Share your experience with the rest of the class, focusing on effects on productivity and your attitude towards your employer and work colleagues.

It’s been a while since I last blogged about labour markets and, in particular, about the effect of automation on wages and employment. My most recent post on this topic was on the 14th of April 2018 and it was mostly a reflection on some interesting findings that had been reported by Acemoglu et al (2017). More specifically, Acemoglu and Restrepo (2017) developed a theoretical framework to evaluate the effect of AI on employment and wages. They concluded that the effect was negative and potentially sizeable (for a more detailed discussion see my blog).

Using a model in which robots compete against human labor in the production of different tasks, we show that robots may reduce employment and wages … According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18–0.34 percentage points and wages by 0.25–0.5 percent.

Since then, I have seen a constant stream of news on my news feed about the development of ever more advanced industrial robots and artificial intelligence. And this was not because of some spooky coincidence (or worse). It has been merely a reflection of the speed at which technology has been progressing in this field.

There are now robots that can run, jump, hold conversations with humans, do gymnastics (and even sweat for it!) and more. It is really impressive how fast change has been happening recently in this field – and, unsurprisingly, it has stimulated the interest of labour economists!

A paper that has recently come to my attention on this subject is by Graetz and Michaels (2018). The authors put together a panel dataset on robot adoption within seventeen countries from 1993 to 2007 and use advanced econometric techniques to evaluate the effect of these technologies on employment and productivity growth. Their analysis focuses exclusively on developed economies (due to data limitations, as they explain) – but their results are nevertheless intriguing:

We study here for the first time the relationship between industrial robots and economic outcomes across much of the developed world. Using a panel of industries in seventeen countries from 1993 to 2007, we find that increased use of industrial robots is associated with increases in labor productivity. We find that the contribution of increased use of robots to productivity growth is substantial and calculate using conservative estimates that it comes to 0.36 percentage points, accounting for 15% of the aggregate economy-wide productivity growth.
 
The pattern that we document is robust to including various controls for country trends and changes in the composition of labor and other capital inputs. We also find that robot densification is associated with increases in both total factor productivity and wages, and reductions in output prices. We find no significant relationship between the increased use of industrial robots and overall employment, although we find that robots may be reducing the employment of low-skilled workers.

This is very positive news for most – except, of course, for low-skilled workers. Indeed, like Acemoglu and Restrepo (2017) and many others, this study shows that the effect of automation on employment and labour market outcomes is unlikely to be uniform across all types of workers. Low-skilled workers are found again to be likely to lose out and be significantly displaced by these technologies.

And if you are wondering which sectors are likely to be disrupted most/first by automation, the rankings developed by McKinsey and Company (see chart below) would give you an idea of where the disruption is likely to start. Unsurprisingly, the sectors that seem to be the most vulnerable, are the ones that use the highest share of low-skilled labour.

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Questions

  1. “The effect of automation on wages and employment is likely to be positive overall”. Discuss.
  2. Using examples and anecdotal evidence, do you agree with these findings?
  3. Using Google Scholar, put together a list of 5 recent (i.e. 2015 or later) articles and working papers on labour markets and automation. Compare and discuss their findings.

Spring has already made its appearance here in Norfolk. Our garden is in full bloom and I am in a particularly spring-philosophical mood today – especially so as I should soon be hearing news from the editorial office of a coveted economics journal. This concerns a paper that I submitted for publication what feels like months ago.

And just as I was reflecting on this thought, a paper by Firmuc and Paphawasit (2018) landed on my desk, evaluating the impact of physical attractiveness on academic research productivity in the field of economics. More specifically, the authors pull together information about the research productivity of about 2000 published economics researchers. They then find photos of them and rate their attractiveness (yes, seriously!) using an online survey. In particular:

Besides collecting some basic information on the authors, we also rated their attractiveness. To this effect, we circulated a number of online survey links to potential participants at Brunel University and elsewhere, using direct communication, email and social networks. Each online survey collected basic background information on the assessor (gender, age, ethnicity, highest education, and whether they are currently enrolled as a student) followed by 30 randomly-chosen and randomly-ordered photos, with each picture placed on a separate page.

…Each rater was asked to rate the attractiveness of the person in the photo on an 11-point scale, from 0 (unattractive) to 10 (very attractive). No information on the photographed individuals was provided and the raters were told that the survey studies the formation of perceptions of beauty. The raters were also asked whether they recognised the person in the picture, or whether the picture did not load properly: in such instances, their scores were excluded from the analysis.

The average beauty score was 3.9, with the most attractive academic scoring 7.6

They even attach photographs of the three most attractive male authors in their sample in an appendix (thankfully the other end of the distribution was left out – I had to check to make sure, as I was worried for a few minutes I would find my photo posted there!).

Their results show that there is a link between authors’ attractiveness and quality of journals where their papers are published, as well as number of citations that they receive. According to their findings, this association matters most for more productive authors (‘of intermediate and high productivity’), whereas there seems to be very small or no effect for less productive authors. Some of these effects disappear once controlling for journal quality:

…attractive authors tend to publish their research in better journals, but once their work is published, it does not attract more citations than other papers published in the same journal by less good-looking authors.

Although there are many methodological parts of this paper that I do not quite understand (probably because it is not my area of specialisation), it does remind us that looks do matter in labour markets. There is a well-established literature in labour economics discussing the association between appearance/beauty and wages and the so-called ‘halo effect’ (referring to the physical attractiveness premium that more attractive workers are likely to command in labour markets – see also Langlois et al., 2000; Zebrowitz et al., 2002; Kanazawa and Kovar, 2004; for a detailed discussion on this).

I was also surprised to read that this beauty bias can be also gender specific. For instance, Cash et al (1977) and Johnson et al. (2010) find that the effect goes the other way (negative impact) when considering female candidates applying for jobs traditionally perceived as ‘masculine’ ones. By contrast, male candidates are more likely to experience a positive return on good looks, irrespective of the type of job that they do (see also Johnson et al., 2010).

No surprise then that ‘guyliners’, ‘make up for men’ and other male beauty products are becoming increasingly popular amongst younger workers – in Europe it is not as common yet as it is in parts of Asia (Japan comes to mind), but I imagine it is a matter of time, as more workers realise that there are positive returns to be made!

References

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Questions

  1. Read some of the papers posted above and explain the main argument about the link between physical attraction and wages. What does the empirical evidence show on this?
  2. Using examples and anecdotal evidence, do you agree with these findings?
  3. If these findings are representative of the real world, what do they suggest about the functioning of modern labour markets?

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.

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References

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?

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.

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References

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.