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.
Articles
Questions
- “The effect of automation on wages and employment is likely to be positive overall”. Discuss.
- Using examples and anecdotal evidence, do you agree with these findings?
- 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
- Beautiful Minds: Physical Attractiveness and Research Productivity in Economics
Institute of Labor Economics conference paper, Jan Fidrmucand Boontarika Paphawasit (July 2018)
- Maxims or myths of beauty? A meta-analytic and theoretical review
Psychological Bulletin, Vol 126(3), pp.390–423, Judith H Langlois, Lisa Kalakanis, Adam J Rubenstein, Andrea Larson, Monica Hallam and Monica Smoot (May 2000)
- Looking Smart and Looking Good: Facial Cues to Intelligence and their Origins
Personality and Social Psychology Bulletin, Leslie A Zebrowitz, Judith A Hall, Nora A Murphy, Gillian Rhodes (February 2002)
- Why beautiful people are more intelligent
Intelligence, Vol 32, pp.227–243, Satoshi Kanazawa and Jody L Kovar (2004)
- Sexism and beautyism in personnel consultant decision making
Journal of Applied Psychology, Vol 62(3), pp.301–10, Thomas Cash, Barry Gillen and D S Burns (January 1977)
- Physical Attractiveness Biases in Ratings of Employment Suitability: Tracking Down the “Beauty is Beastly” Effect
The Journal of Social Psychology, 150(3), pp.301–18, Stefanie K Johnson, Kenneth E Podratz, Robert L Dipboye and Ellie Gibbons (April 2010)
Article
Questions
- 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?
- Using examples and anecdotal evidence, do you agree with these findings?
- If these findings are representative of the real world, what do they suggest about the functioning of modern labour markets?
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
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.
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Data
Questions
- What factors are likely to encourage workers to work longer hours?
- Give some examples of jobs where workers have flexibility in the amount of hours they work per week and jobs where the working week is of a fixed length.
- For what reasons are annual working hours longer in the USA than in Germany?
- Would it be in employers’ interests if the government legislated so as to reduce the maximum permitted working week? Explain.
- What is meant by ‘efficiency wages’? How relevant is the concept to the issue of the average number of hours worked per year from country to country?
- Explain why people in poorer countries tend to work more hours per year than people in richer countries.
- If workers’ wages equalled their marginal revenue product, why might some workers choose to work more and others choose to work less (assuming they had a choice)?
- Are jobs in the gig economy and zero-hour contract jobs in the interests of workers?
- Is South Korea wise to cut its work limit from 68 hours a week to 52?
The UK’s Low Pay Commission has just published its annual report. This shows that the lowest-paid 20% of workers aged 25 and over benefited from last April’s 4.4% rise in the ‘National Living Wage (NLW)’, the name the government gives to the statutory minimum wage for people in this age group. Although only around 6.5% of such workers are paid at the NLW, when it rises this tends to push up wage rates which are just above the NLW as employers seek to maintain the differential.
If the new NLW is above the equilibrium rate for those receiving it, it would be expected that firms would respond by employing fewer workers. However, the Low Pay Commission found no evidence that rises in the NLW caused unemployment. Instead, employers responded by combinations of increasing prices, accepting lower profit margins, restructuring their workforce and reducing the gaps between pay bands.
Over the longer term, employers often seek to increase labour productivity to offset the higher cost per worker of paying increased minimum wage rates. This, however, could lead to a reduction in employment if it involves substituting capital for labour or if greater labour efficiency does not result in a sufficient increase in total output to compensate for an increase in output per worker.
Articles
Report and data
Questions
- Demonstrate on a supply and demand diagram for a perfectly competitive labour market the impact of a rise in the minimum wage on employment and unemployment in that market. Assume that the market is initially in equilibrium at the previous minimum wage rate.
- For what reasons in such markets may a rise in the minimum wage not lead to a rise in unemployment?
- Now demonstrate the effect of a rise in the minimum wage in a monopsonistic market. Assume that the previous minimum wage was previously being paid by the employer.
- For what reasons may the employer in the previous question choose to retain employment at the current level?
- For what reasons may the effect of a rise in the minimum wage be different in the long run from the short run?
- How can employers avoid paying the minimum wage (a) when workers work in the ‘gig’ economy; (b) when workers have to travel as part of their job: e.g. care workers moving from house to house; (c) workers working from home producing items for an employer, such as clothing or jewelry, or providing a service such as telesales?
I admit it, the title of my blog today is a little bit misleading – but at the same time very appropriate for today’s topic. Nancy Sinatra certainly wasn’t thinking about emigration when she was singing this song – it had nothing to do with it, after all. It is, however, very relevant to economists: Indeed, there are many economics papers discussing the effects of skilled immigration on host and source economies and regions.
Economists often use the term ‘brain drain’ to describe the migration of highly skilled workers from poor/developing to rich/developed economies. Such flows are anything but unusual. As The Economist points out in a recent article, ‘[I]n the decade to 2010–11 the number of university-educated migrants in the G20, a group of large economies that hosts two-thirds of the world’s migrants, grew by 60% to 32m according to the OECD, a club of mostly rich countries.’.
The effects of international migration are found to be overwhelmingly positive for both skilled migrant workers and their hosts. This is particularly true for highly skilled workers (such as academics, physicians and other professionals), who, through emigration, get the opportunity to earn a significantly higher return on their skills that what they might have had in their home country. Very often their home country is saturated and oversupplied with skilled workers competing for a very limited number of jobs. Also, they get the opportunity to practise their profession – which they might not have had otherwise.
But what about their home countries? Are they worse off for such emigration?
There are different views when it comes to answering this question. One argument is that the prospect of international migration incentivises people in developing countries to accumulate skills (brain gain) – which they might not choose to do otherwise, if the expected return to skills was not high enough to warrant the effort and opportunity cost that comes with it. Beine et al (2011) find that:
Our empirical analysis predicts conditional convergence of human capital indicators. Our findings also reveal that skilled migration prospects foster human capital accumulation in low-income countries. In these countries, a net brain gain can be obtained if the skilled emigration rate is not too large (i.e. it does not exceed 20–30% depending on other country characteristics). In contrast, we find no evidence of a significant incentive mechanism in middle-income, and not surprisingly, high-income countries.
Other researchers find that emigration can have a significant negative effect on source economies (countries or regions) – especially if it affects a large share of the local workforce within a short time period. Ha et al (2016), analyse the effect of emigration on human capital formation and economic growth of Chinese provinces:
First, we find that permanent emigration is conducive to the improvement of both middle and high school enrollment. In contrast, while temporary emigration has a significantly positive effect on middle school enrollment it does not affect high school enrollment. Moreover, the different educational attainments of temporary emigrants have different effects on school enrollment. Specifically, the proportion of temporary emigrants with high school education positively affects middle school enrollment, while the proportion of temporary emigrants with middle school education negatively affects high school enrollment. Finally, we find that both permanent and temporary emigration has a detrimental effect on the economic growth of source regions.
So yes or no? Good or bad? As everything else in economics, the answer quite often is ‘it depends’.
Articles
- Open future: What educated people from poor countries make of the “brain drain” argument
The Economist, R.S. (27/8/18)
- Brain drain, brain gain, and economic growth in China
China Economic Review, Wei Ha, Junjian Yi and Junsen Zhang (April 2016)
- A Panel Data Analysis of the Brain Gain
World Development, Michel Beine, Ric Docquier and Cecily Oden-Defoort (Vol 39, No 4, pp 523–532, 2011)
Questions
- ‘The brain drain makes a bad situation worse, by stripping developing economies of their most valuable assets: skilled workers’. Discuss.
- Using Google, find data on the inflows and outflows of skilled labour for a developing country of your choice. Explain your results.
- ‘Brain drain’ or ‘brain gain’? What is your personal view on this debate? Explain your opinion by using anecdotal evidence, personal experience and examples.
- Referring to the previous question, write a critique of your answer.
