This website and the course

ICT, Labour, and Inequality (EBC2130) is a course taught in period 2 at Maastricht University each year. The course forms a mandatory element in the second year of the curriculum of students who follow the specialisation Economics and Management of Information. Usually, the course is also attended by exchange students, or those who should have taken the course a year earlier, like me. Huub Meijers is the course coordinator, assisted by tutor Albert Vijghen. As part of this course, in order to gain some basic ICT knowledge, students are required to prepare a website containing course-related literature; hence this page.

The page is structured rather easily. Below you will find the literature of this course, summarised by my fellow students in the course. Subsequently there is a short section that offers some recent English and Dutch news articles on the course's main subject: wage inequality. After providing these sources of information, this page concludes with my personal blog on the topic of IT developments in labour markets. For more general content, the header redirects to an overview of the articles (with full reference) and the university website.

Article summaries

Below, you will find all summaries of this course, written by ourselves. Enjoy!

  • Katz (2000), summarised by Sven Bergmann
  • Topel (1997), summarised by Finnian Stanley
  • Johnson (1997), summarised by Luisa Menezes Flores
  • Açemoglu (2002), summarised by Basile Damian
  • Leuven, Oosterbeek & van Ophem (2004), summarised by Tijn de Smit
  • Levy & Murnane (2013), summarised by Matilde Pesce
  • Açemoglu & Restrepo (2019), summarised by Simon Kratz
  • Cheng, Jia, Li & Li (2019), summarised by Jeremias Gilgenberg
  • Jerbashian (2019), summarised by Lino Wiede
  • Autor (2015), summarised by Julius Körfgen
  • Goos (2018), summarised by Paul Hagemeier
  • Wage inequality in the news

    The first article is an NBC news article from last September. It reports on growing wage differentials between the "haves" and "have-nots" in the United States. It mentions the Gini coefficient and discusses several causes of this growing inequality.

  • NBC News, 26 September 2019

    Income inequality is also very high in the UK. The Independent discusses Gini coefficients and Lorenz-like interpretations of income distributions (richest 10% hold 50% of wealth). The Independent argues that these conditions make the UK particularly unstable for the political struggles they experience.

  • The Independent, 21 October 2019

    As many articles in the course propose, policy-making has an effect on wages and inequality, besides supply and demand. The New York Times article below provides a poignant example of the Lebanese policy, that seems to promote the rich to get richer by increasing public debt. It discusses the tremendous consequences this can have for the Lebanese economy.

  • The New York Times, 2 December 2019.

    Another aspect of inequality that we touched upon only briefly in the course, is gender wage inequality. Earlier this year, the UK was fascinated by the BBC and their wage inequality scandal, as elaborated on in this article of The Guardian.

  • The Guardian, 12 March 2019.

    The issue of gender wage inequality is also present in the Netherlands. This recent (Dutch) article elaborates on it, as magnitude of the problem does not seem to decrease. A few cures are proposed.

  • NOS, 19 November 2019
  • My blog on ICT and wage inequality

    For this website, I wrote a threefold blog that evaluates the concept of wage inequality as a result of technological change. It is structured in a chronologic fashion. The first part reflects on the past, and is in fact my personal review on the course literature. Part 2 is a reflection on present-day equality. I elaborate here on a specific form of wage inequality, namely gender wage inequality. The final part forms a preview on what I suspect is to come regarding technological progress and wage inequality. Enjoy reading!

    In my opinion, this course approaches the literature in a very natural, logical manner. It follows a clear path in the evolution of theory of wage inequality over the past two decades. The Borjas chapters develop a framework in which there is an equilibrium competitive wage and in which education is seen as merely a human capital investment decision. This makes perfect sense, however it is obvious that this is a model, so it only approaches the actual market. Because productivity levels differ across labour markets, inequalities arise.

    The papers form some sort of quest for the actual causes of this inequality, and I find it very enjoyable to join this quest that evaluates all kinds of ‘tweaked’ versions of the supply and demand framework. The first proposal is that technological change may be skill-biased. The supply and demand framework predicts that if more people engage in higher education, the increase in skill supply will lower the wage for skilled labour. However, empirical data shows that skill supply as well as the college wage premium have increased, which imply that demand for skilled labour must also have been increased. The proposed reason for this around 2000 was that technological change was skill-biased: skilled workers became more productive by it relative to unskilled workers. At this point in the course, this sounded as a more than reasonable explanation.

    Johnson (1997) and Açemoglu (2002) extend this idea by addressing the elasticity of substitution more in-depth. They reason that technological change can either substitute or complement skilled and unskilled labour. They conclude that in general, technological change tends to substitute for unskilled labour more easily, and to often be rather complementary to skilled labour. This ‘capital-skill complementarity’, as Goos (2018) calls it, results generally in a higher relative demand for high-skilled labour, increasing the skill wage premium.

    It is only in 2013 when Levy and Murnane start to hint at a further specification of the effects of technological change. They define 5 types of tasks in the workplace, and conclude that only routine-based tasks are susceptible to computerisation or automatization. This results in what Goos (2018) calls routine-biased technological change. These routine-based tasked are concentrated in the medium-wage segment of the wage distribution, resulting in what is coined job polarisation. Employment shares rise in high- and low-wage jobs, as medium-wage jobs are increasingly replaced by IT. There is discussion if this skill polarisation corresponds to wage polarisation, as seen in the contradictory conclusions of Autor (2015) versus Goos (2018)). It seems that high-skill jobs are more complementary to IT, as their wage premium seem to increase more than in low-skill jobs.

    After following the course and the different ways of reasoning, I am convinced that routine-biased technological change is most resembling empirical data, since I think explains a lot of the patterns seen. How I think this will develop? That is explained in blog III.

    To evaluate the status quo of labour inequality beyond recent articles, I turn to the news section on my website. Here, I have included two articles on gender inequality, an apparently persistent phenomenon is hinted upon in some of the course literature. In table 3 of Jerbashian (2016), it is shown that men are more often in high-wage occupations than are women. He provides some wishful reasoning that as a result of job polarisation, women might see a higher increase in high-skill employment share relative to men, because of their apparent advantage in social and communication skills. He observes this trend indeed, but it is an increase that is relatively larger men’s increases. However, it does not conclude anything on absolute employment shares after this development, which may still be lower than men in high-wage occupations.

    Topel (1997) also finds a sorrowing effect: skill supply of women increases, but wage inequality shows inconclusive movement. The general movement of the population of having the skill supply shift offset by a larger demand shift, appears not to hold when only looking at women. We might tend to think then, that demand for skilled female labour is not growing as much as general demand for skilled labour.

    The (Dutch) NOS-article in the news section mentions that women indeed tend to work in low-wage sectors, and more often in part-time contracts. Nevertheless, even when correcting for those facts, the gender wage gap in the Netherlands appears to be growing! An explanation offered is that women tend to negotiate less about salary when applying for a new job, relative to men. A stunning fact is, that women that do tend to succeed relatively less than men when negotiating for a higher salary. This corresponds to the problem in the BBC-wage scandal from the other article. The issue appears to have little to do with a supply and demand framework, but rather with a persistent, stubborn paradigm in western society, wherein women systematically earn less than men. A mere reflection of this is the fact that almost all course literature only involve male data: apparently there has not been enough data from female wages over the past few decades.

    I think this issue is hardly resolvable by technical market solutions. As the NOS-article indicates, it is prohibited by law to discriminate wages by gender, yet it still appears to happen. A reason for this may be unobservable skills, or experience, but it seems questionable that women generally have fewer unobservable skills or job experience than men. For now, it seems like a firm’s responsibility to ensure gender wage equality. But as Hian Li Ko states in the NOS-article: awareness is key. So let us, as aspiring economists, who will end up in businesses and maybe even wage setting be pioneers in gender wage equality awareness!

    Some of the course literature hints toward future developments. For example, Johnson (1997) asks if wage inequality is likely to continue rising in the future. He argues that inequality may be reversed when computers become self-sufficient, making skilled labour superfluous and increasing demand for unskilled labour. Goos (2018) is not that rigorous, but he also advises higher investment in occupations in non-routine social, motivational, and interaction skills, since they will probably be hard to automate in the near future. Autor (2015) thinks it will not get that far, mostly because of environmental control and the limits of machine learning, captured in the Polanyi´s paradox.

    Personally, I also think fears of extreme job replacement may be a bit farfetched. Autor (2015) has a fair point with the Polanyi paradox, we do know much more than we know. However, I think the role of workers may change radically in the very near future. As several authors in the course literature said, IT does not always take over entire jobs, but is often able to take over specific, simplified tasks. The role of a worker may well evolve to some sort of an IT operator more and more over time.

    Think of the 5 types of tasks of that Levy and Murnane (2013) develop, of which we already agreed that routine tasks are at risk of being substituted by IT. Manual non-routine tasks are hard to codify entirely, but specific tasks within jobs are easily codifiable. For example, think of health care for elderly, where many tasks are very physically demanding (therapy, washing, et cetera). Machines may take over the heavy tasks, and an ‘operator’ ensures that all goes well, or talks the elder person through the process. Lately, there have been tests in the Netherlands where robots were used to give some personal attention and assistance to lonely elderly, so even the human, or social part can be substituted for.

    Meanwhile, some threatening developments are also going on for working with new information and solving unstructured problems. Artificial intelligence is characterised by its ability to adopt information and learn to see patterns. Pattern recognition is thus very much at risk of being substituted by artificial intelligence in the very near future. Because of artificial intelligence’s ability to learn exponentially, I expect that a certain degree of common sense will be achieved anytime soon as well. These developments enable IT to penetrate the high-wage occupations as well. Managerial optimisation decision are made quicker and more efficiently by computers, perhaps a manager is only needed to check the feasibility of the proposed solution. But then again, if this machine can learn inductively, and creates new rules for itself after being corrected, this manager is no longer needed in the future either.

    Concluding, I think that routine-based technological change may seem like a good theory for the labour market present-day, but it will not hold for upcoming decades. Artificial intelligence and machine learning will grow exponentially and thereby change all existing models into something not imaginable yet. This may sound somewhat scary, but we are creating IT that is able to think autonomously, and that might have grave consequences for society as we know it. For the near future, maybe we will be much more reliant on IT, and some IT will be harder to operate than other technologies, so wage differentials may still prevail. But where we are in 30 years? That may depend on IT development, but also on societal developments. Think of the problems mentioned in blog II, but also about recent increases in part-time contracts and the like. The future is exciting…