Abstract
This chapter focuses on the impact of digitalisation on employees, labour markets and the distribution of incomes. It distinguishes between occupations and tasks and shows that digitalisation may cause displacement effects, but can hold the potential for employment growth. It also treats qualitative aspects and how digital technology effects the structure of employment. It discusses changes in working conditions and employment relationships and discusses the extent to which the opportunities offered by digitalisation are leading to concentration trends. Accordingly, it draws some conclusions about the corresponding implications for policy.
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Notes
- 1.
The effect of technology on markets is not a one-way street, of course there are reciprocal effects. For the problem area targeted in this chapter, for example, the ageing of the population and the resulting shortage of skilled workers would be a motive for investing in digital technology to compensate for this shortage. Such connections were discussed in Sect. 2.1.3. Here we will now look at the extent to which the changes made possible by digital technology are specific.
- 2.
There is a separate discussion on the topic of such waves of development. However, the fact that the intervals are getting smaller seems to be a common, undisputed assumption of the type of waves diagnosed.
- 3.
Appropriate insofar as this technology has for some time been able to master specific tasks that could previously only be solved by human cognition, the complexity of the potential tasks to be mastered is growing dramatically. As shocking as the successes in chess or the Chinese board game “Go” were (Koch 2019), they now may seem downright boring in view of the achievements of even the free version of ChatGPT.
- 4.
It is not possible here to adequately reflect the relevant discussion on the extent to AI, for example, is similar to human cognitive processes or not; AI and machine learning are ultimately simulations of human intelligence, which, however, come about under both different conditions and function differently. AI forms an own research agenda within economics (see e.g. Agrawal et al. 2019), and we will concentrate here on the effects within the framework of the chosen principles. The author of this book took the liberty of asking the large language model-based ChatGPT the following, everyday language question: “Is Artificial Intelligence intelligent?” The answer was the following: “Artificial Intelligence (AI) refers to the field of computer science that deals with the development of intelligent machines or systems that can perform tasks that normally require human intelligence. However, it is important to note that AI in its current form does not have true human-like intelligence. (…) AI may have impressive capabilities, but they lack the broader understanding, reasoning and awareness associated with human intelligence. (…) They lack subjective experience, emotion and the ability to comprehend the world beyond their specific task or domain. (…) In summary, while AI can perform certain tasks with great efficiency and accuracy, it does not have the same level of general intelligence, awareness and understanding as humans” (OpenAI 2023). Well done, ChatGPT!
- 5.
Here, too, we are dealing with a somewhat problematic agglomerative term, which is already evident from the fact that the “makers” of the digital transformation differentiate the concept of and terms for knowledge differently and concretely in a problem-oriented way and, in order to capture more world complexity, naturally also strive for appropriate integration. See, for example, Wang et al. (2021), on “Linguistic Knowledge, World Knowledge and Common Sense Knowledge”. For the purposes of this chapter, “world knowledge” will be used as a pragmatic working term, whose heuristic function is sufficiently evident from the above paragraph. Irrespective of this, it is of course quite clear that such world knowledge and—however accumulated—experience can also be increasingly absorbed by AI.
- 6.
At this point, this term refers to Luhmann’s social systems theory (comprehensively Luhmann 1995; for an introduction, see recently Baraldi et al. 2021), with the help of which this problem area can be demonstrated quite vividly. From this theory’s point of view, the phenomenon of “society” consists of communications which, differentiated by binary codes (see Luhmann 1982, 169f.), each form their own subsystems and maintain themselves. These few references must be left here; a discussion of systems theory approaches with regard to the subject matter of this presentation is simply not called for.
- 7.
The fact that the use of robots does not mean the disappearance of jobs will be discussed in detail. Irrespective of this, a robot is of course nothing other than “(…) a specific type of automation that is precisely designed to replace human work” (Aghion et al. 2022, 9), in other words “(…) any automatically operated machine that replaces human effort, though it may not resemble human beings in appearance or perform functions in a humanlike manner” (Moravec 2023), whereby the word “humanlike” is a good indication of the problems of knowledge and interaction mentioned above.
- 8.
Moreover, in order to gain insight into the extent of automation, artificial intelligence is used for text analysis. Publications often refer to data provided by the International Federation of Robotics (IFR), which also provides useful definitions (see World Robotics 2022, 27f.). For the purposes of this chapter, however, it is not necessary to work on such a fine-grained basis.
- 9.
The optimistic Marininiello (2022, 284) points out, however, that “(…) instead, their job was transformed, with more time dedicated to direct personal advice and less cash handling”. The fact that machines can replace jobs is of course not a problem as long as the people taking these jobs can then make themselves useful elsewhere and not at a significantly lower wage. The question is rather to what extent the known displacement effects of a technologised economy has come under new conditions as a result of digitalisation.
- 10.
A prerequisite for this argumentation to be successful is, of course, that all products can be sold and this also depends on a number of other factors. The cited study of Aghion et al. focuses on “evidence from France”. They have referred to their own study again (Aghion et al. 2022, 10), in which they themselves state that “(w)hether it be the robot count or the patent measure, the aggregate measures of automation/robotization at the country or industry level provide inconclusive evidence”. Accordingly, they also point to the study by Bonfiglioli et al. (2020), which relativises the possibility that lower prices are actually passed on to consumers.
- 11.
Independently of their research objective, their contribution also already provides a good indication for the considerations pursued here by documenting that the number and share of patents in this area increased strongly in their research period from 1976 to 2014.
- 12.
This statement is quite isolated in this form, since the technological absorption capacity in general, as well as that for digital technologies in particular, is tied to a whole series of important preconditions, such as those described very prominently by Acemoglu and Robinson (2012), focusing the institutional setting, but also to the given endowment with material- and immaterial resources, the degree of economic development as well as the position of a country in the international division of labour (see also the following footnote). In view of the generality of the present considerations, it is probably appropriate to point out that both scenarios, positive and negative, exist in principle.
- 13.
With a view to the international division of labour and the different levels of development of economies, these problems are discussed in detail by Carbonero et al. (2018), who also include trade aspects and “re-shoring”, which is ultimately made easier by automation, in their analysis. These primarily international aspects cannot be discussed in detail here, but should be mentioned where appropriate.
- 14.
This study is also often cited because the figures are so alarming. Methodologically, however, it should be taken into account that Frey/Osborne start at the level of occupations, whereas a reference to tasks, as Anrtz et al. do, leads to different results, if only because not all workers are the same, i.e. they can take on different tasks.
- 15.
It could also be asked to what extent digital technology itself can be useful in mitigating frictions, for example by making better simulations available to political decision-makers. However, this raises a number of model-theoretical questions that cannot be dealt with here.
- 16.
Cultural limits are abstract, therefore cannot be rationalised away ad hoc and naturally vary considerably. Relatively well known in the context under consideration here is the very different acceptance of the use of robots in elderly care, which is relatively high in Japan, a country where on the one hand the threshold of shame is extremely high and on the other hand no one wants to be a burden to anyone else, while in European countries robots are not accepted as care workers. In gastronomy or show business, robotisation is sometimes encountered—see, for example, the bizarre “Robot Restaurant” in Tokyo, which is currently under maintenance—but rather to cause a sensation than to replace jobs. The same might apply to robotic brothels, which have been opening worldwide in the past few years (see Frank 2018; Spitznagel 2018) and naturally raise, above all other considerations, controversial ethical questions (see Hancock 2020; Johnson and Verdicchio 2020).
- 17.
Of course, this also means an important distribution problem. See the following sub-chapter.
- 18.
With this potential, Fernández-Macías identifies a problem area that cannot be dealt with in detail here. Zuboff (2019) has prominently discussed the fundamental problem of omnipresent surveillability through digital technology and offers an instructive overview of a new problem area, but takes a less comprehensive view than the title would lead one to expect (see critique by Johns 2020).
- 19.
Although Williamson coined this elegant term and conclusively drew attention to a phenomenon that is possibly more topical in the age of digitalisation than it was in his time, a theoretically consistent elaboration of this problem area is still open. Asked about it by Geoffrey Hodgson and David Gindis, three years before his Nobel Prize, Williamson replied, laconically, “(o)n the one hand it seemed an important concept and on the other it was hard to give a specific content” (Williamson, 2007, 378). That is where it has remained. Chassagnon (2021, 3), focusing on questions of organisation, points out that “Williamson’s organisational atmosphere has barely been exploited in the economic literature”.
- 20.
Carbonero et al. (2018) provide a study focusing on the developing countries, which, as they specialise in production steps that require simpler activities within the international value chain, are of course particularly affected (ibid., 9). However, they attest that robotisation has “(…) a detriental effect on employment growth at the global level” (ibid., 1).
- 21.
Whether there is a new increase in productivity through digitalisation is by no means unambiguously answered. This may be due to the fact that the productivity effects affect different sectors or different sections of the value chains to different degrees. This phenomenon is also referred to as the “Solow paradox”, because Solow said that one could “(…) see the computer age everywhere but in the productivity statistics” (Solow 1987, 2). See also Triplet (1999). In fact, since the 1970s, a stagnation of productivity has been observable in the developed countries, which, remarkably, has not been changed by the rapid spread of digital technology. The phenomenon is widely discussed in the literature, and opinions differ. Brynjolfsson and Hit (1996) were quick to see the end of this paradox. Gordon (2015, 2012) attests only a minor impact of digitalisation on productivity and, although the USA in particular performed relatively well compared to other countries for the period around the turn of the millennium, he sees more of a secular stagnation for the USA. Gal et al. (2019), on the other hand, do attribute an increase in productivity at the company level to digitalisation. The European Central Bank (2021, 42) points out that after the industrial revolution was completed, substantial productivity were still realised, especially in the service sector.
- 22.
The phenomenon of polarisation to the disadvantage of the middle class is particularly explosive in the United States, although it can also be observed in other developed economies and also in developing countries. Certainly, the contribution by Autor et al. (2006) points to important causes to which the effects of digitalisation can be well connected. However, this phenomenon, which has been observable for some time, is also based on a whole bundle of causes. One key element is to be found in the Cantillon effect of inflation, see e.g. Sieroń (2019, 204f). This aspect is a specific phenomenon of the problem of monetary stability discussed in Chap. 5.
- 23.
Nota bene: This is a much-discussed finding. However, it seems plausible to assume that digital technology tends to promote this process rather than slow it down.
- 24.
The process of digitalisation is accompanied by a broad discourse on the necessary reform of education systems, the dominant element of which is the focus on competence instead of lexical knowledge. In the context considered here, even Mariniello (2022, 297) cannot refrain from repeating the usual nonsense: “So educational systems should shift from simply providing direct knowledge to students to providing the non-cognitive tools needed to acquire new knowledge autonomously”. If these “tools” are “non-cognitive”, what are they supposed to be? Haptic? Or rather olfactory? Even sniffing would require cognition. The dramatic impact of the mass use of digital media on the entire range of human cognitive and social abilities cannot be discussed here, but it is not too much to say that a digital downgrading of human abilities is taking place in the developed countries on such a scale that the boldest science fiction dystopian authors would not have dared to imagine it even a short time ago.
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Dötsch, J.J. (2024). Labour and Incomes. In: Economic Policy in the Digital Age. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-53047-0_8
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