By Klaus Prettner and David E. Bloom
Over the last decade, increased utilization of automation and digital technologies have allowed a full substitution of capital for labor for many tasks (see, for example, Acemoglu & Restrepo, 2018; Hémous & Olsen, 2018; Prettner & Strulik, 2020). Projections point to considerable potential for further replacement of humans by robots, 3-D printers, and artificial intelligence (AI) (Arntz et al., 2017; Frey & Osborne, 2017; Bloom et al., 2019). For example, Abeliansky et al. (2020) calculate in their baseline scenario that 37.9 million jobs worldwide will be automated from 2020 to 2030.
The consequences of the rise in adoption of these new technologies are widely and generally agreed to be i) increasing productivity and economic growth on the upside (Graetz & Michaels, 2018), and ii) rising inequality and challenging job prospects for low-skilled workers on the downside (Cords & Prettner, 2021). While the productivity and growth effects are straightforward, increasing inequality in this context has two sources. First, automation and digital technologies constitute a form of capital such that the income they generate is predominantly earned by the capital owners. Since capital income is much more unequally distributed than labor income, a rise in automation and digitalization that substitutes for raw labor generates a shift of the income share from low- to high-income individuals (Prettner, 2019). Second, since the substitution of low-skilled workers by automation and by digital technologies remains easier than the corresponding substitution of high-skilled workers (Arntz et al., 2017; Frey & Osborne, 2017), this technological change also has the effect of widening income gaps (Lankisch et al., 2019). In Prettner and Bloom (2020), we provide a detailed overview of the economic consequences of these developments and of policy responses to cope with some of the downsides of automation and digitalization, in general, and AI and robots in particular.
The COVID-19 pandemic has provided another strong boost to the use of automation and digitalization technologies. Modern information and communication technologies (such as remote teamwork facilitated by teleconferencing) have allowed firms and public agencies to continue operating even when social distancing protocols are in force. Only two decades ago, such a shift toward remote work would have been impossible because of the absence of suitable technologies. In addition, the threat of infection at the workplace and an ongoing labor shortage that is—to some degree—caused by fear of infection accelerated the adoption of automation technologies in the production and distribution of goods. These trends could further exacerbate inequalities because low-skilled workers typically benefit less from the enabling of remote work; they are also more likely to be substituted by the robots and 3-D printers that are increasingly relied upon in the wake of the COVID-19 pandemic.
There is, however, one area of the digital transformation that has the potential to lead to huge benefits across the socioeconomic spectrum: healthcare innovation.
Fleming (2018) reports that typically nine in ten attempts at developing new therapeutic drugs fail between phase I trials and regulatory approval. Thus, many firms have started using AI in the search for successful candidate drugs to reduce costs and raise success rates. Successful applications of such AI-based searches range from the development of drugs against cancer to the treatment of cardiovascular diseases. In addition, firms employ AI to classify genes and their roles in the future development of diseases, potentially offering a stimulus to personalized medicine in the future (Fleming, 2018). “Medical robots,” such as IBM’s Watson, are already highly proficient in diagnosing diseases and screening X-rays, tasks that require large storage capacities for medical knowledge and advanced pattern recognition skills. Soon, these devices could be of enormous help to doctors. Eventually, they may even come to outperform physicians due to the ongoing improvement in computing power and the increasing availability of training datasets relative to the limited capacity of human brains to store accumulated and relevant medical knowledge. Moreover, the scope and scale-up of 3D-printing of medical implants, prostheses, hearing aids, braces, replacement teeth, personalized drugs, and potentially even full-scale organs could make these precious items widely available and, in the long run, lower their prices to levels at which increasingly wide segments of the population could benefit (Abeliansky et al., 2020; PEW, 2020; Prettner and Bloom, 2020).
Automation and digitalization could also alleviate some of the burdens that confront individuals in an ageing society (Abeliansky & Prettner, 2017; Acemoglu & Restrepo, 2021) with a deteriorating health status. While robots could assume the physically demanding jobs that older adults have difficulties performing, robotic exoskeletons might help care assistants and nurses to lift patients on and off beds, toilets, and examination tables (Financial Times, 2019; The Economist, 2019). Such developments, in turn, would imply better working conditions for several professionals in the healthcare sector, particularly those who are expected to be in high demand in ageing societies. Even the loneliness of older adults or patients with illnesses that limit their social participation could, to some degree, be counteracted by humanoid robots and those in the form of therapeutic companion animals. Autonomous cars and delivery robots could be very helpful to those who are unable (or lack confidence) to drive due to illnesses. This innovation would help serve the high demand for mobility of older members of the population, particularly in rural areas. In addition, wearable sensors are already widely used to monitor medication plans and worrisome changes in gait and bodily functions, as well as to call for help in emergency cases (see Prettner and Bloom, 2020).
Healthcare innovation in automation and digital technologies has likewise been accelerated by the COVID-19 pandemic. Robots and smart technologies are increasingly used for remote disease surveillance, contact tracing, diagnosing diseases without face-to-face contact, and even disinfecting potentially contaminated areas (Blake 2020). Telemedicine — a previously small niche — is now much more widely accepted by both doctors and patients.
It seems clear that automation and digitalization will see further progress in the years to come. Coping with potentially negative effects of these shifts, particularly with respect to increasing inequality, is a highly challenging task. The hope is that digital transformation of healthcare and healthcare innovation will benefit considerably wider parts of the population and thereby alleviate some of the negative distributional effects of new technologies.
About the Authors
Klaus Prettner is Professor of Economics (especially Macroeconomics and Digitalization) at the Vienna University of Economics and Business (WU). His research is primarily concerned with the economic consequences of technological change and with the interrelations among health, demographic developments, and economic growth.
David Bloom is Clarence James Gamble Professor of Economics and Demography at the Harvard T.H. Chan School of Public Health. Much of his recent work focuses on the interplay of health, demographics, and economic wellbeing, and on health technology assessment.
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