New technologies and employment: The state of the art

Vivarelli, Marco; Arenas Díaz, Guillermo Working Paper New technologies and employment: The state of the art UNU-MERIT Working Papers, No. 2025-005 Provided in Cooperation with: Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), United Nations University (UNU) Suggested Citation: Vivarelli, Marco; Arenas Díaz, Guillermo (2025) : New technologies and employment: The state of the art, UNU-MERIT Working Papers, No. 2025-005, United Nations University (UNU), Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, https://doi.org/10.53330/KFFP5948 This Version is available at: https://hdl.handle.net/10419/326934 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-sa/4.0/ #2025-005 New technologies and employment: the state of the art Marco Vivarelli and Guillermo Arenas Díaz Published 10 February 2025 DOI: https://www.doi.org/10.53330/KFFP5948 Maastricht Economic and social Research institute on Innovation and Technology (UNU-MERIT) email: [email protected] | website: http://www.merit.unu.edu Boschstraat 24, 6211 AX Maastricht, The Netherlands Tel: (31) (43) 388 44 00 UNU-MERIT Working Papers ISSN 1871-9872 Maastricht Economic and social Research Institute on Innovation and Technology UNU-MERIT UNU-MERIT Working Papers intend to disseminate preliminary results of research carried out at UNU-MERIT to stimulate discussion on the issues raised. https://creativecommons.org/licenses/by-nc-sa/4.0/ 1 New technologies and employment: the state of the art Marco Vivarellia and Guillermo Arenas Díazb a Department of Economic Policy, Catholic University of the Sacred Hearth, Milano, Italy; UNU-MERIT, Maastricht, The Netherlands; IZA, Bonn, Germany. Corresponding author: [email protected] b Department of Economic Policy, Catholic University of the Sacred Hearth, Milano, Italy. Abstract The relationship between technology and employment has long been a topic of debate. This issue is even more pertinent today as the global economy undergoes a technological revolution driven by automation and the widespread adoption of Artificial Intelligence. The primary objective of this paper is to provide insights into the relationship between innovation and employment by proposing a conceptual framework and by discussing the state of the art of the debates and analyses surrounding this topic. JEL classification: O33 Keywords: Technology, employment, compensation theory, AI, robot Acknowledgements and disclaimers The authors acknowledge the support by the Italian Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN-2022, project 2022P499ZB: “Innovation and labor market dynamics”; principal investigator: Marco Vivarelli; funded by the European Union - Next Generation EU, Mission 4, Component 2, CUP: J53D23004830008 ). The views and opinions expressed are only those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. 2 1. Introduction The relationship between technology and employment has always been a “hot” topic both for social scientists and policy makers, at least since the first industrial revolution. Indeed, claims of technologically caused unemployment tend to re-emerge at times of radical technological change such as countries are currently experiencing, facing the arrival of automation and artificial intelligence (AI) technologies. Today, debate focuses on three main questions: What are the roles of technology and innovation in explaining the long-term declining trend of manufacturing as a share of the modern economy? Are new technologies, such as robots and artificial intelligence, replacing humans? Are job losses due to the advent of robots and AI structural and therefore inevitable? Contextualizing, McKinsey (2017) forecasts that nearly 50% of work activities could be automated by 2055. Specific sectors, such as “Accommodation and Food Services” (66%), “Manufacturing” (64%), and “Transportation and Warehousing” (60%), are particularly susceptible to automation (Figure 1). A more recent report from Goldman Sachs (2023) estimates that 25% of current jobs in the United States and 24% in the European Union could be automated. In the U.S., industries most exposed to AI include “Office and Administrative Support” (46%), “Legal” (44%), and “Architecture and Engineering” (37%), while sectors such as “Building and Grounds Cleaning and Maintenance” (1%), “Installation, Maintenance, and Repair” (4%), and “Construction and Extraction” (6%) are among the least exposed (Figure 2). This somewhat pessimistic outlook has gained significant attention among scholars to explore the potential impacts of new technologies on the labor market. New statistical tools and machine learning methods are enabling researchers to analyze more granularly which technologies affect specific jobs and tasks. For instance, Felten et al. (2018) and Felten et al. (2021) found that white-collar workers in the U.S. are more exposed to AI-driven automation. Conversely, Webb (2020) shows that robots primarily affect low-wage occupations, software alters medium-wage occupations, and high-wage occupations are most vulnerable to AI. More recently, Montobbio et al. (2023) found that low-wage jobs concentrated in production are particularly exposed to robotic labor-saving technologies, especially in installation and maintenance roles. Their study also notes that service-based activities, such as those performed by logistic and healthcare workers, are increasingly exposed to robotic technologies. However, the impact of innovation on employment is not trivial, and it requires understanding the all-possible theoretical mechanisms involved in this relationship: labor-creating (mainly from product innovation), labor-saving (mainly from process innovation), and the so-called market compensation mechanisms, potentially able to counterbalance the initial labor-saving impact of innovation (see Section 2.2). 3 Figure 1. Potential automation by 2055 in different sectors Source: Oxford Economic Forecasting; US Bureau of Labor Statistics; McKinsey analysis (2017) Figure 2. Share of the industry employment exposed to automation by AI: US Source: taking from Goldman Sachs’ report (2023). Goldman Sachs Global Investment Research 66 64 60 54 54 50 50 45 44 44 44 43 42 41 41 39 38 37 34 010 20 30 40 50 60 70 Accommodation and food services Manufacturing Transportation and warehousing Mining Retail trade Agriculture, forestry, fishing and hunting Wholesale trade Utilities Finance and insurance Construction Real estate and rental and leasing Other services (except federal, state and local… Arts, entertainment, and recreation Information Administrative and support and government Professional, scientific, and technical services Health care and social assistance Management of companies and enterprises Educational services Percentage (%) 46 44 37 36 35 33 32 31 29 28 28 28 27 26 26 25 19 12 11 9 6 4 1 0 5 10 15 20 25 30 35 40 45 50 Office and Administrative support Legal Architecture and Engineering Life, Physical, and Social Science Business and Financial Operations Community and Social Service Management Sales and Related Computer and Mathematical Farming, Fishing, and Forestry Protective Service Healthcare Practitioners and Technical Educational Instruction and Library Healthcare Support Arts, Design, Entertainment, Sports and Media All industries Personal Care and Service Food Preparation and Serving Related Transportation and Material Moving Production Construction and Extraction Installation, Maintenance, and Repair Building and Grounds Cleaning and Maintenance Percentage (%) 4 In more detail, while process innovation can be job-destroying, product innovation can imply the emergence of new firms, new sectors, and thus new jobs. But even for process innovation, the final impact on labor demand is shaped by market mechanisms that can compensate for the direct jobdestroying impact, if market and institutional rigidities do not impede them. Furthermore, a Schumpeterian vision is essential for better understanding the relationship between innovation and employment. Schumpeter (1939, 1947) argues that technical unemployment arises from disparities between the skills and abilities of workers displaced from old sectors and those required by emerging ones. This view - focusing on “creative destruction” - emphasizes that while some jobs disappear, new roles are simultaneously created (Díaz, Guerrero, et al., 2024). Additionally, as highlighted by Dosi et al. (2021), innovation should not be viewed as an exogenous or isolated phenomenon. Instead, it influences the entire socio-economic system. In other words, innovation not only impacts the firms or sectors that introduces it, but also might affect related firms and industries. For instance, a robot might serve as a process innovation for downstream sectors while simultaneously functioning as a product innovation for upstream sectors (Díaz, Barge-Gil, et al., 2024; Dosi et al., 2021). Drawing on the previous literature, the main objective of this study is to analyze the relationship between innovation and employment through a comprehensive conceptual framework, considering on the one hand the possible labor-saving impact of technological change, the scope for job creation on the other hand and focusing in particular on the market and institutional mechanisms that can shape the final labor demand outcomes. To achieve this, we first put forward an interpretative theoretical framework and then we critically discuss the key empirical studies that have explored this relationship (including studies that focus on new technologies, such as robotics and artificial intelligence, to provide a comprehensive understanding of how these recent innovations impact employment). The remainder of the paper is structured as follows: in Section 2 we discuss the main theoretical mechanisms determining the relationship between innovation and employment. Section 3 discusses the empirical literature through a selection of previous studies. In Section 4 we summarize the main findings, while Section 5 concludes. 2. A theoretical framework 2.1 A comprehensive conceptualization One of the main drivers of the long-term deindustrialization trend in developed countries is the productivity gap between manufacturing and services. Indeed, technological change is singled out as the main determinant of the productivity improvements that entail job losses in manufacturing and that therefore lead to the declining share of industrial employees in total 5 employment. However, more recently, automation and AI diffusion have made possible a similar labor-saving prospect in service industries ranging from the financial sectors to trade and retail. Referring to the theory put forward by the economists of innovation, there are two basic innovation inputs: research and development (R&D), which may lead to product innovation, and embodied technological change, which may lead to process innovation. R&D investments are the key innovation input in the approach originally proposed in 1979 by Zvi Griliches, who identified the concept of the “knowledge production function” (Griliches, 1979). In this functional relationship linking innovative inputs to innovative outputs, firms pursue new economic knowledge as an input into generating innovative activities. Indeed, a vast literature has identified a strong significant link between R&D investment, innovation, and productivity gains, demonstrating that R&D is a main driver of technological progress at macroeconomic, sectoral, and microeconomic levels (Crepon et al., 1998). Meanwhile, embodied technological change involves process innovation, or innovation that is incorporated in investments in capital goods (machinery and equipment, for instance robots and other automation devices) (Freeman & Soete, 1987). Moreover, the innovation literature suggests that it is mainly large high-tech firms that rely on formal R&D to drive complex product innovation, while embodied technological change plays a key role in smalland medium-size firms in more traditional industries (Pavitt, 1984). As mentioned above, of the two main drivers of technological change, R&D is mainly related to product innovation, and embodied technological change is more closely related to process innovation. However, in some circumstances, the distinction between product innovation and process innovation is ambiguous from an empirical point of view (consider, for instance, the diffusion of ICT in the past decades, and artificial intelligence nowadays), and in many cases the two forms of innovation are interrelated. Moreover, both R&D and embodied technological change participate in mixed innovative activities that entail both product and process innovation. Figure 3 illustrates the main links between innovative inputs, innovative outputs and their eventual impact on the labor market. Obviously enough, process innovation and product innovation involve different employment impacts (as shown in the right panel of Figure 3). Process innovation results in a direct laborsaving (job-destroying) effect, related mainly to the introduction of machinery and equipment that can substitute for labor and allow the production of the same amount of output with fewer inputs (generally workers). On the one hand, product innovation can entail a job-creating effect through the emergence of new industries and new markets. However, on the other hand, the same innovation can play the role of product innovation in a given sector (supply side) and the role of process innovation in another industry (demand/adoption side). For example, the design and implementation of a new AI algorithm is a product innovation in the supplier industries and may entail job creation (e.g. an increase in the demand for data scientists). However, the same algorithm may imply job losses when is adopted in the user sectors as a process innovation (e.g. a drop in the demand for bank clerks). 6 Figure 3. The two faces of innovation: how product and process innovation affect employment Source : Author’s own illustration. 2.2 The labor market implications of process innovation and the compensation mechanisms Since by definition process innovation means producing the same amount of output with less labor (and sometimes other) inputs, the direct impact of process innovation is job destruction when output is fixed. However, economic analysis has demonstrated the existence of countervailing economic forces that can compensate for the reduction in employment arising from technological progress. Indeed, the classical economists put forward a theory that Marx later called the “compensation theory”(Pianta, 2005; Vivarelli, 1995, 2013, 2014). These compensation mechanisms include new machinery, lower prices, new investments, and lower wages. 2.2.1 The compensation mechanism via new machinery The effect of the introduction of new machinery (for instance robots) is ambiguous. On the one hand, process innovations displace workers in downstream industries that introduce the embodied technological change incorporated in the new capital goods. On the other hand, additional workers are needed in the upstream industries that produce the new machinery. However, there are at least three arguments against the efficacy of this compensation mechanism. First, for the introduction of the new machinery to be profitable, the cost of labor associated with the construction of the new machinery has to be lower than the cost of labor displaced by the new capital goods. Second, labor-saving technologies spread to the capital goods sector as well as to the product sector, so this compensation can be an endlessly repeating story, 13 statistical significance depend on various circumstances (for instance, developing vs. developed countries, sectors, period of crisis, different methodologies). Indeed, only few studies found out a labor-saving impact of process innovation (Díaz et al., 2020; Lim & Lee, 2019). One of the main critique addressable to this bunch of studies is that the dummy variable “sole process innovation“ fails to fully capture firm’s process innovation strategy, its actual size and its variability (Díaz, Guerrero, et al., 2024). Other studies have not adopted the two main approaches mentioned above. For instance, a study - using a dynamic employment model and a longitudinal data set on German manufacturing firms over the period 1982–2002 - has found a significantly positive impact of various current and past product and process innovation variables on labor demand (Lachenmaier & Rottmann, 2011). According to this work, innovation is homogeneously employment friendly. More recent studies have used different types of measures of innovation. A study that used patents as a proxy of innovation for 20,000 European companies from 2003 to 2012 found a positive impact of innovation on employment, but only for firms in high-tech manufacturing sectors (Van Roy et al., 2018). Another study from Spain from 1991 to 2012 found a positive effect of product innovation on employment growth and no significant impact of process innovation (both using dummy variables as proxies of innovation) (Bianchini & Pellegrino, 2019). A most recent study used the Enterprise surveys dataset from the World Bank and found that R&D expenditure and process innovation foster firm’s employment growth (Goel & Nelson, 2022). 3.4 Empirical evidence for specific technologies: robots and artificial intelligence The emergence of the current new technological paradigm has generated a desire to explore the empirical effect of specific technologies (namely robots and artificial intelligence) on the labor market. In the case of robots, studies at the industrial level that used data from the International Federation of Robotics and EUKLEMS for developed countries have found a negative effect on employment (specifically for low-skilled workers and in services sectors) (Acemoglu & Restrepo, 2020a; Chiacchio et al., 2018; Graetz & Michaels, 2018). In contrast, most studies at the firm level found positive impacts of robots on employment (mainly in countries such as France, Spain, Canada, and Germany) (Dauth et al., 2021; Dixon et al., 2021; Domini et al., 2021; Koch et al., 2021). However, optimistic employment results obtained at the firm level of analysis can be entirely due to the “business stealing effect” (see above) and job creation at the firm level can well coexist with job destruction at the industry level (Acemoglu et al., 2020). New empirical methodologies (such as natural language processes and text analyses) allow to explore other sources of information (e.g., job posts and patents). These types of studies analyze the exposure and the impact of artificial intelligence (robots) on the labor market. Also, these 14 types of studies can assess the proximity between specific innovations, occupations, and tasks. For instance, one study tries to look at AI-exposed establishments by combining job posts using Burning Glass Technology data and SOC occupational codes. The study found no apparent effect at the industry and occupational levels, but it did find a re-composition toward AIintensive jobs (Acemoglu et al., 2022). Other studies using patents (AI-related inventions) show a moderative positive employment impact of AI patenting within the industries which patent in AI, that is the upstream sectors which provide the new technologies (see above) (Damioli et al., 2024). Other approaches distinguish between labor-saving innovations and labor-complementary technologies. For instance, one study that uses the textual description of tasks in the fourth edition of the Dictionary of Occupation Titles (DOT) and the breakthrough innovations (through patents) found that the most exposed occupations experienced a decrease in wage and employment level (mainly white-collar workers relative to blue-collar workers) (Kogan et al., 2021). More recently, another study identified labor-saving innovations using textual analysis of USPTO patent applications in robotics. The main results show that some activities are more exposed to labor-saving innovation, such as those related to transport, storage, packaging, and moving objects. Along the same line, an update of the previous study shows that occupations most exposed to robotic labor-saving technologies are associated with lower employment and wage rates (Montobbio et al., 2022, 2023). 4. Main findings Theoretical models cannot claim to have a clear answer on the final employment impact of process and product innovation. While the price and income mechanisms described here have the potential to compensate, fully or in part, the direct labor-saving impact of process innovation, the precise outcome is uncertain. Determining factors include such variables as the degree of competition, demand elasticity, elasticity of substitution between capital and labor, and expectations of consumers and employers. Overall, depending on market structure and institutional contexts, compensation mechanisms can be more or less effective, and the unemployment impact of process innovation can be totally, partially, or not at all neutralized. Similarly, the findings of empirical studies are not fully conclusive about the possible employment impact of innovation and technological change. However, most recent panel investigations support a positive link. This positive link is especially evident when R&D or product innovation are adopted as proxies for technological change and when the focus is on high-tech sectors and high-growth firms (Vivarelli, 2013, 2014). In many sectors, however, especially in services, product and process innovation are intermingled and difficult to disentangle. Moreover, while process innovations display clear direct labor-saving effects, some product innovations may also involve job displacement. Therefore, it is not always easy and straightforward to design industrial and innovation policies that can effectively maximize the 15 positive employment impact of innovation. Additional microeconometric studies of the type addressed by the current research literature are needed to further disentangle the labor impact of innovation across different sectors and different types of firms. Indeed, new statistical techniques and sources of information are being used nowadays to construct different measures of labor market exposure to technological change. With specific regard to the AI technologies, the scarce available evidence (see above) suggests that technological leaders within the emergence of the AI paradigm can realize (moderate) laborfriendly outcomes. However, other companies (particularly in manufacturing) may reveal to be unable to couple product innovation with job creation. Moreover, compared with the labor-saving impact implied by the adoption of AI and automation technologies (massive according to some studies, see above), the labor-friendly extent in the supply industries appears limited in magnitude and scope (just as a narrative example: the hiring of data scientists in upstream services and AI big-tech would hardly compensate job losses due to robots in downstream manufacturing). As a gap in the current literature, much is needed in terms of additional empirical evidence able to compare the actual magnitude of possible employment complementary effects within the providers of new AI technologies with the possible job-losses due to the substitution effects within the users of new AI and automation technologies. Finally, one crucial aspect is that most studies that analyze the relationship between employment and innovation focus on developed countries. However, the larger effects of automation and innovation might be in developing countries where many activities and job tasks can be easily substituted by robots and AI algorithms (think about manufacturing jobs displaced by robots or call-center jobs displaced by chatbots). With few exceptions (see above (Goel & Nelson, 2022)), there is a lack of empirical studies for developing countries that can provide more evidence of these phenomena. 5. Conclusion and policy implications The literature, both theoretical and empirical, has examined the main technological drivers that can play a role in the loss of jobs and the creation of technological unemployment. Indeed, innovation affects the economy through both process and product innovation, both of which can have employment impacts. For the most part, R&D expenditures that result in product innovation are generally labor-friendly, creating new jobs, while embodied technological change that results in process innovation is generally job-destroying. A clear policy implication would seem to be that economic policy should try to foster job creation by supporting R&D investments and product innovation. In the AI era this means to foster emerging industries and innovative startups, active in AI design, engineering and patenting. However, the picture is more complicated than that. Product and process innovation are often interrelated, and process innovation does not always lead to job destruction. Indeed, much of 16 the theoretical literature on the employment impact of technology has focused on various market compensation mechanisms that can counteract most if not all of the technological unemployment impacts of process innovation (see the classical compensation theory discussed above, and its recent revival put forward by Acemogulglu and Restrepo). Thus, a general theoretical and empirical conclusion is that compensation mechanisms are always at work but that the full reabsorption of workers dismissed as a result of technological change cannot be assumed ex ante. In particular, to work properly, compensation mechanisms require competition (to facilitate the compensation mechanism that works through lower prices), optimistic expectations (to facilitate the compensation mechanisms that work through lower prices and new investments), and a high elasticity of substitution between capital and labor. In this framework, competition policies that lower entry barriers and reduce monopolistic rents, along with expansionary policies targeting intermediate and final demand for new products, can be important drivers of job creation. In this respect, the concentration of AI research and patenting in the hands of the “big tech” is extremely worrying and should be contrasted by a fierce antitrust policy. Since economic theory offers no clear-cut answer on the employment effect of innovation, answers need to come from empirical analyses. Empirical studies can consider different forms of technological change, their direct effects on employment, various compensation mechanisms at work, and any possible impediments to these mechanisms. In particular, microeconometric studies have the great advantage of enabling direct and precise firm-level mapping of input and output innovation variables (Vivarelli, 2013, 2014) . Overall, the empirical literature, particularly the most recent microeconometric panel data analyses, tends to support a positive link between technological advances and employment, especially when the focus is on R&D, product innovation and upstream high-tech firms. These positive employment outcomes of evidence-based studies are consistent with a lifecycle view of different industries, with emerging sectors characterized by product innovation (mostly labor-friendly) and more traditional, mature industries more likely to experience process innovation (mostly labor-saving). As a policy implication, policy makers should foster the emergence and the strengthening of upstream AI-intensive industries, where the job creation impact is concentrated (see above). However, while supporting R&D investments and promoting knowledge and AI-intensive industries (Antonelli et al., 2023), can be a mean of fostering competitiveness, economic growth and job creation, both industrial policies and innovation policies need carefully to take into account a series of complex interactions between process innovation and product innovation, between mature sectors and new sectors, and between job-creation effects in the upstream industries and job-destruction effects in the downstream industries (see above). These complex interrelationships, difficult to predict in advance, highlight the need for a continuous monitoring of policy implementation. For instance, safety nets and active labor market policies are necessary to deal with the employment displacement due to the widespread diffusion of AI and automation technologies in the user industries. 17 Finally, as discussed above, it is urgent to generate more empirical analysis to know the actual effects of new technologies on the labor markets outside Europe and the United States. Indeed, the most vulnerable countries to new technological changes are those that execute routine tasks (for instance, assembly plants or traditional services), and those are usually developing countries. Policymakers' vision should consider this global effect of technological change on the labor markets, particularly within international organizations such as the UN, World Bank, IMF, etc. References Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial Intelligence and Jobs: Evidence from Online Vacancies. 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