This paper examines the impact of industrial robots on jobs. We combine data on robot adoption and occupations by industry in thirty-seven countries for the period from 2005 to 2015. We exploit differences across industries in technical feasibility – defined as the industry’s share of tasks replaceable by robots – to identify the impact of robot usage on employment. The data allow us to differentiate effects by the routine-intensity of employment. We find that a rise in robot adoption relates significantly to a fall in the employment share of routine manual task-intensive jobs. This relation is observed in high-income countries, but not in emerging market and transition economies.
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Rapid improvements in robot capabilities have fuelled concerns about the implications of robot adoption for jobs. While the creation of autonomous robots with flexible 3D movement continues to be a major challenge to engineers, rapid progress is being made. Robots can now perform a variety of tasks, such as sealing, assembling, and handling tools. As robot capabilities continue to expand and unit prices fall, firms are intensifying investment in robots ( Frey and Osborne, 2017 ; Graetz and Michaels, 2018; Acemoglu and Restrepo, 2020). What is the impact of robot adoption on labour demand? Do robots substitute for tasks previously performed by workers? The main contribution of this paper is to empirically study the impact of industrial robots on the occupational structure of the workforce across industries in a set of high-income as well as Emerging Market and Transition Economies (EMTEs). We combine a large and detailed occupations database with data on industrial robot deliveries from the International Federation of Robotics. The database on occupational employment from Reijnders and de Vries (2018) allows us to examine the share of employment in occupations with a high content of routine tasks – i.e. tasks that can be performed by following a well-defined set of procedures. We delineate occupations along two dimensions of the characteristics of tasks performed, namely ‘analytic’ versus ‘manual’, and ‘routine’ versus ‘non-routine’. We thus distinguish four key occupational groupings, namely routine manual, routine analytic, non-routine manual, and non-routine analytic task-intensive occupations (as in Autor et al. 2003 ; Reijnders and de Vries 2018 ; Cortes et al. 2020). We follow Graetz and Michaels (2018) in constructing measures of robot adoption by country-industry pairs and relate these to changes in occupational employment shares. Our sample covers 19 industries for 37 countries at varying levels of development from 2005 to 2015, and includes major users of industrial robots, such as the Peoples Republic of China (PRC), Japan, South Korea, Germany, and the United States. Our main finding is that country-industry pairs that saw a more rapid increase in robot adoption experienced larger reductions in the employment share of routine manual jobs.
Our approach is motivated by the following economic considerations. Firms produce a variety of products using a continuum of tasks (Acemoglu and Autor, 2011), and these products differ in the number of tasks that can be performed by robots (Graetz and Michaels, 2018). For example, the share of replaceable tasks by robots differs between apparel and automotive and appears larger in the latter. This gives rise to differences across industries in the technical feasibility of robots substituting tasks previously performed by humans. Advances in machine capabilities expand the set of tasks carried out by machines ( Acemoglu and Restrepo, 2018 ). Firms will adopt robots if it is technically feasible and the profit gains exceed the costs of purchasing and installing robots. Given higher wages in advanced countries, the technical constraints to robots replacing tasks are more likely to bind for firms in these countries. Hence, improvements in robot capabilities would result in a larger employment response in advanced countries compared to developing countries.
We use these economic insights in our analysis. In particular, the technical feasibility of adopting robots guides our instrumental variables (IV) strategy to identify the causal relation between robots and labour demand. Economic feasibility motivates our distinction of the impact of robot adoption between advanced and developing countries. Using two-stage least squares (2SLS) estimation, we find that robot adoption lowers the employment share of routine manual occupations. This relation is observed in high-income countries, but not in emerging market and transition economies.
This paper relates to recent studies that examine the impact of robot adoption on socio-economic outcomes. Graetz and Michaels (2018) find that robot adoption contributed to an increase in productivity growth across industries in high-income countries between 1993 and 2007. Their findings suggest that robot adoption did not reduce employment, which is corroborated in this paper. This is also observed by Dauth et al. (2019) , but not by Acemoglu and Restrepo (2020) , who examine geographic variation in robot adoption across the United States and find that robots are labour replacing. Dauth et al. (2019) use detailed linked employer-employee data for Germany to show that displacement effects are cancelled out by reallocation effects, such that in the aggregate no employment effects from robot adoption are observed. Data availability did not allow Graetz and Michaels (2018) to examine the impact of robots on workers that perform different tasks. Yet, Autor (2015) emphasizes that workers with routine task-intensive occupations are most likely to be affected by automation. This paper aims to contribute to our understanding of the impact of robots on such occupational shifts.
The remainder of this paper is organized as follows. Section 2 reviews the key theoretical mechanisms between automation and labour demand. Section 3 describes the methodology and instrumental variables. Section 4 documents patterns in the occupational structure of the workforce and robot adoption. Section 5 empirically studies the impact of robot adoption on the task content of labour demand. Section 6 concludes.
