Abstract
The debate about the impact of robots on employment has been lively. In this paper, I examine the effect of robots on local labor demand in South Korea, one of the most technologically advanced countries in terms of robotics. Using the regional variation in robot exposure constructed from national industry-level robot adoption data and the initial distribution of industrial employment in cities, I find that robots did not reduce local labor demand. However, I estimate declines in labor demand in the manufacturing sector and routine jobs. An increase in one robot per 1000 workers in terms of exposure to robots is correlated with a decline in the job vacancy growth rate of 2.6%p in the manufacturing sector and of 2.5%p in routine jobs. No significant relationship is found between robot exposure and labor demand in the service sector or non-routine jobs.
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Notes
South Korea consists of 17 provinces, and these are further divided into 228 cities.
The OLFSE does not cover establishments with fewer than five permanent employees, agriculture, forestry and fishing, households, and the public sector. The location information is only available at the province-level, not the city-level.
The 18 IFR industries are the following. Outside manufacturing, there are agriculture, mining, utilities, construction, research, and services. In the manufacturing sector, the IFR industries include food and beverages, textiles, paper and printing, plastics and chemicals, minerals, basic metals, metal products, metal and machinery, electronics, automotive, other vehicles (for example, shipbuilding and aerospace), and other manufacturing (including wood and furniture). Acemoglu and Restrepo (2020) use 19 IFR industries, and here, I aggregate “wood and furniture” and “other manufacturing.”
The automotive and electronic industry experienced an increase in robot exposure of 189.65 and 177.98 robots per 1000 workers between 2010 and 2019 in South Korea. (Please refer to Column 1 in Table 8.) This significant increase is due to the huge projects aimed at manufacturing batteries for hybrid and electric cars, as well as the rise in the production of semiconductors and displays.
The 2010 control variables could potentially be endogenous since they are measured at a time when robotization may have already been underway. Therefore, I check the robustness of the results by including 1995 industry shares and demographic characteristics as control variables, since 1995 was before robotics technology advanced significantly, and find qualitatively similar results.
The possible threat to the IV strategy is that there could be common shocks affecting the same industries in South Korea and Singapore, such as declining international demand or other technological changes, and these shocks could induce the same industries in two countries to adopt robots. In this case, the estimates may confound the impact of robots with these pre-existing industry characteristics. The result in Panel A of Table 9 shows that robot adoption in Singapore is not associated with industry characteristics in 2010, suggesting that these confounders are not responsible for the results.
The LZs are geographical units referring to aggregated regions characterized by intense economic interactions.
Additionally, I further disaggregate routine labor demand into manual and analytic task-intensive occupations and estimate the negative impacts for both routine-manual and routine-analytic occupations. When I split up the non-routine labor demand into several manual and analytic task-intensive occupations, none of the estimates are significant.
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Appendix
Appendix
See Tables 8 and 9 and Fig. 7.
Industry and occupation shares in WorkNet and OLSFE, 2019. Note: Panel A plots the share of vacancies by broad industry in 2019 WorkNet data and in OLSFE data. Panel B plots the share of vacancies by broad occupation in 2019 WorkNet data and in OLSFE data
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Kim, H. The impact of robots on labor demand: evidence from job vacancy data in South Korea. Empir Econ (2024). https://doi.org/10.1007/s00181-024-02585-0
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DOI: https://doi.org/10.1007/s00181-024-02585-0