• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.33-51
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• 2023

This study aims to investigate the changes in the employment structure of occupational groups by job characteristics and analyze the factors influencing wage premiums in local labor markets from 2010 to 2020. This study's analysis involves three primary steps. First, the occupational characteristics data from the Korea Network for Occupations and Workers are subjected to an exploratory factor analysis, and then a non-routine task intensity index is calculated by each occupations. Then, we conduct an exploratory analysis of changes in the distribution of employment by occupation from 2010 to 2020 by combining data from the Population Census with data from the Korean Labor and Income Panel Study to construct individual-level and regional-level data. Thirdly, we employ a hierarchical linear model to examine the individual-level and regional-level factors influencing wage premiums. Since 2010, the proportion of employment in occupations requiring non-routine task has continued to rise and now dominates the metropolitan labor market. Moreover, agglomeration effects resulting from urbanization produce a substantial wage premium for wage workers in occupations requiring non-routine tasks. This study seeks to provide policy implications to mitigate inequality and polarization in local labor markets by empirically analyzing the transition of occupational structure and wage inequality in relation to the local labor market context.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.73-90
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• 2023

This research examines the impact of robotics integration on job dynamics in the U.S. manufacturing sector, adding to the critical dialogue on technological evolution and the future of jobs. Anchored in the task-model framework, the study hypothesizes that robotic integration exerts differential influences on diverse occupational clusters, each identified by their unique task-specific attributes. An in-depth examination was undertaken to elucidate the interplay between robotic integration and the occupation clusters. Employing a multilevel growth curve model, our empirical investigation tracked employment dynamics from 2012 to 2022 across 52 U.S. regions, covering 307 manufacturing occupations. The findings suggest a pronounced job decline within occupations necessitating manual dexterity. Nonetheless, the evidence does not conclusively support that the extent of robotics integration exacerbates this trend. These findings imply that the employment shifts in the U.S. manufacturing sector are predominantly driven by long-standing trends of deindustrialization and functional specialization, rather than by the recent diffusion of robotic technologies.