The Impact of Automation and Artificial Intelligence on Employment in Germany’s Manufacturing Sector
Autor(ka) práce:
Do, Thi Ngoc Huyen
Typ práce:
Diploma thesis
Vedoucí práce:
Mazouch, Petr
Oponenti práce:
Lichard, Tomáš
Jazyk práce:
English
Abstrakt:
This thesis investigates the impact of automation and artificial intelligence (AI) on employment within Germany's manufacturing sector, distinguishing between high-tech and low-tech industries. Drawing on regional panel data from 2012 to 2022, the study employs multiple panel models to evaluate the role of technological advancement, controlling for education, demographics, and export dependence. The findings reveal a dual reality: R&D-driven automation and AI positively influence employment in high-tech manufacturing, underscoring the role of innovation in sustaining employment growth. Conversely, low-tech employment is shaped less by technology and more by workforce education, aging populations, and global trade exposure. These contrasting dynamics highlight the need for targeted, sector-specific policy interventions that foster inclusive labor market outcomes in response to rapid technological change.
Klíčová slova:
Technological change; employment dynamics; workforce transformation; panel data analysis
Název práce:
The Impact of Automation and Artificial Intelligence on Employment in Germany’s Manufacturing Sector
Autor(ka) práce:
Do, Thi Ngoc Huyen
Typ práce:
Diplomová práce
Vedoucí práce:
Mazouch, Petr
Oponenti práce:
Lichard, Tomáš
Jazyk práce:
English
Abstrakt:
This thesis investigates the impact of automation and artificial intelligence (AI) on employment within Germany's manufacturing sector, distinguishing between high-tech and low-tech industries. Drawing on regional panel data from 2012 to 2022, the study employs multiple panel models to evaluate the role of technological advancement, controlling for education, demographics, and export dependence. The findings reveal a dual reality: R&D-driven automation and AI positively influence employment in high-tech manufacturing, underscoring the role of innovation in sustaining employment growth. Conversely, low-tech employment is shaped less by technology and more by workforce education, aging populations, and global trade exposure. These contrasting dynamics highlight the need for targeted, sector-specific policy interventions that foster inclusive labor market outcomes in response to rapid technological change.
Klíčová slova:
Technological change; workforce transformation; panel data analysis; employment dynamics