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Intelligent automation uptake and labor productivity in United Kingdom

Chai, Melvin Jenn Yaw and Gooi, Cheryl Su Yi (2019) Intelligent automation uptake and labor productivity in United Kingdom. Final Year Project, UTAR.

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    Growth of labor productivity is at historic lows in the United Kingdom and the decline has accelerated since the Great Economic Recession in year 2008. Weak productivity growth in United Kingdom has raised concerns as a developed nation relies heavily on productivity growth in order to promote and sustain lasting growth as well as prosperity in a globalizing economy. This study attempts to shed light on how minimum wage have sparked an early adoption of artificial intelligence (AI) automation, bringing about most important societal changes in labor productivity in each industry of UK as well as the job distribution in the landscape of UK industries. The determinants of labor productivity in each sector is examined from year 2008 to year 2015 in order to grasp the relationship between labor productivity and AI automation along with other control variables which consists of non-AI related capital stock, expenditure of research and development, fraction of workforce with tertiary education, average weekly earnings and average actual working hours. The findings of this study aims to provide a clearer picture of the potential of AI automation in improving the current labor productivity shortfall for economists and policy makers with a glimpse of what AI automation can do to improve their daily tasks and the firms to know where to target their investments. However, continued research will be required to accurately capture the effects of AI automation on labor productivity as there are no explicit measurements for AI due to unavailability of standardized methods.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: H Social Sciences > HD Industries. Land use. Labor
    Divisions: Faculty of Business and Finance > Bachelor of Economics (Honours) Financial Economics
    Depositing User: ML Main Library
    Date Deposited: 21 Aug 2019 17:11
    Last Modified: 21 Aug 2019 17:11
    URI: http://eprints.utar.edu.my/id/eprint/3525

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