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Extracting Dimensions of Job Quality from Online Employee Reviews

Author

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  • Sleeman, Cath

Abstract

This paper uses a novel dataset of online employee reviews, from Indeed's UK website, to enhance our understanding of job quality. Keywords within the reviews were extracted and then clustered to form a taxonomy of job quality. It is the first UK taxonomy that is derived from employee reviews and the first to be updatable in real time. Analysis of the taxonomy shows that the emphasis placed on different dimensions of work quality has shifted over time, with a marked increase in references to culture, atmosphere and the broader workplace environment. The paper also reveals differences between occupations; workers in some occupations value pay and rewards, while others value the workplace atmosphere and environment. The dataset and methods presented here could form a useful early warning system to detect changes in the dimensions of work quality and to measure the impact of new working practices. More broadly, the paper shows the potential of a novel dataset to improve our understanding of job quality.

Suggested Citation

  • Sleeman, Cath, 2024. "Extracting Dimensions of Job Quality from Online Employee Reviews," Discussion Papers escoe-dp-2024-01, Economic Statistics Centre of Excellence.
    • Handle: RePEc:eoe:escoed:escoe-dp-2024-01
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy

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