Nine-nine-six Work System and People's Movement Patterns: Using Big Data Sets to Analyse Overtime Working in Shanghai
Land Use Policy: the international journal covering all aspects of land use
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Silva, E., & Xiao, C. (2020). Nine-nine-six Work System and People's Movement Patterns: Using Big Data Sets to Analyse Overtime Working in Shanghai. Land Use Policy: the international journal covering all aspects of land use, 90 (104340) https://doi.org/10.1016/j.landusepol.2019.104340
Although topics regarding “996 work system” and overtime working have aroused hot arguments, there is scant literature that analyses the spatial distribution and movement patterns of people who work overtime. This article fills this gap by adopting big data analysis and examining the mobile phone signal data which allow the calculation of the approximate spatial position of the mobile-phone user, and the generation of transportation flows and individuals’ origin-destination (OD) flows. The findings show that no less than one third of employees in Shanghai work overtime, and that overtime workers face higher job-housing imbalance than workers who have normal work durations or flexible schedules. This corroborates David Harvey’s time-space compression theory. Going beyond that, we further discover the interchangeability between exploitation in the time dimension, and that in the spatial dimension, resulting in dual exploitation. This article has important policy implications for optimizing the urban spatial system of Shanghai, as it advocates that in addition to strengthening the enforcement of labor law, the government also needs to improve the public service such as strengthening the underground system’s capacity, and construct affordable houses, so as to alleviate the employees’ sufferings caused by temporal and spatial exploitation. Moreover, the research points out the necessity for Chinese cities to enhance the vertical mixing, in order to shorten the job-housing distance.
External DOI: https://doi.org/10.1016/j.landusepol.2019.104340
This record's URL: https://www.repository.cam.ac.uk/handle/1810/299873
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