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Demographic Variables of Corruption in the Chinese Construction Industry: Association Rule Analysis of Conviction Records

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Abstract

Corruption in the construction industry is a serious problem in China. As such, fighting this corruption has become a priority target of the Chinese government, with the main effort being to discover and prosecute its perpetrators. This study profiles the demographic characteristics of major incidences of corruption in construction. It draws on the database of the 83 complete recorded cases of construction related corruption held by the Chinese National Bureau of Corruption Prevention. Categorical variables were drawn from the database, and ‘association rule mining analysis’ was used to identify associations between variables as a means of profiling perpetrators. Such profiling may be used as predictors of future incidences of corruption, and consequently to inform policy makers in their fight against corruption. The results signal corruption within the Chinese construction industry to be correlated with age, with incidences rising as managers’ approach retirement age. Moreover, a majority of perpetrators operate within government agencies, are department deputies in direct contact with projects, and extort the greatest amounts per case from second tier cities. The relatively lengthy average 6.4-year period before cases come to public attention corroborates the view that current efforts at fighting corruption remain inadequate.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant No. 71371131).

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Authors and Affiliations

  1. Business School, Sichuan University, Wangjiang Campus, Wuhou District, Chengdu, Sichuan, China

    Yao Yu & Chuan Chen

  2. School of Architecture and Built Environment, Deakin University, Geelong, VIC, 3220, Australia

    Igor Martek & M. Reza Hosseini

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  1. Yao Yu
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Correspondence to Chuan Chen.

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Yu, Y., Martek, I., Hosseini, M.R. et al. Demographic Variables of Corruption in the Chinese Construction Industry: Association Rule Analysis of Conviction Records. Sci Eng Ethics 25, 1147–1165 (2019). https://doi.org/10.1007/s11948-018-0024-6

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