PPP project procurement model selection in China: does it matter?

Abstract

Procurement models play a key role in determining the success of a public–private–partnership (PPP) project because they can help the government select the best bidder. Yet, few studies have systematically examined the selection of different procurement models. A content analysis is performed to discuss the institutional and legal framework along with the features of different procurement models in China. A multinomial logistic regression model is then applied to empirically investigate the relationship between the selection of different procurement models and the internal characteristics of the projects, including the investment, duration, operation mode, sector and region. Besides, a questionnaire survey to Chinese officials participating in PPP project procurement is conducted to identify the critical factors affecting the selection preferences. The regression analysis shows that the PPP project procurement model selection in China varies significantly with the internal characteristics of the projects. Moreover, the three most important factors identified are the laws and regulations, internal characteristics of the projects and advice from consultant agencies. These results indicate that the PPP project procurement model selection in China does matter. Helpful policy suggestions for the governments of China and other countries are also provided.

Keywords:

• Public–private partnerships
• procurement
• public sector procurement
• China

Acknowledgements

The authors acknowledge the technical support of Lei Wang and the survey participation of all the respondents. The authors are also grateful to Prof. Carlos Cruz, Prof. João Silva and Dr Francisco Pinto for their helpful suggestions. The authors’ special thanks go to the editors and the anonymous referees for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the Ministry of Science and Technology of the People’s Republic of China [grant number 2017YFB1401401].