GOVERNMENT SUBSIDIES IN PUBLIC-PRIVATE PARTNERSHIP PROJECTS BASED ON ALTRUISTIC THEORY

Author:

Wang Yinglin1,Gao Ruolan2,Hwang Bon-Gang3,Liu Jicai4

Affiliation:

1. Department of Transportation and Civil Engineering , Fujian Agriculture and Forestry University, Fuzhou, China

2. Scholl of Economics and Management, Southwest Jiaotong University, Chengdu, China

3. Department of Building, National University of Singapore, Singapore, Singapore

4. School of Economics and Management, Southwest Jiaotong University, Chengdu, China

Abstract

Nowadays, the public-private partnership (PPP) scheme has been widely adopted in infrastructure projects around the world. In PPP projects, the governments participate as a principal and the investors play the role of an agent, and therefore their behaviours and incentive strategies can be explained and designed by the principal-agent theory. As “economic men” with limited rationality, both the governments and the investors have altruistic preferences during cooperation. This paper studies how project participants’ altruistic preferences affect government subsidies based on the principal-agent theory. To this end, a principal-agent model in the presence of altruism is developed. The results show that the amount of government compensation is related to the altruistic preferences, the expected revenue, costs and investors’ efforts. Contrary to intuition, the governments’ altruism actually undermines the investors’ enthusiasm in cooperation and the risk-sharing propensity, although it increases the utilities of both parties. Moreover, when selecting the investors, governments should examine their operating capacity carefully, which has a significant impact on the sustainable development of the projects and even PPP arrangements. The findings contribute new insights into the development of incentive mechanisms between governments and private investors from the perspective of the behavioural preferences.

Publisher

Vilnius Gediminas Technical University

Subject

Strategy and Management

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