Data-driven quantification of public–private partnership experience levels under uncertainty with Bayesian hierarchical model

Abstract

Public–private partnership (PPP) is increasingly encouraged to deliver public services in developing countries. Many studies have been conducted to identify factors that affect PPP contract failure. Although a country's PPP experience is of great importance in controlling the contract failure rate, most of the current studies are based on a qualitative perspective. This research develops a data-driven approach to quantify countries’ PPP experience levels through the Bayesian hierarchical model with uncertainties considered. First, detailed data exploration and selection have been carried out to clean the data source. Second, the number of change points in the dataset is identified based on the binary segmentation method. Third, the Bayesian hierarchical model is developed to locate the positions of the change points, and different experience levels are divided based on the location of change points. Findings show that: (i) PPP experience level is widely varying depending on PPP sectors. Four experience levels are suggested for the energy sector, while five levels are found for the transportation sector, and water & sewerage sector, (ii) PPP experience level is dispersed around the world, for example, Latin America and Caribbean (LAC) and East Asia and Pacific (EAP) regions have higher PPP experience levels than other regions, (iii) a country may have various experience levels in different sectors, such as India, and (iv) the learning rate will decreases as more PPP projects are initiated. This research can contribute to (a) a novel approach that could detect the change points in PPP project experience, and (b) support investors in the decision making process, such as selecting the most appropriate investment direction, contributing to the development of PPP projects in developing countries.