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Purpose The paper aims to provide insight into the opportunities and impact of political risks in China and selected Asian countries on opportunities in infrastructure projects under public‐private partnership (PPP) schemes. Design/methodology/approach The impact of political risks on PPPs was investigated through an international survey among senior staff of international lenders, investors, insurers, and legal and financial advisors from the public and private sector. The surveyed political risk categories base on the Multilateral Investment Guarantee Agency Convention and literature review. They comprise six categories: currency inconvertibility and transfer restriction, expropriation, breach of contract, political violence, legal, regulatory and bureaucratic risks, and non‐governmental action risks. The survey evaluation uses fuzzy sets and non‐parametric statistics. Findings The findings comprise rankings of political risk factors within China and Asian countries as well as rankings of these countries with respect to the risk categories and rankings of future PPP opportunities over time. Also, survey comments are discussed. Research limitations/implications The survey response rate is relatively low with 29 respondents. This is attributed to the specialized nature of the survey questions and topic area. It also indicates that political risks are little understood and paid attention to, though significant in impact. Owing to the chosen evaluation methods for small sample sizes, the results are robust and show high correlations with market data. Practical implications The results provide insight into the impact of political risks and the perceived magnitude of each risk category. It supports decision makers prioritizing and analyzing country risks. Originality/value The value and originality is in the use of a fuzzy‐coded survey scale to quantify perceptions on single political risk factors across China and Asian countries with focus on PPP infrastructure projects. The paper demonstrated that robust results from small sample sizes can be derived with the employed methods.
Sachs et al. (Tue,) studied this question.