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The construction industry consistently ranks amongst the highest contributors to global gross domestic product, as well as, amongst the most corrupt. Corruption therefore inflicts significant risk on construction activities, and overall economic development. These facts are widely known, but the various sources and nature of corruption risks endemic to the Iranian construction industry, along with the degree to which such risks manifest, and the strength of their impact, remain undescribed. To address the gap, a mixed methods approach is used; with a questionnaire, 103 responses were received, and these were followed up with semi-structured interviews. Results were processed using social network analysis. Four major corruption risks were identified: (1) procedural violations in awarding contracts, (2) misuse of contractual arrangements, (3) neglect of project management principles, and, (4) irrational decision making. While corruption risks in Iran align with those found in other countries, with funds being misappropriated for financial gain, Iran also shows a strong inclination to champion projects that serve the government's political agenda. Root cause identification of corruption risks, namely, the noticeable impact of authoritarianism on project selection in Iran, over criterion of economic benefit or social good, is a significant outcome of this study.

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.