Are different types of corruption tolerated differently?

In the past two decades, abundant research on corruption has established its negative impact on human well-being. Indeed, general scholarship finds that it is appropriately shunned across contexts, with citizens in different cultures and contexts expressing a general aversion to corruption. However, what is less explored is whether different types of corruption are tolerated differently across contexts. Building on this nascent literature, we empirically explore the idea that people will generally have little tolerance for corruption types that impinge on the public goods that citizens understand as having a right to as part of their social contract. To address the central question of this study, we selected six cases—Romania, Spain, Brazil, Mexico, India, and Indonesia—based on the generally assumed incidence of corruption affecting them and the diverse cultural, social, and political traditions that they represent. These factors are expected to provide a larger degree of variation in how each type of corruption is tolerated. Data were collected via an online survey administered between October 2020 and January 2021 using advertisements on the social media platform Facebook. A total of 2,347 subjects completed the survey: Romania (379), Spain (388), Brazil (394), Mexico (389), India (398), and Indonesia (399). Participants were presented with vignettes describing cases of nepotism, clientelism, patronage, state capture, and conflict of interest, and asked to score them on an 11-point Likert scale. We used a neutral narrative in the vignettes to minimize the bias that may be introduced through the wording, thus leaving the assignment of moral weight entirely to the survey participants. The results show that, while the average level of corruption tolerance varies somewhat from country to country (with Indonesia at the top of the rank and Spain at the bottom), the degree of local tolerance of different types of corruption is not homogeneous. These results point to the value of context when assessing the relative prevalence of corruption.