Because of shared norms of ethics, trust, and coordination prevailing in a given society or a social group, corrupt micro-level decisions are related to each other. In micro empirical analysis, this interdependence of corruption decisions can be addressed through the hierarchical modelling of corruption data. Exploiting a baseline sample of 34,358 bribe reports of firms from 71 developing and transition countries, I use a three-level estimation framework to re-examine the contribution of five major determinants of corruption emphasized by the literature: the economic and human development levels, the size of governments, trade openness and democracy. Multilevel estimations stress that the negative effect of income per capita on bribery is found to be mostly driven by improvement in human capital, more particularly by the decline in fertility rates. They also allow reconciling some contrasting findings of the literature on other corruption determinants. First, higher school attendance may increase corruption incidence by inducing larger public spending, but may also reduce the size of bribes by improving the scrutiny over public agents. Second, public intervention has a positive effect on bribery through public spending, but a negative effect through taxation. Third, trade openness has a positive effect on bribery, explained by state interventions on the one hand and by the country’s geographical distance from main world markets on the other hand. Fourth, results reveal a negative effect of political rights and press freedom on corruption, but also show that young democracies may experience higher corruption levels because of larger scope for private transactions. All in all, the significance of random coefficients across estimations, and the sensitivity of some coefficient estimates to their inclusion, point that the contribution of key corruption determinants emphasized in this study is nevertheless highly context-dependent.