Transnational collaboration between regulatory agencies has proliferated rapidly within the last three decades. However, given that information regarding the motives, trustworthiness, and capabilities of potential partners is typically imperfect, decisions about with whom to collaborate are inevitably characterized by a degree of uncertainty. To better capture these dynamics, this article uses a network analytical perspective and hypothesizes that agencies are more likely to form agreements with agencies to whom they are already indirectly connected (transitivity), that are highly connected (preferential attachment), or with whom they share tie-characteristics (assortativity). To test these hypotheses, a stochastic actor-oriented model is used to analyze an original, self-coded data set in which bilateral information exchange agreements between national securities agencies (n = 143) are mapped out over a 18-year period. The results show that the formation of agreements between regulatory agencies is driven by (i) the number of shared partners (i.e. triadic closure); and (ii) similarity regarding agency characteristics (i.e. homophily)
