ABSTRACT
While case study analysis of non-state actors abounds, including studies of intergovernmental organizations (IGOs), non-governmental organizations (NGOs), international non-governmental organizations (INGOs), transnational advocacy networks (TANs) and multinational corporations (MNCs), quantitative analysis is more difficult to find. The reasons for this are unclear. This chapter questions whether this is due to a lack of empirical data. There are several very good sources of global data on non-state actors, particularly the Union of International Associations (UIA) and the United Nations (UN), as well as excellent local-level data on a variety of issues and types of organizations, and increasingly national data on the non-profit sector. Problems arise, however, in trying to compare data on organizations across levels (local/national/global), across countries and over time. The data requirements become exponentially greater and may exceed the capacity of any one organization or research centre, much less a single scholar. The qualitative focus of current studies of non-state actors may also be due to the theoretical and methodological convictions of the scholars which examine these organizations. These scholars often come from sociological traditions which focus on the particular characteristics and evolution of discrete organizations and their agency, rather than their environment, in the fields of political science, economics and sociology (Risse-Kappen 1995a, Josselin and Wallace 2002, Arts et al. 2002, Florini 2000). Alternatively the current focus on particular organizations may be due to the fact that these are ‘nons’, a residual category of things which cannot be explained or included in the dominant concepts and theoretical traditions or within dominant methodologies. Non-state actors are often noticed when they do things which are exceptional and do not conform to expectations but instead change their environments and produce outcomes in unexpected ways (Evangelista 2002, Clark 2001, Cameron et al. 1998, Scarce 2005). This exceptionality makes it difficult to use quantitative methods that focus on commonalities across cases, places and time.
