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This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

This chapter explores the characteristics of emerging environmental movement organizations in China, and more specifically the role of guanxi – or personal networks – in Chinese environmental activism. While organizational networks of environmental NGOs are still weak in Chinese environmental activism, personal networks of environmental activists are instrumental in building the first sprouts of a green civil society. We explore this via an in-depth case study of relatively successful environmental activism to halt the construction of a number of hydro-electric projects on the Nu River. The case study illustrates that in China, more so than in western countries, informal personal networks, rather than formal organizational networks, play a crucial role in the organization and success of contemporary environmental campaigns. This is partly explained by the immature environmental movement, and partly by the specifics of Chinese social networks.