The Chinese economy has transformed significantly since 1979, and the spread of mixed-ownership firms since the late 1990s is a notable, if under-explored part of it. Mixed ownership, combining state and private capital, presents a challenge to mainstream economic ideology and, as widely used as it is in China, may represent an alternative mode of capitalism. This dissertation draws from the varieties of capitalism, post-socialist transition, and developmental state literatures to explore economic strategies that may guide the implementation of mixed-ownership across all industries, competing with, and replacing both state- and privately-owned businesses.

Using the Chinese Economic Censuses from 2004 and 2008, the Annual Survey of Industrial Firms from 1998-2007, and my own impressions and information gathered from fieldwork in China, I approach this topic with industry-level data from across the entire economy, as well as 361,450 firm-years’ worth of detailed firm-level data. I use summary statistics to show that the implementation of mixed-ownership as a general type of firm is both widespread, occurring in every sector of the economy, and making a significant proportion of firms within many industries. However, various regression models show that, while mixed ownership is progressing and evolving as a significant part of the economy, any cohesive strategy for the utilization of these firms– by state or private investors- is only beginning to become evident by the end of the period in the study. Furthermore, I show that firm registration is only occasionally based upon the sources of capital within that firm, a fact that shows the complexity and ephemerality of the very specific forms of institutions in the economy.

At this time, it remains difficult to categorize the Chinese economy because, even after more than thirty years, the transformation continues. I argue that China does not conform well to existing models of capitalism in part due to mixed ownership, and that the state is only now evolving mixed-ownership firms into a proper development-oriented institution. In the coming decades, it is likely that these firms persist and grow into a larger role mediating state and private entrepreneur interests while promoting necessary economic goals.

This dissertation considers credible models for social network data. In the classical socialnetwork analysis setting, nodes are connected to each other with the connections between the nodes stochastic and interdependent. As a result social network modeling has a sample size of one, where each network observed must be regarded as a single realization of a stochastic process. I consider three distinct but related topics in this field in each of the chapters.

I first consider the Latent Order Logistic (LOLOG) model, a recently proposed model classfor social network modeling. I take the data centric viewpoint, that is, how does the LOLOG model perform in terms of fit, qualitative interpretation and scientific conclusions on data that practitioners fit with commonly used exponential-family random graph models (ERGM).

I also propose a method for Bayesian inference for the LOLOG model. This method alsoyields insight into the posterior distribution of the latent edge ordering, which is fundamental to the LOLOG model. This approach allows for deeper insight into the LOLOG process, as well as insight into the reasons for LOLOG’s desirable properties for social network analysis.

The final chapter of the thesis considers a separate topic. I consider an approach for causalinference with observational data in a network setting. Current approaches assume the network is observed and exogenous. The main contribution of this chapter is to regard the edges and nodal covariates as jointly distributed in a causal model. We consider the causal structure of this problem, and provide an extension to current approaches to account for this . Throughout this dissertation, I use real-data networks to demonstrate the ideas and methodology. Examples include the National Longitudinal Study of Adolescent to Adult Health and a well known network of New England Lawyers.

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