Empirical studies of social and economic networks are facilitated by the growing availability of network data. This area of research focuses on understanding two major questions: how networks affect economic outcomes and how networks are formed. This dissertation studies these questions respectively. The first chapter examines the impact of social networks on agents’ economic outcomes in the context of job referrals in the labor market. The second chapter relates to the formation of financial networks with latent traits in the context of U.S. campaign contributions. In the first chapter, “A Structural Analysis of Job Referrals and Social Networks: The Case of the Corporate Executives Market”, I develop and structurally implement a labor market search model in which workers, in addition to directly receiving job offers, also receive referrals from their social contacts. In the model, referrals are generated endogenously: an external referral occurs when a friend rejects an offer he/she receives, and an internal referral occurs when a friend leaves his/her current job. I estimate the model by Generalized Method of Moments using data on the labor market history and the social connections of executives in S&P 500 firms. Using the estimated model, I find that referrals play a substantial role in the executive labor market. More than one quarter of the job transitions and raises are driven byvreferrals. Shutting down referrals reduces executives’ welfare by an equivalence of a two to seven percentage points reduction in income. I also evaluate the impacts of the social networks’ structure by comparing the outcomes under the observed networks and alternative randomly formed networks. I find that the welfare distribution is more unequal under the random networks. I further investigate the mechanisms for these effects through the lens of two network statistics: friends’ popularity and local community clustering. In the second chapter, “Inferring the Ideological Affiliations of Political Committees via Financial Contributions Networks” (co-authored with Hanming Fang), we address the missing data problem for about two thirds of the political committees that do not self-identify their party affiliations in their registration with the Federal Election Commission. In this chapter, we propose and implement a novel Bayesian approach to infer the ideological affiliations of political committees based on the network of financial contributions among them. In Monte Carlo simulations, we demonstrate that our estimation algorithm achieves very high accuracy in recovering these committees’ latent ideological affiliations when the pairwise difference in ideology groups’ connection patterns satisfy a condition known as the Chernoff-Hellinger divergence criterion. We illustrate our approach using the campaign finance records from the 2003-2004 election cycle. Using the posterior mode to categorize the ideological affiliations of the political committees, our estimates match the self-reported ideology for 94.63% of those committees who self-reported to be Democratic and 89.49% of those committees who self-reported to be Republican.