Social networks have a profound impact on individual labor market outcomes. This paper establishes a theoretical framework where firms, workers, and applicants are linked through a social network of friendships and family links, which can be used by firms to reduce information asymmetries and increase match quality during the recruitment process. Here, the bayesian signaling game focuses on the decision of the worker, who is employed inside the firm and has links with the applicant outside the firm; the model proposes a set of equilibria where firms are able to generate matches with increased match quality through a social network. The theoretical framework pays special attention to potential incentive problems that arise due to nepotism and favoritism. The empirical model brings these predictions to the data and focuses on labor market outcomes at the individual level. The analysis exploits the panel dimension of the German Socio Economic Panel and highlights differences in labor market outcomes that can be linked to the existence of the aforementioned social networks. Empirical results focus on three key issues: First, findings allow for further insight on the determinants of the event I found my last job using either friends or relatives. Second, the paper evaluates the relationship between the use of a social networks and the starting wage of applicants. Third, results shed light on the question of how far long term labor market outcomes such as the duration of the job and the earnings profile differ by the entry channel into a job. Findings illustrate fluctuations in the use of social networks over the business cycle which highlight the decision making process of the worker who acts an an intermediary between the firm and the applicant. More specifically, the higher level of the unemployment rate reduces the probability to find a job through a social network. Additionally, results from the empirical model suggest that social networks improve match quality, resulting in longer employment spells and higher starting wages.