Promotion through Connections: Favors or Information?

Connections appear to be helpful in many contexts such as obtaining a job, a promotion, a grant, a loan, or publishing a paper. This may be due to favoritism or to information conveyed by connections. Building on earlier work on discrimination, we propose a new method that identifies these channels using data observed at the time of promotion. The method exploits distinct implications of the two effects on the relationship between observables and success. We show that extra information on connected candidates generates excess variance in latent errors while favors yield different promotion thresholds. We characterize the conditions under which both effects are identified and operationalize these ideas econometrically within a semiparametric framework. We also derive testable restrictions of the model and show how to account for connection endogeneity. We reanalyze data on academic promotions in Spain and Italy and political promotions in China. We detect evidence of favoritism for all types of candidates and of information effects for candidates applying to junior positions. We find strong support for the model's testable restrictions.