In many different contexts, connected candidates are more likely to be hired or promoted than unconnected ones. This may be due to favoritism or better information on candidates' abilities. Attempts at identifying both effects have generally relied on productivity measures collected after hiring. In this paper, we develop a new method to identify favors and information from data on hiring. Under natural assumptions, we show that observable characteristics have a lower impact on the probability to be hired for connected candidates and that this reduction precisely captures the information effect. We then show how to recover biases due to favors from overall shifts in hiring probabilities by estimating parameters of our simple structural model. We apply this new method on data on academic promotions in Spain. We find some evidence of information effects and strong evidence of favoritism. These results are consistent with those obtained from later productivities.