Promotion through Connections: Favors or Information?

Connections appear to be helpful in many contexts such as obtaining a job, apromotion, a grant, a loan or publishing a paper. This may be due to favoritismor to information conveyed by connections. Attempts at identifying both effectshave relied on measures of true quality, generally built from data collectedlong after promotion. This empirical strategy faces important limitations.Building on earlier work on discrimination, we propose a new method to identifyfavors and information from classical data collected at time of promotion.Under natural assumptions, we show that promotion decisions look more randomfor connected candidates, due to the information channel. We obtain newidentification results and show how probit models with heteroscedasticity canbe used to estimate the strength of the two effects. We apply our method to thedata on academic promotions in Spain studied in Zinovyeva & Bagues (2015). Wefind evidence of both favors and information effects at work. Empirical resultsare consistent with evidence obtained from quality measures collected fiveyears after promotion.