The Role of Referrals in Inequality in Labor Markets

We study labor markets in which firms can hire either via referrals or open applications. Referrals help screen candidates and so lead to better matches and increased productivity, but disadvantages workers who apply via open applications. We identify the different conditions under which distributing referrals more evenly across a population not only reduces inequality, but also increases productivity and also improves economic mobility across generations. We use the model to examine optimal policies, and show that one-time affirmative action policies have long-term impacts due the induced changes in future referral networks. We also show how the possibility of firing workers improves hiring decisions and lowers inequality.

SPEAKERS Nicole Immorlica’s research lies broadly within the field of algorithmic game theory. Using tools and modeling concepts from both theoretical computer science and economics, Nicole hopes to explain, predict, and shape behavioral patterns in various online and offline systems, markets, and games. Her areas of specialty include social networks and mechanism design. Nicole received her Ph.D. from MIT in Cambridge, MA in 2005 and then completed three years of postdocs at both Microsoft Research in Redmond, WA and CWI in Amsterdam, Netherlands before accepting a job as an assistant professor at Northwestern University in Chicago, IL in 2008. She joined the Microsoft Research Northeast Labs in 2012.

Emmanuel Midy is the Community Lead of RadicalxChange Foundation. He is a writer and consultant on the intersection of media and technology.