Social networks and labour match quality: a search approach

Individuals often rely on their informal personal contacts - social networks - to find
employment or improve their labour market prospects. The general direction of the
theoretical literature predicts a positive effect of networks on the quality of the match
between workers and firms, and thus wages and turnover, because they reduce information
and search frictions. However, the empirical literature gives mixed and heterogenous results.
My objective is to explore alternative channels of transmission from networks to match
quality. I will study the role of networks in wage bargaining as a determinant of the worker's
outside option, and as a social risk sharing mechanism against the incidence of
unemployment, to explain the differentials of the effect of networks on wages and turnover
by skill level. To address these issues, I will follow a structural approach to identify and
quantify the economic mechanisms behind the relationship. For the theoretical part, I will
formulate three models incorporating networks into a directed search framework, that
includes wage renegotiation and learning about match quality. Using individual level data
from the UK and Germany, I will empirically test the predictions of these models using
advanced quantitative methods. This project will contribute to the strand of theoretical
literature on social networks in search models, and the empirical literature that evaluates
them using large individual level datasets. From a policy point of view, this project will be
useful when designing programs for employment agencies to fill the information gap for
those who have less access to job referrals.