Electronic Thesis and Dissertation Repository


Doctor of Philosophy




Salvador Navarro

2nd Supervisor

Nirav Mehta

Joint Supervisor


My dissertation aims to contribute the migration and education literatures, my thesis contains two main topics and three papers. In the first two papers, I study the job search process of rural migrants in urban labor markets when social networks present. Then, I analyse how social networks affect both migration decisions and individuals' labor market outcomes. In the last paper, I examine the determinants of an individual's college education decision, with particular attention to the uncertainty faced by individuals.

The first paper examines the job search process of rural migrants, who have the option of returning home. This paper focuses on analysing the effect of social networks on their labor market outcomes. I build a dynamic structural model of job search for migrants. I estimate the model using ``Rural Urban Migration in China" dataset. The estimation results show that rural migrants with social networks receive more job offers and that migrants with social networks also have higher wages on average

The second paper analyses the effect of social networks on both migration decisions and individuals' labor market outcomes. I develop and estimate a dynamic model of return and repeated migration, social network investment decisions, and labor market transitions. The model distinguishes two channels through which social networks may affect migration decisions: (1) a direct effect on migration costs, and (2) an indirect effect on labor market outcomes through the job arrival rate. I estimate the model using panel data from the Chinese Household Income Project (2007-2009). The estimation results show that social networks increase arrival rates, and decrease migration costs. I also show that policies that directly lower migration costs may be more cost effective at increasing rural-urban migration in China than those focused on unemployment benefits.

In the third paper, we use economic theory and estimates of a semiparametrically identified structural model to analyze the role played by uncertainty and its interaction with credit constraints and preferences in explaining education choices. We develop a methodology that distinguishes information unknown to the econometrician but forecastable by the agent and information unknown to both at each stage of the life cycle. Using microdata on earnings, we estimate a model of college choice, labor supply and consumption, under uncertainty with repayment constraints. We find that 52% and 56% of the variances of college and high school log-wages respectively are predictable by the agent at the time schooling choices are made. When people are allowed to smooth consumption, college increases to nearly 58%.