Bipartite network models for marriage and labour markets
Research proposal MARNET aims at improving our empirical knowledge of markets structured as bipartite networks (all connections involve two different categories of agents) by providing better statistical models of network formation and the effects of the network structure on outcomes. 
The use of random graphs, in which an edge between two nodes is the realization of a random draw, to model networked social, technological and biological systems has been subject to a novel and vigorous effort over the last twenty years. I will contribute to the knowledge corpus on networked economic systems with two separate research lines: first, in studying the interaction between social norms and marriage (monogamous and heterosexual); second, in studying how to improve models for linked employer-employee data. 
The first research line aims at developing empirical models of marriage formation and intra-household resource allocation in order to better understand why different marriage markets (regions, countries, time periods) look different. My aim is to disentangle and quantify the sources of differentiation that relate to standard socio-economic variables, such as education and wages, from those that relate to culture and social norms. Various extensions will be considered to improve the design and realism of the model (aging and fertility, lots of heterogeneity, unobserved heterogeneity, housing and asset accumulation).
In the second research line, I will use recent advances in dynamic random graphs to model individual wage dynamics and the mobility of workers across firms. The main empirical objective is to quantify the degree of assortative matching of workers and firms in the labour market, and the relative contributions of worker and firm heterogeneity in wages. Several extensions will be considered (time-varying types, subjecting the network structure to pre-determined firm sizes, opening firm nodes by modelling wage dynamics and occupational mobility within firms).