In my thesis, I study the effects of agents' heterogeneity on labor market outcomes, with particular focus on sorting, performance, wages, and inequality. Chapter one studies multidimensional matching between workers and jobs. Workers differ in manual and cognitive skills and sort into jobs that demand different combinations of these two skills. To study this multidimensional sorting, I develop a theoretical framework that generalizes the unidimensional notion of assortative matching. I derive the equilibrium in closed form and use this explicit solution to study biased technological change. The key finding is that an increase in worker-job complementarities in cognitive relative to manual inputs leads to more pronounced sorting and wage inequality across cognitive relative to manual skills. This can trigger wage polarization and boost aggregate wage dispersion. I then estimate the model for the US during the 1990s. I identify a significant increase in complementarities of cognitive inputs and in cognitive skill-bias in production. Counterfactual exercises suggest that these technology shifts can account for observed changes in worker-job sorting, wage polarization and a significant part of the increase in US wage dispersion. Chapter two develops a theory that links differences in men's and women's social networks to disparities in their labor market performance. We are motivated by our empirical finding that men's and women's networks differ. Men have a higher degree (more network links) than women, but women have a higher clustering coefficient (a woman's friends are also friends among each other). In our model, a worker with a higher degree has better access to information. In turn, a worker with a higher clustering coefficient faces more peer pressure. Both peer pressure and access to information can attenuate a team moral hazard problem in the work place. But whether peer pressure or access to information is more important depends on the work environment. We find that, in environments where uncertainty is high, information is crucial and, therefore, men outperform women / in line with findings from sectors with high earnings' uncertainty like the financial or film industry.