I study how city fundamentals, like amenities and housing restrictions, contribute to aggregate wage inequality through the sorting of heterogeneously skilled workers. I develop a ``system of cities'' model that features workers who differ along a continuum of skills and who compete for limited housing. This model is quantitatively tractable, and can replicate patterns in the dispersion of wages and housing prices both between and within cities. I calibrate this model to match different moments of the distributions of talent and wages for a cross-section of US cities, and I use it to understand the importance of sorting when accounting for patterns of regional inequality. Spatial sorting accounts for 7.5% of the aggregate wage dispersion and makes the economy 1.9% more productive. I then evaluate the general equilibrium effects of an important place-based policy, namely housing policy, and find that a 1% expansion in the supply of houses in more constrained cities can improve aggregate productivity between 0.2% and 0.4%. These effects would be larger in the absence of sorting. Moreover, relaxing housing constraints in those cities also tends to increase aggregate wage inequality.
Firm clustering is positively correlated with productivity, and it exhibits significant cross-sectional variation across industries. Thus, it is important to understand the industry characteristics that drive firms' decisions to co-locate. We develop a model of knowledge sharing and derive the prediction that riskier and more complex industries experience the greatest gains from knowledge spillovers. Using tests that account for the non-randomness of location decisions, we find a strong positive relationship between industry risk or complexity and the clustering of: 1) firms' headquarters, 2) patent inventors, and 3) R&D expenses. Customer/supplier proximity is also significantly and positively related to industry risk and complexity.
“Playing Checkers in Chinatown” with Jose Antonio Espin-Sanchez.
© Santiago Truffa 2016.