Urban density boosts productivity and innovation, improves access to goods and services, reduces typical travel distances, encourages energy-efficient construction and transport, and facilitates sharing of scarce amenities. However, density is also synonymous with crowding, makes living and moving in cities more costly, and concentrates exposure to pollution and disease. We explore the appropriate measurement of density and describe how it is both a cause and a consequence of the evolution of cities. We then discuss whether and how policy should target density and why market and political forces unhappily resolve the trade-off between its pros and cons.
A key reason why the trade-off between the benefits and costs of bigger and denser cities is not resolved in a socially optimal way is that the interests of existing and potential city residents are generally misaligned. Incumbent residents in the most productive cities often use planning regulations to limit entry into their city to maximise their welfare at the expense of potential newcomers. To study this, we develop an urban growth model where human capital spillovers foster entrepreneurship and learning in heterogeneous cities. Incumbent residents limit city expansion through planning regulations so that commuting and housing costs do not outweigh productivity gains from agglomeration. The model is grounded in strong microfoundations, matches key regularities at the city and economy-wide levels, and generates novel predictions that we provide evidence for. It can be quantified using few parameters that we estimate based on its key equations. Through counterfactual scenarios, we quantify the relevance of planning regulations and the effect of cities on economic growth and aggregate output.
One of the fundamental decisions individuals make in cities is the choice of the neighbourhood where to live. We spend about two-thirds of our time at home and around one-third of our income buying or renting it. Depending on our residential location choice, there are also substantial differences in the extent to which jobs, education opportunities and amenities are within reach and with whom we can interact. As circumstances change, so do our residential location choices, and in many countries, 5% or more of the population moves each year. Research on residential location choices tends to focus on common determinants across individuals or broad groups, such as job opportunities, housing costs, accessibility, amenities, and taxes. In this project, we also explore less traditional factors specific to each individual, such as their' self-perception and the location of their family and friends, which turn out to be crucial determinants of location choices.
Bigger cities offer more valuable experience and opportunities in exchange for higher housing costs. While higher-ability workers benefit more from bigger cities, they are not more likely to move to one. Our model of urban sorting by workers with heterogeneous self-confidence and ability suggests flawed self-assessment is partly to blame. Our empirical analysis shows that, consistent with our model, young workers with high self-confidence are more likely to initially locate in a big city. For more experienced workers, ability plays a stronger role in determining location choices, but the lasting impact of earlier choices dampens their incentives to move.
Using anonymised cellphone data, we also study how social networks shape residential mobility decisions. Individuals with few local contacts are more likely to change residence. Movers strongly prefer neighbourhoods where they already know more people nearby. Contacts matter because proximity to them is valuable and makes attractive locations more enjoyable. They also provide hard-to-find local information and reduce frictions, especially in home-search. Effects are not driven by similar people being more likely to be friends and move between certain locations. Recently-moved and more central contacts are particularly influential. With age, proximity to family gains importance over friends.
