I’m Ready to Be Automated
How AI can shift supply and demand—perhaps with benefits for everyone.
I’m Ready to Be AutomatedAssociated Press
Economic activity isn’t evenly distributed across geographical space. This idea is reflected in the existence of cities, as well as the concentration of economic functions in specific locations within cities, such as Manhattan in New York and the Square Mile in London. Understanding the strength of the forces of agglomeration that underlie these concentrations of economic activity is central to a range of policy questions, including one overarching question: What makes cities thrive?
Is it proximity to natural resources—such as rivers, oceans, and energy sources—that makes places attractive for firms to locate production? Is it shared amenities—such as leafy streets and scenic views—that make them attractive places for people to live? Or does the cumulative effect of growing population density itself make cities more productive, thereby attracting more firms and workers, boosting productivity further and raising demand for services, such as shops, cafés, and theaters?
Because of the complex history of many cities, identifying the sources of urban development is difficult. But Gabriel M. Ahlfeldt and Daniel M. Sturm of the London School of Economics, Princeton’s Stephen J. Redding, and Humboldt University’s Nikolaus Wolf developed a model that indicates a positive relationship between urban density and productivity growth in a virtuous circle of “cumulative causation.” The researchers applied their model to the unique natural experiment of the construction and demolition of the Berlin Wall—and the impact on economic activity in neighboring locations.
When Berlin was divided at the end of the Second World War, the western part lost access to the heart of the city; when the wall came down in 1989, the city was reunified. The researchers tracked the fortunes of West Berlin, which remained a market economy during the 41-year period of division, collecting data on employment, population, and rents between the 1930s and the 2000s.
They find that property prices and economic activity in the eastern side of West Berlin, close to the historic central business district in East Berlin, began to fall when the city was divided. Then, after reunification, the same area began to redevelop: West Berlin suddenly had access to all the knowledge and public resources in the resurgent central business district it had been denied. This spurred development in these areas, raising land prices close to the central business district and demonstrating the positive effect of exposure to density in neighboring areas.
The model is successful in explaining the observed reorganization of economic activity within West Berlin not only qualitatively but also quantitatively. What’s more, it has practical applications for urban planners making decisions on infrastructure and housing. For example, if a city is considering a subway, the model can be used to demonstrate how property prices are likely to increase.
The model also makes it possible to simulate what will happen to places that are close to proposed new infrastructure, and what the potential economic spillovers to other locationsmay be. And it can predictwhen improving one area is likely to hurt another area, when firms and workers might move away to better connected and more desirable locations.
This article was produced in collaboration with Microeconomic Insights. For more on this research, see Microeconomic Insights’ full description of it here.
Gabriel M. Ahlfeldt, Daniel M. Sturm, Stephen J. Redding, and Nikolaus Wolf, “The Economics of Density: Evidence from the Berlin Wall,” Econometrica, November 2015.
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