Economists have long been interested in how space – or distance – affects economic activity between buyers and sellers, firms and workers, or global trading partners. But until recently, traditional models have struggled to incorporate spatial complexity, even as more and more data have become available.
For Costas Arkolakis, an economics professor at Yale and an Economic Growth Center and MacMillan Center affiliate, this tension has been a major research motivation.
“There was this ever-increasing gap between the potential of using new data to address interesting questions and the tools we had to answer them,” said Arkolakis. “The methods, models, and even empirical approaches differed widely across different fields.”
Thanks in part to Arkolakis and his colleagues’ work, however – as well as advances in computing and data analysis – spatial economics is a rich and rapidly expanding research area. Innovative new models and methods are being developed, often drawing on diverse disciplines like mathematics, physics, computer programming, and network science.
By using new tools to answer classic economics questions, Arkolakis’s research has broad and far-reaching implications for a range of policy areas – from international trade and urban planning to infrastructure investment, immigration, and beyond.