Manor Road Seminar Room G
Recent research has shown a strong link between the economic complexity and economic development in terms of productivity or inequality. However, the mechanisms underpinning this relationship remain poorly understood. While it is often argued that complex ecosystems provide a fertile ground for novel combinations of ideas leading to the emergence of impactful new technologies and industries, this process is hard to measure with aggregate data structured around industrial codes oering a lagging view of the economy. We seek to overcome this challenge by combining social data about industrial activity in UK Travel to Work Areas (TTWAs) with a novel dataset with content and meta-data from over a million UK business websites. We measure economic complexity using the Economic Complexity Index, which captures a location's specialisation in unique, knowledge intensive sectors, proxy emergence with the presence of keywords related to novel technologies in local business websites, and model the relation between both variables and productivity using a Bayesian regression framework. Although our analysis reveals evidence of a link between our key variables, this eect is confounded with the scale of local economies. This is consistent with the idea that larger, urban areas have an advantage in nurturing economic complexity and technological emergence in a reinforcing cycle that could be expected to increase geographical concentration and economic inequality.