Economics of innovation, economic history, and labour economics

Revolutionary ideas often emerge in particular times and places, such as Ancient Greece, Renaissance Italy, England during the Industrial Revolution, or the Silicon Valley today. I will be investigating the geographic and chronologic diffusion of scientific ideas during the twentieth century. 

In order to understand these issues, I am collaborating with Alessandro Iaria (CREST-ENSAE) and Carlo Schwarz (University of Warwick) and we will collect the first world-wide dataset of all university scientists in the world for the period 1900 to today from historical sources. These data will cover about half a million observations on all university professors of the world and contain their names, their research fields, and their university affiliation. I will combine the detailed data on university professors with publication and citation data in all leading science journals from the ISI Web of Science. 


This figure shows the number of university professors by city in 1914. I will collect similar data for 1900, 1925, 1933, 1950, 1970, and for today.

Second, I will use machine learning tools, i.e. dynamic topic models, developed by computer linguists to identify new scientific ideas from analysing titles and abstracts of scientific papers. 

In the third step, I will analyse the spread of these ideas across geographic space and investigate the factors that make certain locations and time periods particularly prone to developing new scientific ideas. This will allow me to answer the following questions: Did the reduction of communication costs, such as the introduction of the internet, increase the speed at which scientific ideas spread across the globe? Do major scientific discoveries require a ‘critical mass’ of scientists in one location? Do established scientists help the gestation of major ideas or do they act as gatekeepers so that science advances ‘one funeral at a time’ as the famous German physicist Max Planck quipped? 

Moreover we can combine these data with data on patents to understand how basic science affects the development of new technology. 

Dr Fabian Waldinger 
London School of Economics and Political Science
Philip Leverhulme Prize