Introduction: Migration and innovation are two phenomena whose ties date back a long time in history. It was Dutch, Walloons, and Italian migrants in the sixteenth and seventeenth centuries who established London as a centre for silk weaving and brew making (Luu 2005). And it was French Huguenots escaping religious persecutions who modernized the textile industry of Prussia at the end of the seventeenth century, with productivity effects still detectable two centuries later (Hornung 2014). Coming to more recent times, Moser et al. (2014) show how Jewish scientists seeking refuge from Nazi Germany were responsible for a significant growth in US patenting activity in several fields for several decades ahead. But it is only in the last twenty years or so that a truly global flow of scientists and engineers (S&Es) has emerged as an important component of total migration flows, especially those having the United States as destination and China and India as origin countries (Freeman 2010). Hence a growing number of papers have been produced that investigate the impact of these high-skilled migrants on innovation in both their destination and origin countries (see Chapters 6 and 7). This emerging literature is mostly empirical and extremely data hungry. While a few questions, mostly framed in terms of productivity effects of high-skilled migration, can be answered with aggregate data (see Chapter 2), others require extensive and detailed microdata analysis. In this chapter we explore the potential of patents as a data source for migration studies, most notably through the information they provide on inventors’ names and addresses. We do so both by reviewing the existing literature and by experimenting with a “pilot” database, which we obtain by combining inventor data with extensive information on the ethnic origin of names and surnames. In doing so, we build on Kerr’s (2008) pioneer effort, but differently from his US-centric approach, we try explicitly to focus on Europe both by making use of European Patent Office (EPO) data and by exploiting a more fine-grained information source.Read less <