Migration of talent is a major driver of innovation . Large-scale bibliometric data have been used to measure international mobility of scholars. Yet , our understanding of internal migration among researchers is quite limited partly due to lack of data aggregated at a suitable sub-national level. In this study, we present a novel method and re-purpose bibliometric data using a neural network which provides a sub-national level for aggregating affiliation data. We analyze internal mobility based on over 1.3 million authorship records from the Scopus database to trace internal movements of over 150,000 scholars in Mexico and provide measures of internal migration such as net migration rates for all states over the period 1996-2019. Internal mobility is a rare event of a specific subset of active scholars. We document a core-periphery structure in the migration network of scholars' states centered around Mexico City, State of Mexico, Hidalgo, Morelos, and Queretaro which account for a major share of the total inter-state scholarly migration flows . Over the past two decades, the migration network has become more dense, but also more diverse, including greater exchange between states along the Gulf and the Pacific Coast. Our analysis of mobility events as a temporal network suggests that Mexican scholarly migration is experiencing a mobility transition in which migration between urban centers is increasing in particular to and from a single metropolitan region .
Migration of scholars is a major driver of innovation and diffusion of knowledge. Although large-scale bibliometric data have been used to measure international migration of scholars , our understanding of internal migration among researchers is very limited. This is partly due to lack of data aggregated at a suitable sub-national level. In this study, we analyze internal migration in Mexico based on over 1.1 million authorship records from the Scopus database . We trace movements of scholars between Mexican states and provide key demographic measures of internal migration for the period 1996-2018. From a methodological perspective, we develop a new framework for enhancing data quality, inferring states from affiliations, and detecting moves from modal states for the purposes of studying internal migration between researchers. Substantively, we combine demographic and network science techniques to improve our understanding of internal migration patterns country boundaries. Migration patterns between states in Mexico appear to be heterogeneous in size and direction across regions. However, while many scholars remain in their regions, there seems to be a preference for Mexico City and the surrounding states as a destination . Over the past two decades, we observed a general decreasing trend in the crude migration intensity. However, the migration network has become more dense, and more diverse, including greater exchange between states along the Gulf and the Pacific Coast. Our analysis , which is mostly empirical in nature, sets the foundations for testing and developing theories that can rely on the analytical framework developed by migration scholars and the richness of appropriately processed bibliometric data .
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for the period 1996-2018. From a methodological perspective, we develop a new framework for enhancing data quality, inferring states from affiliations, and detecting moves from modal states for the purposes of studying internal migration between researchers. Substantively, we combine demographic and network science techniques to improve our understanding of internal migration patterns country boundaries. Migration patterns between states in Mexico appear to be heterogeneous in size and direction across regions. However, while many scholars remain in their regions, there seems to be a preference for Mexico City and the surrounding states as a destination
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of mobility events as a temporal network suggests that Mexican scholarly migration is experiencing a mobility transition in which migration between urban centers is increasing in particular to and from a single metropolitan region
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