Dataset of Multi-Aspect Integrated Migration Indicators

Diletta Goglia (), Laura Pollacci and Alina Sîrbu
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Diletta Goglia: Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Laura Pollacci: Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Alina Sîrbu: Department of Computer Science, University of Pisa, 56127 Pisa, Italy

Data, 2023, vol. 8, issue 9, 1-31

Abstract: Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. New knowledge extracted from these data must be validated using traditional data, which are however distributed across different sources and difficult to integrate. In this context we present the Multi-aspect Integrated Migration Indicators (MIMI) dataset, a new dataset of migration indicators (flows and stocks) and possible migration drivers (cultural, economic, demographic and geographic indicators). This was obtained through acquisition, transformation and integration of disparate traditional datasets together with social network data from Facebook (Social Connectedness Index). This article describes the process of gathering, embedding and merging traditional and novel variables, resulting in this new multidisciplinary dataset that we believe could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers.

Keywords: international migration; migration flows; migration stocks; bilateral migration; Facebook social connectedness index; migration nowcasting; migration drivers (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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