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Abstract: Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100,000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users’ frequent locations and show that commuting distances can be reasonably well explained by a gravity model. [Copyright &y& Elsevier]

In the context of the debate on the labor-market consequences of globalization, we adopt an original approach toward the identification of the wage differences between foreign and domestic firms: worker mobility. Using matched employer-employee panel data for Portugal, we consider virtually all spells of interfirm mobility over a period of 10 yr. We find that foreign firms offer significantly more generous wage policies, although there is also a (smaller) selection effect. The results are robust to the consideration of displaced workers, wage growth differences in the new firms, and different subsets of workers. ( JEL J31, J63, F23) [ABSTRACT FROM AUTHOR]