Analyzing the effect of time in migration measurement using geo-referenced digital trace data
Guy L. Abel,
Emmanuel Letouzé and
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Emilio Zagheni: Max Planck Institute for Demographic Research, Rostock, Germany
No WP-2020-024, MPIDR Working Papers from Max Planck Institute for Demographic Research, Rostock, Germany
Geo-referenced digital trace data oﬀer unprecedented ﬂexibility in migration estimation. Due to their high temporal granularity, many diﬀerent migration estimates can be generated from the same dataset by changing the deﬁnition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along quasi-continuous time-scale, analogous to a survival function. From migration theory,we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three diﬀerent platforms in two internal migration contexts: geo-tagged Tweets and Gowalla check-ins in the U.S., and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical datasets point to an emergent research agenda using digital trace data to study the speciﬁc functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.
JEL-codes: J1 Z0 (search for similar items in EconPapers)
Pages: 41 pages
New Economics Papers: this item is included in nep-mig, nep-ore and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:dem:wpaper:wp-2020-024
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