Searching for a better life: Predicting international migration with online search keywords

Marcus H. Böhme; André Gröger; Tobias Stöhr

doi:https://doi.org/10.1016/j.jdeveco.2019.04.002

Searching for a better life: Predicting international migration with online search keywords

Marcus H. Böhme, André Gröger, Tobias Stöhr

Department of Economics and Economic History

Research output: Contribution to journal › Article › Research

Abstract

© 2019 Elsevier B.V. Migration data remains scarce, particularly in the context of developing countries. We demonstrate how geo-referenced online search data can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Our approach provides strong additional predictive power for international migration flows when compared to reference models from the migration and trade literature. We provide evidence, based on survey data, that our measures partly reflect genuine migration intentions and that they outperform any of the established predictors of migration flows in terms of predictive power, especially in the bilateral within dimension. Our findings contribute to the literature by (1)providing a novel way for the measurement of migration intentions, (2)allowing real-time predictions of current migration flows ahead of official statistics, and (3)improving the performance of conventional models of migration flows.

Original language	English
Journal	Journal of Development Economics
DOIs	https://doi.org/10.1016/j.jdeveco.2019.04.002
Publication status	Published - 1 Jan 2019

Keywords

Google trends
International migration
Migration intention

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title = "Searching for a better life: Predicting international migration with online search keywords",

abstract = "{\textcopyright} 2019 Elsevier B.V. Migration data remains scarce, particularly in the context of developing countries. We demonstrate how geo-referenced online search data can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Our approach provides strong additional predictive power for international migration flows when compared to reference models from the migration and trade literature. We provide evidence, based on survey data, that our measures partly reflect genuine migration intentions and that they outperform any of the established predictors of migration flows in terms of predictive power, especially in the bilateral within dimension. Our findings contribute to the literature by (1)providing a novel way for the measurement of migration intentions, (2)allowing real-time predictions of current migration flows ahead of official statistics, and (3)improving the performance of conventional models of migration flows.",

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AU - Gröger, André

AU - Stöhr, Tobias

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N2 - © 2019 Elsevier B.V. Migration data remains scarce, particularly in the context of developing countries. We demonstrate how geo-referenced online search data can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Our approach provides strong additional predictive power for international migration flows when compared to reference models from the migration and trade literature. We provide evidence, based on survey data, that our measures partly reflect genuine migration intentions and that they outperform any of the established predictors of migration flows in terms of predictive power, especially in the bilateral within dimension. Our findings contribute to the literature by (1)providing a novel way for the measurement of migration intentions, (2)allowing real-time predictions of current migration flows ahead of official statistics, and (3)improving the performance of conventional models of migration flows.

AB - © 2019 Elsevier B.V. Migration data remains scarce, particularly in the context of developing countries. We demonstrate how geo-referenced online search data can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Our approach provides strong additional predictive power for international migration flows when compared to reference models from the migration and trade literature. We provide evidence, based on survey data, that our measures partly reflect genuine migration intentions and that they outperform any of the established predictors of migration flows in terms of predictive power, especially in the bilateral within dimension. Our findings contribute to the literature by (1)providing a novel way for the measurement of migration intentions, (2)allowing real-time predictions of current migration flows ahead of official statistics, and (3)improving the performance of conventional models of migration flows.

KW - Google trends

KW - International migration

KW - Migration intention

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M3 - Article

JO - Journal of Development Economics

JF - Journal of Development Economics

SN - 0304-3878

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