Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.

Google 趋势 (GT) 会随时间和地理位置整理搜索关键词的数量。从理论上讲，这些数据可以洞察人们事前的移民意图，因此有助于对未来移民的预测分析。然而，从经验来看，GT 的预测能力是敏感的，它可能会因地理环境、选择分析的搜索关键字以及谷歌的市场份额及其用户特征和搜索行为等而变化。与之前大多数试图证明使用 GT 来预测移民流的好处的研究不同，本文解决了一个关键但讨论较少的问题：当 GT无法预测移民流量时提高迁移模型的性能。使用 EUROSTAT 对首次庇护申请的统计数据以及从各种数据源收集的一组推拉指标，我们训练了移民文献中常用的三类引力模型，并研究了 GT 的包含如何影响模型的预测难民目的地选择的能力。结果表明，纳入 GT 的效果很大程度上取决于不同模型的复杂性。具体来说，GT 只能提高相对简单模型的性能，而不能提高通过流固定效应或自回归效应增强的模型的性能。这些发现要求在建模和预测迁移的背景下，对使用 GT 以及其他数字追踪数据的优势和局限性进行更全面的分析。