Forecasting Forced Displacement Flows Using Machine Learning with Text Data

Laura Mayoral, Hannes Mueller, Christopher Rauh, Ben Seimon and Ramón Talvi Robledo

No 1573, Working Papers from Barcelona School of Economics

Abstract: Forced displacement is an important policy challenge, yet forecasting is hindered by sparse, annually observed flow data and reporting delays. This article proposes a forecasting method for country outflows and dyadic flows tailored to this sparse data setting. We combine slow-moving structural predictors with high-frequency text-based signals, compress high-dimensional news into low-dimensional topic representations via Latent Dirichlet Allocation to mitigate overfitting, and estimate a stacked ensemble of gradient-boosted trees that captures non-linear origin–destination interactions while making optimal use of the available data. We further apply conformal prediction to construct statistically valid prediction intervals for bilateral flows. Analyzing the text component yields that destination-specific search intensity of migration terms is a central predictor of subsequent dyadic displacement flows.

Keywords: forecasting; machine learning; Google trends; forced displacement; early warning; dyadic; conformal prediction (search for similar items in EconPapers)
JEL-codes: C53 D72 P16 (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-inv and nep-mig
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Persistent link: https://EconPapers.repec.org/RePEc:bge:wpaper:1573

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