Including the gender dimension of migration is essential to avoid systematic bias in migration predictions

• 成果类型: Article
• 署名作者: Anastasiadou, Athina; Zagheni, Emilio; de Valk, Helga A. G.
• 署名单位: Max Planck Society; University of Groningen; Royal Netherlands Academy of Arts & Sciences; Netherlands Interdisciplinary Demographic Institute (NIDI-KNAW)
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10324
• DOI: 10.1073/pnas.2500874122
• 发表日期: 2025-07-15
• 关键词: international migration; women; US; networks; immigration; moroccan

摘要

This study examines the theoretical and methodological limitations of migration research in understanding gender-specific trends of migration. In particular, theory-driven methods suffer from the gender blindness of migration theories, while data-driven methods suffer from the scarcity of gender disaggregated migration data. This research aims to evaluate how these dual limitations affect the accuracy of commonly used migration prediction models. By analyzing migration flows disaggregated by gender, the study compares the performance of deterministic methods and probabilistic gravity-type models in predicting migrant flows with varying gender compositions. The findings reveal significant differences in the predictive performance of gravity-type models based on the gender composition of migration flows. Drawing on migration theories and case studies, the study contextualizes these findings, concluding that the lack of robust theoretical frameworks and the limited availability of gender-specific migration data have critically undermined the accuracy of current prediction and forecasting methods. The implications of this research highlight the urgent need for a critical reassessment of migration theories and methodologies through the lens of gender biases, paving the way for more inclusive and accurate migration predictions.