Laboratory
Migration and Mobility
At a Glance
Projects
Publications
Team
Project
Sex and Gender Differences in Migration Patterns
Athina Anastasiadou, Jisu Kim, Asli Ebru Sanlitürk, Emilio Zagheni, Helga A.G. de Valk (Netherlands Interdisciplinary Demographic Institute / University of Groningen, Netherlands)
Detailed Description
Our understanding of how migration patterns vary by gender and sex is fragmented, in part due to a lack of disaggregated data on migration. Data sources that have emerged from the digital revolution, such as social media data or web searches, combined with survey data, register data, and official statistics provide new opportunities to enrich the fragmentary data landscape, and they and offer new avenues for studying gender differences at various stages of the migration process.
This project will include a sequence of interconnected papers, starting with a systematic literature review of the quantitative research on sex and gender differences in migration patterns. The purpose is to summarize the current state of scientific knowledge of these differentials and identify the gaps in this research area. Moreover, it will provide the state of the art of our theoretical understanding of gender and sex differences in migration processes.
Another part of this project evaluates statistical forecasting models, based on their predictions of migration flows by sex. We apply the standard models for predicting migration flows to the available sex-disaggregated datasets. Their performance will be assessed by comparing their out-of-sample forecasts with the underlying data. If gender biases result, we will use theoretical insights gained from the reviewed literature to inform these forecasting models and improve methods for forecasting migration flows by sex.
We will also explore how digital trace data can contribute to the predictive power of migration forecasting models by sex. To this end, we integrate gender-disaggregated innovative data sources into forecasting models and evaluate their performance, based on their improvement. Digital trace data will include Facebook and Instagram data by age and sex, as well as online search data provided by Google or Yandex. By improving predictions of migration flows by sex in this way, we can reach more timely forecasts due to the availability of innovative data.
We will initially rely on data-rich settings, but the long-term perspective of this project is to apply such methods to data-scarce settings and close the knowledge gaps about data-scarce settings and countries that have low data quality. The resulting research will contribute to moving from viewing humans in transit as a homogenous group to acknowledging gender differences.
Methodologically, the innovative data sources we plan to leverage often form samples that are highly biased and not representative of the general population. We plan to address these limitations by generating appropriate correction factors for gender, age, and educational gaps between online and offline populations.
The project outcomes are expected to contribute to our understanding of migration patterns as gendered processes and advance theoretical underpinnings and methodological applications about vulnerable groups in transit.
Migration, Projections and Forecasting
Publications
Anastasiadou, A.; Kim, J.; Şanlitürk, A. E.; de Valk, H.; Zagheni, E.:
MPIDR Working Paper WP-2023-039. (2023)
