Maintainer Xinyi Zhao.
Date of the last update: 2023-01-15
ORCID: 0000-0002-2552-7795
Institution1: Max Planck Institute for Demographic Research, Rostock, Germany
Institution2: Leverhulme Centre for Demographic Science, Department of Sociology, University of Oxford, Oxford, UK
WWW: https://www.demogr.mpg.de/en/about_us_6113/staff_directory_1899/xinyi_zhao_4083/
Email: zhao@demogr.mpg.de
Email2: xinyi.zhao@st-hughs.ox.ac.uk
Title: A gender perspective on the global migration of scholars
Authors: Xinyi Zhao, Aliakbar Akbaritabar, Ridhi Kashyap, Emilio Zagheni
DOI: 10.1073/pnas.2214664120
Publication venue and date: Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2023
Abstract: Although considerable progress toward gender equality in science has been made in recent decades, female researchers continue to face significant barriers in the academic labor market. International mobility has been increasingly recognized as a strategy for scientists to expand their professional networks, and that could help narrow the gender gap in academic careers. Using bibliometric data on over 33 million Scopus publications, we provide the first global and dynamic view of gendered patterns of transnational scholarly mobility, as measured by volume, distance, diversity and distribution, from 1998 to 2017. We find that while female researchers continued to be under-represented among internationally mobile researchers and migrate over shorter distances, this gender gap was narrowing at a faster rate than the gender gap in the population of general active researchers. Globally, the origin and destination countries of both female and male mobile researchers became increasingly diversified, which suggests that scholarly migration has become less skewed and more globalized. However, the range of origin and destination countries continued to be narrower for women than for men. While the US remained the leading academic destination worldwide, the shares of both female and male scholarly inflows to that country declined from around 25% to 20% over the study period, partially due to the growing relevance of China. This study offers a cross-national measurement of gender inequality in global scholarly migration that is essential for promoting gender-equitable science policies, and for monitoring the impact of such interventions.
If you use the data in this repository, please cite our publication using the following APA style or BibTex information.
Zhao, X., Akbaritabar, A., Kashyap, R., & Zagheni, E. (2023). A gender perspective on the global migration of scholars. Proceedings of the National Academy of Sciences, 120(10), e2214664120. https://doi.org/10.1073/pnas.2214664120
@article{zhao2023GenderPerspectiveGlobal,
title = {A gender perspective on the global migration of scholars},
volume = {120},
copyright = {All rights reserved},
url = {https://www.pnas.org/doi/10.1073/pnas.2214664120},
doi = {10.1073/pnas.2214664120},
abstract = {Although considerable progress toward gender equality in science has been made in recent decades, female researchers continue to face significant barriers in the academic labor market. International mobility has been increasingly recognized as a strategy for scientists to expand their professional networks, and that could help narrow the gender gap in academic careers. Using bibliometric data on over 33 million Scopus publications, we provide a global and dynamic view of gendered patterns of transnational scholarly mobility, as measured by volume, distance, diversity, and distribution, from 1998 to 2017. We find that, while female researchers continued to be underrepresented among internationally mobile researchers and migrate over shorter distances, this gender gap was narrowing at a faster rate than the gender gap in the population of general active researchers. Globally, the origin and destination countries of both female and male mobile researchers became increasingly diversified, which suggests that scholarly migration has become less skewed and more globalized. However, the range of origin and destination countries continued to be narrower for women than for men. While the United States remained the leading academic destination worldwide, the shares of both female and male scholarly inflows to that country declined from around 25\% to 20\% over the study period, partially due to the growing relevance of China. This study offers a cross-national measurement of gender inequality in global scholarly migration that is essential for promoting gender-equitable science policies and for monitoring the impact of such interventions.},
number = {10},
urldate = {2023-02-27},
journal = {Proceedings of the National Academy of Sciences},
author = {Zhao, Xinyi and Akbaritabar, Aliakbar and Kashyap, Ridhi and Zagheni, Emilio},
month = mar,
year = {2023},
pages = {e2214664120}
}- 0_gendered_net_migration_all.csv: Country-level inflows and outflows by gender for four time periods (1998-2002, 2003-2007, 2008-2012 and 2013-2017).
- 0_OD_migration_flow.csv: Migration Origin-Destination (OD) flows between countries by gender for four time periods.
- 1_aggregated_global_level_migrant.csv: Global-level academic migration tally and gender ratios among migrant researchers and all researchers (including non-movers) for four time periods.
Note on Figures 1-4:
Replication script will generate the figures as presented in the manuscript. But, a few tweaks and decorative additions (including labels, annotation arrows) are added afterwards to clarify the most important points without changing the underlying data or results.
- 1_aggregated_country_level_migrant.csv: Country-level academic migration tallies and gender ratios among migrant researchers and all researchers for four time periods, which will be used for Fig. 1. Gender ratios among all published researchers (X-axis) and migrant researchers (Y-axis).
- 2_country_level_spread.csv: Country-level migration spreads among researchers, which will be used for Fig. 2.Scholarly emigration and immigration spreads, by gender and across four periods, for selected countries and at the global level.
- 2_global_spread_withweighted.csv: Global-level migration spreads, which will be used for Fig. 2. Scholarly emigration and immigration spreads, by gender and across four periods, for selected countries and at the global level.
- 3_female_preferred_D_global.xlsx: Destination countries ordered by female immigrant researchers' preferences at the global level.
- 3_female_preferred_D_global_top12.xlsx: A further processed version of 3_female_preferred_D_global.xlsx, which will be used for Fig. 3. The 10 most preferred destinations for global mobile researchers by gender.
- 3_male_preferred_D_global.xlsx: Destination countries ordered by male immigrant researchers' preferences at the global level
- 3_male_preferred_D_global_top13.xlsx: A further processed version of 3_male_preferred_D_global.xlsx, which will be used for Fig. 3. The 10 most preferred destinations for global mobile researchers by gender.
- 4_female_preferred_D_ctr.xlsx: First three destinations for emigrant female researchers from different countries, which will be used for Fig. 4. Top three destinations for mobile researchers by gender at the country level.
- 4_male_preferred_D_ctr.xlsx: First three destinations for emigrant male researchers from different countries, which will be used for Fig. 4. Top three destinations for mobile researchers by gender at the country level.
Source code (Python v3.9) for performing analyses of global-level and country-level gender ratios (Fig. 1.), global-level and country-level migration spreads (Fig. 2.), global-level and country-level preferred destinations by gender (Fig. 3. and Fig. 4.). The details of methods can be found in the paper.
- Python 3*
- numpy
- Pandas
- plotly
- pycountry_convert
Source code (R v4.2.1) for plotting Fig. 1., Fig. 2., Fig. 3. and Fig. 4..
Replication script will generate the figures as presented in the manuscript. But, a few labels and annotations are added afterwards to clarify the most important information and serve an aesthetic purpose without changing any underlying data or results.
- plotly
- grid
- gridExtra
- scales
- tidyverse (dplyr, ggplot2)
- ggpmisc
- magrittr
- ggh4x
- rlang
- viridis
- patchwork
- hrbrthemes
- circlize
- alluvial
- ggalluvial
- readxl
- RColorBrewer
- ggflags (devtools::install_github('rensa/ggflags'))
- here
These are the figures exported from R script Plot_Code_for_PNAS.R.
These are the figures from published manuscript.
This study was granted access to the bibliometric data through the Project "Kompetenzzentrum Bibliometrie" of the German Competence Network for Bibliometrics (https://bibliometrie.info/). The authors also acknowledge their funder Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) with grant number 16WIK2101A.
By the license terms, we are not allowed to publish any individual level data. All aggregated data to replicate our results, analysis and figures in the published manuscript are uploaded to this repository (as described above).
- python; 3.10.8
- numpy; 1.23.5
- pandas; 1.5.1
- plotly; 5.11.0
- pycountry-convert; 0.7.2
> sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows Server x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 LC_MONETARY=German_Germany.1252
[4] LC_NUMERIC=C LC_TIME=German_Germany.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] RColorBrewer_1.1-3 readxl_1.4.0 ggalluvial_0.12.3 alluvial_0.1-2 circlize_0.4.15 hrbrthemes_0.8.0
[7] patchwork_1.1.2 viridis_0.6.2 viridisLite_0.4.0 gridExtra_2.3 rlang_1.0.6 ggh4x_0.2.3
[13] magrittr_2.0.3 ggpmisc_0.5.2 ggpp_0.5.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[19] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 tidyverse_1.3.1 scales_1.2.0
[25] plotly_4.10.1 ggflags_0.0.2 ggplot2_3.4.0 here_1.0.1
loaded via a namespace (and not attached):
[1] fs_1.5.2 lubridate_1.8.0 httr_1.4.3 rprojroot_2.0.3 tools_4.2.1 backports_1.4.1
[7] utf8_1.2.2 R6_2.5.1 DBI_1.1.3 lazyeval_0.2.2 colorspace_2.0-3 withr_2.5.0
[13] tidyselect_1.1.2 grImport2_0.2-0 compiler_4.2.1 extrafontdb_1.0 cli_3.4.1 rvest_1.0.2
[19] quantreg_5.93 SparseM_1.81 xml2_1.3.3 systemfonts_1.0.4 digest_0.6.29 rmarkdown_2.14
[25] base64enc_0.1-3 jpeg_0.1-9 pkgconfig_2.0.3 htmltools_0.5.2 extrafont_0.18 dbplyr_2.2.1
[31] fastmap_1.1.0 htmlwidgets_1.5.4 GlobalOptions_0.1.2 rstudioapi_0.13 shape_1.4.6 generics_0.1.3
[37] jsonlite_1.8.0 Matrix_1.4-1 Rcpp_1.0.8.3 munsell_0.5.0 fansi_1.0.3 gdtools_0.2.4
[43] lifecycle_1.0.3 stringi_1.7.6 MASS_7.3-57 crayon_1.5.1 lattice_0.20-45 haven_2.5.0
[49] splines_4.2.1 hms_1.1.1 knitr_1.39 pillar_1.7.0 reprex_2.0.1 XML_3.99-0.10
[55] glue_1.6.2 evaluate_0.15 data.table_1.14.2 modelr_0.1.8 png_0.1-7 vctrs_0.5.1
[61] tzdb_0.3.0 MatrixModels_0.5-0 Rttf2pt1_1.3.11 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[67] xfun_0.31 broom_1.0.0 survival_3.3-1 ellipsis_0.3.2