Research Papers

Domestic brain circulation in China: Impact on publication, citation, collaboration and university prestige

Expand
  • 1Industrial Training Center, Shenzhen Polytechnic University, Shenzhen 518000, Guangdong, China;
    2Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China;
    3Peng Cheng Laboratory, Shenzhen 518066, Guangdong, China
†Yifang Ma (Email: mayf@sustech.edu.cn; ORCID: 0000-0003-0326-7993).

Received date: 2025-04-14

  Revised date: 2025-07-22

  Accepted date: 2025-08-22

  Online published: 2025-09-29

Abstract

Purpose: This study investigates the impact of domestic mobility on Chinese scientists’ academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.
Design/methodology/approach: Using publication and affiliation data from OpenAlex, we identified 2,896 scientists who relocated between cities in China from 2014 to 2017. We applied propensity score matching (PSM) to compare their academic outcomes post-mobility with a matched group of non-mobile peers. Multiple performance metrics were examined, including publication count, citation impact, number of collaborators, and university prestige. Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.
Findings: Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact. Scientists were more likely to move to lower-prestige universities. However, prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions, while the number of publications did not.
Research limitations: This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data, lacking personal or qualitative variables such as gender, discipline-specific norms, or institutional culture. Data coverage for Chinese-language publications may also be limited.
Practical implications: This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility. It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention, as well as for researchers considering career transitions.
Originality/value: This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators. The integration of university prestige metrics (Double First-Class and citation-based rankings) offers a nuanced view of career dynamics within the Chinese higher education system.

Cite this article

Yurui Huang, Jialong Guo, Chaolin Tian, Shibing Xiang, Yongshen He, Yifang Ma . Domestic brain circulation in China: Impact on publication, citation, collaboration and university prestige[J]. Journal of Data and Information Science, 0 : 20250048 -20250048 . DOI: 10.2478/jdis-2025-0048

References

Ackers, L. (2005). Moving people and knowledge: Scientific mobility in the European Union.International migration, 43(5), 99-131.
Amelina, A. (2013). Hierarchies and categorical power in cross-border science: Analysing scientists’ transnational mobility between Ukraine and Germany.Southeast European and Black Sea Studies, 13(2), 141-155.
Appelt S., van Beuzekom B., Galindo-Rueda F., & de Pinho R. (2015). Which factors influence the international mobility of research scientists? In Global mobility of research scientists (pp. 177-213). Elsevier.
Austin, P. C. (2008). A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003.Statistics in medicine, 27(12), 2037-2049.
Austin, P. C. (2009). Some methods of propensity‐score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations.Biometrical Journal, 51(1), 171-184.
Azoulay P., Ganguli I.,& Zivin, J. G.(2017). The mobility of elite life scientists: Professional and personal determinants. Research policy, 46(3), 573-590. https://doi.org/10.1016/j.respol.2017.01.00.
Beine M., Docquier F., & Rapoport H. (2001). Brain drain and economic growth: theory and evidence.Journal of Development Economics, 64(1), 275-289.
Bornmann, L. (2014). Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics.Journal of Informetrics, 8(4), 895-903.
Cao C., Baas J., Wagner C. S., & Jonkers K. (2020). Returning scientists and the emergence of China’s science system.Science and Public Policy, 47(2), 172-183.
Cao C.,& Simon, D. F. (2021). China’s talent challenges revisited. In E. Baark, B. Hofman, & J. Qian (Eds.), Innovation and China’s global emergence (pp. 90-112). NUS Press.
Carlson, T., & Martin-Rovet, D. (1995). The implications of scientific mobility between France and the United States.Minerva, 211-250.
Chand, M., & Tung, R. L. (2019). Skilled immigration to fill talent gaps: A comparison of the immigration policies of the United States, Canada, and Australia.Journal of International Business Policy, 2, 333-355.
Chepurenko, A. (2015). The role of foreign scientific foundations’ role in the cross-border mobility of Russian academics.International Journal of Manpower, 36(4), 562-584.
Clauset A., Arbesman S., & Larremore D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks.Science Advances, 1(1), e1400005.
Conchi, S., & Michels, C. (2014). Scientific mobility: An analysis of Germany, Austria, France and Great Britain (Fraunhofer ISI Discussion Papers Innovation Systems and Policy Analysis, No. 41). Fraunhofer-Institut für System- und Innovationsforschung ISI. https://hdl.handle.net/10419/9437.
Cooke F. L., Saini D. S., & Wang J. (2014). Talent management in China and India: A comparison of management perceptions and human resource practices.Journal of world business, 49(2), 225-235.
Costas R., Van Leeuwen T. N., & Bordons M. (2010). A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact.Journal of the American society for information science and technology, 61(8), 1564-1581.
Czaika, M., & Orazbayev, S. (2018). The globalisation of scientific mobility, 1970-2014.Applied Geography, 96, 1-10.
Deng X., Liang L., Wu F., Wang Z., & He S. (2022). A review of the balance of regional development in China from the perspective of development geography.Journal of Geographical Sciences, 32(1), 3-22.
Deville P., Wang D., Sinatra R., Song C., Blondel V. D., & Barabási A.-L. (2014). Career on the move: Geography, stratification and scientific impact.Scientific reports, 4(1), 1-7.
Edler J., Fier H., & Grimpe C. (2011). International scientist mobility and the locus of knowledge and technology transfer.Research policy, 40(6), 791-805.
Florida R., Mellander C. P., & Stolarick K. M. (2010). Talent, technology and tolerance in Canadian regional development.The Canadian Geographer/Le Géographe canadien, 54(3), 277-304.
Fortunato S., Bergstrom C. T., Börner K., Evans J. A., Helbing D., Milojević S., Petersen A. M., Radicchi F., Sinatra R., & Uzzi B. (2018). Science of science. Science, 359(6379), eaao0185.
Fullerton, A. S. (2009). A conceptual framework for ordered logistic regression models.Sociological Methods & Research, 38(2), 306-347.
Gates, A. J., & Barabási, A.-L. (2023). Reproducible Science of Science at scale: pySciSci.Quantitative Science Studies, 1-17.
Geuna, A. (2015). Global mobility of research scientists: The economics of who goes where and why. Academic Press.
Gomez C. J., Herman A. C., & Parigi P. (2020). Moving more, but closer: Mapping the growing regionalization of global scientific mobility using ORCID.Journal of Informetrics, 14(3), 101044.
Guan, J., & Chen, Z. (2012). Patent collaboration and international knowledge flow.Information Processing & Management, 48(1), 170-181.
Haunschild, R., & Bornmann, L. (2023). Identification of potential young talented individuals in the natural and life sciences: a bibliometric approach.Journal of Informetrics, 17(3), 101394.
Hill, J., & Reiter, J. P. (2006). Interval estimation for treatment effects using propensity score matching.Statistics in medicine, 25(13), 2230-2256.
Hu B., Liu Y., Zhang X., & Dong X. (2020). Understanding regional talent attraction and its influencing factors in China: From the perspective of spatiotemporal pattern evolution.Plos One, 15(6), e0234856.
Huang Y., Tian C., & Ma Y. (2023). Practical operation and theoretical basis of difference-in-difference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors.Journal of Data and Information Science, 8(1), 29-46.
Issa, H., & Kogan, A. (2014). A predictive ordered logistic regression model as a tool for quality review of control risk assessments.Journal of Information Systems, 28(2), 209-229.
Jałowiecki, B., & Gorzelak, G. J. (2004). Brain drain, brain gain, and mobility: Theories and prospective methods.Higher Education in Europe, 29(3), 299-308.
Jin C., Ma Y., & Uzzi B. (2021). Scientific prizes and the extraordinary growth of scientific topics.Nature communications, 12(1), 1-11.
Jonkers, K., & Tijssen, R. (2008). Chinese researchers returning home: Impacts of international mobility on research collaboration and scientific productivity.Scientometrics, 77, 309-333.
Kalsø Hansen, H. (2007). Technology, Talent and Tolerance - The Geography of the Creative Class in Sweden. (RAPPORTER OCH NOTISER; Vol. 169). Department of Social and Economic Geography, Lund University.
Kato, M., & Ando, A. (2017). National ties of international scientific collaboration and researcher mobility found in Nature and Science.Scientometrics, 110, 673-694.
Kwok L., Adams C. R., & Price M. A. (2011). Factors influencing hospitality recruiters’ hiring decisions in college recruiting.Journal of Human Resources in Hospitality & Tourism, 10(4), 372-399.
Lee, J. T. (2014). Education hubs and talent development: Policymaking and implementation challenges.Higher Education, 68(6), 807-823.
Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing [Computer software]. Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s432001.htm.
Li, F., & Tang, L. (2019). When international mobility meets local connections: Evidence from China.Science and Public Policy, 46(4), 518-529.
Li, Z. (2021). ORCID-based study of researcher mobility trends in China [基于ORCID的中国科研人员流动趋势研究](Master’s thesis, Nanjing Agricultural University). Nanjing Agricultural University. https://doi.org/10.27244/d.cnki.gnjnu.2021.00083.
Liu J., Wang R., & Xu S. (2021). What academic mobility configurations contribute to high performance: an fsQCA analysis of CSC-funded visiting scholars.Scientometrics, 126, 1079-1100.
Liu, M., & Hu, X. (2021). Will collaborators make scientists move? A Generalized Propensity Score analysis.Journal of Informetrics, 15(1), 101113.
Liu, M., & Hu, X. (2022). Movers’ advantages: The effect of mobility on scientists’ productivity and collaboration.Journal of Informetrics, 16(3), 101311.
Liu Q., Turner D., & Jing X. (2019). The “double first-class initiative” in China: Background, implementation, and potential problems.Beijing International Review of Education, 1(1), 92-108.
Luo Z., Gardiner J. C., & Bradley C. J. (2010). Applying propensity score methods in medical research: pitfalls and prospects.Medical Care Research and Review, 67(5), 528-554.
Mellander, C., & Florida, R. (2011). Creativity, talent, and regional wages in Sweden.The Annals of Regional Science, 46, 637-660.
Millard, D. (2005). The impact of clustering on scientific mobility: A case study of the UK.Innovation, 18(3), 343-359.
Moed, H. F., & Halevi, G. (2014). A bibliometric approach to tracking international scientific migration.Scientometrics, 101, 1987-2001.
Nishikawa-Pacher A., Heck T., & Schoch K. (2022). Open editors: a dataset of scholarly journals’ editorial board positions. Research Evaluation, 32(2), 228-243. https://doi.org/10.1093/reseval/rvac03.
Pao, M. L. (1992). Global and local collaborators: a study of scientific collaboration.Information Processing & Management, 28(1), 99-109.
Pellens, M. (2012). The motivations of scientists as drivers of international mobility decisions (FBE Research Report MSI_1202). KU Leuven - Faculty of Business and Economics.
Peters M. A.,& Besley, T.(2018). China’s double first-class university strategy: 双一流2018.143882.
Petersen, A. M. (2018). Multiscale impact of researcher mobility.Journal of The Royal Society Interface, 15(146), 20180580.
Priem J., Piwowar H., & Orr R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv. https://arxiv.org/abs/2205.0183.
Qian, H. (2010). Talent, creativity and regional economic performance: The case of China.The Annals of Regional Science, 45, 133-156.
Ren W., Xue B., Yang J., & Lu C. (2020). Effects of the Northeast China revitalization strategy on regional economic growth and social development.Chinese Geographical Science, 30, 791-809.
Robertson, S. L. (2006). Brain drain, brain gain and brain circulation. Globalisation, Societies and Education, 4(1), 1-5. https://doi.org/10.1080/1476772060055490.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects.Biometrika, 70(1), 41-55.
Saxenian, A. (2005). From brain drain to brain circulation: Transnational communities and regional upgrading in India and China.Studies in Comparative International Development, 40, 35-61.
Séguin B., Singer P. A., & Daar A. S. (2006). Scientific diasporas.Science, 312(5780), 1602-1603.
Shi D., Liu W., & Wang Y. (2023). Has China’s Young Thousand Talents program been successful in recruiting and nurturing top-caliber scientists? Science, 379(6627), 62-65.
Solimano, A. (2006). The international mobility of talent and its impact on global development (Discussion Paper No. 2006/08). UNU World Institute for Development Economics Researc.
Tarique, I., & Schuler, R. S. (2010). Global talent management: Literature review, integrative framework, and suggestions for further research.Journal of World Business, 45(2), 122-133.
Tejada Guerrero, G. (2012). Mobility, Knowledge and Cooperation: Scientific Diasporas as Agents of Change.Migration and Development, 10(18), 59-92.
Thorn K.,& Holm-Nielsen, L. B. (2008). International mobility of researchers and scientists: Policy options for turning a drain into a gain In A Solimano (Ed) The international mobility of talent: Types, causes, and development impact (pp145-167) Oxford University Press Policy options for turning a drain into a gain. In A. Solimano (Ed.). The international mobility of talent: Types, causes, and development impact (pp.145-167). Oxford University Press.
Trippl, M. (2013). Scientific mobility and knowledge transfer at the interregional and intraregional level.Regional studies, 47(10), 1653-1667.
Tymon Jr W. G., Stumpf S. A., & Doh J. P. (2010). Exploring talent management in India: The neglected role of intrinsic rewards.Journal of world business, 45(2), 109-121.
Venturini S., Sikdar S., Rinaldi F., Tudisco F., & Fortunato S. (2023). Collaboration and topic switches in science. arXiv. https://doi.org/10.48550/arXiv.2304.0682.
Verginer, L., & Riccaboni, M. (2021). Talent goes to global cities: The world network of scientists’ mobility.Research policy, 50(1), 104127.
Waltman, L., & van Eck, N. J. (2012). The inconsistency of the h-index.Journal of the American society for information science and technology, 63(2), 406-415.
Wang K., Shen Z., Huang C., Wu C.-H., Eide D., Dong Y., Qian J., Kanakia A., Chen A.,& Rogahn, R.(2019). A review of microsoft academic services for science of science studies. Frontiers in Big Data, 2, 45. https://doi.org/10.3389/fdata.2019.0004.
Wang Q., Tang L., & Li H. (2015). Return migration of the highly skilled in higher education institutions: A Chinese university case.Population, Space and Place, 21(8), 771-787.
Wang Y., Luo H.,& Yang, G.(2022). An analysis of the inter-provincial mobility network of scientific researchers in China and its evolution. Science Research Management, 43(3), 79-88. https://doi.org/10.19571/j.cnki.1000-2995.2022.03.01.
Wei, F., & Zhang, G. (2020). Measuring the scientific publications of double first‐class universities from mainland China.Learned publishing, 33(3), 230-244.
Wong, K.-y., & Yip, C. K. (1999). Education, economic growth, and brain drain. Journal of Economic Dynamics and Control, 23(5-6), 699-726. https://doi.org/10.1016/S0165-1889(98)00040-.
Yin, X., & Zong, X. (2022). International student mobility spurs scientific research on foreign countries: Evidence from international students studying in China.Journal of Informetrics, 16(1), 101227.
Yuret, T. (2017). An analysis of the foreign-educated elite academics in the United States.Journal of Informetrics, 11(2), 358-370.
Zeng A., Shen Z., Zhou J., Wu J., Fan Y., Wang Y.,& Stanley, H. E.(2017). The science of science: From the perspective of complex systems. Physics Reports, 714-715, 1-73. https://doi.org/10.1016/j.physrep.2017.10.00.
Zhang F., Liu H., Zhang J., & Cheng Y. (2022). The evolution of China’s high-level talent mobility network: A comparative analysis based on school and work stage. Complexity, 2022, 7353462. https://doi.org/10.1155/2022/735346.
Zhao Z., Bu Y., Kang L., Min C., Bian Y., Tang L., & Li J. (2020). An investigation of the relationship between scientists’ mobility to/from China and their research performance.Journal of Informetrics, 14(2), 101037.
Zhao Z., Li J., Min C., Bu Y., Kang L., & Bian Y. (2019). Scientists’ academic disruptiveness significantly increased after they moved to China.Proceedings of the Association for Information Science and Technology, 56(1), 852-854.
Zhou J., Zeng A., Fan Y., & Di Z. (2018). Identifying important scholars via directed scientific collaboration networks.Scientometrics, 114(3), 1327-1343.
Zhu W., Jin C., Ma Y., & Xu C. (2023). Earlier recognition of scientific excellence enhances future achievements and promotes persistence.Journal of Informetrics, 17(2), 101408.
Zweig D., Fung C. S., & Han D. (2008). Redefining the brain drain: China’s ‘diaspora option’.Science, Technology and Society, 13(1), 1-33.
Zweig D., Siqin K.,& Huiyao, W.(2020). ‘The best are yet to come:’State programs, domestic resistance and reverse migration of high-level talent to China. Journal of Contemporary China, 29(125), 776-791. https://doi.org/10.1080/10670564.2019.1705003
Outlines

/

京ICP备05002861号-43

Copyright © 2023 All rights reserved Journal of Data and Information Science

E-mail: jdis@mail.las.ac.cn Add:No.33, Beisihuan Xilu, Haidian District, Beijing 100190, China

Support by Beijing Magtech Co.ltd E-mail: support@magtech.com.cn