Analysis of the International Mobility of Researchers through a Markov Chain model
Event: EURO 2025 - 32nd European Conference on Operational Research
Location: Leeds, United Kingdom
Date: June 23-25, 2025
This presentation introduces a novel Markov chain analysis of academic mobility patterns revealing how university reputation influences researchers’ career trajectories and institutional barriers to mobility.
This presentation explores the complex dynamics of international academic mobility using advanced Markov chain modelling to analyse career trajectories of researchers across different university rankings and institutional affiliations.
Research Objectives:
- Institutional Barriers: Investigate how university reputation creates mobility barriers for researchers
- Career Trajectories: Analyse the influence of initial institutional affiliation on career development
- Global Patterns: Understand international mobility patterns across different university rankings
- Policy Implications: Provide insights for addressing mobility inequalities in academia
Methodology:
- Large-Scale Data: Analysis of 3.8+ million individual records from ORCID database
- University Rankings: Global ranking system to classify institutional standing
- Markov Chain Modelling: Transition probability analysis for inter-institutional mobility
- Longitudinal Analysis: Year-by-year career tracking and trajectory modelling
Key Findings:
- Mobility Constraints: Evidence of significant barriers between high and low-ranked institutions
- Career Persistence: Strong influence of initial institutional affiliation on career development
- Transition Patterns: Systematic analysis of movement probabilities across university rankings
- Global Inequalities: Documentation of institutional hierarchies affecting researcher mobility
Data and Methods:
- ORCID Database: Comprehensive researcher career tracking across millions of academics
- Ranking Integration: Global university ranking data for institutional classification
- Statistical Framework: Markov chain transition matrices and probability analysis
- Temporal Analysis: Multi-year career progression and mobility patterns
Academic Impact: This research contributes to understanding systemic inequalities in academic career development and provides empirical evidence for policy interventions to improve researcher mobility and career equity across institutional hierarchies.
Collaborative Research: Joint work with Vincenzo Scardigno, demonstrating interdisciplinary collaboration in applying quantitative methods to higher education research and academic career analysis.
