Algorithmic Stratification in Finance Degrees: Global Patterns of Python Adoption in University Curricula

Abstract Universities and business schools are under growing pressure to equip finance graduates with the algorithmic literacy required for data-intensive, Python-based work in quantitative finance and financial technology, yet little is known about how far Python has been institutionalized in finance curricula, and for whom. This research conceptualizes Python as a form of curricular infrastructure and an axis of algorithmic stratification and provides the first systematic cross-national map of its documented presence in finance degrees. Using publicly available programme and module descriptions, the study codes 241 finance-related programmes offered by 140 universities in 14 countries. A three-dimensional rubric captures the depth, scope and authenticity of Python integration and is combined into a Python adoption index (0–12; normalized 0–1). Cross-national and programme-level patterns are examined with descriptive statistics, analysis of variance and regression, complemented by mixed-effects models with random intercepts for country and thematic analysis of programme-level comments. The results reveal a highly stratified landscape: around one third of programmes document no Python, another third show limited or elective-only provision, and a smaller group exhibit deep, authentic, programme-wide integration. More than half of the variance in the adoption index lies between countries, and Python is strongly concentrated in graduate and specialist quantitative/FinTech programmes. Among programmes that do adopt Python, however, the intensity of integration converges across degree levels. Empirically, the study offers the first systematic cross-national map of Python integration in finance education; methodologically, it develops a transferable Depth–Scope–Authenticity framework; and conceptually, it demonstrates how Python structures access to algorithmically intensive segments of the finance labor market, with implications for curriculum design, professional standards, and equity in higher education and management education.