Who bears retirement-preparedness inequality? Concentration-index decomposition and machine-learning attribution by gender
Retirement preparedness is a multidimensional asset shaping post-employment well-being, yet its socioeconomic distribution among knowledge workers in developing economies remains undocumented. This paper quantifies and decomposes economic inequality in retirement preparedness among academicians in Indian central universities, comparing male and female faculty to reveal its gendered architecture. A cross-sectional survey of 736 mid- to late-career academicians across 11 central universities supported a 0–10 retirement preparedness index spanning financial, psychological, health, social, and lifestyle domains. Generalized linear models estimated determinants within a five-level socio-behavioral framework. Inequality was measured using concentration curves and the concentration index (CI) and decomposed by the Wagstaff method. An interpretable gradient-boosting model with SHapley Additive exPlanations (SHAP) was added as a specification diagnostic; predictive SHAP importance and Wagstaff contributions are treated as conceptually distinct quantities. Preparedness was pro-rich for both sexes, with greater inequality among women (CI = 0.1156) than men (CI = 0.0742), and among defined-contribution National Pension System members (CI = 0.1283) than defined-benefit Old Pension Scheme members (CI = 0.0611). Household income and financial literacy together explained more than 40% of inequality in each group; spousal employment and financial self-efficacy were distinctive contributors among women, and risk tolerance among men, while outstanding debt and dependency burden were equalizing. The learning layer located where the decomposition’s additive-linear assumption is weakest, recovered a literacy threshold and income saturation, and identified four preparedness archetypes, with women over-represented in the two least-prepared. This is the first concentration-index decomposition of retirement preparedness among Indian academicians to apply an explicit gender lens, identifying behavioral and institutional leverage points for equitable healthy aging. Methodologically, it formalizes a rank-weighted SHAP decomposition as the quantity that would render explainable artificial intelligence attribution commensurable with Wagstaff contributions, and identifies its estimation as the framework’s principal extension.
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