This study presents a dynamic, model-based view of consumers’ ageing developments, focused on gender differences, to uncover the pathways and socioeconomic transitions that female and male consumers take through old age. The analysis of longitudinal survey data spanning 15 years uses a latent Markov dynamic cluster model with transitions over time. The resulting life courses allow an exploration of lifestyle-related changes in multiple consumer well-being variables beyond age 50. Substantial well-being differences appear in the ageing paths of men and women. In both cases, a dominant chronological sequence through old age is complemented by less common transitions, rarely associated with advanced age. Although the model does not use chronological age as an independent variable, it outperforms purely agebased, or age- cohort-, and period-based models in predicting old-age consumer wellbeing. These results highlight the importance of considering within-cohort diversity when modelling the accompaniments of old age: while some older consumers enjoy active lifestyles, others of similar age succumb to depression and loneliness, rendering age an insufficient predictor of well-being states. In the future, the presented model could be matched with other, even cross-sectional, consumer survey data to help predict various dynamics in the ageing consumer population.

本研究展示了消费者老龄化发展的动态、基于模型的观点，重点关注性别差异，以揭示女性和男性消费者在老年期间所经历的途径和社会经济转型。对跨越 15 年的纵向调查数据的分析使用具有随时间变化的潜在马尔可夫动态聚类模型。由此产生的生命历程允许探索 50 岁以后多个消费者福祉变量中与生活方式相关的变化。男性和女性的衰老路径中出现了巨大的福祉差异。在这两种情况下，贯穿老年的主要时间顺序由不太常见的过渡补充，很少与老年相关。尽管该模型不使用实足年龄作为自变量，但它的表现优于纯粹基于年龄或年龄队列，和基于时期的模型预测老年消费者福祉。这些结果强调了在对老年伴奏进行建模时考虑群组内多样性的重要性：虽然一些老年消费者享受积极的生活方式，但其他年龄相仿的消费者屈服于抑郁和孤独，使得年龄不足以预测幸福状态。未来，所提出的模型可以与其他甚至是横截面的消费者调查数据相匹配，以帮助预测老龄化消费者群体的各种动态。使年龄不足以预测幸福状态。未来，所提出的模型可以与其他甚至是横截面的消费者调查数据相匹配，以帮助预测老龄化消费者群体的各种动态。使年龄不能充分预测幸福状态。未来，所提出的模型可以与其他甚至是横截面的消费者调查数据相匹配，以帮助预测老龄化消费者群体的各种动态。