Identifying populations at risk of deprivation is crucial for effective policy design. Yet, much existing research focuses on single aspects, such as income or material deprivation, and often abstracts from deprivation dynamics. This study addresses this gap by analyzing the dynamics and socio-economic gradient of cumulative deprivation using data from the 2005–2021 waves of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. Employing copula-based techniques and two econometric approaches—a Conditional Maximum Likelihood Estimator (CMLE) estimator and a two-stage Generalized Method of Moments (GMM) procedure—the analysis reveals significant state dependence, where past cumulative deprivation strongly predicts future deprivation. Schooling, employment, and parenthood emerge as key determinants. These findings underscore the importance of adopting multidimensional and temporal perspectives on deprivation, offering critical insights for more targeted and effective policy interventions.