Multidimensional economic mobility reveals uneven post-pandemic recovery in Europe

Poverty research has long theorized “welfare basins” and tipping points but lacked a data-driven approach to locate them or to test how policy sequencing shifts trajectories. Our ``economic landscape" framework fills that gap. Here, we introduce a Markov chain framework that models welfare transitions across twenty-two European countries using two decades (2004 - 2024) of longitudinal household data. This approach constructs multidimensional ``economic landscapes” spanning income, education, and health, revealing stable welfare basins, tipping points, and optimal escape pathways from low welfare. By integrating multiple dimensions, our framework captures threshold effects whereby modest shocks can entrench disadvantage. Our analysis predicts key metrics, including poverty duration, efficient mobility pathways, and the profound impact of COVID-19 on social resilience. Post-pandemic escape times from low-income and health states increased by 0.56 years on average, with greater variability across countries than pre-COVID. We find that the pandemic disruption reshaped welfare basins, with European countries requiring 2 to 10 years to restore pre-pandemic welfare levels. Crucially, we find that sequencing matters: prioritizing improvements in health and education before income yields more effective poverty alleviation. Metric-agnostic and non-parametric, this tool enables policymakers and organizations worldwide to apply their own data for forward-looking poverty reduction strategies, transforming poverty alleviation into a dynamic design problem.