Emergent poverty traps at multiple levels impede social mobility.

Eradicating extreme poverty and inequality are the key leverage points to achieve the seventeen Sustainable Development Goals (SDGs). However, the reduction of extreme poverty and inequality remains vulnerable to shocks such as pandemics and climate change. Numerous models have been developed to examine the complex social interactions giving rise to inequality and persistent poverty, yet few approaches include multilevel dynamics. Here, we introduce a heterogeneous agent-based model to identify conditions underlying poverty traps at different levels, which manifest as distinct statistical steady-state outcomes. We find that vulnerabilities emerge from the interaction between individual and institutional mechanisms. Individual characteristics like risk aversion, attention, and saving propensity can lead to sub-optimal diversification and low capital accumulation. These individual drivers are reinforced by institutional mechanisms such as lack of financial inclusion, access to technology, and economic segregation, leading to persistent inequality and poverty traps. Our experiments demonstrate that addressing the above factors yields a "double dividend"-reducing poverty and inequality within and between communities and creating positive feedback that can withstand shocks. Finally, we demonstrate that our theoretical model can be used as a sandbox for cost-benefit analysis of intervention strategies.