We study the accumulation of financial competencies in a model of dynamic skill formation. We find evidence of complementarities between financial literacy and risk attitudes. Risk tolerance facilitates experimentation and learning-by-doing. Latent risk attitudes and financial literacy are unevenly distributed across households and do not align with general human capital. Linking estimates with data on household portfolios, we show that early-life differences in financial literacy may account for more than half of the standard deviation of wealth by age 60. Dynamic complementarities in skill for- mation imply that early interventions could reduce later-life inequality while boosting wealth growth.
In the long run, we are all dead. Nonetheless, even when investigating short-run dynamics, models require boundary conditions on long-run, forward-looking behavior (e.g., transversality and no-bubble conditions). In this paper, we show how deep learning approximations can automatically fulfill these conditions despite not directly calculating the steady state, balanced growth path, or ergodic distribution. The main implication is that we can solve for transition dynamics with forward-looking agents, confident that long-run boundary conditions will implicitly discipline the short-run decisions, even converging towards the correct equilibria in cases with steady-state multiplicity. While this paper analyzes benchmarks such as the neoclassical growth model, the results suggest deep learning may let us calculate accurate transition dynamics with high-dimensional state spaces, and without directly solving for long-run behavior.