Recent influential work finds large increases in inequality in the United States based on measures of wealth concentration that notably exclude the value of social insurance programs. This paper shows that top wealth shares have not changed much over the last three decades when Social Security is properly accounted for. This is because Social Security wealth increased substantially from $7.2 trillion in 1989 to $40.6 trillion in 2019 and now represents nearly 50% of the wealth of the bottom 90% of the wealth distribution. This finding is robust to potential changes to taxes and benefits in response to system financing concerns.

We study the distributional consequences of student debt forgiveness in present value terms, accounting for differences in repayment behavior across the earnings distribution. Full or partial forgiveness is regressive because high earners took larger loans, but also because, for low earners, balances greatly overstate the benefits of debt cancellation. Consequently, forgiveness would benefit the top decile as much as the bottom three deciles combined. Enrolling households who would benefit from income-driven repayment is less expensive and distributes more funds to lower-income households.

What explains wealth and portfolio differences between black and white Americans? We find that disparities in economic factors explain portfolios well, but only partly explain the wealth gap. In a dynamic setting, economic factors often change optimal saving rates in ways that offset their effects on income and returns. Consequently, their net wealth effect is often limited, making the wealth gap harder to explain. We estimate that differences in income levels, income risk, family structures, mortality, health expenditures, property taxes, mortgage rates, and asset returns explain half of the differential between the racial wealth gap and the racial income gap.

This paper quantifies the aggregate effects of financing constraints. We start from a standard dynamic investment model with collateral constraints. In contrast to the existing quantitative literature, our estimation does not target the mean leverage ratio to identify the scope of financing frictions. Instead, we use a reduced‐form coefficient from the recent corporate finance literature that connects exogenous debt capacity shocks to corporate investment. Relative to a frictionless benchmark, collateral constraints induce losses of 7.1% for output and 1.4% for total factor productivity (TFP) (misallocation). We show these estimated losses tend to be more robust to misspecification than estimates obtained by targeting leverage.