Using French administrative data on job-creating entrepreneurs, I estimate a life-cycle model in which risk-averse individuals can start businesses and return to paid employment. Then, I use the dynamic model to value the option of returning to the labor market in case of failure. For new entrepreneurs, this option is worth 6.4× the average net wage in the country, which represented 136,000€ in 2018. This option value is explained by the unobserved heterogeneity in entrepreneurial abilities and the random-walk component of productivity. Estimated unobserved benefits of entrepreneurship represent 38.6% of the average net wage pre-tax per year (some 15% of profits), or 8,250€ in 2018. Unobserved benefits add up to 90,700€ over the average entrepreneurial spell. Together, unobserved benefits and the option value of returning to paid employment explain 42% of firm creations.

I estimate a life cycle model of portfolio choices that incorporates the relationship between market returns and the skewness of idiosyncratic income shocks. The cyclicality of skewness can explain (a) low stock market participation among young households, (b) why the equity share of participants slightly increases until retirement, and (c) why renters invest less in stocks than do homeowners. With a relative risk aversion of 6 and yearly participation cost of $250, the model matches the evolution of wealth, participation, and conditional equity shares over the life cycle. Nevertheless, cyclical skewness increases the equity premium by at most 0.5 percentage points.

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.

Using Swedish administrative panel data, we document that workers facing higher left‐tail income risk when equity markets perform poorly have lower portfolio equity share. In line with theory, the relationship between cyclical skewness and stock holdings increases with the share of human capital in a worker's total wealth and vanishes as workers get closer to retirement. Cyclical skewness also predicts portfolio differences within pairs of identical twins. Our findings show that households hedge against correlated tail risks, an important mechanism in asset pricing and portfolio choice models.

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.