We present new estimates of earnings volatility over time and the life cycle for men and women by race and human capital, using Social Security earnings linked to the Current Population Survey. From the late 1970s to the mid-1990s, there is a strong negative trend in earnings volatility driven by a decline in transitory variance. From the mid-1990s, there is relative stability in trends of male earnings volatility due to an increase in the variance of permanent shocks. Cohort analyses indicate that earnings volatility is U-shaped, driven by large permanent shocks early and later in the life cycle.

We investigate the effect of welfare reform on intergenerational welfare participation, using mother-daughter pairs in the Panel Study of Income Dynamics. We find that a mother’s Aid to Families with Dependent Children/Temporary Assistance for Needy Families (AFDC/TANF) participation increased her daughter’s odds of adult participation in that program by roughly 25 percentage points or more, but that welfare reform attenuated this transmission by at least 50%. However, there is no diminution of transmission after welfare reform when we consider the wider safety net or other outcomes. Daughters who grew up with mothers on AFDC/TANF were no better off after reform, with substitution toward other welfare programs over generations.

We address long-standing concerns in the literature on compensating wage differentials: the econometric properties of the estimated value of statistical life (VSL) and the wide range of such estimates. We confront prominent econometric issues using panel data, a more accurate fatality risk measure, and systematic application of panel data estimators. Controlling for measurement error, endogeneity, latent individual heterogeneity possibly correlated with regressors, state dependence, and sample composition yields VSL estimates of $4 million to $10 million. The comparatively narrow range clarifies the cost-effectiveness of regulatory decisions. Most important econometrically is controlling for latent heterogeneity; less important is how one does it.

Earnings nonresponse in household surveys is widespread, yet there is limited knowledge of how nonresponse biases earnings measures. We examine the consequences of nonresponse on earnings gaps and inequality using Current Population Survey individual records linked to administrative earnings data. The common assumption that earnings are missing at random is rejected. Nonresponse across the earnings distribution is U-shaped, highest in the left and right tails. Inequality measures differ between household and administrative data due in part to nonresponse. Nonresponse biases earnings differentials by race, gender, and education, particularly in the tails. Flexible copula-based models can account for nonrandom nonresponse.