We analyze changes in the gender structure at the top of the earnings distribution in the United States over the last 30 years using a 10% sample of individual earnings histories from the Social Security Administration. Despite making large inroads, females still constitute a small proportion of the top percentiles: the glass ceiling, albeit a thinner one, remains. We measure the contribution of changes in labor force participation, changes in the persistence of top earnings, and changes in industry and age composition to the change in the gender composition of top earners. A large proportion of the increased share of females among top earners is accounted for by the mending of, what we refer to as, the paper floor – the phenomenon whereby female top earners were much more likely than male top earners to drop out of the top percentiles. We also provide new evidence at the top of the earnings distribution for both genders: the rising share of top earnings accruing to workers in the Finance and Insurance industry, the relative transitory status of top earners, the emergence of top earnings gender gaps over the life cycle, and gender differences among lifetime top earners.

We use a massive, matched employer-employee database for the United States to analyze the contribution of firms to the rise in earnings inequality from 1978 to 2013. We find that one-third of the rise in the variance of (log) earnings occurred within firms, whereas two-thirds of the rise occurred between firms. However, this rising between-firm variance is not accounted for by the firms themselves: the firm-related rise in the variance can be decomposed into two roughly equally important forces—a rise in the sorting of high-wage workers to high-wage firms and a rise in the segregation of similar workers between firms. In contrast, we do not find a rise in the variance of firm-specific pay once we control for worker composition. Instead, we see a substantial rise in dispersion of person-specific pay, accounting for 68% of rising inequality, potentially due to rising returns to skill. The rise in between-firm variance, mostly due to worker sorting and segregation, accounted for a particularly large share of the total increase in inequality in smaller and medium firms (explaining 84% for firms with fewer than 10,000 employees). In contrast, in the very largest firms with 10,000+ employees, 42% of the increase in the variance of earnings took place within firms, driven by both declines in earnings for employees below the median and a substantial rise in earnings for the 10% best-paid employees. However, because of their small number, the contribution of the very top 50 or so earners at large firms to the overall increase in within-firm earnings inequality is small.

We study the evolution of individual labor earnings over the life cycle using a large panel data set of earnings histories drawn from U.S. administrative records. Using fully nonparametric methods, our analysis reaches two broad conclusions. First, earnings shocks display substantial deviations from lognormality–the standard assumption in the incomplete markets literature. In particular, earnings shocks display strong negative skewness and extremely high kurtosis–as high as 30 compared with 3 for a Gaussian distribution. The high kurtosis implies that in a given year, most individuals experience very small earnings shocks, and a small but non-negligible number experience very large shocks. Second, these statistical properties vary significantly both over the life cycle and with the earnings level of individuals. We also estimate impulse response functions of earnings shocks and find important asymmetries: positive shocks to high-income individuals are quite transitory, whereas negative shocks are very persistent; the opposite is true for low-income individuals. Finally, we use these rich sets of moments to estimate econometric processes with increasing generality to capture these salient features of earnings dynamics.

The magnitude of and heterogeneity in systematic earnings risk has important implications for various theories in macro, labor, and financial economics. Using administrative data, we document how the aggregate risk exposure of individual earnings to GDP and stock returns varies across gender, age, the worker’s earnings level, and industry. Aggregate risk exposure is U-shaped with respect to the earnings level. In the middle of the earnings distribution, aggregate risk exposure is higher for males, younger workers, and those in construction and durable manufacturing. At the top of the earnings distribution, aggregate risk exposure is higher for older workers and those in finance. Workers in larger employers are less exposed to aggregate risk, but they are more exposed to a common factor in employer-level earnings, especially at the top of the earnings distribution. Within an employer, higher-paid workers have higher exposure to employer-level risk than lower-paid workers.

This paper studies the nature of business cycle variation in individual earnings risk using a confidential dataset from the U.S. Social Security Administration, which contains (uncapped) earnings histories for millions of individuals. The base sample is a nationally representative panel containing 10 percent of all U.S. males from 1978 to 2010. We use these data to decompose individual earnings growth during recessions into “between-group” and “within-group” components. We begin with the behavior of within-group shocks. Contrary to past research, we do not find the variance of idiosyncratic earnings shocks to be countercyclical. Instead, it is the left-skewness of shocks that is strongly countercyclical. That is, during recessions, the upper end of the shock distribution collapses—large upward earnings movements become less likely—whereas the bottom end expands—large drops in earnings become more likely. Thus, while the dispersion of shocks does not increase, shocks become more left-skewed and, hence, risky during recessions. Second, to study between-group differences, we group individuals based on several observable characteristics at the time a recession hits. One of these characteristics—the average earnings of an individual at the beginning of a business cycle episode—proves to be an especially good predictor of fortunes during a recession: prime-age workers that enter a recession with high average earnings suffer substantially less compared with those who enter with low average earnings (which is not the case during expansions). Finally, we find that the cyclical nature of earnings risk is dramatically different for the top 1 percent compared with all other individuals—even relative to those in the top 2 to 5 percent.