The Federal Reserve Board’s Survey of Consumer Finances for 2019 provides insights into the evolution of family income and net worth since the previous time the survey was conducted in 2016. The survey shows that over the 2016–19 period, the median value of real (inflation-adjusted) family...

Administrative tax data indicate that U.S. top income and wealth shares are substantial and increasing rapidly (Piketty and Saez 2003, Saez and Zucman 2014). A key reason for using administrative data to measure top shares is to overcome the under-representation of families at the very top that...

Using 2019 SCF data, we update estimates of the U.S. wealth and income distributions. These data indicate that wealth concentration in 2019 was similar to the levels seen in 2016 and near the historical high over the 1989–2019 period. Income concentration similarly remains high but declined...

Wealth concentration in the U.S. has increased over the past 25 years across multiple methodologies for measuring wealth. But the reasons for the increase—and the timing of the increase—are quite different. In this note, we show that most available estimates are fairly consistent in level...

This paper describes the construction of the Distributional Financial Accounts (DFAs), a new dataset containing quarterly estimates of the distribution of U.S. household wealth since 1989, and provides the first look at the resulting data. The DFAs build on two existing Federal Reserve Board...

Participation in household surveys has fallen over time, making it harder to produce a household survey-like the Survey of Consumer Finances (SCF)-in a timely manner. To address these challenges, the reference year of the sampling frame data for the 2016 SCF wealthy oversample was shifted back...

Well known research based on capitalized income tax data shows robust growth in wealth concentration in the late 2000s. We show that these robust growth estimates rely on an assumption---homogeneous rates of return across the wealth distribution---that is not supported by data. When the...