Gabriel ZUCMAN, University of California - Berkeley



11.45 am to 1.00 pm


Classroom: D2-034

Real-time inequalit


This paper constructs high-frequency and timely income distributions for the United States. We develop a methodology to combine the information contained in high-frequency public data sources—including monthly household and employment surveys, quarterly censuses of employment and wages, and monthly and quarterly national accounts statistics—in a unified framework. This allows us to estimate economic growth by income groups, race, and gender consistent with quarterly releases of macroeconomic growth, and to track the distributional impacts of government policies during and in the aftermath of recessions in real time. We test and successfully validate our methodology by implementing it retrospectively back to 1976. Analyzing the Covid-19 pandemic, we find that all income groups recovered their pre-crisis pretax income level within 20 months of the beginning of the recession. Although the recovery was primarily driven by jobs rather than wage growth, real wages experienced significant gains at the bottom of the distribution in 2021 and 2022, highlighting the equalizing effects of tight labor markets. After accounting for taxes and cash transfers, real disposable income for the bottom 50% was nearly 20%higher in 2021 than in 2019, but fell in 2022 as the expansion of the welfare state during the pandemic was rolled back.