Complete methodological details are provided in our forthcoming paper “Real-Time Inequality” (download slides here).
Realtime Inequality provides timely and high-frequency estimates of the distribution of income and wealth in the United States. Our statistics distribute the totality of national income and household wealth across socio-economic groups and are updated each quarter when new macroeconomic numbers are published. This makes it possible to estimate economic growth by socio-economic groups 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. Our series adjust for price inflation and are expressed in March 2023 dollars. Just like GDP, our estimates for 2023 are preliminary and will be adjusted as more comprehensive data come out in the coming months.
We consider four definitions of income:
Our definition of wealth includes all financial and non-financial assets owned by households, net of all debts. Assets include all funded pensions (IRAs, 401(k)s, and funded defined benefits pensions). Vehicles and unfunded pensions (such as promises of future Social Security benefits and other unfunded defined benefits pensions) are excluded.
We consider three different populations:
Our methodology to estimate the distribution of income in real time combines 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.
The result of this combination is a set of harmonized monthly micro-files in which an observation is a synthetic adult (obtained by statistically matching public micro-data) and variables include income, wealth, and their components. These variables add up to their respective national accounts totals and their distributions are consistent with those observed in the raw input data. The micro-files are available here.
Our work only uses public data. All our programs and raw data are available here. We thank in advance users for their comments and suggestions to help us improve this work.