¶ Real-time inequality

nber link

¶ BibTeX

@techreport{blanchet2022real,
  title={Real-time inequality},
  author={Blanchet, Thomas and Saez, Emmanuel and Zucman, Gabriel},
  year={2022},
  institution={National Bureau of Economic Research}
}

¶ Abstract

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. All estimates are available at https://realtimeinequality.org and are updated with each quarterly release of the national accounts, within a few hours.

¶ Notes and Excerpts

An impetus for this project is the Covid-19 pandemic. The pandemic dramatically affected the US economy and led to large-scale government intervention, with federal government deficits of around 15% of GDP in both 2020 and 2021, the greatest as a share of the economy since World War II.

Main motivation is also follows:

To uncover the dynamics of inequality in real time, we combine these and other publicly available sources systematically. A historical analogy is helpful to understand our objectives and the gist of our approach. Before the creation of the US national income and product accounts in the context of the Great Depression, several business surveys existed, each providing valuable information on aspects of the business cycles. But these surveys were not integrated in a consistent system, making it hard to capture the dynamics of the economy as a whole. The national accounts solved this issue and became a reference tool for the study of the business cycle, with numerous applications in macroeconomics and for policymaking. Today, high-frequency distributional information is available from various sources, but there is no unified framework in which comprehensive (i.e., capturing all sources of income) growth statistics could be computed for the different social groups. Our paper proposes a methodology to bridge this gap.

I suppose that’s a compelling motivational story for the project, yeah.

First, all social groups recovered their real pre-crisis pretax income levels within 20 months of the start of the Covid recession.

I can cite that in my PUI intro, sure.

After accounting for taxes and cash and quasi-cash transfers, disposable income for adults in the bottom 50% was 20% higher in 2021 than in 2019. However, disposable income fell in the beginning of 2022 and then flatlined, as the expansion of the welfare state enacted during the pandemic—e.g., an expanded child tax credit and earned income tax credit—was rolled back.