Tracking US inequality in real time

The inequality statistics lag behind the growth statistics. This column presents new real-time inequality statistics for the United States, synchronized with growth statistics, showing that all income groups recovered their pre-crisis pre-tax income levels within 20 months of the start of the Covid-induced recession. Covid-related transfers have drastically but temporarily increased disposable incomes for the bottom 50%, above their pre-Covid levels. Real wages experienced significant gains at the bottom of the distribution, highlighting the equal impact of tight labor markets.

A major gap in government economic statistics around the world is the lack of timely data on income distribution. Although detailed macroeconomic data—from quarterly national accounts to high-frequency labor market statistics—are published in near real time, they are not disaggregated by income level. We know how GDP evolves quarterly, but we don’t know which social groups benefit from this growth, or which are most affected by the economic crisis. Today, for example, inflation in the United States is the highest since the early 1980s (e.g. Ballhuis et al. 2022). But the distributional impact of this inflationary phase is not yet known. Are real wages rising despite inflation, and if so for which socio-economic groups? Have all groups of the population recovered from the Covid-19 crisis, or are some of them still below their pre-crisis income levels?

The lack of timely inequality statistics relative to growth statistics diverts public debate on the state of the economy toward macroeconomic growth concerns and away from distributional considerations. In a recent paper (Blanchet et al. 2022), we attempted to address this discrepancy by constructing a high-frequency and temporal distribution of income in the United States. We propose an approach for aggregating information contained in high-frequency public data sources, including the monthly household and employment surveys, quarterly censuses of employment and wages, and monthly and quarterly national accounts series. The result of this study is a set of monthly micro-files containing an observation of a synthetic adult (statistically obtained from public micro-data matching) and variables including income and its components. These variables add up to their respective national account totals and their distributions are consistent with those observed in the raw input data. Using these files, we can estimate quarterly and even monthly economic growth by social group as official macroeconomic growth statistics are released.

After a recession, it is possible to estimate the ‘distributive output gap’, that is, how much income remains below its pre-recession level or the bottom 50% of the distribution, the next 40% and the trend % for the top 10 since our files include comprehensive tax and government transfer variables. As such, they can be used to monitor how harms to different social groups are reduced during a crisis by stabilization policies as they are implemented.

Our files available at and real-time distributional growth statistics are updated within hours with each release of the national accounts. We automated the code and website to be able to provide high-frequency updates sustainably. This will allow us to analyze future business cycles in real time, maximizing the utility of this tool for economists, policymakers and the public at large. In the meantime, we can use this new tool to analyze the recent dynamics of the post-Covid US economy. Three interesting results emerged.

First, while all groups, especially the bottom 50% and the top 1%, experienced a sharp loss in pre-tax income at the onset of the Covid crisis, all social groups recovered their actual pre-crisis pre-tax income levels within 20 months (Figure 1). . The recovery was more even than the recovery from the Great Recession of 2008-2009, during which it took almost ten years to recover 50% of pre-crisis pre-tax income levels – although GDP per adult recovered in four years. was equal These results illustrate the fact that a given trajectory of GDP growth corresponds to widely different market income dynamics for different social groups, highlighting the utility of distributional growth statistics over time.

Figure 1 Increase in real pre-tax income across distributions during Covid-19

Comment: This figure shows the monthly dynamics of real factor income (before tax capital and labor income) for adults (with income equally divided between married spouses) around the Covid-19 pandemic. The pandemic led to strong income declines for the bottom 50% and the top one percent. All groups had recovered their pre-crisis income levels by October 2021.

Second, government programs during the pandemic led to dramatic — but short-lived — improvements in living standards for the working class (Figure 2). After accounting for taxes and cash and quasi-cash transfers, disposable income for the bottom 50% of adults was 20% higher in 2021 than in 2019. In particular, the very generous unemployment insurance supplement ($600/week from April to July 2020 and $300/week from January to August 2021) more than compensates the bottom 50% for their lost earnings due to the Covid crisis. When adding the extended additional child tax credit and earning income tax credits and especially Covid stimulus checks, the disposable income of the bottom 50% temporarily rises. These massive shifts towards the bottom 50% were far greater than in other advanced economies. This demonstrates the enormous power of government to redistribute income. However, the disposable income of the bottom 50% declined in early 2022, as expansions of the welfare state during the pandemic – notably the expanded child tax credit and earned income tax credit – were rolled back. The only reason the disposable income of the bottom 50% was higher in 2022 than in 2019 (about 10% in real terms) was the group’s higher market income, driven by wage gains.

Figure 2 Pre-tax vs disposable real income of the bottom 50% during Covid

Comment: This figure decomposes the average real monthly income of the bottom 50% from July 2019 to May 2022. We are limited to the working population (ages 20 to 64). Individual adults are ranked by their factor income (labor and capital income before taxes) and income is divided equally between married spouses. The figure reveals the relative importance of various government programs enacted during the Covid-19 pandemic, most notably the three waves of Covid-relief (April 2020, January 2021 and March 2021), the expansion of unemployment insurance, the expansion of refundable tax credits (EITC and the Child Tax Credit). , and the Paycheck Protection Program. All of these programs expired in early 2022, and the only reason that the average bottom 50% disposable income was higher than pre-Covid (by about 10%) was the higher level of factor income.

Third, the tight labor market in the Covid recovery has benefited low-wage workers, whose wage growth has outpaced higher inflation. Figure 3 shows the evolution of real labor income of adults in the working population (ages 20-64) by quartile from January 2019 to May 2022. Focusing on the full working population, including non-workers, captures both wage growth and employment growth. Since about a quarter of working-age adults do not work (and therefore have no earnings), we exclude the bottom quarter from the figure. Hence, the second quartile corresponds to low paid workers. Due to massive job losses during Covid, this group’s real earnings fell sharply. However, this group recovers very quickly. Even with rapid inflation it experienced real income growth in 2022 while other groups saw no real growth in 2022.

Between January 2019 and May 2022 the two months between January 2019 and May 2022 at the macro-level almost equal employment rates among the working-age population—real average labor income for low-wage workers increased by more than 10%, faster than all other groups in the population. Thus, the Covid recovery was characterized by a reduction in wage inequality, a break from the prevailing trend since the early 1980s that highlights the parallel effects of the tightest labor market in the US since WWII (Michaillat and Saez 2022).

Figure 3 Increase income across distribution during covid

Comment: This figure depicts average real labor income in the second, third and fourth quartiles and the top 1% of the labor income distribution for adults aged 20 to 64 (including non-workers) from January 2019 (base 100) to May. 2022. Labor income is individualized (ie, not divided equally between married spouses) and includes all wages and salaries, wage and salary supplements, and 70% of self-employment income. The first quartile is not depicted because about ¼ of adults aged 20 to 64 do not work. The second quartile corresponds to low-wage workers. Low-wage workers continue to gain real wages in 2022 despite higher inflation.

Because our method uses only public data, it can be easily replicated, tested, and extended. Looking ahead, it could be enriched by combining administrative datasets within government agencies or by including additional data sources such as private sector data (Chetty et al. 2020). We see our paper as building a prototype of a real-time distribution that combines currently publicly available data sources—a prototype that can be refined using additional data and eventually incorporated into official national accounts statistics. Our approach can also be applied to other countries, thus improving the ability to understand business cycles across the globe.


Blanchet, T, E Saez, and GabrSiel Zucman (2022), “Real-Time Asymmetry”, NBER Working Paper No. 30229.

Bolhuis, MA, JNL Cramer, and LH Summers (2022), “Past and current inflation are more similar than you think”,, 22 June.

Chetty, R, JN Friedman, N Hendren, M Stepner, and the Opportunity Insights Team (2020), “The Economic Impacts of Covid-19: Evidence from a New Public Database Using Private Sector Data”, NBER Working Paper No. 27431 .

Michaillat, P and E Saez (2022) “Figure out skilled unemployment”,, 19 April.

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