NBER WORKING PAPER SERIESREAL-TIME INEQUALITY
Thomas Blanchet
Emmanuel Saez
Gabriel Zucman
Working Paper 30229
http://www.nber.org/papers/w30229

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
July 2022, Revised November 2022

We thank Akcan Balkir, Anand Bharadwaj, and James Feng for outstanding research assistance,and Heather Boushey, Dennis Fixler, Marina Gindelsky, Damon Jones, Robert Kornfeld, Greg
Leiserson, Danny Yagan, and numerous conference participants for helpful comments andreactions. We acknowledge financial support from the Center for Equitable Growth at UC
Berkeley, the Carnegie Foundation, NSF grant SES-1559014, the Stone Foundation, and theEuropean Research Council. This paper is supplemented by a website, https://realtimeinequality.org, with timely inequality and distributional growth estimates updated every
quarter and detailed visualizations. All our data and programs are also posted online at thisaddress. The views expressed herein are those of the authors and do not necessarily reflect theviews of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2022 by Thomas Blanchet, Emmanuel Saez, and Gabriel Zucman. All rights reserved. Shortsections of text, not to exceed two paragraphs, may be quoted without explicit permission
provided that full credit, including © notice, is given to the source.

Real-Time Inequality
Thomas Blanchet, Emmanuel Saez, and Gabriel Zucman
NBER Working Paper No. 30229
July 2022, Revised November 2022
JEL No. E01,H2,H5,J3
ABSTRACTThis 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 genderconsistent 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 andsuccessfully 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 labormarkets. 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.
Thomas Blanchet
University of California
Evans Hall #3880
Berkeley, CA 94720
United States
thomas.blanchet@icloud.com

Gabriel Zucman
Department of Economics
University of California, Berkeley
530 Evans Hall, #3880
Berkeley, CA 94720
and NBER
zucman@berkeley.edu

Emmanuel Saez
Department of Economics
University of California, Berkeley
530 Evans Hall #3880
Berkeley, CA 94720
and NBER
saez@econ.berkeley.edu

Real-Time Inequality website is available at https://realtimeinequality.org
Code repository is available at https://github.com/thomasblanchet/real-time-inequality

1

Introduction

A major gap in global economic statistics is the lack of timely information on the distributionof income. Thanks to a sophisticated system of national accounts and labor market statistics,
detailed macroeconomic data are published almost in real time. In the United States, estimatesof quarterly gross domestic product are released less than a month after the end of each quarter;
monthly personal income, jobs, and unemployment statistics within a month; unemployment
claims data weekly. These figures, scrutinized by the business community, are a vital input for
the analysis of the business cycle and the conduct of monetary and fiscal policy. But they are
not disaggregated by income level. While we know how GDP evolves quarterly, we do not know
which social groups benefit from this growth, or which are most affected by economic crises as
they unfold. This gap limits the ability of governments and central banks to design effective
policies in crisis situation and in the aftermath of recessions. Moreover, because of this gap,
macroeconomic statistics get a lot more attention than inequality in the press and the publicdebate, which naturally focuses on the most recent data.
Our paper attempts to address this gap by creating high-frequency and timely distributionsof income for the United States, thus putting distributional statistics on an equal footing withmacroeconomic statistics. We propose a methodology to combine the information containedin high-frequency public data sources, including monthly household and employment surveys,
quarterly censuses of employment and wages, and monthly and quarterly national accounts
series. The result of this combination is a set of harmonized monthly micro-files in which anobservation is a synthetic adult (obtained by statistically matching public micro-data) and variables include income and its components. These variables add up to their respective national
accounts totals and their distributions are consistent with those observed in the raw input data.
Using these files, we can estimate quarterly economic growth by social group as soon as official macroeconomic growth is released. Following a recession, it becomes possible to estimate“distributional output gaps,” that is the extent to which income remains below its pre-recession
level or trend for the bottom 50% of the distribution, the next 40%, and the top 10%. Since
our files incorporate tax and government transfer variables, they can be used to monitor how
losses for different social groups during a crisis are mitigated by stabilization policies as they
are implemented. Looking forward, these files could be used to estimate parameters of macroeconomic models of the business cycle with heterogeneous agents and to calibrate such models.
Our files and distributional growth statistics, available at https://realtimeinequality.org,
are updated with each release of the national accounts, within a few hours. We automated the1code and website with a view to being able to provide high-frequency updates sustainably. Thisshould allow us to analyze future business cycles in real time, maximizing the usefulness of thistool for economists, policymakers, and the public.
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 sinceWorld War II. There is clear evidence that by the spring of 2022 the economy had on aggregate
recovered from the shock, but inflation was at its highest since the beginning of the 1980s.
Did government spend too much to support incomes at the height of the crisis? During therecovery, did all groups of the population benefit from running the economy hot, or was therecovery unequal? And in the aftermath of the recession, were real wages rising despite inflation,and if so for which socio-economic groups?
Addressing such questions in a timely manner is difficult, for two main reasons. First, income
tax data—a crucial source to study income dynamics, especially at the top of the distribution—
are only available with a lag of almost two years, due to late tax returns filings by top earners andprocessing times at the Internal Revenue Service. Second, the different data sources that containhigh-frequency distributional information are incomplete and dispersed. The monthly Current
Population Survey captures less than half of US national income, because it excludes certain
forms of income (such as employment benefits and business profits) and does not capture highearners well (due to top-coding, small sample sizes, and income under-reporting). Employment
and establishment surveys contain information on high earners, but only indirectly as they
are not tabulated by income brackets. High-frequency national account series also indirectly
contain a great deal of distributional information—since different forms of income are distributed
differently across households—but are typically analyzed separately from survey data.
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 andthe gist of our approach. Before the creation of the US national income and product accounts inthe 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 aconsistent 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 in2

which comprehensive (i.e., capturing all sources of income) growth statistics could be computedfor the different social groups. Our paper proposes a methodology to bridge this gap.
The main steps of our methodology can be summarized as follows. We start with theannual distributional national accounts micro-files of Piketty, Saez and Zucman (2018), which
allocate 100% of annual national income, household wealth, and many components of thesemacroeconomic aggregates using primarily individual tax data. We then statistically matchthese files to Current Population Survey and Survey of Consumer Finance micro-data using
optimal transport matching methods. To our knowledge this is the first time such a “one-to-one”
statistical match is conducted. This allows us to bring gender, race, and education variables—
which are missing in tax data—into the Piketty, Saez and Zucman (2018) distributional national
accounts, thus allowing us to produce the first statistics on the distribution of national income
by gender, race, and educational attainment.
We then move to higher frequency and estimate real-time statistics in three steps. First,
taking moving averages of current and adjacent-year micro-data, or using the latest file (2019)from 2020 onwards, we create monthly files by rescaling each component of national income to its
monthly seasonally-adjusted aggregate value. Second, we incorporate high-frequency changes in
the wage distribution using monthly survey micro-data and tabulations of monthly and quarterly
surveys and administrative records. Building on the important work of Lee (2020), we showthat public tabulations of the Quarterly Census of Employment and Wages by 6-digits NAICS
industry × county × ownership sector (public vs. private) can be used to predict changes in wage
inequality remarkably well, including at the top of the distribution which is not well covered inexisting household surveys. For example, the share of wages earned in the top 1% of industries
× counties × ownership sector with the highest average wage (e.g., securities brokerage in New
York county; Internet publishing and broadcasting in Santa Clara county) is strongly correlatedwith the share of wages earned by the top 1% workers. This allows us to project high-frequencychanges in wages within the top 10% of the distribution reliably. For the bottom 90%, which iswell covered by household surveys, we estimate real-time wage levels by averaging predictions
from tabulated employment surveys and from the monthly Current Population Survey.
Third, we model changes in other components of pretax and posttax national income. For
business and capital income, we account for changes in the aggregate value of each component(rental income, corporate profits, etc.) and assume that within-component distributions areunchanged in the short term. For government transfers, we model the distribution of new programs using program parameters, eligibility rules, and public sources, paying special attention3

to the new programs created during the Covid-19 pandemic such as the Paycheck Protection
Program, for which detailed public data about beneficiaries and studies of incidence exist.
We test and successfully validate our methodology by applying it retrospectively back to
1976. Comparing predicted to observed income changes, we find that we correctly anticipate
whether income is growing or falling 89% of the time for the top 1%, 93% for the next 9%, and
82% for the next 40% and for the bottom 50% (which often have negligible income growth over
our sample period). We provide extensive quantification of the bias and noise of our projections,which for all income concepts (e.g., pretax vs. posttax) and income groups are found to belimited. Our methodology delivers accurate predictions during and in the immediate aftermathof recessions, when real-time estimates are most valuable from a policy perspective. Even toughit does no rely on tax data, our methodology is unbiased for the top 1%.
The intuition for why our methodology delivers reliable results is the following. About 30%
of national income is capital income. Because wealth is a stock variable, the concentration of thevarious components of capital income is relatively slow-moving at high frequency. The impact of
capital income on total income inequality is mostly driven by changes in the size of the different
components of aggregate capital income—such as corporate profits and housing rents—overthe business cycle, changes which are captured by our methodology. For labor income, which
accounts for about 70% of national income, short-term changes in the distribution can be large,
as unemployment spikes in recessions. But in contrast to capital income, for labor income we
do not assume stable distributions within component: we capture high-frequency distributionalchanges thanks to our combination of household and employment surveys.
Because our methodology only uses public data, it can easily be replicated, tested, and extended. Looking forward, it could be enriched by combining administrative datasets within government agencies or by incorporating additional data sources, such as private sector information(Chetty et al., 2020). We view our paper as constructing a prototype of real-time distributions
combining all currently publicly available data sources—a prototype that could be refined using
additional data and eventually incorporated into official national account statistics.1
Using our monthly micro-files to examine the Covid-19 pandemic yields three main findings.
First, all social groups recovered their real pre-crisis pretax income levels within 20 months ofthe start of the Covid recession. The recovery was much more equal than the recovery from1

The Federal Reserve has published Distributional Financial Accounts since 2019, distributing aggregate
household wealth quarterly (Batty et al., 2019). For income, the Bureau of Economic Analysis (BEA) distributes
annual personal income and has explored the feasibility of higher-frequency statistics (Fixler, Gindelsky, andKornfeld, 2021). We have greatly benefited from discussions with the Federal Reserve and BEA teams.

4the Great Recession of 2008–2009, during which it had taken nearly 10 years for the bottom
50% to recover its pre-crisis pretax income level—even though GDP per adult recovered in 4
years. The Covid recovery was also more equal across gender and racial groups. These findings
illustrate the fact that a given trajectory of GDP growth is compatible with widely different
market income dynamics for the various social groups, highlighting the usefulness of timely
distributional growth statistics.
Second, labor earnings experienced significant gains at the bottom of the labor income distribution during the Covid recovery, in a context of loose monetary policy until the spring of2022 and tight labor market. Between February 2020 (the eve of the recession) and September
2022—two months with equal employment rates—real average labor income for low-wage workers increased by more than 10%, faster than for all other groups of the population (except the
top 1% which grew slightly faster). Gains were limited elsewhere. The Covid recovery was thus
characterized by a reduction in wage inequality among the bottom 99%, a break from the trend
prevailing since the early 1980s that highlights the equalizing effects of tight labor markets.
Third, government programs enacted during the pandemic led to an unprecedented—but
short-lived—improvements in living standards for the working class. 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 thenflatlined, 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. The only reason why disposable
income for the bottom 50% was higher in 2022 than in 2019 (by about 10% in real terms) was
the higher market income for this group, driven by wage gains.
The rest of this paper is organized as follows. In Section 2 we relate our work to theliterature. Sections 3 and Sections 4 detail our methodology. Section 5 provides validation
tests. In Section 6 we study the dynamics of income inequality during the Covid-19 pandemic
and in its aftermath, and contrast it with the Great Recession of 2008–2009. We discuss racialinequality in Section 7 and conclude in Section 8.

2

Related Literature

2.1

Previous Attempts at Estimating Inequality at a High Frequency

There has been and there are ongoing efforts to provide timely estimates of inequality in the
United States.

5

The Federal Reserve Bank of Atlanta maintains a monthly wage growth tracker, constructedusing microdata from the Current Population Survey following a methodology developed in Dalyet al. (2011).2 The tracker reports the median percent change in the hourly wage of employedindividuals observed 12 months apart. Breakdowns by, e.g., wage quartiles, gender, occupation,and census divisions are shown. Although a useful tool, this wage tracker has some limitations.
First, it does not account for non-workers, hence the statistics do not map onto overall income
inequality. During recessions, the median wage of employed workers in the bottom quartile often
rises through composition effects as low-wage workers are laid off; even though bottom wages
may appear to be growing relatively fast, inequality may in fact be rising. Second, the data
are top-coded at $150,000 in annual wage, roughly the 95th percentile of the wage distribution.
They miss the dynamic of income in the top 5%, a group that earns about a quarter of all
wages. In contrast to the Atlanta wage growth tracker, our statistics include non-workers, top
earners, and all other forms of income beyond wage income (e.g., capital income and transfers),
making it possible to distribute all of national income and to decompose its growth.
Since 2019, the Federal Reserve has published Distributional Financial Accounts (DFA), distributing aggregate household wealth at the quarterly frequency (Batty et al., 2019). Following
Saez and Zucman (2016), the DFA allocate the official Federal Reserve Financial Accounts totals
across the population. In contrast to Saez and Zucman (2016) who primarily rely on individual
income tax data and the capitalization method for this allocation, the Federal Reserve uses theSurvey of Consumer Finances, a triennal survey of about 6,000 families. In this paper, although
our focus is primarily on income we also construct real-time estimates of wealth inequality. As
detailed in Section 6.4, the evolution of wealth inequality we obtain is consistent with the DFA
estimates. Our value-added is to capture the top of the distribution all the way to the top
0.01%, to provide longer time series (back to 1976, while the DFA starts in 1989), to have more
distributional information at the annual frequency (due to the annual nature of tax data, as
opposed to the triennal nature of the Survey of Consumer Finances), and to provide current-day
estimates of wealth inequality (updated daily on https://realtimeinequality.org), basedon daily changes in stock market indices.
Recently, Fixler, Gindelsky, and Kornfeld (2021) build on the annual distributional personal income statistics created by the Bureau of Economic Analysis (Fixler et al., 2017) to
explore the feasibility of a quarterly distribution of personal income. The main methodological difference with our work is that Fixler, Gindelsky and Kornfeld (2021) do not attempt2

See https://www.atlantafed.org/chcs/wage-growth-tracker.

6to project changes in distributions within components, but simply rescale the annual personal
income totals component by component to match the corresponding quarterly totals. As theyshow (and as we confirm in Section 5.3 below), this methodology produces reasonable resultsin years of normal growth but significantly underestimates inequality during recessions. A key
contribution of our work is to demonstrate that a more sophisticated methodology—projectingchanges in the distribution of labor income using high-frequency household and employment
surveys—overcomes this issue. There are a number of additional methodological differences
between the two projects. In contrast to Fixler, Gindelsky and Kornfeld (2021) who distributepersonal income, we distribute national income, the aggregate used to compute macroeconomic
growth.3 We start from annual estimates which are largely based on individual tax return data,
while Fixler, Gindelsky and Kornfeld (2021) rely primarily on the Current Population Survey,
making it harder to provide estimates within the top 10%. These differences notwithstanding,both projects share the same objective of creating timely inequality statistics consistent with
the national accounts. Our work was inspired by discussions with staff of the Bureau of Economic Analysis and the ongoing dialogue between academics and researchers within government
agencies is in our view highly valuable.

2.2

Impacts of the Covid-19 Pandemic on Inequality

Our work also relates to the literature on the impact of the Covid-19 pandemic on inequality,
recently surveyed in Stantcheva (2022). The literature emphasizes the equalizing effects of government intervention in high-income countries, while suggesting several channels through which
the pandemic may, once these interventions fade out, eventually widen economic disparities.
In the US context, Parolin et al. (2022) use the monthly Current Population Survey and theSurvey of Income and Program Participation to produce monthly poverty rates in real time.
Several studies attempt to analyze inequality in real time in countries other than the UnitedStates. For example, Aspachs et al. (2020) and Bounie et al. (2020) use micro-level bankaccount data to construct high-frequency distributional data for Spain and France respectively.
Relative to this body of work, our main contribution is to provide a general methodology thatcan be applied to all business cycles and could be implemented throughout the world. The mainfeature of our methodology is its comprehensive character (capturing 100% of national income),
timeliness (estimates are available online within a month), and granularity (with estimatesavailable from the bottom 50% to the top 0.01% for pretax income, posttax income, disposable
3

See Saez and Zucman (2020) for a discussion of the differences between these two concepts and their implications.

7

income, and wealth). Applied to the Covid-19 crisis in the US context, our methodology delivers new insights, such as the fast recovery of working class incomes even before government
intervention, and the decline in wage inequality in the tight post-Covid labor market.

3

Rescaling to Match Monthly Income Aggregates

There are two main steps in our methodology. First, we rescale existing annual income distributions to match monthly macroeconomic income totals, income component by component.
Second and most importantly, we incorporate information on changes in the distribution ofincome within key components, most notably wage income. In this Section we define and construct our monthly aggregates and explain how we rescale annual distributions to match theseaggregates, before turning to changes in distributions within components in Section 4.

3.1

Definition of Income

Our goal is to estimate the monthly and quarterly distributions of the income concepts studied in
Piketty, Saez and Zucman (2018) and in the distributional national accounts literature (Blanchet
et al., 2021): factor, pretax, posttax, and disposable income.4 Factor income is the income
earned from labor and capital, before any tax and government spending and before the operationof the pension system. Pretax income is factor income after the operation of the pension
system (public and private), disability insurance, and unemployment insurance. Contributions
to pensions (including Social Security taxes) and to unemployment and disability insurance areremoved, while the corresponding benefits are added. Pretax income thus in particular captures
the effect of expanded unemployment insurance during the Covid-19 pandemic. Posttax income
is pretax income minus all taxes (other than Social Security taxes, already subtracted frompretax income), plus all government transfers (other than Social Security and unemployment
benefits, already included in pretax income) and the government deficit.
Factor, pretax, and posttax income all add up to national income. National income is themost comprehensive and harmonized notion of income: it includes all income that accrues toresident individuals, no matter the legal nature of the intermediaries through which this income
is earned. In contrast to personal income, national income is not affected by business decisions tooperate as corporations vs. non-corporate businesses such as partnerships, a decision influenced
by the tax system. This feature of national income maximizes comparability over time. National
4

Detailed definitions are presented Piketty, Saez and Zucman (2018) and Saez and Zucman (2020) in the US
context, and in Alvaredo et al. (2016) and Blanchet et al. (2021) in the international context.

8

income is computed following internationally-agreed methods, maximizing comparability acrosscountries. Last, it is closely related to GDP, the aggregate most often used to compute economic
growth: National income is GDP minus capital depreciation plus net income received fromabroad. Since capital depreciation and net foreign income account for a relatively small fractionof GDP, the growth of national income is conceptually close to the growth of GDP.5 Our focus onnational income is in line with recommendations made by the Commission on the Measurementof Economic Performance and Social Progress (Stiglitz, Sen and Fitoussi, 2009).
Factor income—the sum of income from labor and capital, the two factors of production—
naturally lends itself to decompositions of economic growth. Pretax income and posttax income
include income which is socialized through social insurance and the tax-and-transfer system. At
the individual level, the growth of pretax and posttax income thus reflects both output growthand changes in transfers. Comparing the growth of posttax income to the growth of factor
income provides a comprehensive view of the extent to which taxes and government spending
equalize growth across the distribution.
We also consider a fourth income concept, disposable income. It is equal to pretax income
minus all taxes, plus all cash and quasi-cash transfers. Disposable income captures the income
individuals have at their disposal to consume private goods and to save. In contrast to posttax
income, disposable income excludes in-kind transfers such as Medicare and Medicaid, collective
consumption expenditures, and the government deficit. Disposable income does not add up tonational income and thus cannot be used to decompose growth. It is, however, a useful concept
to study the distributional impacts of stabilization policies during economic crises.6

3.2

Construction of Monthly Income Aggregates

To construct aggregate monthly factor, pretax, disposable, and posttax income and their components, we use the monthly and quarterly national accounts published by the Bureau of Economic
Analysis. We start from the most detailed components of personal income (published monthly)and domestic product and income (published quarterly) available. All the monthly and quar5
Conceptually, GDP and gross domestic income GDI (from which national income is derived by subtracting
depreciation and adding net foreign income) are identical, but in practice they are estimated using largely
independent sources in the United States and hence their growth can diverge; see Section 3.2 below.
6
In periods of crisis, posttax income—which includes government spending other than cash transfers but adds
back the government deficit—can be lower than disposable income. This was the case in the second quarter of
2020, due to the massive federal deficits induced by the economic response to the Covid pandemic. Disposable
income has two advantages relative to posttax income in this context. First, it does not require one to make(necessarily debatable) assumptions about who bears the burden of the government deficit. Second, it is more
directly informative of the consumption possibilities of households and of the extent to which government policies
manage to smooth them over the business cycle.

9terly aggregates used in this paper are seasonally-adjusted and expressed in real dollars using
the national income price deflator.
Four remarks about the construction of our aggregates are in order. First, factor income isestimated using the income approach of the US national income and product accounts, not the
product approach. As is well known, there is a statistical discrepancy between gross domestic
income (GDI) and gross domestic product (GDP) in the United States (e.g., Fixler, de Francisco and Kanal, 2021). We do not allocate the statistical discrepancy. Our estimates match
income growth, not product growth; whenever the statistical discrepancy is not zero, we do
not exactly capture GDP growth.7 Second, corporate profits and GDI are only available one
month after the publication of the first estimate of GDP.8 As a result, national income and ourestimates of quarterly growth by social group are published one month after the initial estimate
of GDP. Third, in addition to being published quarterly in the context of GDP statistics, mostcomponents of factor income—such as compensation of employees, proprietors’ income, andrental income, but not corporate profits—as well as government transfers are also publishedmonthly as part of personal income, about four weeks after the end of each month. This allows
us to estimate certain monthly statistics within a month, such as factor and disposable income
growth for the bottom 50% (where corporate profits are negligible) and the distribution of labor income. Fourth, for the components of income that are only available quarterly but notmonthly—e.g., for factor income, corporate profits; for postttax income, collective government
expenditure—we disaggregate the quarterly series when they become available using Denton’s(1971) method, following the International Monetary Fund (2017) recommendations to compile
high-frequency national accounts.

3.3

From Annual to Monthly Micro-Files

Just as monthly and quarterly national accounts data are seasonally adjusted and annualized(i.e., presented in levels equivalent to a full year), our monthly and quarterly distributional data
are seasonally adjusted and annualized so that they are also directly comparable in level to7

Appendix Figure A1 compares the growth of GDI to the growth of GDP. Both track each other closely but
not perfectly. The statistical discrepancy has been significant during the recovery from the Covid-19 recession,
during which GDI has recovered faster than GDP. The other reason why we do not exactly match GDP growthis the fact that net foreign income (included in national income but not in GDI) and depreciation (included in
GDI but not in national income) can grow at different rates than GDI.
8
The first estimate of quarterly GDP is available near the end of the first month after each quarter. A second
estimate is released about a month after, and a third and final estimate about a month after the second estimate,i.e. about three months after the end of the quarter. Relative to GDP, quarterly GDI is produced with a a lagof an additional month (2 months for the fourth quarter) as it requires estimating corporate profits, which isdone using the detailed (but not quite as timely) Census Bureau Quarterly Financial Report (US Census, 2022).

10

annual inequality estimates. Seasonal adjustment is important because some forms of income
such as executive bonuses are highly seasonal (e.g., most bonuses are paid once a year in January). Annualization means that we estimate the distribution of what annual income would
be if seasonally-adjusted monthly income totals and their distributions remained stable over 12months.9 Concretely, seasonal adjustment means that a January bonus is spread out over the
12 months of the year; annualization means that if bonuses double from one January to thenext, this doubling is spread out smoothly over 12 months.
Another way to measure inequality at a high frequency would be to estimate the inequalityof actual monthly income. Because of income mobility (e.g., losing or starting a job in the
middle of a year), this approach would lead to more inequality at the monthly frequency thanat the annual frequency. By contrast, our procedure which annualizes income makes inequality
statistics comparable at high vs. low frequency.10
Our starting point to estimate monthly distributions is the annual distributional national
accounts synthetic micro-data of Piketty, Saez and Zucman (2018). These files combine IRS tax
micro-data, surveys, and national accounts data to construct annual distributions of income andwealth consistent with national accounts aggregates. Since their first publication, a literature hasdeveloped to test assumptions, conduct robustness tests, develop improvements, and maximize
comparability with other countries where similar methods are followed (Blanchet et al., 2021).
The current files, updated in Saez and Zucman (2020b), incorporate the results of this body ofwork. A new file is created each year when the most recent tax statistics and annual national
accounts become available. The last annual micro-file is for the year 2019, the year preceding theCovid-19 pandemic.11 This file currently serves as our baseline for 2020 and onwards estimations.
To convert these annual files to the monthly frequency, we normalize the population and the
distribution of each income component to one. We then create monthly versions of the annual
files mixing samples from two adjacent years with unequal weights. Specifically, to create afile corresponding to month m in year y, we combine the micro-data for the year y with its
weights multiplied by m/12 with the micro-data for the year y − 1 with its weights multipliedby 1 − m/12. Therefore, each monthly file is a moving average of the yearly files over the lasttwelve months. This procedure smoothes out short-run, year-specific, mean-reverting variations,
9

Quarterly income is computed as the average of monthly income over the three months of the quarter.
There is no micro-data in the United States allowing one to track the longitudinal evolution of householdincome month after month or quarter after quarter (Fixler, Gindelsky and Kornfeld, 2021). Our approach that
focuses on monthly and quarterly distributions of annualized incomes does not require longitudinal data andusefully by-passes this issue.
11
The Covid pandemic caused a backlog at the Internal Revenue Service so that paper returns for 2020 incomes
were not yet fully processed as of June 2022.
1011

which are not informative of the distribution for a given month, and would otherwise introduce
discontinuities in the monthly series. Like in the annual micro-files, each observation in themonthly micro-files represents an adult individual, defined as an individual aged 20 or more.
We then rescale the components of factor, pretax, posttax, and disposable income so thatthey add up to their seasonally-adjusted monthly total value, component by component and at
the most granular level possible. Specifically, for factor income we rescale wages and salaries,
supplements to wages and salaries, proprietor’s income, rental income, corporate profits, interest
income, production taxes, production subsidies, non-mortgage interest payments, and government interest payments to their respective monthly totals.12 For pretax income we additionallyrescale private pension contributions, Social Security taxes, contributions to unemployment
insurance, private pension benefits, Social Security benefits, and unemployment insurance benefits; for disposable income, Medicare taxes, direct taxes, the estate tax, veteran benefits, andother cash benefits; and for posttax income, Medicare, Medicaid, other in-kind transfers, collective expenditures, and the government deficit. Components of household wealth are similarly
rescaled to their end-of-month values, as detailed in Appendix B.
Because the various components of aggregate income do not grow at the same rate from one
month to another, the mere act of rescaling to match monthly totals changes the distributionof income. From the second to the third quarter of 2020, for example, corporate profits grew byclose to 25%, much faster than wages. Since profits are more concentrated than wages toward
the top of the distribution, this pushes towards a higher top 1% pretax income share. Rescaling
to match aggregates, however, is not sufficient to accurately capture high-frequency changes ininequality, especially during recessions. With publicly available data it is possible to do more,
namely to project changes in distributions within key components—a task we now turn to.

4

Incorporating Changes Within Income Components

The most important step of our methodology involves incorporating information on the monthto-month evolution of the distribution of labor income, which accounts for about 70% of national income. We estimate both changes in the extensive margin (number of employed vs.
non-employed individuals, including recipients of unemployment insurance benefits) and in theintensive margin (changes in the wage distribution). We estimate these changes by cells of race× education × gender × 5-year age group × marital status. To be able to do so, we need to12

Appendix A provides a detailed mapping of these National Income and Product Accounts concepts to thevariables used in our micro-files.

12bring in race, education, and age variables into the Piketty, Saez and Zucman (2018) annual
distributional national accounts files. We do so by statistically matching these files to the MarchCPS and the Survey of Consumer of Finances. This Section begins by describing this statisticalmatching, before turning to how we incorporate changes in the extensive margin and intensive
margin, and in the distribution of other income components (government transfers).

4.1

One-to-One Statistical Matching to Survey Data

Method. Consider two datasets: A (sometimes referred to as the base file, the annual distributional national accounts micro-files in our case) and B (sometimes referred to as the supplemental file, e.g., the March CPS). Assume A and B have common variables, denoted by X (e.g.,income and its components). The remaining variables are denoted by Y in file A and Z in file B.
The goal is to bring the Z variables (e.g., education) into file A. The optimal way to do so is toimplement a constrained statistical match (e.g., Rodgers 1984), in which each observation from
B is matched “one-to-one” with an observation in A, while minimizing the sum of distances
over the X variables between matched observations. This constrained statistical match can be
implemented using optimal transport methods.
Formally, assume the two datasets are of size n and m respectively, and that observations
in each datasets have weights u = (u1 , u2 , . . . , un ) and v = (v1 , v2 , . . . , vm ). Without loss ofgenerality, assume weights sum to one. Denote by Dij the distance between observation i in thefirst dataset and j in the second over the X variables (in our application we use the L1 norm, i.e.,the sum of the absolute values of the differences). The optimal transport map Γ ∈ Rn×m whichmatches observations “one-to-one” while minimizing the sum of distances between matchedobservation is the solution of the following linear programming problem:minΓ∈Rn×mn X
mX

Γij Dijsuch thatΓ1 = uΓ0 1 = v

Γ≥0i=1 j=1

The main appeal of constrained statistical matching is that the multivariate distributions of
all the Z variables are preserved in the matched dataset. In that sense, there is no less of distributional information, in contrast to other matching procedures.13 The main practical obstacle
to implementing constrained statistical matches so far has been computational requirements.
Finding the optimal Γ involves solving for a large-scale linear programming problem: if thetwo datasets to be matched each have 100,000 observations (which is the order of magnitude
13

One-sided matching does not respect the distributions seen in the second dataset. One-sided matching
without replacement is inefficient as match quality becomes very poor for the last observations matched.

13

in our case) then the matrix of pairwise distances has 10 billion entries. Thanks to recently
developed implementations of optimal transport algorithms, solving this type of problem has
become doable in a reasonable amount of time.
Implementation. To statistically match the distributional national accounts micro-files tosurvey data, we start by supplementing the March CPS with group quarter observations fromthe American Community Survey: individuals living in correctional facilities, nursing homes,
college dormitories, etc., who are not sampled by the March CPS. This allows us to capture
the entire population of US residents, as in our distributional micro-files, which is importantto capture income dynamics at the bottom of the distribution. We then statistically match theaugmented March CPS to our annual micro-files over the following X variables (observed inboth datasets): wage income, pension income, business income, interest, dividend and rents,
Social Security benefits, welfare benefits, and government transfers other than Social Security
and welfare benefits. We bring in the following Z variables: race, education, age. The match isdone at the household level, separately for married vs. singles, individuals aged more vs. lessthan 65, and employed vs. unemployed individuals.
We similarly match our micro-files to the Survey of Consumer Finances (SCF), a triennal
survey of about 6,500 families that over-samples wealthy households. We annualize the SCF
by taking moving averages of the two closest waves of the SCF and match it to our micro-files
over wage income, pension and Social Security income, business income, interest and dividend
income, capital gains, financial and business assets, housing assets, and debts. This allows us tobring socio-economic characteristics for high-income households, which are not well covered in
the March CPS. We use the socio-economic variables transported from the SCF for households
in the top 5% of the income or wealth distribution, and those transported from the March CPS
for the bottom 95%.
Value and limitation. The key value of our one-to-one statistical match is to join together
several micro-databases (including individual tax data, the CPS, and the SCF) in a single file.
Variables that are common across databases exist in versions corresponding to each dataset(e.g., SCF net wealth and net wealth estimated from tax data) and are close to each otherrecord by record thanks to the matching procedure. This makes it straightforward to switchfrom one database to another as needed. For example, researchers who are used to working
with the CPS can primarily focus on CPS variables and replace them with tax-data variables
whenever the analysis concerns the top of the distribution.
14

However, we emphasize that this matched database cannot provide reliable information
about the joint distribution of variables that are not jointly included in at least one of theoriginal database. For example, because neither the public-use individual tax data, nor the CPS
or the SCF provides comprehensive information on the joint distribution of income and state,14
we cannot analyze patterns in income growth by state × social group. As another example,
because the only information on the joint distribution of wealth and race comes from the SCF
and the SCF is noisy above the top 1% (due to small sample sizes), it is not possible to use thematched dataset to obtain reliable estimates of the racial composition of wealth above the 99th
percentile. The limitations of the source files carry over to the matched file and knowledge ofthese limitations is critical to make an informed used of the matched database. An obviously
superior way to construct a unified database would be to proceed to exact matches acrossadministrative sources and surveys, as can be done for research purposes in, e.g., Scandinavian
countries. However, this is not yet fully feasible in the United States even within government
agencies (see, e.g., Chetty et al., 2010) and certainly not using public data. Furthermore, anysuch match could not be produced quickly and thus would not help with the production realtime inequality estimates. The statistical match we implement leverages the strength of US
public data, which are rich but scattered.
Test of the matching procedure. We test and validate the quality of our matching procedure by comparing the demographic composition of income and wealth by income and wealthgroup in the survey micro-data and the matched file. To understand the logic of the test, consider the case of gender and wage earnings. The CPS provides good information on gendercomposition by annual wage earnings deciles (i.e., the fraction of individuals in each decile ofthe earnings distribution who are women). We can check that this gender composition is preserved when using the annual wage earnings variable coming from the tax data (as opposed to
the CPS) in the matched file. This will naturally be the case if the matched observations have
similar earnings in the CPS and the tax data.15
Figure 1 reports the results of this test. The figure compares demographic composition
along one key income (or wealth) variable of interest, as measured in the original survey data
vs. in the matched file tax data. Panels (a) and (b) consider gender and racial composition byannual wage earnings measured in the CPS vs. tax data; in both cases, the sample includes
14

The state variable is not available for top earners in the public-use tax files.
By contrast, this test could fail if the statistical match is not excellent on the wage earnings variable, e.g.,
because the samples matched are too small to find close matches, or because the matching procedure puts highweight on other variables.
15

15

the full population of working-age individuals (aged 20–64), including individuals with no wage
earnings. Panel (c) considers racial composition by annual total income in the CPS, the SCF,
and the matched file. For the purpose of this exercise, total income is the sum of wages,
pensions, Social Security benefits, business income, interest, dividends, and rents. Last, panel(d) considers racial composition by wealth in the SCF vs. the matched file. In both panels (c)and (d), we consider the entire CPS (and SCF) samples. All four panels show that demographic
compositions are very close in the original survey data and the matched file. The reason for thissuccess is that our one-to-one matching procedure preserves ranks in the income and wealthdistribution well, as illustrated in Appendix Figure A2 in the case of wage earnings.

4.2

Changes in Employment

To capture high-frequency changes in the distribution of labor income, the first step of our
methodology involves adjusting employment status at the micro-level each month. To do so wecompute employment rates by race × education × gender × 5-year age group × marital status
cells in the monthly CPS, and use these tabulations to impute employment rates by cells in ourmonthly distributional national accounts micro-files. We proceed in four steps.
Estimation of aggregate employment rate First, we estimate the number of workers eachmonth using the BLS monthly release of non-farm employment at the national level. Since thenumber of employed people in a given year is mechanically higher than in a given month, we
adjust monthly employment numbers to make them commensurable with yearly estimates fromSocial Security tax data. We do so using the strong and consistent linear relationship observed
between the BLS and the Social Security numbers over time. Appendix Figure A3 depicts theraw monthly employment rates from the BLS, the annual employment rates from the Social
Security tax data, and the monthly employment rates adjusted to match annual rates (in allcases for the working-age population, age 20–64). In recent decades, raw monthly employment
rates are around 75% while adjusted monthly employment rates (that track annual levels) are
10 percentage points higher, around 85% (because of part-year workers).
Estimation of the aggregate number of UI recipients Second, we estimate the numberof unemployment insurance (UI) recipients each month. To do so, we use the Department ofLabor’s weekly publication of unemployment claims. We aggregate this data by month andadjust it for seasonal variations using the X11 procedure (Shiskin et al., 1967). Since thenumber of UI recipients in a given week is mechanically lower than in a given year, we adjust
16

the number of UI claims by a constant coefficient to match the annual levels recorded in the
tax data (consistent with our goal of constructing monthly distributions of annualized income).
Estimation of labor force status by individual characteristic Third, using the monthly
CPS, we estimate monthly series of labor force status by race × education × gender × 5-year
age group × marital status. In each monthly CPS dataset, we run a logistic regression of(i) employment status and (ii) unemployment status against race, education, age by 5-year
group, and marital status interacted with gender. We use the prediction from these regressions
to estimate employment and unemployment rates by cell. We adjust this data for seasonalvariations using the X11 procedure (Shiskin et al., 1967).
Adjustment of employment and UI recipients in the monthly microfiles. Last, in ourmonthly microfiles, we adjust at the margin whether someone (i) is employed and (ii) receives
UI benefits based on the information collected in the three preceding steps. The procedure,
described in detail in Appendix C.1, reproduces relative changes in labor force status by race× education × gender × 5-year age group × marital status, while also matching the aggregate
levels of employment and number of UI benefit recipients.

4.3

Changes in the Distribution of Earnings

The second step of our methodology to capture high-frequency changes in the distribution oflabor income involves estimating changes in the distribution of wages at the monthly frequency.
We do this by combining all the available evidence on this issue: monthly and quarterly employment surveys, and the monthly CPS.
Predicting wage inequality from tabulated employment surveys. We first estimate
wage inequality monthly using the timely employment censuses and establishment surveys of the
Bureau of Labor Statistics. The Quarterly Census of Employment and Wages (QCEW) provides
employment and wage statistics for about 95% of employees, based on state and federal unemployment insurance administrative records. At the monthly frequency, the Current Employment
Statistics (CES) survey provides similar information based on a representative sample of about
144,000 businesses and government agencies. Both the QCEW and the CES are published in
the form of tabulations by industries × geographical areas, up to 6-digits NAICS industry ×
county × type of ownership (i.e., public or private) in the case of the QCEW. Although the underlying individual-level micro-data are not publicly available, valuable information about the17

distribution of wages, including at the top, can be retrieved from these granular tabulations.
Building on Lee (2020), we construct quarterly wage income distributions using the QCEW
data. The idea is to use the QCEW as if it were a micro-level dataset, treating each 6-digitsNAICS-industry × county × type-of-ownership cell as an observation whose weight is the employment count and whose value is the average wage. Each wave of the QCEW contains about
a million such observations in recent years, much more than a typical wage survey. We remove
outliers, defined as cells whose wage is less than half of a full-time minimum wage job. We
then estimate the average wage by percentile, after implementing three adjustments described
below. As detailed in Section 5 below, our procedure delivers remarkably accurate predictionsof trends in wage inequality.
Adjustments to the QCEW data. The adjustments we apply to the QCEW data are thefollowing. We first convert the QCEW wage data from quarterly to monthly. Employment
counts are reported monthly in the QCEW, but wage earnings only quarterly. This is not asignificant issue since wages are sticky, so changes in the wage distribution in the short run are
driven by changes in the relative employment of low-wage and high-wage workers rather thanby changes in their respective salaries. We run the wage data in the QCEW through a moving
average of the last twelve months to get smooth monthly wages and to get rid of the seasonality
in the wage data (due, for example, to end-of-year bonuses).16
Second, because the QCEW data is aggregated in cells, it understates the level of inequality
between individual workers. This issue is illustrated on Figure 2. In the annual tax microdata, the top 1% wage share (among individuals with positive wages) increases from 6% in
the late 1970s to about 12% in the 2000s. In the raw but de-seasonalized QCEW data, thetop 1% wage share is lower. We fix this discrepancy by implementing a simple adjustment
to the monthly series. Specifically, we regress the tax data real wage against the QCEW real
wage for each percentile, and use the prediction from these regressions as our monthly estimate
for each percentile. This procedure works well because the relationship between the average
wage of a given percentile in the QCEW data and the tax data is strongly linear. Importantly,
this correction does not vary with time and thus does not weaken the predicting power of the
QCEW. Figure 2 illustrates this correction in the case of the top 1% share, which is typicallyhard to capture with non-tax data. Adjusting the raw de-seasonalized QCEW wage series with
16

Whenever this procedure introduces missing values, we impute them back by regressing log wages on county,
time, industry, and type-of-ownership fixed effects. Remaining seasonal variations (introduced by seasonality inemployment numbers) is corrected by running the average wage for each percentile through the X11 procedure.

18

a multiplier and level shift (constant over the period) generates the adjusted QCEW series
depicted in blue. This adjusted series almost perfectly aligns with the actual top 1% wage shareobserved in the micro tax data in both levels and trends over the entire period.
Third, we supplement the QCEW with the Current Employment Statistics (CES) survey to
get timely estimates. The QCEW is published with a lag of one to two quarters. The CES isreleased every month, albeit with a coarser level of aggregation: about 19,000 monthly series
covering up to 300 industries and 450 areas, compared to about a million series in the QCEW.
We match each QCEW cell to three CES series. The first matches the location of the QCEW
cell as precisely as possible, the second matches it at the state level, and the third at the national
level. Because there is a trade-off in the CES between the level of geographical and industry
disaggregation, using these three series allows us to extract as much information as possible.
We average the trend from these three series in each cell and use this average trend to extend
the QCEW data in the most recent quarters. Appendix Figure A4 depicts the quality of thisprojection by comparing bottom 50% and top 10% wage shares estimated using the raw QCEW
data vs. projected using the CES up to 6 months forward (in dashed lines). By definition,
the projection matches the QCEW level in the last month of each quarter, and then projects
forward over the next two quarters. Overall, the CES projection comes close to the QCEW,
although the fit is a bit weaker for the top 10% than for the bottom 50%.
Wage inequality in the monthly CPS. In addition to estimating high-frequency changes in
the wage distribution using the QCEW, we also estimate changes in average wages by percentile
using the weekly earnings variable of the monthly CPS. Our final estimates of average wage by
percentile in a given month is obtained as the average of the CPS and QCEW predictions. For
the bottom 80% of the wage distribution, QCEW and CPS predictions are weighted equally.
The weight on the CPS prediction then linearly falls to 0 as we move to the 90th percentile, andis 0 above the 90th percentile where the CPS is not informative because of top-coding. Once wehave our final estimate of wages by percentile, the distribution within percentiles is interpolatedusing generalized Pareto interpolation methods (Blanchet et al., 2022).
Adjustment of wage levels in the monthly microfiles. We use the resulting marginal
wage income distribution to update our monthly micro-files as follows. In the monthly CPS, we
compute the average rank in the wage distribution of each race × education × gender × age ×marital status cell. We also compute these average ranks in the 12 preceding months. In ourmonthly micro-files, we adjust the average rank by cell to replicate the evolution seen in the19

CPS. Once the ranks are adjusted, we assign each individual observation the wage corresponding
to his or her adjusted rank. Appendix C.2 provides complete details.

4.4

Distribution of New Government Transfers

In the last step of our methodology, we model the distribution of new government transfers.
We simulate the key components of the government response to the Covid crisis: the Paycheck
Protection Program, Covid relief payments, and expanded refundable tax credits.
The Paycheck Protection Program was a loan program designed to keep small businesses
afloat, representing about $1,000 billion, or 5% of national income. The government forgave
most of these loans, assuming companies kept their employees and wages stable. Following
Autor et al. (2022), we distribute 70% of the program’s expenditures to business owners and theremaining 30% to wage earners. We construct a novel estimate of the program’s distributional
effect for the incidence on wages. We use the publicly available data on each loan, which wematch to the QCEW data based on the date of the loans, the industry, and the location of thebusiness. We manage to match about 5,700,000 loans to 5,500,000 QCEW cells. We estimate
both an extensive margin (fraction of the workforce covered) and an intensive margin (fractionof the wage bill covered) for each percentile of the labor income distribution, which we use tosimulate the effect of the Paycheck Protection Program on workers.
The three waves of Covid relief payments (“economic impact payments”) are allocated basedon program rules using taxable income as reported in the updated Piketty, Saez and Zucman(2018) files. Finally, we allocate the expanded refundable tax credits (child tax credit and earned
income tax credit) based on income and eligibility using our micro-data.17

4.5

Summary of All the Sources Used and Timing of Release

Table 1 summarizes all the sources used to construct our real-time estimates, including frequencyof publication and timing of availability. Our approach only relies on public data sources.
Appendix D describes the structure of the programs used to construct our real-time estimates.
The programs are available online at https://realtimeinequality.org, making it possible
for researchers to assess and improve any aspect of the methodology.
17

Refundable tax credits corresponding to incomes earned in year t are generally paid out in year t + 1 andcounted as transfers in year t + 1 in the national accounts, a convention we follow. In 2021, however, half of the
expanded child tax credit was paid monthly (in the last 6 months of 2021) and is assigned to 2021 (not 2022).

20

5

Validation Tests

Since we apply our methodology back to 1976 (the first year of the QCEW), we have a large
number of monthly micro-files that can be used to test the accuracy of our approach. This
Section presents the results of these tests.

5.1

Wage Distribution Prediction

We begin by examining how well our monthly wage inequality series match the actual distribution of wage income—by far the largest component of national income—over the 1976–2019
period. Figure 3 compares the wage distribution in the updated Piketty, Saez and Zucman(2018) micro-files, which are based on public tax micro-files, to the distribution constructed inthis paper using the QCEW and the CPS. Each panel depicts the share of total wage income
earned by a specific group (bottom 50%, middle 40%, next 9%, and top 1%) among adultindividuals with positive wage income.
Our monthly estimates track the annual tax-data-based statistics well for all groups, including for the top 1% which is not measured well in traditional household surveys. First, thelong-run trend in rising wage concentration is accurately captured by our combination of QCEW
and CPS data: as in the annual tax-data-based statistics, the top 1% gains 5.5 points between
the late 1970s and 2019 and the next 9% gains 3 points, while the middle 40% loses 7 points
and the bottom 50% loses 1.5 point. Second, our methodology accurately predicts short-run
variation, the focus of this paper. Over the 44 years t from 1975 to 2018, we correctly capture
whether a share has risen or fallen between t and t + 1 82% of the time for the top 1%. It is
most instructive to focus on inequality dynamics during economic downturns, the periods when
the real-time methodology is most valuable from a policy perspective. From 1976 to 2019, there
are 8 recession years (1980, 1981, 1982, 1990, 1991, 2001, 2008, 2009). Focusing on these years
and the year following each recession (1983, 1992, 2002, 2010), we correctly predict whether the
top 1% and bottom 50% shares are falling or rising in 19 cases out of 24. These results are
consistent with the analysis of Lee (2020) who first used the QCEW to create quarterly wage
distributions for the macroeconomic analysis of the business cycle.

5.2

Volatility of Capital Income

Next, we provide support for our treatment of capital income by comparing the volatility ofaggregate capital income components and the volatility of their distributions. Recall that forcapital income (corporate profits, rental income, proprietors’ income, interest), to form our next
21

year(s) projection we assume that within-component distributions are stable. For example, if
the top 10% earns 70% of aggregate corporate profits in 2019, we project that the top 10% still
earns 70% of corporate profits in 2020 and 2021. If corporate profits shrink in 2020, individuals
in the top 10% of the total income distribution are more affected than individuals in the bottom
90% and all else equal, inequality falls. If aggregate corporate profits soar, inequality increases.
To assess the merits of this procedure, Appendix Figure A5 compares the size of capitalincome components (as measured by their share of national income) and the concentration ofthese income components (as measured by the share going to the top 10% of the pretax income
distribution). All series are normalized to 100 in 1976 to contrast volatilities. The top left
panel reports results for corporate profits, the largest form of capital income, and shows that
aggregate profits are highly volatile. During or just before recessions, it is common for the shareof profits in national income to fall by 10%–20% (e.g., 1980, 2000, 2008). The share of profits
earned by the top 10% exhibits comparatively little year-to-year variation. The same conclusion
holds for other components of capital income, as reported in the other three panels. Becausechanges in aggregates swamp short-term changes in distributions, movements in macroeconomic
aggregates capture the bulk of the contribution of capital income to changes in inequality.

5.3

Retrospective Validation

Last and most importantly, we retrospectively check whether our methodology combining prioryear annual micro-files with current-year high-frequency data sources provides accurate estimates of current-year distributions. This test incorporates all forms of income, as opposed to
wages or components of capital income only.
Methodology. For each year t from 1975 to 2018 (or 2017), we start from the year t updated
annual micro-files of Piketty, Saez and Zucman (2018) and implement our real-time methodology
to age these data into a year t + 1 (or year t + 2) simulated annual micro-dataset—using thesame sources (high-frequency national accounts aggregates, household and employment surveys,etc.) and assumptions as in our real-time methodology.18 Using these simulated micro-data foryear t + 1 or t + 2, we can compute any distributional statistics and compare them with thestatistics coming out of the actual annual micro-data. The most directly relevant statistic for
18

The only exception is that for simplicity, we do not attempt to specifically model the distribution of new
government transfers created between t and t+1 (or t+2), while in our real-time methodology we carefully modelthe distribution of post-2019 new transfers (see Section 4.4). This means that for disposable and posttax income,our real-time methodology (post-2019) is likely to be more highly predictive than implied by our retrospective
tests.

22

our purposes is real income growth. For each group of the population, we compute actual income
growth using the annual distributional national account micro-files for both years t and t + 1(or t + 2), and predicted income growth rates using the actual annual micro-files for year t but
the simulated micro-data for year t + 1 (or t + 2). The t + 2 simulations are relevant given that
our real-time inequality estimates for year t are typically based on annual micro-files for yeart − 2, due to the nearly 2-year delay in the availability of tax data.
Results. Figure 4 compares actual and predicted real income growth rates for the bottom
50% (top panels) and the middle 40% (bottom panels) of the factor income distribution, withincome equally split among married spouses. Left panels show actual vs. predicted growth fromyear t to t + 1 and right panels from t to t + 2. In all cases the dots are closely scattered aroundthe 45-degree line, showing that our predictions are highly informative of the actual growth inbottom 50% and middle 40% incomes. For example, we correctly predict whether bottom 50%
is rising or falling 82% of the time one year forward and 91% of the time 2 years forward.19 One
can condition on certain actual growth rates to visually ascertain how well our methodology
performs in different contexts. For instance if one conditions on actual growth below -2.5% (orbelow -5% in the 2-year ahead graph), typically corresponding to recession years, the correlation
between actual and predicted growth remains very high. Figure 5 repeats this analysis for top
1% incomes and next 9% incomes. The dots again align well with the 45-degree line. We
correctly predict whether top 1% incomes are rising or falling 89% of the time one year forward
and 93% of the time 2 years forward.
One caveat when considering the top of the distribution is that predicting short-run growthduring tax reforms can be challenging. The largest errors are observed in 1987 and 1988, when
predicted growth—though strongly positive—is significantly lower than observed growth. As
shown by Figure 3, in those years the QCEW predicts large gains in the top 1% wage share but
fails to capture the full magnitude of the rise in this top share. One possible interpretation isthat the Tax Reform Act of 1986, which reduced the top marginal income tax rate from 50% in1986 to 28% in 1988, led to an immediate and across-the-board increase in top-end wages andbonuses for executives within industries × counties (in addition to gains in specific high-paying
industries × counties, by construction captured by our methodology). More broadly, it canbe challenging to predict the growth of top incomes in years of significant tax reforms, which
19

The 2-year forward prediction performs better by that metric because the bottom 50% often has little annual
income growth during our sample period, so that in a few cases predicted growth is slightly negative when actual
growth is barely positive (or vice-versa), a problem that attenuates when one considers growth over 2 years.

23

in addition to real responses can generate avoidance responses, such as inter-temporal income
shifting. As shown by Figure 5, the prediction errors in top 1% growth are concentrated during
tax reform years (although not all tax reform years lead to errors). In non-tax-reform years our
methodology delivers accurate predictions for the top 1%.
Rescaling vs. adjustments of distributions. To better understand which aspects of our
methodology matter to generate accurate predictions, Appendix Figure A6 reports similar figures of actual vs. predicted growth rates but using a simplified prediction methodology that only
rescales macroeconomic aggregates (following Section 3) without incorporating any changes inthe distribution of labor income. The simplified methodology performs well in years of normal
growth, but delivers significantly worse results than our full methodology for the bottom 50%
during recessions, when it significantly over-estimates growth. This finding echoes the results of
Fixler, Gindelsky, and Kornfeld (2021) discussed in Section 2.1 and shows that adjusting the labor income distribution is critical to project inequality during recessions. Once the distributionof labor income is adjusted, our methodology accurately predicts growth during downturns.
Goodness of fit summary. To summarize the performance of our approach, Table 2 reports
detailed statistics for goodness of fit and noise. For each group (bottom 50%, middle 40%,
next 9%, and top 1%) and income concept (and wealth), we compute the fraction of years inwhich we correctly predict whether income (or wealth) is growing or falling, the mean difference
between predicted and observed 1-year growth, the standard error of this mean, and the root
mean square error. For reference we also report the standard deviation of actual 1-year growth
rates. We consider three samples of years: all 44 years from 1976 to 2019, the 34 years which
are not tax reform years, and the 12 years corresponding to recessions and their immediate
aftermath. Appendix Table A1 reports the same statistics for growth over 2 years. A numberof results are worth noting.
First, the good fit and limited noise obtained for factor income (Figures 4 and 5) extends toother income concepts—pretax, disposable, and posttax—and to wealth. Across concepts andsamples of years, we correctly predict the sign of growth around 90% of the time.20 Bias inannual growth is limited to a few tenth of a percentage point, which is reasonable considering
the sample sizes involved. Remarkably, our methodology is unbiased for the top 1% even though20

As noted in footnote 19 for factor income, predictions are slightly worse for the bottom 50% because growthis often close to zero for that group in those decades. If one considers growth over 2 years, we correctly predict
the sign of growth for the bottom 50% about 90% of the time just like for other groups (Appendix Table A1).

24

it does not rely on any tax data. Standard errors for top 1% predictions fall when excluding
tax-reform years; for other groups (where avoidance possibilities and incentives to avoid are
more limited), including tax reforms does not make a difference. Second, these results carryover to recessions: our methodology is highly predictive of income dynamics during downturns
and the ensuing recoveries. We under-estimate disposable and posttax income growth for thebottom 50% during past recessions, but this is because we do not attempt to incorporate thecreation of new government transfers during past recessions (e.g., $600 individual tax credits in2008) in our simulated micro-files. Post-2019 monthly files, by contrast, carefully incorporate all
new government programs (as detailed in Section 4.4), maximizing the accuracy of disposableand posttax income predictions at the bottom during and after the Covid-19 pandemic.

6Inequality During the Covid-19 Pandemic

This Section uses our real-time estimates to analyze the dynamics of income and wealth during
the Covid-19 pandemic and in its aftermath. We start by studying the dynamic of income
and wages before government intervention, then move to disposable income, before turning to
wealth inequality. Unless otherwise noted, all the statistics we report are for “equal-split adults,”
defined as individual adults with income and wealth equally split between married spouses. On
https://realtimeinequality.org, we also report statistics at the household level, where ahousehold is a tax unit as defined by the tax code, i.e., either a single person aged 20 or above
or a married couple, in both cases with children dependents if any. All growth numbers areadjusted for inflation using the official national income deflator. The same deflator is used for
all groups of the population.

6.1

The Dynamic of Factor Income During the Covid-19 Recession

Dynamic across the income distribution. The Covid-19 pandemic led to a dramatic collapse in average national income. Between February 2020 (the last month before the recession)and April 2020 (its trough), annualized real national income per adult fell 15%. Average income then rebounded sharply. But as Figure 6a shows, the fall and recovery were uneven. The
economic downturn caused by the pandemic led to the strongest factor income decline for theworking-class (-33% for the bottom 50% between February 2020 and April 2020) and to a lesser
extent for the top one percent (-19%) due to the collapse of business profits, a key source ofincome at the top. The crisis affected the middle class and upper-middle class relatively less,because individuals in these groups were more likely to remain employed.
25

The groups with the largest losses in 2020 experienced the largest gains in 2021. On average,
real factor income per adult grew almost 6% in 2021, but by close to 8% for the bottom half ofthe distribution and by 11.5% for the top one percent. Growth was lower for the middle 40%(close to 3%). According to our estimates, all income groups recovered their pre-crisis factor
income level within 20 months, but not at the same pace. Top groups recover faster than the
middle class and the working class. For the top 10% and the top 1%, the recovery took twelve
months; but for the middle 40% and the bottom 50% it took twenty one months. Because thebottom 50% was hit the hardest and recovered last, the pandemic, had, by the end of 2021,
exacerbated factor income inequality. The share of factor income earned by the top 1% was
19.5% in December 2021, its highest level in the post-World War II era.
Comparing the Covid-19 and Great Recession recessions and recoveries. It is wellknown that in the aggregate, the recovery from the Covid-19 crisis (18 months) was much fasterthan the recovery from the Great Recession (4 years and a month). Our real-time estimatesallow us to move beyond aggregates and compare recovery patterns for the working-class. To
do so, Figure 6b focuses on the working-age population (to control for population aging in the2010s, during the long recovery of the Great Recession) and normalizes income to 100 in themonth preceding each recession. Two main results emerge.
First, in the aftermath of the Great Recession it had taken a staggering 8 years and 1 months
for the bottom 50% of the working-age population to recover its pre-crisis real factor income
level. From 2008 to 2012, a period during which the economy rebounded and crossed its pre-crisis
output level, the bottom 50% of working-age adults experienced virtually no growth. Income
started growing in 2013 but slowly, so that it only exceeded its December 2007 level in January
2016. The slow recovery of the working class is a robust feature of the Great Recession.21 It is
not an artifact of population aging (since we restrict to the working-age population), but rather
reflects the stagnation of wages at the bottom of the distribution (detailed in Section 6.2 below).
Second and by contrast, real factor income for the bottom 50% rebounded more quickly
after the Covid recession. By the time average income had recovered from the Great Recession,
average income for the bottom 50% was still 10% below its pre-crisis income level and still four
years away from a full recovery. By the time average income had recovered from the Covid19 crisis, by contrast, the bottom 50% was only 4% below its pre-crisis income level and was
growing fast. These results vividly illustrate the fact that a given trajectory of GDP growth21

The top fiscal income shares of Piketty and Saez (2003), which have been used to study the fraction of
growth accruing to top income groups (see Saez, 2008, and subsequent updates), also revealed it.

26

is compatible with widely different market income dynamics for the working class, highlighting
the usefulness of timely and disaggregated growth statistics.

6.2

Wage Growth After Recessions: Covid-19 vs. Great Recession

In the aftermath of the pandemic, the unemployment rate reached historically low levels (3.6%in March 2022), in a context of loose monetary policy (with interest rates of 0% until March
2022) and expansionary fiscal policy. Who benefited most from the tight labor market? To shedlight on this issue we can use our micro-files to study the month-to-month dynamics of labor
income. The main finding is that the Covid recovery was characterized by a reduction in wage
inequality—a break from the trend prevailing since the early 1980s—due to strong wage growthat the bottom of the distribution.
Methodology to study labor income inequality. To establish this result, we analyzechanges in labor income inequality in the working-age population (including non-workers) andcompute growth rates of labor incomes by percentiles of labor income.22 Our goal is not tocharacterize wage growth for a given worker or to fix the composition of the workforce; rather,
we want to describe the evolution of the distribution of labor income as comprehensively as
possible. The growth statistics by percentile we compute capture the effect of changes in both
employment and wages. Our measure of labor income includes wages, supplements to wages
and salaries (such as health insurance and retirement benefits) and the labor income of selfemployed individuals (defined as 70% of self-employment income), before any tax or deduction
for pension contributions. Conceptually, it corresponds to the total cost, for employers, of
employing a worker. Our analysis, as always in this paper, is cross-sectional in nature: we do
not follow individuals over time.23
To provide the context required to interpret changes in the distribution of labor income,Figure 7a depicts the raw monthly employment-to-working-age population ratio from BLS fromJanuary 2019 to September 2022. This rate was 78% before the Covid-19 pandemic, fell to 68%
at the trough of the recession, and by September 2022 had returned to its pre-Covid level.24
22
To better connect to the labor economics literature and because we have good measures of individual wages,
for the analysis of labor income inequality we focus on individualized income series, i.e., we do not split income
equally between married spouses.
23
We build on a long long tradition in labor economics studying changes in cross-sectional wage inequality;see, e.g., Katz and Murphy (1992). The main difference is that our statistics incorporate the entire working-age
population including non-workers (as opposed to workers only). This approach allows one to comprehensively
capture how government policies affect the labor market, including changes in the extensive margin. With our
micro-files, it is also possible to focus on employed individuals only. We view both approaches as complementary.
24
Employment rates in our micro-files are higher by about 10 percentage points throughout because our micro-

27

This means that although the composition of the workforce may not be the same in September
2022 as in January 2019, a comparison of the level of labor income in these two months is notconfounded by changes in the level of employment, which facilitates the interpretation of labor
income growth.
The decline of labor income inequality in 2019–2022. Figure 7c depicts the evolutionof average labor income by labor income group from January 2019 to September 2022. Since
the bottom quartile of the working-age population is mostly unemployed, we focus on the next
three quartiles. We also report income for the top 1%, which earns a sizable fraction of totallabor income and cannot be studied with available household surveys. Average labor income
in each group is normalized to 100 in January 2019. We can see that from January 2019 to
September 2022, real labor income grew significantly in the second quartile, the group with thelowest-wage workers. Because the same number of adults was employed in both months, thisgrowth does not reflect a jobs (i.e., quantity) effect; it reflects the fact that low-paying jobs paid
more in September 2022 than in January 2019, by about 10% in real terms. Income grew muchless for the third and fourth quartiles: in these groups real average labor income increased bya mere 3% over this 3 years and 9 months period. The relatively strong growth at the bottom
illustrates the equalizing effects of tight labor markets.
One caveat is that there is heterogeneity at the top of the labor income distribution. While
the top quartile experienced little growth over 2019-2022, the top 1% grew as fast as the second
quartile, even slightly more (+12% between January 2019 and September 2022). Consistent
with the updated Kopczuk, Saez, and Song (2010) statistics (based on Social Security records)
constructed and analyzed by Mishel and Kandra (2021), we find that the top 1% grew fasterthan other groups in 2020. Moreover, our real-time estimates suggest that the top 1% grew
especially fast in the first half of 2021, before plateauing in the second semester and fallingslightly in the first three quarters of 2022. Thus, although wage inequality fell from 2019 to
September 2022 within the bottom 99%, the share of labor income earned by the top 1% rose.25
Analyzing month-to-month changes between January 2019 and September 2022, we can seethat these dynamics largely reflect changes in employment. The rise in unemployment during
Covid led to a drop in average labor income in all groups, particularly marked at the bottom.
files match annual employment rates, while Figure 7a reports raw actual monthly employment rates (which aremechanically lower); see appendix Figure A3 and Section 4.2 for a discussion.
25
Another caveat is that, as reported in Appendix Figure A10, we do not find a significant decline of thecollege labor income premium: in the working-age population, individuals with at least some college education
earn on average twice as much labor income as individuals without a college education since the late 2010s, withno trend. This number captures differences in both wages and employment by educational attainment.

28

The recovery in the second quartile between the trough of the recession and September 2022
was primarily driven by jobs gains, although wage gains played a significant additional role. By
the end of 2021 real labor income was stagnating or falling for the third and fourth quartile ofthe labor income distribution, while the second quartile kept rising.
Labor income inequality: Covid vs. Great Recession. The dynamics of labor income
inequality observed during and after the Covid pandemic contrast sharply with the one observed
during and after the Great Recession, as depicted in Figure 7. Panel (b) of that figure showsthat it took almost 10 years for the employment rate to get back to its pre-Great Recession level.
Panel (d) shows that second quartile labor incomes fell the most during Great Recession (asobserved during the Covid-19 pandemic), but—in sharp contrast to the Covid-19 recession—
continued to fall in real terms until the beginning of 2012, even though the employment rate
had already started to recover. Second quartiles real labor incomes did not recover their real
pre-Great Recession levels until 2016.
Finally, Figure 8 depicts real labor income growth rates by vingtiles of the labor income
distribution above the 25th percentile, from the eve of the Covid recession (February 2020) to
September 2022 (as of writing the latest available month, which turns out to have an employment
rate equal to that of February 2020), and from the eve of the Great Recession (December 2007)
to May 2017 (the month when the employment rate recovered its December 2007 level). In bothcases we capture a full employment cycle and labor income growth statistics are not confoundedby changes in aggregate employment.
The Great Recession and ensuing recovery were characterized by modest gains in the middleof the labor income distribution—and a stagnation at the bottom and at the top. The Covid
cycle is the mirror image: large earnings gains at the bottom and top of the distribution—andlosses in the middle. More precisely, during the Covid cycle, labor income grew at annual rates
of close to 2% at the bottom. By contrast, between December 2007 and May 2017, average labor
income for the second quartile of the working-age population grew only 0.2% a year. By thetime the employment rate had recovered its pre-Great Recession level (in May 2017), average
earnings for low-wage workers were barely higher than a decade before. Growth was stronger
from the 75th to the 95th percentile during the Great Recession, while these groups experienced
real losses during the Covid cycle. Finally, the top 1% grew fast during Covid while it stagnatedfrom 2007—a peak year for top labor incomes, which include stock options—to 2017.

29

6.3

The Effects of Government Intervention

Government intervention during recessions affect the level and distribution of disposable income,sometimes massively. In 2021, average real disposable income per adult in the United States
was about 10% higher than in 2019 due to large government deficits.
The bottom 50% most benefitted from the increase in government spending. After accounting for taxes and cash and quasi-cash transfers, average disposable income for the bottom 50%
was nearly 20% higher in 2021 than in 2019. Figure 9 shows a step-by-step decomposition of thisevolution. To facilitate the interpretation of the results, we focus on the working-age population(aged 20 to 64) and we always rank by factor income so that all figures for a given month referto the same group.26 The figure reveals the relative importance of the different government
programs enacted during the pandemic.
In the early months of the crisis, the Paycheck Protection Program lifted incomes. But
available evidence on the incidence of the program (Autor et al., 2022) implies that the effectis limited: by our estimates, the Paycheck Protection Program increased the average monthly
income of the bottom 50% of working-age adults by about $100. It replaced about a fifthof the decline in factor income that occurred in the first months of the crisis for this group(from about $2,000 in February 2020 to about $1,500 in April and May 2020). Unemployment
insurance, which was expanded during the crisis, had much larger effects, lifting average bottom
50% monthly income by about $800 in May, June, and July 2020, and by up to $400 a monththrough to the summer of 2021.
The three waves of Covid-relief payments (April 2020, January 2021, and March 2021) had
massive but temporary effects on monthly income. Disposable monthly income for the bottom
50% peaked in March 2021 following the third payment, to reach $4,000—twice as much as
before the pandemic ($2,000). For the bottom 50%, disposable income that month was twiceas large as factor income (about $2,000). This gap between disposable and factor income washistorically high: disposable income is usually close to factor income for the bottom 50% (thatis, this group usually pays about as much in taxes as it receives in cash and quasi-cash transfers).
By the fall of 2021, disposable monthly income for the bottom 50% had declined to $2,400. The
main reason why disposable income was higher for the working class in the end of 2021 than
before the pandemic was the expanded child tax credit and the expanded earned income tax
credit for adults with children.27 In the beginning of 2022, disposable income fell as the expanded
26

Appendix Figure A7 shows disposable income in the full adult population (i.e., not restricting to working-age
adults) ranking by factor income; the results are similar.
27
As in the national accounts, refundable tax credits—i.e., cash transfers administered through the tax

30

tax credits expired. The only reason why it remained higher than pre-Covid (by about 10%)is that factor income was higher—driven by the real wage gains documented above. In sum,government programs enacted during the pandemic led to a dramatic and unprecedented—but
short-lived—improvements in living standards for the lower half of the income distribution.28

6.4

Changes in Wealth Concentration

Last, we study the effect of the Covid-19 crisis on wealth inequality. We produce monthly
and daily estimates of wealth levels across the distribution. Monthly estimates are obtainedby rescaling quarterly Financial Accounts aggregates to their monthly value using real estateand equity indices, and daily estimates by updating equity values using daily stock indices.29
This makes it possible to track changes in wealth inequality during periods of turmoil in asset
markets, which is valuable to simulate wealth effects on consumption in real time and to improve
the analysis and management of the business cycle.
Figure 10 shows the monthly dynamic of wealth across the distribution from July 2019 to
September 2022, with projections to October 31th , 2022 using our daily methodology. Two
distinct phases can be observed. First, until the end of 2021, wealth grew strongly for all groups
and wealth concentration rose. From the end of 2019 to the end of 2021, average real wealth per
adult grew 26%, primarily due to the rise in asset prices, both in housing and equity markets.
For the top 1% the increase was 31% and for the top 0.01% it reached 34%. The share of wealthowned by the top 0.1% adults increased 1.2 point from the end of 2019 to the end of 2021, toreach 18.8%—the highest level recorded in the post-Word War II era. Wealth gains were thenpartly erased by the decline in stock prices in the first three quarters of 2022. In a context
of still rising real estate prices (until the summer of 2022), this decline affected the wealthiest
disproportionately, leading to a fall in top wealth shares. More than two-thirds of the wealthgains made in 2020 and 2021 by the top 0.1% were erased in the first 10 months of 2022. By
October 2022, the top 0.1% wealth share was below its pre-Covid level.
Our findings on the dynamics of wealth inequality are consistent with other existing evidence.
system—are categorized as cash transfers (not negative taxes); thus the child tax credit and the earned income tax credits show up as “regular cash transfers” in Figure 9.
28
Appendix Figure A8 shows that the same conclusion holds true when looking at total posttax income, i.e.,
including Medicaid and Medicare, other in-kind government spending, collective consumption expenditures, andthe government deficit.
29
For housing wealth we use the quarterly Case-Schiller index, projected to the most recent month with
the Zillow Home Value index. For equities, we use the Wilshire 5000 Total Market Index, the most extensive
representative index of US public companies. We also adjust the wealth of the top 400 daily to match thereal-time estimates published by Forbes. See Appendix B for complete details.

31

The Federal Reserve Distributional Financial Accounts (DFA) show similar patterns, both for
the Covid-19 crisis and over the long run. If anything, the DFA suggest an even stronger increase
in wealth concentration since 1989. From the third quarter of 1989 (the start of the DFA) tothe second quarter of 2022, the top 1% wealth share grew 8.6 points according to the Federal
Reserve (vs. +5.7 in our series over the same period of time); the next 9% lost -0.4 point (-0.6
in our series); the middle 40% lost -7.4 points (-4.3 in our series); the bottom 50% lost -0.6
point (-0.7 in our series). Given that these two series rely on different distributional sources (atriennal survey of about 6,000 families in the case of the DFA, annual tax data in our case), thissimilarity suggests that the rise of wealth concentration in the United States since the 1980s is
highly robust.30 In both the DFA and our series, the top 1% wealth share was at its highest
recorded level at the end of 2021 and fell by 1–2 percentage points in the first half of 2022.

7

Racial Economic Disparities

The statistical match between survey and tax data implemented in this paper allows us to
study the real-time dynamics of racial income disparities, in particular whether Black andwhite households recovered at the same pace after the Covid-19 recession.

7.1

A Comprehensive Measure of the Black-white Income Gap

To provide context for the analysis of the Covid-19 pandemic, we start by describing the mediumrun dynamics of the Black-white income gap. Although a large literature studies racial income
disparities (e.g., Bayer and Charles, 2018; Chetty et al., 2020; Derenoncourt and Montialoux,2021), to date there is no estimate of how average national income—the broadest notion ofincome—differs for Black vs. white Americans. Due to data limitations (the lack of information
on race in tax data and the poor coverage of capital income in household surveys, in particular),most existing statistics focus on earnings or some measure of disposable income. Our approach,by contrast, allows us for the first time to provide a comprehensive measure of the Black-white
income gap. Concretely, in 2021 average national income per adult in the United States wasaround $79,000; with our files we can ask: How does this number differ across racial groups?
30

As detailed in Saez and Zucman (2020), top wealth shares, although they exhibit the same trend, are lower
throughout in level in the DFA than in Saez and Zucman (2016); e.g., the top 1% wealth share rises from 22.5%
in 1989Q3 to 31.1% in 2022Q2 in the DFA (vs. 28.6% to 34.3% in our series). This is because in contrast to Saez
and Zucman (2016) and this paper, the DFA include unfunded defined benefit pensions and vehicles in wealth,both of which are relatively equally distributed. Once the same definition of wealth is used, the level, trend,and composition of top wealth shares are nearly identical in the two projects (see, e.g., Saez and Zucman, 2020,
Figure 1).

32

Figure 11 shows the average pretax national income of Black adults relative to white adults.
On average Black Americans earn half of what white Americans do: $48,000 in 2021 vs. $95,000.
This gap is significantly larger than the Black-white earnings gap that is the traditional focusof the literature. As Figure 11 shows, Black Americans (including nonworkers) aged 20 to 64
on average earn 65% of what working-age white Americans do.31 The Black-white national
income gap is even larger because racial disparities in capital income are larger than disparities
in labor income: on average Black adults earn only about 20% of the average capital incomeof white adults. This gap itself primarily reflects the major disparities in wealth reported
in Figure 11 and recently studied in, e.g., Derenoncourt et al. (2022).32 Property ownership
remains much more unequal than labor market incomes and this inequality is a key contributor to
the persistent racial income disparities that characterize the United States.33 Capital income iseven more unequally distributed than wealth because of differences in yields, in turn coming fromdifferences in asset composition. Relative to white households, a greater fraction of the wealthof Black households is in relatively low-yield assets, namely housing and pensions. Businessassets and corporate equity, which have a higher yield, are more concentrated among whitehouseholds.
Figure 11 also shows that there has been no reduction in the Black-white income gap since
the late 1980s. If anything, racial disparities are slightly higher in 2022 than in 1989. Inequality
increased from 1989 to 2013; it then started falling in the mid-2010s, in both cases driven bychanges in labor income disparities. The recent decline in inequality was not enough to offset
the previous increase, so that the average pretax income of Black adults relative to white adults
remains lower in 2022 than in 1989.
31

Restricting to workers, the Black-white earnings gap is lower (Black workers earn about 75% of what white
workers do). As shown by Bayer and Charles (2018), taking nonworkers into account is critical to analyze thedynamics of the Black-white earnings gap, due to differential trends in employment.
32
The Black-white wealth gap displayed in Figure 11 is close to the one in the Federal Reserve Distributional
Financial Accounts, the most comparable statistics. In 2019 the average wealth of Black households is 22% of
the average wealth of white households in the DFA vs. 25% in Figure 11. The difference is due to the unit ofobservation (households in DFA vs. adult individuals in Figure 11). Because of differences in household size,
racial wealth disparities are slightly larger at the household level. Using our microfiles one can also study racial
wealth disparities at the household level; results are identical to the DFA. In the Survey of Consumer Finances(used, e.g., by Derenoncourt et al., 2022), racial wealth disparities are higher than in the DFA and our series
because of the different wealth totals (in particular the higher total for private business wealth, whose ownershipis concentrated among white households). Trends are similar in all series.
33
Similarly, Figure A9 show that even though Black individuals account for 12% of the entire adult population,
they account for less than 8% of the top 10% of the wage distribution and 4.5% of the top 10% of the wealthdistribution.

33

7.2

Racial Disparities Over the Business Cycle

Turning to the recent dynamic, Figure 12 contrasts income growth at the quarterly frequency
during the Great Recession and its aftermath and during the Covid crisis. During the GreatRecession, the average income of Black people experienced a prolonged decline of 4 years. In
the third quarter of 2011, it was 8% lower than on the eve of the Great Recession, while average
income for white working-age adults had already fully recovered. This year corresponds to thepeak in Black-white income disparities over the last three decades. Average Black income thenstarted recovering, first at the same pace as for whites, then faster after 2014, leading to a
decline in the Black-white income gap that continued until the Covid pandemic hit. The trend
for Hispanics mirrors the one seen for Blacks, except that growth was even stronger from 2014.
During Covid, racial disparities were less pronounced than during the Great Recession. The
collapse in income in the second and third quarter of 2020 was similar for Black and white
people; it was less marked for Hispanics. The different groups then recovered at roughly thesame pace: by the first quarter of 2021 all had recovered their pre-crisis pretax income level.
We thus again see an illustration of the fact that the Covid-19 recovery was much more equal
than the Great Recession recovery.
Appendix Figure A11 reports similar comparisons of average income for men vs. women in
the two downturns and recoveries. In both cases income dropped more for men than for women.
During the Great Recession, women recovered faster than men (3 years vs. more than 4 years),
while during Covid both groups recovered in less than a year. We do not find evidence that theCovid recession had, by the end of 2021, exacerbated gender income inequality.

8

Conclusion

Macroeconomic growth statistics are not necessarily informative of how income grows for mostsocial groups. Yet government statistics currently available globally do not make it possible
to know who benefits from economic growth in a timely manner. Our paper attempts toaddress this gap in the United States by creating monthly income distributions, available withina few hours of the publication of official high-frequency national accounts aggregates. Our
methodology, which we retrospectively test and successfully validate back to 1976, combines all
publicly available high-frequency data in a unified framework.
Real-time distributional growth statistics could play a critical role in guiding stabilization
policies during and in the aftermath of recessions. For example, following a recession, they could34

be used to estimate “distributional output gaps,” that is the extent to which income remains
below its pre-recession level or trend for the bottom 50% of the distribution, the next 40%, andthe top 10%. Since our files incorporate all taxes and government transfers, they could be used
to study whether fiscal policy enacted during a crisis mitigates income losses for the working
class on a month-to-month basis.
This project only uses publicly available datasets and our programs are available online athttps://realtimeinequality.org, making it possible for interested users to examine all the
aspects of our approach and refine it. Although our estimation procedure appears to deliver
reliable results, our estimates could be improved by complementing the data we use with realtime private sector data (Chetty et al., 2020), by leveraging internal administrative data, or bycollecting new administrative data.
A number of potential data improvements are worth highlighting. First, high-frequency national accounts totals are—like all important economic statistics—still a work in progress andcould be refined. It would be valuable to develop processes to remove the statistical discrepancy
between GDI and GDP, i.e., to systematically reconcile the income and expenditure approach.
This would allow BEA to produce a single unified estimate of quarterly growth, as many countries do. Progress in that direction could be facilitated by a more systematic exploitation ofhigh-frequency data on corporate profits, such as listed companies’ quarterly earnings statements.34 The national accounts could also be improved by reporting separate profits estimatesfor public vs. private companies (or C-corporations vs. S-corporations). Because profits of private companies tend to be more concentrated towards the top of the income distribution, thisadditional breakdown would improve the accuracy of distributional estimates.35 Last, government agencies could produce additional high-frequency inequality estimates. Most importantly,
the Bureau of Labor Statistics could compute a quarterly individual-level wage distribution using the administrative unemployment insurance micro-data that underly the QCEW. We view
our real-time statistics as a prototype which we hope will be refined, enriched, and eventually
incorporated into official national account statistics.
34

Public companies must submit quarterly earnings to the Securities and Exchange Commission within 40
days. If this delay was reduced to less than 30 days, these data could be used by BEA as a key input to form
an estimate of corporate profits within a month of the end of each quarter. In turn, this would make it possiblefor BEA to simultaneously estimate growth from both the income and expenditure approach, making it easier
to integrate data from these two approaches into a single number.
35
Krakower et al. (2021) present prototype NIPA estimates of profits for S-corporations, but there are noquarterly estimates to date. The Census Bureau Quarterly Financial Report—which serve as a key input for
the estimation of quarterly profits by BEA—could include tabulations for public vs. private firms separately.

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37

Figure 1: Testing the One-to-One Statistical Match(a) Gender by Wage Earnings(b) Race by Wage Earnings

80%

40%
Bottom 50%

70%

35%
% of blacks & hispanics% of women

60%
50%
40%
30%
20%
10%

30%
Bottom 50%
25%
20%
15%
10%
5%

Top 10%

Top 10%
0%

0%
1975

1985
1980

1995
1990

2005

2015

2000

2010

DINA (matched)

1975
2020

1995
1990

CPS

2005

2015

2000

2010

DINA (matched)(c) Race by Income

2020

CPS

(d) Race by Wealth

40%

40%

35%

35%

30%

% of blacks & hispanics% of blacks & hispanics

1985
1980

Bottom 50%
25%
20%
15%
10%
5%

30%
Bottom 50%
25%
20%
15%
10%
5%

Top 10%

Top 10%

0%

0%
1975

1985
1980

1995
1990

DINA (matched)

2005
2000

2015
2010

CPS

1975
2020

SCF

1985
1980

1995
1990

2005
2000

DINA (matched)

2015
2010

2020

SCF

Notes: This figure compares the demographic composition along one key income or wealth variable of interestin the original survey data vs. the same key income or wealth variable from the tax data in our statisticallymatched dataset. Panel (a) considers gender composition by annual wage earnings brackets in the CPS vs.
matched data. Panel (b) considers racial composition by annual wage earnings brackets. In both panels (a) and(b), the sample includes zeros (individuals with no wage earnings) but only includes working-age individuals(20–64). Panel (a) uses individual earnings, while panel (b) divides earnings equally among married spouses.
Panel (c) considers race composition by annual total income bracket in the CPS, the SCF, and matched data.
For the purpose of that exercise, total income is the sum of wages, pensions, Social Security benefits, businessincome, interest, dividends, and rents. Panel (d) considers the same race composition by wealth bracket in the
SCF vs. matched data. In both panels (c) and (d), we consider the entire CPS (and/or SCF) samples.

38

Figure 2: Top 1% Wage Share: Adjusting the QCEWAnnual Tax Data

39

Top 1% share of wages (%)

12

QCEW
NAICS
(current version)
SIC(old version)
Adjusted

10
8
6
4
2
0
1980m1

2000m1
1990m1

2020m1
2010m1

Notes: This figure depicts the top 1% wage share (among individuals with positive wages) in the annual tax data (in red circles) and in the raw but
de-seasonalized QCEW data (orange series for the old QCEW based on SIC industry codes, and green series for the modern QCEW based on more granular
NAICS industry codes). The top 1% wage share is substantially lower in the raw QCEW because the data is aggregated by county×industry cells, but thetrends are similar. Adjusting the QCEW series with a multiplier plus level shift constant over the period (based on a time series regression approach as
described in the text) generates the blue adjusted QCEW series that almost perfectly aligns with the tax data both in levels and trends over the full period.

Figure 3: Wage Distributions: Tax Data vs. QCEW & CPS Based Estimates

40

share of wage income (%)

Bottom 50%

Middle 40%
55

17
16
15
14

50
45

1960m1

1980m1

2000m1

2020m1

1960m1

Next 9%1980m1

2000m1

2020m1

Top 1%
14
12
10
8
6

27
26
25
24
23
1960m1

1980m1

2000m1

2020m1

Public-use Tax Data [Yearly]

1960m1

1980m1

2000m1

2020m1QCEW + CPS [Monthly]

Notes: This figure compares the wage distributions in the annual distributional national accounts of Piketty, Saez and Zucman (2018, updated), which
are based on public tax micro-data, and those obtained in our monthly micro-files using the QCEW and the CPS as described in Section 4.3. Each panel
depicts the share of total wage income earned by a specific group (bottom 50%, middle 40%, next 9%, and top 1%) of individuals with positive wage income.
Wages are individualized (they are not equally split between married spouses). The monthly series estimated from the QCEW and CPS track the annual
micro-data closely for all groups, including the top 1% which is not measured well in traditional survey data.

Figure 4: Actual vs. Predicted Growth at the Bottom(b) Bottom 50%, 2 years(a) Bottom 50%, 1 year
10

1985

1984

5

Actual growth rate (%)

Actual growth rate (%)

10
2017

0
2012

-5

5
0
-5
-10

-10
-10

-5

0

5

10

-10

Predicted growth rate (%)510(d) Middle 40%, 2 years

6

8
6

4

Actual growth rate (%)

Actual growth rate (%)

0

Predicted growth rate (%)(c) Middle 40%, 1 year

2
1988
1987

0
-2

-5

1991

2012

2009

1994

4

1993

2

1991

0
-2

1988

1992

2009

-4
-4
-4

-2

0

2

4

6

-4

Predicted growth rate (%)• Regular year

-2

0

2

4

6

8

Predicted growth rate (%)

N Tax reform year

Notes: This figure compares predicted to actual growth in average real factor income per adult (with income
equally split among married spouses) for the bottom 50% (top panels) and the next 40% (bottom panels).
Growth is computed from year t to t + 1 (left panels) and from t to t + 2 (right panels) for each year t from 1975
to 2018 (2017 in the right panels). Actual growth is obtained using the annual distributional national accountmicro-data for both years t and t + 1 (or t + 2). Predicted growth is obtained using the annual micro-data foryear t but the projected micro-data using our methodology for t + 1 (or t + 2). Years of significant tax reforms(which can generate income shifting) are shown in red. Years t + 1 (or t + 2) with significant prediction errors arelabelled. Overall, the dots align well with the 45-degree line depicted on the graphs: our methodology accurately
predicts growth at the bottom and in the middle of the distribution.

41

Figure 5: Actual vs. Predicted Growth at the Top(b) Top 1%, 2 years(a) Top 1%, 1 year
1988

35

1987

30

15

Actual growth rate (%)

Actual growth rate (%)

20

2012

10
1979

5

1994

0
1993

-5

1988

25
20
15
10
1993

5

1992
1994

0
-5

1991

-10

1991

-15

-10
-10

-5

0

5

10

15

20

-15 -10 -5 0 5 10 15 20 25 30 35

Predicted growth rate (%)

Predicted growth rate (%)(c) Next 9%, 1 year(d) Next 9%, 2 years

6
Actual growth rate (%)

Actual growth rate (%)

8
4
2000

2

2013

0
-2

2009

6

2000

4

2013

2
0
-2
-4

2009

-4
-4

-2

0

2

4

6

-4

Predicted growth rate (%)• Regular year

-2

0

2

4

6

8

Predicted growth rate (%)

N Tax reform year

Notes: This figure compares predicted to actual growth in average real factor income per adult (with income
equally split among married spouses) for the top 1% (top panels) and the next 9% (bottom panels). Growth iscomputed from year t to t + 1 (left panels) and from t to t + 2 (right panels) for each year t from 1975 to 2018(2017 in the right panels). Actual growth is obtained using the annual distributional national account micro-data
for both years t and t + 1 (or t + 2). Predicted growth is obtained using the annual micro-data for year t but
the projected micro-data using our methodology for t + 1 (or t + 2). Years of significant tax reforms (which cangenerate income shifting) are shown in red. Years t + 1 (or t + 2) with significant prediction errors are labelled.
Overall, the dots align well with the 45-degree line depicted on the graphs: our methodology accurately predicts
the growth of top incomes.

42Figure 6: Factor Income: Covid-19 vs. Great Recession(a) Real Factor Income Around the Covid-19 Pandemic

Average income per adult (constant)
07/2019 = 100

110

100

90

80

Top 1%
Next 9%Middle 40%
Bottom 50%

70
2019m7

2020m1

2020m72021m12021m72022m12022m7

Real average factor income per working-age adult(Index, 100 in the month preceding the recession)(b) Income Dynamics: Covid-19 vs. Great Recession
110

July 2021(1 year,
10 months)

Working-age adults(Great recession)

Working-age adults(COVID recession)

100

90

February 2017(8 years, 1 months)
Bottom 50%(Great recession)

80
Bottom 50%(COVID recession)

70
0

12

24

36

48

60

72

84

96

108

120

Months after recession started

Notes: Panel (a) shows the monthly dynamic of real factor income per adult (with income equally split among
married spouses) around the Covid-19 pandemic. The pandemic led to the strongest income declines for thebottom 50% and to a lesser extent for the top one percent. By October 2021 all groups had recovered theirpre-crisis income level. Panel (b) compares the growth of real factor income per working-age adult (with income
equally split among married spouses) on average and for the bottom 50% of the working age population during
the Great Recession and the Covid-19 recession. We restrict to the working-age population (20 to 64) to control
for population aging in the 2010s. Income is normalized to 100 in December 2007 for the Great Recession andFebruary 2020 for the Covid-19 recession, corresponding to the month immediately preceding each recession.
The x-axis counts the number of months since the start of each recession.

Figure 7: Employment and Earnings Growth: Covid vs.
Great Recession

Employment to working-age population ratio (%)

Employment to working-age population ratio (%)

Macro Employment Rates(a) Covid(b) Great Recession
79
78
77
76
75
74
73
72
71
70
69
68
67
2019m1 2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

79
78
77
76
75
74
73
72
71
70
69
68
67
2008m1

2010m1

2012m1

2014m1

2016m1

2018m1

Real Labor Income by Group(c) Covid(d) Great Recession
120

120

110
4th quartile100

3rd quartile90

2nd quartile

80

Average real labor income
Index (2019m1 = 100)

Average real labor income
Index (2019m1 = 100)

Top 1%

110
4th quartile
Top 1%

100
3rd quartile90

2nd quartile

80

2019m1

2020m1
2019m72021m1
2020m72022m12021m72022m7

2008m1 2010m1 2012m1 2014m1 2016m1 2018m1

Notes: The top panels depict the evolution of the monthly employment to working-age population ratio, defined
as seasonally-adjusted nonfarm employment (from the BLS Current Employment Statistics establishment survey)
divided by the number of adults aged 20 to 64 during Covid in panel (a) and the Great Recession in panel(b). This measure of employment excludes proprietors, private household employees, unpaid volunteers, farm
employees, and the unincorporated self-employed. The bottom panels depict the average real labor income forvarious fractiles of the labor income distribution among adults aged 20 to 64 (including non-workers) during
Covid in panel (a) and the Great Recession in panel (b), with base 100 at the start of each period. Labor
income is individualized (i.e., not equally split between married spouses) and includes all wages and salaries,
supplements to wages and salaries, and 70% of self-employment income.

Figure 8: Earnings Growth Across the Distribution: Covid-19 vs. Great Recession

45

Annualized real labor income growth (%)

4

3

2

1

COVID recession
and recovery(02/2020 to 09/2022)
Great recession
and recovery(12/2007 to 05/2017)

0

-1

-2
25-30%
35-40%
45-50%
55-60%
65-70%
75-80%
85-90%
95-99%
30-35%
40-45%
50-55%
60-65%
70-75%
80-85%
90-95% Top 1%

Percentiles (working-age population)
Notes: This figure shows the annualized growth rate of real labor income by vingtile of the working-age population (with a zoom on the top 1%), from theeve of the Covid recession (February 2020) to September 2022, and from the eve of the Great Recession (December 2007) to May 2017. In both cases, we
capture a full employment cycle (i.e., May 2017 is the month when the employment rate had returned to its pre-Great Recession level; and by September
2022 the employment rate had returned to its pre-Covid level). Labor income is individualized (i.e., not equally split between married spouses) and includes
wages, supplements to wages and salaries, and 70% of self-employment income. We include all working-age adults (aged 20 to 64), including non-workers.
The graph starts at the 25th percentile since the bottom quartile of working-age adults is mostly unemployed. The figure shows that the bottom (andtop) of the labor income distribution experienced fast growth from the beginning of 2020 to September 2022, in contrast with the recovery from the GreatRecession.

Figure 9: Income of the Bottom 50% during the Covid Crisis

Monthly income per adult (constant USD)

4,000
3,500

Post-tax disposable income
COVID stimulus checks
Regular cash transfers(net of taxes)
Subsidized pretax national income
Other benefits (net)(pensions & DI, minus contributions)
Unemployment insurance benefits
Subsidized factor national income
Paycheck Protection Program
Factor national income(matching national income)

3,000
2,500
2,000
1,500
1,000
500
0
2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

Notes: This figure decomposes the average real monthly income of the bottom 50% working age (20-64) adults
from July 2019 to September 2022. Individual adults are ranked by their factor income, and income is equally
split between married spouses. The figure reveals the relative importance of the different government programs
enacted during the Covid-19 pandemic, most importantly the three waves of Covid-relief payments (April 2020,
January 2021, and March 2021), the expansion of unemployment insurance, the expansion of refundable tax
credits (EITC and child tax credit), and the Paycheck Protection Program. By the beginning of 2022 all of these
programs had expired, and the only reason why average bottom 50% disposable income remained higher thanpre-Covid (by about 10%) was the higher level of factor income.

Average wealth per adult (constant USD)
07/2019 = 100

Figure 10: Wealth Growth During and After Covid
140

130

120

110

100

Top 0.1%
Top 1%
Top 10%
Middle 40%

2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

Notes: This figure shows the evolution of average real wealth per adult (with wealth equally split between
married spouses) in the middle 40% of the wealth distribution (i.e., from the median to the 90th percentile), thetop 10%, the top 1%, and the top 0.1%, from July 2019 to September 2022 (plain line) and projected values for
October 31st , 2022 (dotted line) using our methodology for producing daily estimates. The average wealth ofeach group is normalized to 100 in July 2019. The figure shows, e.g., that the wealth of the top 0.1% increased
by close to 40% (adjusted for inflation) from July 2019 to December 2021 and that more two thirds these gains
46
were erased in the first ten months of 2022.

Figure 11: Black-White Economic Disparities
100
Black average / White average (%)

90
80
70

Labor income (working-age population)60
50

Pretax income

40
30
20

WealthPretax capital income

1001990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1

Notes: This figure shows Black-white differences in income and wealth. The unit of observation is the individual
adult. The labor income line restricts to the working-age population (individuals aged 20 to 64); other series
include the entire adult population. The series presented are quarterly and start in 1989, the first year of theSurvey of Consumer Finances.

Pretax income(constant USD, pre-recession peak = 100)

Figure 12: Income Dynamics by Race: Covid vs. Great Recession
COVID Recession (2020-2022)

Great Recession (2007-2016)

110

105

100

95

90
2020q1

2020q32021q12021q32022q1Non-hispanic whites

2007q3Blacks

2010q32013q3

2016q3

Hispanics

Notes: This figure shows the evolution of average real pretax national income by racial group during the Covid
recession and its aftermath (left panel) and the Great Recession and its aftermath (right panel). Income isnormalized to 100 in the quarter preceding each recession.

47

Table 1: Overview of Sources
Usage

Construction ofannual microdata

48

Intra-annual
distribution
adjustments

Estimation andrescaling to macroaggregates

SourceProducer

Usage

Frenquency Lag

Notes

Public-use tax data

Internal Revenue
Service

Main microdata source.

Yearly

1–2 years

The last public-use microfile dates from 2014. For later years, weupdate the microdata using IRS tabulations of income.

Social Security Wage
Statistics

Social Security
Administration

Complementary data on the wage
income distribution.

Yearly6 months

We use the wage income distribution from the SSA because it isbetter at capturing low wages.

Current Population
Survey (ASEC)

Census Bureau (via
IPUMS)

Integration of socio-demographic
information into the tax data (bottom
95%).

Yearly6 months

We match the CPS to the tax data using optimal transport on
detailed income variables.

Survey of Consumer
Finances

Federal Reserve

Integration of socio-demographic
information into the tax data (top5%).

Triennial

1 year

We match the SCF to the tax data using optimal transport on
detailed income and wealth variables.

Quarterly Census of
Employment andWages (QCEW)

Bureau of Labor
Statistics (BLS)

Estimation of the wage income
distribution.

Quarterly

5 months

The wage data in the QCEW is quarterly but the employment
data is monthly. Therefore we treat the QCEW as a monthly
dataset.

Current Employment
Statistics (State andArea)

Bureau of Labor
Statistics (BLS)

Estimation of the wage income
distribution.

Monthly

3 weeks

This data is similar to the QCEW but coarser and released more
frequently. We match it to the QCEW to extrapolate theevolution of earnings and employment in the most recent months.

Current Employment
Statistics (National)

Bureau of Labor
Statistics (BLS)

Estimation of the number of wage
earners.

Monthly

3 weeks

We use aggregate nonfarm employment statistics to adjust the
number of wage earners monthly.

Unemployment
Insurance Weekly
ClaimsDepartment of Labor

Estimation of the number of
unemployment insurance claims.

Weekly1 week

We average the series by month, adjust for seasonal variations,and use it to adjust the number of unemployment insurance
recipients by month.

Current Population
Survey (Monthly)

Census Bureau (via
IPUMS)

Estimation of the wage income
distribution, and the ranks in theearnings distribution, and of thenumber of wage earners by age,gender, education, race and marital
status.

Monthly

2 weeks

We use the CPS to estimate wage income for the bottom 90% of
the distribution only (because of top-coding), and average that
estimate with the one from the QCEW. For the top 10% we only
rely on the QCEW.

Real-time Billionaires

Forbes Magazine

Wealth for the 400 richest Americans.

Daily

None

We adjust the overall wealth of the 400 wealthiest households tomatch the real-time Forbes estimate.

National Income andProduct Accounts(NIPA)

Bureau of Economic
Analysis (BEA)

Estimation of monthly income
aggregates.

Monthly /Quarterly

1 monthWhen needed, we disagreggate the quarterly or yearly data at themonthly frequency using Denton’s (1971) method. With the
exception of corporate profits, the most important income
components are available monthly.

Financial Accounts

Federal Reserve

Estimation of quarterly wealthaggregates.

Quarterly

2 months

When needed, we disagreggate yearly data at the quarterly
frequency using Denton’s (1971) method. The most important
wealth components are avaialble quarterly.

Wilshire 5000 Total
Market Index

Wilshire Associates(via FRED)

Disagreggation of the value of stocks
at the monthly frequency.

Daily

None

We use the index to disagreggate the quarterly estimate of stocks
values at the monthly level using Denton’s (1971) method.

Case-Shiller National
Home Price Index

Standard & Poor’s(via FRED)

Disagreggation of the value of housing
at the monthly frequency.

Monthly

2 months

We use the index to disagreggate the quarterly estimate of housing
values at the monthly level using Denton’s (1971) method.

Zillow Home Value
Index

Zillow

Disagreggation of the value of housing
at the monthly frequency.

Monthly

2 weeks

We use the Zillow index to extrapolate the Case-Shiller in thelatest months.

Table 2: Prediction Errors for Growth Rates of Income and WealthAll years

Excl. tax reforms

Recessions

BracketStd. Dev.

Correct sign

Std. Err.

Correct sign

Std. Err.

Correct sign

Factor Income

Bottom 50%
Middle 40%
Next 9%Top 1%

4.0 pp.
1.5 pp.
1.8 pp.
6.0 pp.

82%
82%
93%
89%

2.1 pp. -0.8 pp.
0.9 pp. -0.3 pp.
1.1 pp. -0.7 pp.
3.4 pp. -0.2 pp.

2.0 pp.
0.9 pp.
0.9 pp.
3.4 pp.

85%
91%
100%
91%

2.1 pp. -0.9 pp.
0.7 pp. -0.2 pp.
1.1 pp. -0.8 pp.
2.5 pp. -0.2 pp.

1.9 pp.
0.7 pp.
0.8 pp.
2.5 pp.

92%
83%
83%
92%

1.7 pp. -0.4 pp.
1.2 pp. -1.0 pp.
1.1 pp. -0.6 pp.
3.6 pp. 1.4 pp.

1.7 pp.
0.7 pp.
0.9 pp.
3.3 pp.

Pretax Income

Bottom 50%
Middle 40%
Next 9%Top 1%

3.0 pp.
1.5 pp.
2.3 pp.
6.2 pp.

75%
80%
91%
89%

2.0 pp. -1.0 pp.
1.0 pp. -0.2 pp.
1.2 pp. -0.7 pp.
3.4 pp. -0.1 pp.

1.7 pp.
0.9 pp.
0.9 pp.
3.4 pp.

76%
88%
100%
91%

2.0 pp. -1.0 pp.
0.8 pp. -0.2 pp.
1.1 pp. -0.8 pp.
2.6 pp. -0.2 pp.

1.7 pp.
0.7 pp.
0.9 pp.
2.6 pp.

83%
83%
75%
92%

1.8 pp. -1.3 pp.
1.1 pp. -0.9 pp.
1.0 pp. -0.3 pp.
3.7 pp. 1.4 pp.

1.3 pp.
0.7 pp.
0.9 pp.
3.4 pp.

Disposable Income

Bottom 50%
Middle 40%
Next 9%Top 1%

2.4 pp.
1.4 pp.
2.2 pp.
6.4 pp.

70%
86%
91%
89%

2.5 pp. -1.6 pp.
0.9 pp. -0.2 pp.
1.4 pp. -0.6 pp.
4.0 pp. 0.7 pp.

1.9 pp.
0.9 pp.
1.3 pp.
3.9 pp.

68%
91%
88%
91%

2.3 pp. -1.4 pp.
0.8 pp. -0.2 pp.
1.4 pp. -0.7 pp.
3.2 pp. 0.6 pp.

1.8 pp.
0.8 pp.
1.1 pp.
3.1 pp.

75%
83%
100%
83%

3.3 pp. -2.8 pp.
0.8 pp. -0.5 pp.
1.1 pp. 0.2 pp.
4.5 pp. 2.1 pp.

1.7 pp.
0.6 pp.
1.1 pp.
3.9 pp.

Post-tax Income

Bottom 50%
Middle 40%
Next 9%Top 1%

2.4 pp.
1.7 pp.
2.6 pp.
6.7 pp.

75%
93%
89%
84%

1.9 pp. -1.2 pp.
0.9 pp. -0.2 pp.
1.3 pp. -0.6 pp.
3.9 pp. 0.2 pp.

1.5 pp.
0.8 pp.
1.1 pp.
3.9 pp.

79%
97%
97%
88%

1.8 pp. -1.0 pp.
0.7 pp. -0.2 pp.
1.2 pp. -0.7 pp.
2.9 pp. 0.2 pp.

1.5 pp.
0.7 pp.
1.0 pp.
2.9 pp.

75%
92%
75%
67%

2.5 pp. -2.0 pp.
0.7 pp. -0.5 pp.
0.9 pp. 0.0 pp.
4.1 pp. 1.4 pp.

1.4 pp.
0.5 pp.
0.9 pp.
3.8 pp.

WealthMiddle 40%
Next 9%Top 1%

4.9 pp.
3.9 pp.
5.8 pp.

82%
95%
82%

1.8 pp. 0.0 pp.
1.2 pp. -0.2 pp.
2.8 pp. -1.5 pp.

1.8 pp.
1.2 pp.
2.4 pp.

88%
97%
82%

1.7 pp. -0.2 pp.
1.1 pp. -0.1 pp.
2.5 pp. -1.4 pp.

1.7 pp.
1.0 pp.
2.1 pp.

75%
100%
75%

1.4 pp. 0.1 pp.
1.4 pp. -0.3 pp.
2.9 pp. -1.4 pp.

1.4 pp.
1.4 pp.
2.5 pp.

49

Concept

RMSE

Bias

RMSE

Bias

RMSE

Bias

Std. Err.

Notes: This table reports statistics for goodness of fit and noise of our 1-year ahead real income and real wealth growth predictions. “Std. dev.” is thestandard deviation of observed 1-year growth. “Correct sign” is the fraction of years in which we correctly predict whether income (or wealth) is growing or
falling. “Bias” is the mean difference between predicted and observed 1-year growth, “Std. Err” is the standard error of this mean, and ”RMSE” is the root
mean square error capturing total error (RM SE 2 = bias2 + std. err.2 ). “All years” includes 44 observations (growth relative to the preceding year in 1976,
1977, ..., 2019). “All years excluding tax reforms” includes 34 observations (it excludes 1987, 1988, 1991, 1992, 1993, 2001, 2003, 2012, 2013). “Recessions”
includes 12 observations, corresponding to recession years (1980, 1981, 1982, 1990, 1991, 2001, 2008, 2009) and their immediate aftermath (1983, 1992,
2002, 2010).

Appendix (for Online Publication)A

Link Between NIPA National Income Components and
DINA Concepts

Our monthly micro-files distribute BEA’s high-frequency national income accounts, startingfrom the annual distributional national accounts micro-files of Piketty, Saez and Zucman (2018).
These files are based on internationally harmonized guidelines (Blanchet et al., 2021), whichthemselves are based on the UN System of National Accounts and definitions of income components that maximize consistency with components of household wealth. The concepts used by
the Bureau of Economic Analysis for the US national accounts are largely consistent with the
System of National Accounts, but sometimes slightly differ. To clarify how the main variablein our micro-files relate to the headline aggregates of the official US national accounts, thisSection provides a mapping of the main components of national income as published by BEA(henceforth NIPA) into the main components of factor national income in our distributional
national accounts micro-files (henceforth DINA).
Recall that national income as published by BEA (NIPA Table 1.12) is decomposed into
compensation of employees, proprietors’ income, rental income of persons, corporate profits,
net interest and misc. payments, taxes on production and imports less subsidies, net business
transfer payments, and current surplus of government enterprises. The main differences with
DINA are the following:• In DINA, business transfer payments are allocated to corporate profits (for corporatetransfers), to proprietors’ income (for non-corporate businesses’ transfers) and to rental
income (for housing transfers).
• in DINA, the small current surplus of government enterprises is treated as a tax on production.
• In DINA, property taxes (business and real estate) are not treated as taxes on production
but as direct taxes (like wealth taxes would be), hence allocated to corporate profits (forproperty taxes paid by corporations), proprietors’ income (for property taxes paid by
non-corporate businesses), and rental income (for residential property taxes).
• In the NIPAs, there are various imputations of interest income (e.g., dividends received
by life-insurance companies; notional interest on underfunded pension plans) that are
re-classified in DINA for consistency with household wealth.
As a result of these and other minor other reclassifications to improve consistency with household
wealth aggregates, NIPA national income concepts map onto DINA factor income concepts asfollows (NIPA Table numbers and DINA variable names in parenthesis):36
36

The mapping for compensation of employees, corporate profits, net interest, and taxes on production andimports less subsidies is exact. The mapping for proprietors’ income and rental income is almost exact (with adiscrepancy of less than 0.1% of national income).

50

NIPA compensation of employees (Table 1.12 line 2) = DINA compensation of employees (flemp)
NIPA proprietors’ income (Table 1.12 line 9) = DINA business asset income (fkbus)+ DINA labor component of mixed income (flmil)– DINA business property taxes allocated to non-corporate businesses (non-corporate businessshare of propbustax)+ NIPA rental income included in proprietors’ income (Table 7.4.5 line 20)– NIPA net non-corporate business transfers paid (Table 1.12 line 21 – Table 1.14 line 10 –Table 7.4.5 line 19)– NIPA royalties (Table 7.9 line 7)
NIPA rental income of persons (Table 1.12 line 12) = DINA housing asset income (fkhou)- NIPA residential property taxes (Table 7.4.5 line 15 = proprestax)- NIPA mortgage interest payments (Table 7.4.5 line 18)- NIPA housing net current transfer payments (Table 7.4.5 line 19)- NIPA rental income included in proprietors’ income (Table 7.4.5 line 20)+ NIPA tenant-occupied rental income of nonprofits (Table 7.9 line 14)+ NIPA royalties (Table 7.9 line 7)
NIPA corporate profits (Table 1.12 line 13) = DINA equity asset income (fkequ)+ DINA equity income earned through pension plans (equity share of fkpen)– DINA business property taxes allocated to corporations (corporate share of propbustax)+ NIPA dividends received by government (Table 3.1 line 14)+ NIPA dividends received by nonprofits (Table 2.9 line 51)– NIPA net corporate business transfers paid (Table 1.14 line 10)– NIPA imputed interest paid by corporations on underfunded pension plans (Table 7.12 line
192)
– NIPA dividend receipts of life-insurance companies included under “imputed interest received
from life-insurance carriers” (part of Table 7.11 line 68, not separately reported).
NIPA net interest and misc. payments (Table 1.12 line 18) = DINA currency, deposits, andbond income (fkfix)+ DINA interest income earned through pension plans (interest share of fkpen)– DINA non-mortgage interest paid (fknmo)+ NIPA misc. corporate payments (Table 1.14 line 9 – Table 7.11 line 101)
+ NIPA imputed interest paid by corporations on underfunded pension plans (Table 7.12 line
192)
+ NIPA dividend receipts of life-insurance companies included under “imputed interest received
from life-insurance carriers” (part of Table 7.11 line 68, not separately reported)+ NIPA interest received by nonprofits (Table 2.9 line 50)– NIPA net interest paid by government, other than imputed for unfunded pension plans (Table
7.11 line 107 - line 50)
NIPA taxes on production and imports less subsidies (Table 1.12 line 19 – line 20) = DINA
51

sales and excise taxes (fkprk + flprl)+ DINA residential property taxes (proprestax)+ DINA business property taxes (propbustax)– DINA subsidies on production and imports (fksubk + flsubl)– NIPA current surplus of government enterprises (Table 1.12 line 25)

B

Real-Time Wealth Distributions

To construct quarterly wealth distributions, we start from the Piketty, Saez and Zucman (2018)
micro-files, last updated in February 2022,37 and re-scale the main components of household
wealth to their end-of-quarter value, using the latest quarterly release of the Financial Accounts.
The wealth components we consider are, on the asset side, tenant-occupied housing, owneroccupied housing, S-corporation equity, C-corporation equity, equity in non-corporate businesses, fixed-income assets, pension assets; and on the liability side, tenant-occupied mortgages,
owner-occupied mortgages, and non-mortgage debt. The aggregate value of all of these components are published quarterly by the Federal Reserve in the Financial Accounts of the UnitedStates, around 70 days after the end of each quarter. Following Saez and Zucman (2016), ourestimates exclude unfunded pensions (such as promises of future Social Security benefits), consumer durables (which are not assets in the System of National Accounts and thus excluded fromwealth in other countries; see United Nations, 2009), and the assets and liabilities of non-profit
institutions such as private foundations.
We then assume that distributions are stable within each of these components from one
quarter to the other and compute the implied distribution of wealth. We use real-time Forbes
data to adjust the wealth of the top 400 tax units so that it matches the Forbes estimate at
the end of each quarter. Thus our estimates of wealth inequality by construction match Forbes
at the very top, like the annual estimates constructed in Saez and Zucman (2020b) and the
SCF-year estimates of Batty et al. (2019). The Forbes estimates contain valuable information
on high-frequency wealth dynamics at the top-end because they combine public information onownership of stock in listed companies (from mandatory Securities and Exchange Commission
filings) with daily stock price changes for these companies. Close to half of the wealth of theForbes 400 is in public equity in recent years. The limitations of annual Forbes estimates (lackof public information on diversified portfolios and on debts, imperfect information on the valueof private businesses) carry over to the real-time Forbes estimates.
Additionally, we construct monthly and daily wealth distributions as follows. Starting fromthe quarterly wealth totals by components described above, we use housing and equity priceindices to update total housing wealth and total equity wealth at the monthly frequency. We do
so using the Denton (1971) method for available quarters, and extrapolating using the indexes’
growth rate in the most recent months, before the last quarter becomes available. The stock
market index that we use is the Wilshire 5000, a comprehensive index of the stock performanceof publicly traded U.S. firms. For housing prices, we use the Case-Shiller index, extrapolated
37
The micro-files are updated annually. A description of each update is available at http://gabriel-zucman.
eu/usdina, as are current micro-files, computer code, and tabulations of key findings. All vintage releases andcorresponding code are also published at this address.

52

using the Zillow house price index in the most recent months. For daily estimates, we simply
update total equity (including S-corporations equity) using the daily Wilshire 5000. For bothmonthly and daily estimates, we keep distributions constant within components and rescale thewealth of the top 400 tax units to match real-time Forbes numbers.

C

Adjustment of Employment Status and Wages

C.1

Adjustment of Employment and UI Recipients

In our monthly microfiles, we adjust at the margin whether someone (i) is employed and (ii)
receives UI benefits based on the information described in Section 4.2, namely (i) Bureau ofLabor Statistics (BLS) monthly releases of non-farm employment at the national level, (ii) the
Department of Labor’s weekly unemployment claims statistics, and (iii) labor force status byrace × education × gender × 5-year age group × marital status cell in the CPS. Here we provide
details on our adjustment procedure.
Consider an annual microfile for date t, which was constructed as a weighted average ofyear y1 (with wealth 1 − λ) and y2 (with weight λ, with y2 = 2019 and λ = 1 for the mostrecent years) to represent the average distribution over the 12-month period {tmin , . . . , tmax }.
Let us use the letter i to denote a given cell of race × education × gender × 5-year age group× marital status. Let xit ∈ [0, 1] be the employment rate of cell i, and let nit ∈ [0, 1] be thePrelative size of cell i, and let xt ≡ i nit xit be the aggregate employment rate in the microfile.
In the CPS-based employment series constructed above, let yit ∈ [0, 1] be the employment rateof cell i at time t. From the BLS-based aggregate employment series, let zt be the aggregate
employment rate at time t.
Ptmax
1
Let yi ≡ 12
t=tmin yit be the average employment rate from the CPS series over the period
it −yinotionally covered by the synthetic microfile. Let ∆yit ≡ yyi (1−ybe the relative differencei)between in the series’ values at date t and their average over {tmin , . . . , tmax }.38 We calculate
the new employment
rate for cell i in the microfile as xit + xit (1 − xit )(∆yit + zt − xt − γ)Pnit xit (1−xit )∆yitiP
where γ =is a renormalization term that ensures that we match the aggregate
i nit xit (1−xit )employment rate. This method has the desirable property of reproducing the relative changes in
employment between cells while matching aggregate employment overall and without modifying
employment within a given cell unless the CPS-based warrants such a change.39
Using this newly calculated employment rate, we adjust the number of employed individuals
within each cell as follows. If the employment rate has increased, we randomly select nonemployed units and mark them as employed to match the new rate. Conversely, if the employment rate has decreased, we randomly select employed units and mark them as non-employed.
If employment has not changed, we do nothing. We give a wage to all the observations wemarked as employed, using the procedure described in Section 4.3.
We follow the exact same procedure as above, using the unemployment rates to adjust the38

Note that we normalize by a factor yi (1 − yi ) and not yi to get relative changes since we are working withratios between 0 and 1.
39
In rare cases where the adjusted employment rate is below 0 or above 1, we truncate it and repeat theprocedure until the constraint is satisfied.

53

number of UI recipients. When we need to increase the number of UI recipients within a cell,
we select in priority individuals who are not employed.

C.2

Adjustment of Wage Income

To attribute a wage to every individual marked as employed in our monthly microfiles, we
combine (i) the marginal wage income distribution estimated by averaging QCEW and CPS
predictions (as described in Section 4.3) with (ii) the rank of each individual in the wage
distribution, which we adjust as explained below.
Estimation of the average wage rank by individual characteristic. We estimate theaverage wage rank by race × education × gender × 5-year age group × marital status in
the CPS. Since the monthly wage data is top-coded in the CPS, we use an interval-censored
regression model. In each monthly CPS file, we estimate the wage rank of each observation (or,for top-coded observations, the lower bound of the rank). We transform these ranks using thelogistic function and then run an interval-censored regression of the rank against race, education,
5-year age groups, and marital status interacted with gender, and assuming normal residuals.
We use the prediction from that regression to construct monthly series of the average wage rankfor each cell. We correct these series for seasonal variations using the X11 procedure.
Adjustment of wage income in the microfiles. In the monthly microfiles, (i) we adjusteach observation’s wage rank at the margin using the relative variations in wage rank from the
CPS, and then (ii) we attribute the wage income corresponding to their rank.
Consider an annual microfile for date t, which was constructed as a weighted average ofyear y1 (with wealth 1 − λ) and y2 (with weight λ, with y2 = 2019 and λ = 1 for the mostrecent years) to represent the average distribution over the 12-month period {tmin , . . . , tmax }.
For individuals with a positive wage, define xikt the wage rank of individual k in cell i at date t.
In the CPS-based wage rank series constructed above, let yit ∈ [0, 1] be the average wage rankof cell i at time t.
Ptmax
1
Let yi ≡ 12
t=tmin yit be the average wage rank from the CPS series over the period notionit −yially covered by the synthetic microfile. Define ∆yit ≡ yyi (1−ybe the relative difference betweeni)in the series’ values at date t and their average over {tmin , . . . , tmax }.40
We calculate the a new rank for observation i, k as xikt + xikt (1 − xikt )∆yit . If an observation
does not have an initial rank because it was previously non-employed, we directly give it the
average wage rank of its cell.41 Then we interpolate the monthly wage distribution estimatedby percentile using the generalized Pareto interpolation (Blanchet et al., 2022) and attribute toeach observation the wage income that corresponds to its rank. We adjust ranks similarly in
the UI benefits distribution and distribute UI benefits by keeping their marginal distribution
identical.
40
Note that we normalize by a factor yi (1 − yi ) and not yi to get relative changes since we are working withranks between 0 and 1.
41
We sort observations according to the new rank, and re-compute the rank from this order, to ensure thatthe distribution of ranks is uniform.

54

D

Structure of Programs and Files

The section describes the overall structure of the program files used in this paper. The programs(as well as a more detailed and up-to-date version of the following description and instructions)are available online at https://github.com/thomasblanchet/real-time-inequality.

D.1

Description of programs/code• The folder raw-data contains the raw input data, primarily in cases where direct download/scraping is not possible or not justified, or in cases where data files are heavy (likethe QCEW) and therefore downloading them over the internet every time is not desirable.
• The folder work-data contains intermediary data files that are produced by the code. It
is divided into subfolders corresponding to each code file, and no intermediary data file ismay be changed by two distinct code files.
• The folder graphs contains the all the figures (and a few tables) generated by the code.
It is divided into subfolders corresponding to each code file.
• The folder programs contains the codes (except those performing the optimal transport).
– The codes named programs/01-* handle the retrieval of the raw data, either directlyfrom the internet or from the folder raw-data.
– The codes named programs/02-* handle preliminary treatments of the data.
– The codes named programs/03-* produce the synthetic microfiles and related outputs.
– The codes named programs/04-* produce the figures and tables used for the analysis.
• The folder transport contains the code and data specifically related to the optimal transport: it is meant to run separately from the main code on a computing cluster.

D.2

License for CodeThe code is licensed under the Modified BSD License.

D.3

Instructions for Replication• Edit the $root global in programs/00-setup.do to correspond to the project’s directory.
• Run the file programs/00-run.do.
• To also run the transport, run programs until programs/02-export-transport-dina.do
and then execute the Python code under transport/transport.py preferably using
Slurm and the Shell script transport/transport.sh. Then resume the execution ofprograms/00-run.do.

55

Programs are organized as follows:• programs/01-*– The codes retrieve the data from the internet directly to the extent that it is possible.
– Unless there has been changes in the structure of the data, they should run without
any change for each update.
– In some cases, the data needs to be manually updated in the raw-data folder at each
update.
– Instruction for each file in included in 00-run.do.
• programs/02-*– The codes primarily generate data in the work-data folder that is used to generate
the synthetic microfiles.
• transport– This folder includes the data and code necessary for the optimal transport.
– These codes are meant to run on the computing cluster.
– They do not need to be updated every time (only when new tax microdata is available).
– The CSV data files included in this folder are produced by the codes before.
• programs/03-*– The codes in that folder produce the synthetic microfiles, including backtesting versions of the microfiles that use older tax data, and rescaling versions that only use
information on macro aggregates.
– The globals $date begin and $date end at the beginning of these files can be used
to generate only the files for specific months. This can be useful since not all the files
need to be constructed for every update.
– Codes in that section also produce the aggregated version of the database by groupthat is used for the website http://realtimeinequality.org/. These files are
stored in the folder website.
• programs/04-*– Use the microfiles and related outputs to create the tables and figures included in
the paper.

56

D.4

Description of Microfiles and Codebook

One output of this paper is a set of harmonized monthly micro-files in which an observation isa 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.
With these micro-files one can reproduce all the results of the paper and the statistics shownat https://realtimeinequality.org exactly. The files are available here.42
There is one file per month starting in January 1976. The files are at the adult individual(aged 20 and above) level, so the sum of weights (variable weight) adds up to the total adult
population, 248.9 million in May 2022. The variable id identifies a household (as defined by
the tax code, i.e., either a single person aged 20 or above or a married couple, in both caseswith children dependents if any). Income is individualized. To compute our benchmark equalsplit adult statistics, compute average income or wealth by id. To compute statistics at thehousehold level, take the sum of income or wealth by id and the average weight by id.
The files include socio-demographic information: age, sex, marital status (married), race,
educational attainment (educ), and the following income and wealth variables:• princ: factor national income• peinc: pretax national income• poinc: posttax national income• dispo: disposable income• flemp: compensation of employees• proprietors: proprietors’ income• rental: rental income• profits: after-tax corporate profits• corptax: corporate income tax• fkfix: interest income• govin: government interest income• fknmo: non-mortgage interest payments• prodtax: production taxes• prodsub: production subsidies• contrib: contributions to pensions, disability insurance, & unemployment• penben: pension and disability insurance benefits• uiben: unemployment insurance benefits• taxes: current taxes on income and wealth• estatetax: estate tax• othercontrib: contributions for government social insurance other than pension, unemployment, and disability• govcontrib: total contributions for government social insurance• vet: veterans’ benefits• othcash: other cash benefits• medicare: Medicare spending
42

Full link in case hyperlinks break:https://www.dropbox.com/home/SaezZucman2014/RealTime/repository/real-time-inequality/work-data/03-build-monthly-microfiles/microfiles.

57• medicaid: Medicaid spending• otherkin: other in-kind government spending• colexp: collective consumption expenditure• covidrelief: covid-19 economic impact payments• covidsub: paycheck protection program• surplus: surplus/deficit of government and private social insurance• surplus ss: surplus/deficit of government social insurance• prisupenprivate: surplus/deficit of private pension system• prisupgov: primary surplus/deficit of government• hweal: net wealth• housing tenant: tenant-occupied housing wealth• housing owner: owner-occupied housing wealth• equ scorp: equity in S-corporations• equ nscorp: equity in corporations other than S-corporations• business: equity in non-corporate businesses• pensions: pension wealth• fixed: currency, deposits, and interest-bearing assets• mortgage owner: mortgages on owner-occupied housing• mortgage tenant: mortgages on tenant-occupied housing• nonmortage: non-mortgage debt
• top400: dummy if observation is in the top 400 of households by wealth• acs: dummy if observation lives in group quarters (nursing homes, dormitories, etc.)

58

59

Quarterly GDP growth (annualized, %)Figure A1: GDP vs. GDI Growth
2020Q33020

102021Q22021Q42021Q12021Q302020Q42022Q1
2020Q1

-10
-10

0

10

20

30

Quarterly GDI growth (annualized, %)Notes: This figure compares the quarterly growth of real annualized GDP to the quarterly growth of real annualized GDI from 1947Q2 to 2022Q1.
Observations corresponding to the Covid-19 recession and recovery are shown in red triangles. The point corresponding to 2020Q2 (an outlier with around-35% annualized GDP growth and -35% annualized GDI growth) is omitted. During the recovery from Covid-19, GDI has recovered faster than GDP.

Figure A2: Comparing Wage Earnings Ranks in the Oneto-One Statistical Match
100

80

60

45deg line
mean CPS rank
90% interval

40

20

0
0

20

40

60

80

100

DINA wage percentile

Notes: This figure compares the ranks in wage earnings between the wage earnings in the DINA data andthe wage earnings in the CPS data in the one-to-one statistically matched dataset. The x-axis is the DINA
wage earnings percentile and y-axis is the average percentile in the CPS data. The figure show averages over
1976–2019. The data refers to household earnings and the sample is limited to households that earn at least afull-time Federal minimum wage. The figure shows that ranks in wage earnings are very close implying that the
one-to-one statistical match respects ranks well along the crucial wage earnings variable.

60

Figure A3: Monthly vs. Annual Employment Rates

85%

80%

75%

70%

BLS (adjusted)

1
m

1
20
20

m
20
15

1
m

1
20
10

1

m
20
05

m
20
00

1
m

1

BLS

19
95

1

m
19
90

m
19
85

1
m
19
80

m

1

65%
19
75

61

Employment rate
Working-age population (20-64)

90%

DINA

Notes: This figure depicts, for the working age population 20-64, the raw monthly employment rates from the BLS based on the monthly CPS (in dashedblue line), the annual employment rates from the Social Security tax data (in red circles), and the monthly employment rates adjusted to match annual
rates (in solid blue line). Employment rates in our monthly data is given by the blue solid series. Quantitatively, in recent decades (when the catching-up
female labor force participation trend stabilizes), raw monthly employment rates are around 75% while adjusted monthly employment rates (that track
annual levels) are 10 points higher around 85% because of part-year workers.

Figure A4: Capturing Most Recent Months Using CES instead of QCEW(b) Top 10% Wage Share
27.5

26

Top 10% wage income share in QCEW (%)

62

Bottom 50% wage income share in QCEW (%)(a) Bottom 50% Wage Share

25.5

25

27

26.5

26

25.5

24.5
2018m12 2019m6 2019m12 2020m6 2020m12 2021m6 2021m12
QCEW

Extrapolation from CES

2018m12 2019m6 2019m12 2020m6 2020m12 2021m6 2021m12
QCEW

Extrapolation from CES

Notes: This figure depicts the quality of the projection using the monthly Current Employment Statistics (CES) data to extrapolate the QCEW data forthe last 2 quarters (when the QCEW is not yet available but the CES is). The blue series depicts the bottom 50% wage share in panel (a) and the top 10%
wage share in panel (b) using the raw (de-seasonalized) QCEW data. For each quarter, the figure also depicts in dashed line the projected shares using the
CES data to extrapolate the QCEW data up to 6 months out. By definition, the projection matches the QCEW level in the last month of the quarter, andthen projects out over the next two quarters. To carry out the projection, we match each QCEW cell to three CES series. The first matches the location of
the QCEW cell as precisely as possible, the second matches it at the state level, and the third at the national level. We average the trend from these threeseries in each cell and use this average trend to extend the QCEW data in the most recent quarters. Overall, the CES projection comes pretty close to the
QCEW. The fit is better for the bottom 50% share than for the top 10% wage share.

Figure A5: Capital Income Volatility(a) Corporate Profits
140130(b) Interest
170

National income’s shareof pretax corporate profits

National income’s shareof interest income

160

Index (1976 = 100)

Index (1976 = 100)

150
120
110
100
90

140
130
120
110
100
90

80

Share ofpretax corporate profits
earned by the top 10%

70
1980q1

1990q1

2000q180
70

2010q1

1980q1(c) Rental Income

2000q1

2010q1120

110

National income’s shareof rental income

Index (1976 = 100)

Index (1976 = 100)

1990q1(d) Proprietor’s Income

400

300

Share ofinterest income
earned by the top 10%

200

National income’s shareof proprietors’ income

100

90
Share ofproprietor's income
earned by the top 10%

100
80

Share ofrental income
earned by the top 10%
070
1980q1

1990q1

2000q1

2010q1

1980q1

1990q1

2000q1

2010q1

Notes: This figure compares the volatility of the size of capital income components (corporate profits, interest,
rental income, proprietor’s income), as measured by their share of national income, and the volatility of theirconcentration, as measured by the share of each component going to the top 10% of the pretax income distribution. For example the top left panel shows the share of pretax corporate profits in national income and theshare of pretax corporate profits accruing to the 10% of adults at the top of the pretax income distribution,from 1976 to 2019. All series are depicted relative to a base 100 in 1976 to compare volatility. The figure showsthat the size of capital income components can be highly volatile at high-frequency while their concentrationis slow-moving. This lends support to our methodology which captures high-frequency changes in aggregate
capital income and assumes that their concentration is unchanged in the short run.

63

Figure A6: Predicted Growth: Our Method vs. Simplified Macro Method(a) Bottom 50%(b) Top 1%
20

Actual growth rate (%)

64

Actual growth rate (%)

10

5

15
10

Full Methodology
5 OnlyRescaling

0

-5

0
-5
-10

-10
-10

-5

0

5

10

-10

0

5

10

15

20

Predicted growth rate (%)

Predicted growth rate (%)• Full methodology-5• Rescaling only

Notes: This figure compares the quality of our baseline estimates with the quality of simpler estimates that only rescales macroeconomic aggregates without
adjusting within-component distributions. This figure depicts predicted to actual growth in average real factor income per adult (with income equally split
among married spouses) from year t to t + 1 for the bottom 50% (left panel) and the top 1% (right panel) for each year from 1976 to 2019. Actual growth isobtained using the annual distributional national account micro-data for both years t and t + 1. Predicted growth is obtained using the annual micro-data
for year t but the projected micro-data using our full methodology for t + 1 (blue full dots) or a simplified methodology that only rescales macroeconomic
aggregates without adjusting within-component distributions (red empty dots). The simplified methodology performs worse, especially in recessions years
for the bottom 50%, showing that adjusting distributions is critical to accurately project real-time inequality during recessions.

65

Average income per adult (constant)
07/2019 = 100

Figure A7: Real Disposable Income Around the Covid-19 Pandemic

180
160
140
120
100
80
2019m7

2020m1

Top 1%

2020m72021m1

Next 9%2021m7

Middle 40%

2022m12022m7

Bottom 50%

Notes: This figure shows the monthly evolution of real disposable income per adult from July 2019 to March 2022 in the full adult population (not restricting
to working-age adults). Individual adults are ranked by their factor income, and income is equally split between married spouses. The figure shows thatfor the bottom 50% of the factor income distribution, monthly disposable income was nearly twice as large in March 2021 as in July 2019 (as a result of
the third wave of Economic Impact Payments). By the spring of 2022, disposable income had returned to its pre-Covid level for all groups except for thebottom 50% for which it was about 15% higher.

66Monthly income per adult (constant USD)

Figure A8: Income of the Bottom 50% During the Covid Crisis

4,000
3,500
3,000
2,500
2,000
1,500
1,000

Post-tax national income(matching national income)Government deficit
Other government spending
Medicaid and Medicare
Post-tax disposable income
COVID stimulus checks
Regular cash transfers(net of taxes)
Subsidized pretax national income
Other benefits (net)(pensions & DI, minus contributions)
Unemployment insurance benefits
Subsidized factor national income
Paycheck Protection Program
Factor national income(matching national income)

500
0
2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

Notes: This figure decomposes the average real monthly posttax national income of the bottom 50% of the working-age population ranked by factor income
from July 2019 to May 2022. This is the same Figure as Figure 9 but adding Medicaid and Medicare, other government spending, and the government
deficit, so as to go all the way to posttax national income. See notes to Figure 9.

Figure A9: Share of Black adults in the Full Population vs. Top 10%

1267Share of black population (%)

Full population

10

8
Top 10% wage earners

6
Top 10% pretax income

4

Top 10% wealth201989q1

1995q1

2001q1

2007q1

2013q1

2019q1

Notes: This figure shows the fraction of Black adults in the full adult population, in the top 10% of the wage distribution, 10% of the pretax income
distribution, and top 10% of the wealth distribution. The series start in 1989, the first year of the Survey of Consumer Finances.

68

College premium(% increase between no college and some college)

Figure A10: College Premium

250

Pretax capital income

200

Wealth150
Pretax income

100

Labor income(working-age population)50

0
1990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1

Notes: This figure shows differences in income and wealth between people with no college education and people with at least some college education. The
unit of observation is the individual adult. The labor income line restricts to the working-age population (individuals aged 20 to 64); other series include
the entire adult population. The series start in 1989, the first year of the Survey of Consumer Finances.

69

Pretax income(constant USD, pre-recession peak = 100)

Figure A11: Income Dynamics by Gender: Covid Recession vs. Great Recession
Covid Recession (2020-2021)

Great Recession (2007-2016)

105

100

95

90

85
2020q1

2020q32021q12021q32022q1

Men

2007q3

2010q32013q3

2016q3

Women

Notes: This figure shows the evolution of average pretax national income by gender during the Covid recession and its aftermath (left panel) and the GreatRecession and its aftermath (right panel). Income is normalized to 100 in the quarter preceding each recession.

Table A1: Prediction Errors for Growth Rates of Income & Wealth (2 Years)
All years

Excl. tax reforms

Recessions

BracketStd. Dev.

Correct sign

Std. Err.

Correct sign

Std. Err.

Correct sign

Factor Income

Bottom 50%
Middle 40%
Next 9%Top 1%

6.6 pp.
2.3 pp.
3.0 pp.
9.1 pp.

91%
93%
98%
93%

2.4 pp. -0.7 pp.
1.3 pp. -0.2 pp.
1.2 pp. -0.6 pp.
5.1 pp. -1.0 pp.

2.3 pp.
1.3 pp.
1.0 pp.
5.0 pp.

91%
97%
100%
94%

2.4 pp. -1.0 pp.
1.0 pp. -0.1 pp.
1.1 pp. -0.6 pp.
3.8 pp. -1.1 pp.

2.2 pp.
1.0 pp.
0.9 pp.
3.6 pp.

92%
83%
92%
83%

1.4 pp. -0.4 pp.
1.6 pp. -1.2 pp.
0.9 pp. -0.5 pp.
4.8 pp. 1.5 pp.

1.4 pp.
1.0 pp.
0.8 pp.
4.5 pp.

Pretax Income

Bottom 50%
Middle 40%
Next 9%Top 1%

4.9 pp.
2.4 pp.
3.7 pp.
9.4 pp.

88%
95%
98%
88%

2.5 pp. -1.2 pp.
1.3 pp. -0.1 pp.
1.2 pp. -0.4 pp.
5.2 pp. -1.0 pp.

2.2 pp.
1.3 pp.
1.1 pp.
5.1 pp.

88%
100%
97%
94%

2.5 pp. -1.1 pp.
1.1 pp. -0.0 pp.
1.1 pp. -0.5 pp.
3.8 pp. -1.1 pp.

2.3 pp.
1.1 pp.
1.0 pp.
3.7 pp.

83%
83%
92%
75%

2.2 pp. -1.8 pp.
1.4 pp. -1.1 pp.
0.7 pp. 0.1 pp.
4.9 pp. 1.5 pp.

1.4 pp.
0.9 pp.
0.7 pp.
4.6 pp.

Disposable Income

Bottom 50%
Middle 40%
Next 9%Top 1%

3.8 pp.
2.2 pp.
3.3 pp.
9.3 pp.

88%
95%
93%
93%

3.5 pp. -2.2 pp.
1.3 pp. -0.2 pp.
1.6 pp. -0.0 pp.
6.4 pp. 0.3 pp.

2.7 pp.
1.2 pp.
1.6 pp.
6.3 pp.

91%
94%
91%
97%

3.1 pp. -1.9 pp.
1.1 pp. -0.2 pp.
1.4 pp. -0.2 pp.
4.4 pp. -0.0 pp.

2.5 pp.
1.1 pp.
1.4 pp.
4.4 pp.

75%
92%
83%
83%

4.8 pp. -4.2 pp.
1.1 pp. -0.5 pp.
1.5 pp. 1.2 pp.
6.7 pp. 1.9 pp.

2.4 pp.
1.0 pp.
0.9 pp.
6.5 pp.

Post-tax Income

Bottom 50%
Middle 40%
Next 9%Top 1%

3.8 pp.
2.9 pp.
4.1 pp.
10.0 pp.

88%
93%
95%
91%

2.7 pp. -1.6 pp.
1.2 pp. -0.2 pp.
1.4 pp. -0.2 pp.
6.2 pp. -0.4 pp.

2.2 pp.
1.2 pp.
1.4 pp.
6.2 pp.

97%
97%
94%
94%

2.3 pp. -1.2 pp.
1.0 pp. -0.2 pp.
1.2 pp. -0.4 pp.
4.3 pp. -0.7 pp.

2.0 pp.
1.0 pp.
1.2 pp.
4.3 pp.

92%
83%
83%
83%

3.5 pp. -2.9 pp.
0.9 pp. -0.5 pp.
1.0 pp. 0.7 pp.
6.0 pp. 1.2 pp.

1.9 pp.
0.8 pp.
0.7 pp.
5.9 pp.

WealthMiddle 40%
Next 9%Top 1%

8.2 pp.
6.6 pp.
9.9 pp.

88%
93%
93%

2.1 pp. 0.6 pp.
1.9 pp. 0.2 pp.
4.7 pp. -3.1 pp.

2.0 pp.
1.9 pp.
3.5 pp.

97%
97%
94%

1.8 pp.
1.7 pp.
4.1 pp.

1.8 pp.
1.7 pp.
3.1 pp.

92%
83%
83%

1.8 pp. 0.5 pp.
2.3 pp. 0.1 pp.
4.5 pp. -2.8 pp.

1.8 pp.
2.3 pp.
3.5 pp.

70

Concept

RMSE

Bias

RMSE

Bias

0.1 pp.
0.2 pp.
-2.7 pp.

RMSE

Bias

Std. Err.

Notes: This table report statistics for goodness of fit and noise of our 2-year ahead real income and real wealth growth predictions. See notes to Table 2.