Real-Time Inequality
Thomas Blanchet

Emmanuel Saez

Gabriel Zucman

University of California, Berkeley

November 2022

Motivation: Growth vs. Inequality Statistics Timeliness
National Accounts provide timely aggregate macro-economic data
I Estimates of US quarterly GDP are released a month after each quarter
I Estimates of US monthly personal income released a month after each month
I Early estimates are subsequently revised but drive the discussion
In contrast, inequality statistics are annual and lag behind
I Official US Poverty and inequality statistics for 2022 will be published in Sept 2023I Income Tax Statistics for 2022 to measure top incomes published in Sept 2024
⇒ Growth stats get a lot more attention than inequality in public debate
Our goal: produce distributional national accounts in real-timeGoal: Produce real-time inequality data
There is a lot of real-time data relevant for inequality:
I Jobs and earnings data from employers’ monthly and quarterly surveys.
I Unemployment, labor force, wage earnings from CPS monthly surveys.
I Components of GDP (e.g. capital vs. labor share)
Our goal: Mobilize all public data to build real-time inequality statistics
I To distribute all national income from national accounts across groups
I High frequency monthly/quarterly but “annualized” like national accounts
I Early estimates subsequently revised when more data available
Prototype: This paper is a first attempt to be further refined/improved

Contribution: Monthly microdata matching macro totals
This project: prototype real-time monthly distributional national accounts:
I Output: Monthly synthetic microdata which distributes all of national income
and wealth (and their components) to individuals, matching macro totals
I Can be used to compute a wide range on inequality and growth statistics
I Following a recession, this can be used to compute “distributional output gaps:”which groups of the population are below their pre-crisis income level or trend
I Incorporate all taxes and government transfers → reveal how national income isdistributed and redistributed month-to-month
Estimates available on realtimeinequality.org within a few hours of thepublication of the national accounts:

Our Website realtimeinequality.org
Who Benefited from Growth Last Quarter?

Related Literature: COVID has boosted the real-time inequality agenda
I Atlanta Fed US Monthly Wage Tracker based on Daly et al. (2011) uses
longitudinal CPS to track wage changes by quartile, gender, education but
excludes non-workers, only wages, and cannot capture top 5%
I Fed Distributional Financial Accounts based on Batty et al. (2019)
interpolates SCF triennal wealth surveys using quarterly financial accounts.
I US BEA has explored feasibility of quarterly distribution of personal income(Fixler, Gindelsky, and Kornfeld 2021): conclude that just rescaling
macro-aggregate doesn’t work well in recessions.
I Monthly US Poverty Rates from Parolin et al. (2022) uses monthly CPS andSIPP to produce monthly poverty rates in real-time, focuses only on bottom
I Outside US: Many studies trying to follow COVID crisis in real time

Methodology and Validation

Methodological Overview
Starting point: annual tax-based Distributional National Accounts microdata ofincome and wealth by Piketty, Saez, Zucman (2018) continuously updated/improved:
I One-to-one statistical match with surveys (March CPS, SCF, ACS) to add
demographics (gender, race, age)
I Monthly files using moving average of current and adjacent yearly microdata.
I Use 2019 (latest pre-Covid year) file as basis for 2020, 2021, 2022.

How we move to high frequency:
I Capital income/wealth: rescaling to macro aggregates. Works because
aggregate volatility much bigger than concentration volatility.
I Labor income: distribution estimated monthly using the Quarterly Census ofEmployment and Wages and monthly CPS.

Statistical Matching Using Optimal Transport
Statistical matching between the public use tax micro data, the annual Current
Population Survey (CPS), the American Community Survey (ACS), and the Survey ofConsumer Finances (SCF) to add demographics
I Optimal transport to match observations “one-to-one” using common variables.
I Resulting micro dataset respects joint distribution of all variables from eachdataset.
I Much better than one sided matching or matching without replacement.
I Much more computationally demanding but doable today.
I Obviously not as good as comprehensive admin socio-economic (confidential)
data: cannot provide reliable joint variable distributions unless variables are jointly
included in one of original databaseCapital Incomes: We Simply Rescale to Macroeconomic AggregatesThis Works Because Aggregate Profits Are Volatile, Their Concentration is Not
140Index (1976 = 100)130

National income’s shareof pretax corporate profits

120110100
9080

Share ofpretax corporate profits
earned by the top 10%

70
1980q1

1990q1

2000q1

2010q1

“Annualized” Monthly and Quarterly Micro-Data

I Our monthly/quarterly data are directly comparable to annual data in both levels
and distribution
I National Accounts also present monthly and quarterly data on an annualized basis
I True monthly/quarterly data have more inequality that gets smoothed out atannual frequency
I Our method bypasses this issue which is good for 2 reasons:
I Limited longitudinal high frequency data
I Our monthly, quarterly, and annual data are all directly comparable

I Our monthly statistics tell us how annual statistics would look like if monthly
distributions stayed the same for 1 year

Data for Estimating High Frequency Wage Inequality
I Quarterly Census of Employment: QCEW is an exhaustive administrative
dataset (nearly all wage earners covered).
I Published quarterly with monthly data.
I Data by (6-digit NAICS industry code) × (county) × (ownership sector).
I ≈ 1,000,000 observations each month.
I Can be used to infer complete wage distribution including top 1% (Lee, 2020).

I Current Employment Statistics: For most recent months, we use the lessdetailed but timely monthly CES
I Monthly Current Population Survey: Monthly CPS is an individual survey onlabor force, unemployment, weekly wage earnings, captures well the bottom 95%

Adjusting Wage Earnings at Monthly Frequency
Extensive margin: Adjust employment rates by race×education×gender×age cells
using monthly CPS and convert into “annualized employment rates”
Intensive margin:
I Remove seasonality from monthly QCEWI Adjust each QCEW wage percentile to match annual tax data wage percentile(using a time invariant linear regression for each percentile)
I For most recent months, use CES to predict QCEW (that lags 1-2 quarters)
I Use similar methodology with monthly CPS
I Take average of CPS and QCEW for each percentile below 95th and only QCEW
for top 5 percentiles

Adjusting Monthly Employment Rates to “Annualized” Employment Rates

85%80%75%70%

1m1202015m

1BLS (adjusted)

202010m

1m

12005m

100
2095

m

1BLS

191990m

1m

185m
1980
1975m

165%

19

Employment rate
Working-age population (20-64)

90%DINA

Rise of Top 1% Wage Income Share (among workers) in Tax Data
Annual Tax Data

Top 1% share of wages (%)

1210864201980m1

1990m1

2000m1

2010m1

2020m1

Top 1% Wage Income Share in QCEW parallels Tax Data series
Annual Tax Data

Top 1% share of wages (%)

12QCEW
NAICS(current version)
SIC(old version)

1086420
1980m1

1990m1

2000m1

2010m1

2020m1

Linearly Adjusted QCEW Captures This Rise Remarkably Well
Annual Tax Data

Top 1% share of wages (%)

12QCEW
NAICS(current version)
SIC(old version)
Adjusted

108
6420
1980m1

1990m1

2000m1

2010m1

2020m1

Bottom 50% wage income share in QCEW (%)

Timely CES predicts QCEW Results Well for Bottom 50%
26

25.5

2524.52019m1

2019m7

2020m1QCEW

2020m72021m12021m7Extrapolation from CESTimely CES predicts QCEW Results Decently for Top 10%
Top 10% wage income share in QCEW (%)

27.5

2726.52625.52019m1

2019m7

2020m1QCEW

2020m72021m12021m7Extrapolation from CES

We Validate Our Methodology by Applying It Retrospectively
Our Methodology Successfully Predicts Annual Growth Rates
Top 1%

Bottom 50%201984
2017

5

Actual growth rate (%)

Actual growth rate (%)

100
2012-51988
1987

152012101979

51994

01993

-51991-10-10-10-50

510-10Predicted growth rate (%)-5051015

Predicted growth rate (%)
Shares20

Just rescaling on macro-aggregates does not work nearly as well

Actual growth rate (%)

105Full Methodology
Rescaling Only

0-5-10-10-505

Predicted growth rate (%)

10

The Distribution and Redistribution ofNational Income During Covid

COVID: All Groups Recover their Pre-tax Income Within 20 Months

Average income per adult (constant)
07/2019 = 1001101009080

Top 1%
Next 9%Middle 40%
Bottom 50%70
2019m7

2020m1

2020m72021m12021m72022m12022m7

Role of Government Transfers: COVID-specific programs
Microdata makes is easy to model new government programs
I Paycheck Protection Program (PPP): loans to keep businesses afloat (70% wentto owners, 30% to workers according to Autor et al. 2022)
I Extra Unemployment benefits:
I Extended duration
I Extra $600 per week from March 2020 to July 2020
I Extra $300 per week from January to September 2021

I More generous Child Tax Credit and Earned Income Tax Credit in 2021 only
I Stimulus checks of $1200 in April 2020, $600 in January 2021, $1400 in March
2021 for bottom 90%

Bottom 50% Incomes (aged 20-64): The Role of Government Transfers

Monthly income per adult (constant USD)4,000
3,500
3,000
2,500
2,000
1,500
1,00050002019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

Factor national income(matching national income)Bottom 50% Incomes (aged 20-64): The Role of Government Transfers

Monthly income per adult (constant USD)

4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

Subsidized factor national income
Paycheck Protection Program
Factor national income(matching national income)Bottom 50% Incomes (aged 20-64): The Role of Government Transfers

Monthly income per adult (constant USD)

4,000
3,500
3,000
2,500
2,000
1,500
1,000
5000
2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

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)Bottom 50% Incomes (aged 20-64): The Role of Government Transfers

Monthly income per adult (constant USD)

4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
02019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

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)The Post-Covid Recovery:
Is Real Labor Income Growing?
For Whom?

Employment to working-age population ratio (%)

Employment Rate (age 20-64) Back to Pre-COVID Levels by mid-2022
79
78
77
76
75
74
73
72
71
70
69
68
67
2019m1 2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7

The Tight Labor Market Benefits Low-Wage Workers
Real Labor Income Per Working-Age (20–64) Adult
120

Average real labor incomeIndex (2019m1 = 100)

Top 1%

110
4th quartile100

3rd quartile90

2nd quartile

80
2019m1

2019m7

2020m1

2020m72021m12021m72022m12022m7

Employment to working-age population ratio (%)

After the Great Recession, Employment Took 8 Years to Recover
79
78
77
76
75
74
73
72
71
70
69
68
67
2008m1

2010m1

2012m1

2014m1

2016m1

2018m1

Low-Wage Workers Recovered Very Slowly from Great Recession
Real Labor Income Per Working-Age (20–64) Adult

Average real labor incomeIndex (2019m1 = 100)

120

110
4th quartile
Top 1%

100
3rd quartile90

2nd quartile

80
2008m1 2010m1 2012m1 2014m1 2016m1 2018m1

COVID vs. Great Recession Recovery
Annual Real Growth In Labor Income by Percentile
Annualized real labor income growth (%)

432
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)

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

Real Wealth Growth per Adult During and After COVID
140130120110100

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

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

Crisis and Recovery by Race and GenderA Comprehensive Estimate of Black/White Economic Disparities
100
Black average / White average (%)

908070Labor income (working-age population)

60
50
4030201001990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1

A Comprehensive Estimate of Black/White Economic Disparities
100
Black average / White average (%)

908070Labor income (working-age population)

60504030

Wealth201001990q1 1995q1 2000q1 2005q1 2010q1 2015q1 2020q1

A Comprehensive Estimate of Black/White Economic Disparities
100
Black average / White average (%)

908070Labor income (working-age population)

6050403020

WealthPretax capital income

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

A Comprehensive Estimate of Black/White Economic Disparities
100
Black average / White average (%)

908070Labor income (working-age population)

6050Pretax income

403020WealthPretax capital income

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

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

Recessions and Recovery by Race and Ethnicity
COVID Recession (2020-2022)

Great Recession (2007-2016)

110

105

100

9590
2020q1

2020q32021q12021q32022q1Non-hispanic whites

2007q3Blacks

2010q32013q3

Hispanics

2016q3Pretax income(constant USD, pre-recession peak = 100)

A Covid “Shecession”? Recessions and Recovery by Gender
Covid Recession (2020-2021)

Great Recession (2007-2016)

105

100

9590852020q1

2020q32021q12021q32022q1

Men

2007q3

Women

2010q32013q3

2016q3

Conclusion

I It’s possible to track inequality in near real-time.
I Estimates based solely on public data.
I Prototype to be improved and run by government agencies down the road.
I Stark contrast between the recoveries from the last two recessions.
I realtimeinequality.org
I Updated daily for wealth, quarterly for income.