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Income Inequality and Income Risk:
Old Myths vs. New Facts1
Fatih Guvenen
University of Minnesota and NBER
JDP Lecture Series on “Dilemmas in Inequality”at Princeton University, Fall 2013
(Updated: October 2014)
1This lecture summarizes research conducted jointly with Serdar Ozkan, Fatih Karahan, Greg Kaplan, and Jae Song.
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Not everything that counts can be counted...
... and not everything that can be counted counts.
Sign on Einstein’s office wall at Princeton
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Motivation
Nature of income inequality/risk: critical for many questions insocial sciences.
Survey-based US panel datasets have important limitations:
I small sample size
I large measurement (survey-response) error
I non-random attrition
I top-coding, etc.
=) myths about income inequality and income risk.
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Data: SSA Master Earnings File
Population sample: Universe of all individuals with a U.S. SocialSecurity number
Currently covers 35 years: 1978 to 2012 (soon to be updated with2013 data)
Basic demographic info: sex, age, race, place of birth, etc.
Earnings data:
I Salary and wage earnings from W-2 form, Box 1
F No topcodingF Unique employer identifier (EIN) for each job held in a given year.F 4–5 digit SIC codes for each employer
I Self-employment earnings from IRS tax forms (Schedule SE)
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Our Sample
10% Representative panel of US males from 1978 to 2012
Salary and wage workers (from W-2 forms)
I exclude self-employed (data top coded before 1994)
I Focus on workers aged 25–60
I Key Advantages:
F Very large sample size (200+ million individual-year observations)
F No survey response error (W-2 forms sent from employer directly toSSA)
F No sample attrition
F No top-coding
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Six Myths
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Six Myths
1 Myth #1: Income risk has been trending up in the past 40 years.
2 Myths #2 and #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
3 Myth #4: Top 1% are largely immune to business cycle risk
4 Myths #5 and #6: Income over the life cycle can be modeled as:
(A polynomial in age... + ...a random walk process...) withGaussian shocks
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Risk and Inequality
Over Time
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Trends in Income Risk
Myth #1:
The volatility of income shocks...
has increased significantly over the past 40 years.
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Upward Trend in Income Risk: Background
This conclusion has been reached by virtually all papers that usePSID data.
Moffitt and Gottschalk (1995) documented it first in a now-famouspaper, and it has been confirmed by a large subsequent literature.
The fact that this finding is robust across various PSID studiessuggests that it is more about the data set rather than themethodology.
Here is how the basic result looks like (from Moffitt-Gottschalk’supdated paper: Moffitt and Gottschalk (2012))
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Myth #1: Upward Trend in Income Risk
Figure 10: Permanent, Transitory, and Total Variances for those 30-39 with Education Greater than 12
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.451970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
year
permanenttransitorytotal
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Fact #1: No Upward Trend in Volatility
When researchers turned to administrative datasets, such as theone described above, the opposite conclusion emerges robustly
See, e.g., Congressional Budget Office (2007); Sabelhaus andSong (2010); Guvenen et al. (2014b)
In fact, looking by age, gender, and industry groups, we see thesame pattern of flat or declining volatility in all groups (with theexception of agriculture, which is very small).
Here is the basic figure from Guvenen et al. (2014b):
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Fact #1: No Upward Trend in Volatility
Year
StandardDeviation
1980 1985 1990 1995 2000 2005 20100.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
yt − yt−1
yt − yt−5
Source: Guvenen, Ozkan, Song (JPE, 2014)Fatih Guvenen (Myths vs. Facts) Myths vs. Facts 13 / 41
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Risk and Inequality Over the
Business Cycle
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Business Cycle Variation in Shocks
Myth #2:
The variance of idiosyncratic income shocks
rises substantially during recessions.
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Myth #2: Countercyclical Shock Variances
−0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8
Density
yt+k
− yt
Recession
Expansion
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Countercyclical Variance
Constantinides and Duffie (1996): countercyclical variance cangenerate interesting and plausible asset pricing behavior.
Existing indirect parametric estimates find a tripling of the varianceof persistent innovations during recessions (e.g., Storesletten et al(2004)).
Our direct and non-parametric estimates show no change invariance over the cycle. See the next figure.
The following figures on Myths 2 to 4 are from Guvenen et al.(2014b).
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Fact #2: No Change in Variance
0 10 20 30 40 50 60 70 80 90 100
0.8
1
1.2
1.4
1.6
1.8
2
Percentiles of 5-Year Average Income Distribution (Y t−1)
Dispersionin
Recession/Dispersionin
Expansion
Std. dev. ratio
L90−10 ratio
Storesletten et al (2004)’s benchmark estimate: 1.75
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Fact #2: Countercyclical Left-Skewness
−0.6 −0.5 −0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5
Density
yt+k
− yt
Expansion
Recession
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Fact #2: Countercyclical Skewness
0 10 20 30 40 50 60 70 80 90 100−0.4
−0.3
−0.2
−0.1
0
0.1
Percentiles of 5-Year Average Income Distribution (Y t−1)
Kelley’s
Skew
nessMea
sure
ofyt+k−
yt,k=
1,5
Expansion
Recession
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Is Business Cycle Risk Predictable?
Myth #3:
Business cycle risk is mostly ex-post risk
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Fact #3: Business Cycle Risk is Predictable
0 10 20 30 40 50 60 70 80 90 100
−0.3
−0.25
−0.2
−0.15
−0.1
−0.05
0
0.05
0.1
Percentiles of 5-Year Average Income Distribution (Y t−1)
Mea
nLogIn
comeChangeDuringRecession
1979-83
1990-92
2000-02
2007-10
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Business Cycle Risk for Top 1%
Myth #4:
The top 1% are largely immune
to the pain of business cycles.
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Fact #4: The “Suffering” of the Top 1%
0 10 20 30 40 50 60 70 80 90 100−0.35
−0.3
−0.25
−0.2
−0.15
−0.1
−0.05
0
0.05
0.1
Percentiles of 5-Year Average Income Distribution (Y t−1)
Mea
nLogIn
comeChangeDuringRecession
1979-83
1990-92
2000-02
2007-10
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Fact #4: 1-Year Income Growth, Top 1%
1980 1985 1990 1995 2000 2005 2010−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
Year
Log1-Y
earChangein
MeanIn
comeLevel
Top 0.1%
Top 1%
P50
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Fact #4: 5-Year Income Growth, Top 0.1%
1980 1985 1990 1995 2000 2005
−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
0.2
0.3
0.4
0.5
Year
Log5-Y
earChangein
MeanIn
comeLevel
Top 0.1%
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Risk and Inequality Over the
Life Cycle
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Lifecycle Profile of Income
Myth #5:
A reasonable specification of income over the life cycle consists of:
1 A common polynomial in age... +
2 ...a random walk process...
3 with Gaussian shocks
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Myth #5: Lifecycle Profile of Income
Age25 30 35 40 45 50 55 60
LogAverageIncome
9.6
9.8
10
10.2
10.4
10.6
127%
rise
Source for the rest of this section: Guvenen et al. (2014a)Fatih Guvenen (Myths vs. Facts) Myths vs. Facts 29 / 41
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Fact #5: Lifecycle Profiles of Income
0 10 20 30 40 50 60 70 80 90 100−1
−0.5
0
0.5
1
1.5
2
2.5
3
Percentiles of Lifetime Income Distribution
log(Y
55)
–log(Y
25)
Top 1%: 15−fold increase!
Random Walk Model
Income Growth from Pooled Regression
HIP (Guvenen (2009))
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Distribution of Income Shocks
Myth #6:
It is OK to model income growth...
...as a lognormal distribution
=) it is OK to assume...
...zero skewness and no excess kurtosis
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Kurtosis
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Myth #6: Lognormal Histogram of yt+1 � yt
−3 −2 −1 0 1 2 30
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
yt+1− yt
Den
sity
N(0,0.432)
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Fact #6: Excess Kurtosis
−3 −2 −1 0 1 2 30
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
yt+1− yt
Den
sity
N(0,0.432)
US Data, Ages 35-54, P90 of Y
Kurtosis: 28.5
Kurtosis: 3.0
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Fact #6: Excess Kurtosis
Prob(|yt+1 � yt | < x)x # Data N(0, 0.432)
0.05 0.39 0.080.10 0.57 0.160.20 0.70 0.300.50 0.80 0.591.00 0.93 0.94
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Fact #6: Excess Kurtosis
0 10 20 30 40 50 60 70 80 90 100
4
8
12
16
20
24
28
32
Percentiles of Past 5-Year Average Income Distribution
Kurtosisof(y
t+1−
yt)
Ages 25-29
Ages 30-34
Ages 35-39
Ages 40-54
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Skewness
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Fact #6: Skewness of yt+1 � yt
0 10 20 30 40 50 60 70 80 90 100−3
−2.5
−2
−1.5
−1
−0.5
0
Percentiles of Past 5-Year Average Income Distribution
Skew
nessof(y
t+1−
yt)
Age=25-34Age=35-44Age=45-49Age=50-54
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Double Pareto Tails of Earnings Growth
yt+1 − yt-3 -2 -1 0 1 2 3
Lo
g D
ensi
ty
-8
-6
-4
-2
0
2US Data
Normal (0.0.482)
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Conclusions
For too long, we have played the “blind men and the elephant.”
But there is hope: some fantastic datasets are becoming moreaccessible.
Challenges: Data on consumption.. still very limited.
We hope these new (or revised) facts will feed back into theoryand policy work.
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References
Congressional Budget Office, “Trends in Earnings Variability over thePast 20 Years,” Technical Report, Congressional Budget Office 2007.
Guvenen, Fatih, Fatih Karahan, Serdar Ozkan, and Jae Song,“What Do Data on Millions of U.S. Workers Say About Labor IncomeRisk?,” Working Paper, University of Minnesota 2014., Serdar Ozkan, and Jae Song, “The Nature of CountercyclicalIncome Risk,” Journal of Political Economy, 2014, 122 (3), 621–660.
Moffitt, Robert A. and Peter Gottschalk, “Trends in the Variances ofPermanent and Transitory Earnings in the U.S. and Their Relation toEarnings Mobility,” Boston College Working Papers in Economics444, Boston College July 1995.
Moffitt, Robert and Peter Gottschalk, “Trends in the TransitoryVariance of Male Earnings: Methods and Evidence,” Winter 2012, 47(2), 204–236.
Sabelhaus, John and Jae Song, “The Great Moderation in MicroLabor Earnings,” Journal of Monetary Economics, 2010, 57,391–403.
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