income inequality and income risk - wordpress.com...fatih guvenen (myths vs. facts) myths vs. facts...

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.

Fatih Guvenen (Myths vs. Facts) Myths vs. Facts 1 / 41

Not everything that counts can be counted...

... and not everything that can be counted counts.

Sign on Einstein’s office wall at Princeton

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.


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)


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


Six Myths

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


Risk and Inequality

Over Time

Trends in Income Risk

Myth #1:

The volatility of income shocks...

has increased significantly over the past 40 years.


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))


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


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):


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

Risk and Inequality Over the

Business Cycle

Business Cycle Variation in Shocks

Myth #2:

The variance of idiosyncratic income shocks

rises substantially during recessions.


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


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).


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


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


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


Kelley’s

Skew

nessMea

sure

ofyt+k−

yt,k=

1,5

Expansion

Recession


Is Business Cycle Risk Predictable?

Myth #3:

Business cycle risk is mostly ex-post risk


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


Mea

nLogIn

comeChangeDuringRecession

1979-83

1990-92

2000-02

2007-10


Business Cycle Risk for Top 1%

Myth #4:

The top 1% are largely immune

to the pain of business cycles.


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


Mea

nLogIn

comeChangeDuringRecession

1979-83

1990-92

2000-02

2007-10


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


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%


Risk and Inequality Over the

Life Cycle

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


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

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))


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


Kurtosis

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)


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



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



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


Skewness

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


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)


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.


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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