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Income Inequality and Income Risk:
Old Myths vs. New Facts1
Fatih Guvenen
University of Minnesota and NBER
12th ECB/CEPR Labour Market WorkshopFrankfurt, Germany
December 12, 2016
1This lecture summarizes research conducted jointly with Jae Song, Serdar Ozkan, Fatih Karahan, Greg Kaplan, Nick
Bloom, Till von Wachter, Luigi Pistaferri, David Price, Sergio Salgado, David Domeij, Rocio Madera, Chris Busch, and Priscilla
Fialho.
Fatih Guvenen Myths vs. Facts 1 / 66
Blind Men and the Elephant
Fatih Guvenen Myths vs. Facts 2 / 66
Motivation
I Nature of income inequality/risk: critical for many questions insocial sciences.
I Survey-based US panel datasets have important limitations:
– small sample size
– large measurement (survey-response) error
– non-random attrition
– top-coding, etc.
I =) myths about income inequality and income risk.
Fatih Guvenen Myths vs. Facts 3 / 66
Motivation
I Nature of income inequality/risk: critical for many questions insocial sciences.
I Survey-based US panel datasets have important limitations:
– small sample size
– large measurement (survey-response) error
– non-random attrition
– top-coding, etc.
I =) myths about income inequality and income risk.
Fatih Guvenen Myths vs. Facts 3 / 66
Motivation
I Nature of income inequality/risk: critical for many questions insocial sciences.
I Survey-based US panel datasets have important limitations:
– small sample size
– large measurement (survey-response) error
– non-random attrition
– top-coding, etc.
I =) myths about income inequality and income risk.
Fatih Guvenen Myths vs. Facts 3 / 66
Data: SSA Master Earnings File
I Population sample: Universe of all individuals with a U.S. SocialSecurity number
I Currently covers 36 years: 1978 to 2013
I Basic demographic info: sex, age, race, place of birth, etc.
I Earnings data:
– Salary and wage earnings from W-2 form, Box 1
I No topcodingI Unique employer identifier (EIN) for each job held in a given year.I 4–5 digit SIC codes for each employer
– Self-employment earnings from IRS tax forms (Schedule SE)
Fatih Guvenen Myths vs. Facts 4 / 66
Data: SSA Master Earnings File
I Population sample: Universe of all individuals with a U.S. SocialSecurity number
I Currently covers 36 years: 1978 to 2013
I Basic demographic info: sex, age, race, place of birth, etc.
I Earnings data:
– Salary and wage earnings from W-2 form, Box 1
I No topcodingI Unique employer identifier (EIN) for each job held in a given year.I 4–5 digit SIC codes for each employer
– Self-employment earnings from IRS tax forms (Schedule SE)
Fatih Guvenen Myths vs. Facts 4 / 66
Data: SSA Master Earnings File
I Population sample: Universe of all individuals with a U.S. SocialSecurity number
I Currently covers 36 years: 1978 to 2013
I Basic demographic info: sex, age, race, place of birth, etc.
I Earnings data:
– Salary and wage earnings from W-2 form, Box 1
I No topcodingI Unique employer identifier (EIN) for each job held in a given year.I 4–5 digit SIC codes for each employer
– Self-employment earnings from IRS tax forms (Schedule SE)
Fatih Guvenen Myths vs. Facts 4 / 66
One Baseline Sample
I Individuals: 10% representative panel of US population from1978 to 2013
I Salary and wage workers (from W-2 forms)
– exclude self-employed (data top coded before 1994)
– Focus on workers aged 25–60
– Key Advantages:
I Very large sample size (400+ million individual-year observations)
I No survey response error (W-2 forms sent from employer directly toSSA)
I No sample attrition
I No top-coding (earnings measure includes exercised stock optionsand vested restricted stock units)
I Firms: Full population (100%) of US firms.
Fatih Guvenen Myths vs. Facts 5 / 66
One Baseline Sample
I Individuals: 10% representative panel of US population from1978 to 2013
I Salary and wage workers (from W-2 forms)
– exclude self-employed (data top coded before 1994)
– Focus on workers aged 25–60
– Key Advantages:
I Very large sample size (400+ million individual-year observations)
I No survey response error (W-2 forms sent from employer directly toSSA)
I No sample attrition
I No top-coding (earnings measure includes exercised stock optionsand vested restricted stock units)
I Firms: Full population (100%) of US firms.
Fatih Guvenen Myths vs. Facts 5 / 66
One Baseline Sample
I Individuals: 10% representative panel of US population from1978 to 2013
I Salary and wage workers (from W-2 forms)
– exclude self-employed (data top coded before 1994)
– Focus on workers aged 25–60
– Key Advantages:
I Very large sample size (400+ million individual-year observations)
I No survey response error (W-2 forms sent from employer directly toSSA)
I No sample attrition
I No top-coding (earnings measure includes exercised stock optionsand vested restricted stock units)
I Firms: Full population (100%) of US firms.
Fatih Guvenen Myths vs. Facts 5 / 66
One Baseline Sample
I Individuals: 10% representative panel of US population from1978 to 2013
I Salary and wage workers (from W-2 forms)
– exclude self-employed (data top coded before 1994)
– Focus on workers aged 25–60
– Key Advantages:
I Very large sample size (400+ million individual-year observations)
I No survey response error (W-2 forms sent from employer directly toSSA)
I No sample attrition
I No top-coding (earnings measure includes exercised stock optionsand vested restricted stock units)
I Firms: Full population (100%) of US firms.
Fatih Guvenen Myths vs. Facts 5 / 66
One Baseline Sample
I Individuals: 10% representative panel of US population from1978 to 2013
I Salary and wage workers (from W-2 forms)
– exclude self-employed (data top coded before 1994)
– Focus on workers aged 25–60
– Key Advantages:
I Very large sample size (400+ million individual-year observations)
I No survey response error (W-2 forms sent from employer directly toSSA)
I No sample attrition
I No top-coding (earnings measure includes exercised stock optionsand vested restricted stock units)
I Firms: Full population (100%) of US firms.
Fatih Guvenen Myths vs. Facts 5 / 66
Five Myths
Five Myths
1. Long-run trends:– Myth #1: Rise in income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up in the past 40 years.
2. Business cycle:
– Myth #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
– Myth #4: Top 1% are largely immune to business cycle risk
3. Life-cycle:
– Myth #5: Idiosyncratic income shocks can be modeled fairly wellwith a lognormal distribution.
Fatih Guvenen Myths vs. Facts 7 / 66
Five Myths
1. Long-run trends:– Myth #1: Rise in income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up in the past 40 years.
2. Business cycle:
– Myth #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
– Myth #4: Top 1% are largely immune to business cycle risk
3. Life-cycle:
– Myth #5: Idiosyncratic income shocks can be modeled fairly wellwith a lognormal distribution.
Fatih Guvenen Myths vs. Facts 7 / 66
Five Myths
1. Long-run trends:– Myth #1: Rise in income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up in the past 40 years.
2. Business cycle:– Myth #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
– Myth #4: Top 1% are largely immune to business cycle risk
3. Life-cycle:
– Myth #5: Idiosyncratic income shocks can be modeled fairly wellwith a lognormal distribution.
Fatih Guvenen Myths vs. Facts 7 / 66
Five Myths
1. Long-run trends:– Myth #1: Rise in income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up in the past 40 years.
2. Business cycle:– Myth #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
– Myth #4: Top 1% are largely immune to business cycle risk
3. Life-cycle:
– Myth #5: Idiosyncratic income shocks can be modeled fairly wellwith a lognormal distribution.
Fatih Guvenen Myths vs. Facts 7 / 66
Five Myths
1. Long-run trends:– Myth #1: Rise in income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up in the past 40 years.
2. Business cycle:– Myth #3: Income risk over the business cycle is...
mostly about countercyclical variance of shocks
– Myth #4: Top 1% are largely immune to business cycle risk
3. Life-cycle:– Myth #5: Idiosyncratic income shocks can be modeled fairly well
with a lognormal distribution.
Fatih Guvenen Myths vs. Facts 7 / 66
Long-Run Trends in
Inequality and Risk
Rise in Income InequalityI 20+ years of research into the determinants of rising wage
inequality.
I Conventional wisdom:– 1/3 is observables (education and age)
– 2/3 residual or unobservables (innate ability? search frictions?)
I Today:
– Rising between-firm or within-firm inequality?
�var(wit ) ⌘ � varj(wj| {z }
)
betw. firm inequality
+�var(wit � wj)| {z }
with.-firm ineq.
– Results from “Firming Up Inequality” with Song, Price, Bloom, vonWachter (2015)
Fatih Guvenen Myths vs. Facts 9 / 66
Rise in Income InequalityI 20+ years of research into the determinants of rising wage
inequality.
I Conventional wisdom:– 1/3 is observables (education and age)
– 2/3 residual or unobservables (innate ability? search frictions?)
I Today:
– Rising between-firm or within-firm inequality?
�var(wit ) ⌘ � varj(wj| {z }
)
betw. firm inequality
+�var(wit � wj)| {z }
with.-firm ineq.
– Results from “Firming Up Inequality” with Song, Price, Bloom, vonWachter (2015)
Fatih Guvenen Myths vs. Facts 9 / 66
Rise in Income InequalityI 20+ years of research into the determinants of rising wage
inequality.
I Conventional wisdom:– 1/3 is observables (education and age)
– 2/3 residual or unobservables (innate ability? search frictions?)
I Today:
– Rising between-firm or within-firm inequality?
�var(wit ) ⌘ � varj(wj| {z }
)
betw. firm inequality
+�var(wit � wj)| {z }
with.-firm ineq.
– Results from “Firming Up Inequality” with Song, Price, Bloom, vonWachter (2015)
Fatih Guvenen Myths vs. Facts 9 / 66
Rise in Income InequalityI 20+ years of research into the determinants of rising wage
inequality.
I Conventional wisdom:– 1/3 is observables (education and age)
– 2/3 residual or unobservables (innate ability? search frictions?)
I Today:
– Rising between-firm or within-firm inequality?
�var(wit ) ⌘ � varj(wj| {z }
)
betw. firm inequality
+�var(wit � wj)| {z }
with.-firm ineq.
– Results from “Firming Up Inequality” with Song, Price, Bloom, vonWachter (2015)
Fatih Guvenen Myths vs. Facts 9 / 66
Where Do the Wage Gains Go?
I Piketty and Saez (2003, QJE) wrote an influential paperdocumenting rise of aggregate income share held by top 1%.
I Today: Media and public debate equate inequality with thefortunes of top 1%
I As an example, Paul Krugman (NY Times, Feb 23 2015):
As for wages and salaries . . . all the big gains are goingto a tiny group of individuals holding strategic positionsin corporate suites...
I Our findings: This view misses the “big picture”.
Fatih Guvenen Myths vs. Facts 10 / 66
Where Do the Wage Gains Go?
I Piketty and Saez (2003, QJE) wrote an influential paperdocumenting rise of aggregate income share held by top 1%.
I Today: Media and public debate equate inequality with thefortunes of top 1%
I As an example, Paul Krugman (NY Times, Feb 23 2015):
As for wages and salaries . . . all the big gains are goingto a tiny group of individuals holding strategic positionsin corporate suites...
I Our findings: This view misses the “big picture”.
Fatih Guvenen Myths vs. Facts 10 / 66
Where Do the Wage Gains Go?
I Piketty and Saez (2003, QJE) wrote an influential paperdocumenting rise of aggregate income share held by top 1%.
I Today: Media and public debate equate inequality with thefortunes of top 1%
I As an example, Paul Krugman (NY Times, Feb 23 2015):
As for wages and salaries . . . all the big gains are goingto a tiny group of individuals holding strategic positionsin corporate suites...
I Our findings: This view misses the “big picture”.
Fatih Guvenen Myths vs. Facts 10 / 66
Fact #1: Rise in Inequality is Fractal
0.2
.4.6
Lo
g C
ha
ng
e,
19
81
−2
01
3
0 20 40 60 80 100Percentile of Indv Total Earnings
Indv Total Earnings
Within Industry
Fatih Guvenen Myths vs. Facts 11 / 66
Our findings
1. Result 1: Inequality Rose Across the Entire Wage Distribution.
– Contradicts typical media accounts that rising inequality == risingtop income shares.
2. Next question: What is the role of employer’s in rising inequality?
Fatih Guvenen Myths vs. Facts 12 / 66
Our findings
1. Result 1: Inequality Rose Across the Entire Wage Distribution.
– Contradicts typical media accounts that rising inequality == risingtop income shares.
2. Next question: What is the role of employer’s in rising inequality?
Fatih Guvenen Myths vs. Facts 12 / 66
Fact #1: What is the Role of Employers?
0.2
.4.6
Lo
g C
ha
ng
e,
19
81
−2
01
3
0 20 40 60 80 100Percentile of Indv Total Earnings
Indv Total Earnings
Avg of Log Earnings at Firm
Within Industry
Fatih Guvenen Myths vs. Facts 13 / 66
Fact #1: What is the Role of Employers?
0.2
.4.6
Lo
g C
ha
ng
e,
19
81
−2
01
3
0 20 40 60 80 100Percentile of Indv Total Earnings
Indv Total Earnings
Avg of Log Earnings at Firm
Indv Earnings/Firm Average
Within Industry
Fatih Guvenen Myths vs. Facts 14 / 66
Our findings, cont’d
1. Result 1: Inequality rose across the entire wage distribution.Contradicts typical media accounts that rising inequality == risingtop income shares.
2. Result 2: Almost all of the rise in wage inequality happenedacross firms, i.e., by rising gap in the average pay across firms.
– Almost no change in pay inequality within employers, except inmega-firms.
– Q: What is driving the rise in between-firm inequality?
I Answer: 1/2 rising segregation, 1/2 increased sorting.
3. Next question: Is the CEO pay driving rising inequality?
Fatih Guvenen Myths vs. Facts 15 / 66
Our findings, cont’d
1. Result 1: Inequality rose across the entire wage distribution.Contradicts typical media accounts that rising inequality == risingtop income shares.
2. Result 2: Almost all of the rise in wage inequality happenedacross firms, i.e., by rising gap in the average pay across firms.
– Almost no change in pay inequality within employers, except inmega-firms.
– Q: What is driving the rise in between-firm inequality?
I Answer: 1/2 rising segregation, 1/2 increased sorting.
3. Next question: Is the CEO pay driving rising inequality?
Fatih Guvenen Myths vs. Facts 15 / 66
Our findings, cont’d
1. Result 1: Inequality rose across the entire wage distribution.Contradicts typical media accounts that rising inequality == risingtop income shares.
2. Result 2: Almost all of the rise in wage inequality happenedacross firms, i.e., by rising gap in the average pay across firms.
– Almost no change in pay inequality within employers, except inmega-firms.
– Q: What is driving the rise in between-firm inequality?
I Answer: 1/2 rising segregation, 1/2 increased sorting.
3. Next question: Is the CEO pay driving rising inequality?
Fatih Guvenen Myths vs. Facts 15 / 66
Rise in Income Inequality
The primary reason for increased income inequality inrecent decades is the rise of the supermanager.
Piketty (2013, p. 315)
Wage inequalities increased rapidly in the United States andBritain because US and British corporations became muchmore tolerant of extremely generous pay packages after1970.
Piketty (2013, p. 332)
A key driver of wage inequality is the growth of chiefexecutive officer earnings and compensation.
Mishel and Sabadish (2014)
Fatih Guvenen Myths vs. Facts 16 / 66
Rise in Income Inequality
The primary reason for increased income inequality inrecent decades is the rise of the supermanager.
Piketty (2013, p. 315)
Wage inequalities increased rapidly in the United States andBritain because US and British corporations became muchmore tolerant of extremely generous pay packages after1970.
Piketty (2013, p. 332)
A key driver of wage inequality is the growth of chiefexecutive officer earnings and compensation.
Mishel and Sabadish (2014)
Fatih Guvenen Myths vs. Facts 16 / 66
Rise in Income Inequality
The primary reason for increased income inequality inrecent decades is the rise of the supermanager.
Piketty (2013, p. 315)
Wage inequalities increased rapidly in the United States andBritain because US and British corporations became muchmore tolerant of extremely generous pay packages after1970.
Piketty (2013, p. 332)
A key driver of wage inequality is the growth of chiefexecutive officer earnings and compensation.
Mishel and Sabadish (2014)
Fatih Guvenen Myths vs. Facts 16 / 66
Rise in Income Inequality
The primary reason for increased income inequality inrecent decades is the rise of the supermanager.
Piketty (2013, p. 315)
Wage inequalities increased rapidly in the United States andBritain because US and British corporations became muchmore tolerant of extremely generous pay packages after1970.
Piketty (2013, p. 332)
A key driver of wage inequality is the growth of chiefexecutive officer earnings and compensation.
Mishel and Sabadish (2014)
Fatih Guvenen Myths vs. Facts 16 / 66
Fact #1A: Top Paid Workers vs Firm Pay
−.5
0.5
11
.5L
n r
atio
of
leve
ls
99 99.2 99.4 99.6 99.8 100Percentile
Individuals
Firms
Individual/Firm
By Individual’s Percentile: Top 1%, 1982−2012
rFatih Guvenen Myths vs. Facts 17 / 66
Fact #1B: Dodd-Frank: CEO/median pay
0.1
.2.3
.4.5
Lo
g C
ha
ng
e S
ince
19
81
1980 1990 2000 2010Year
Highest−Paid Empl
5th−Highest Paid
10th−Highest Paid
25th−Highest Paid
50th−Highest Paid
100th−Highest Paid
Median at Firm
Subgroup: 100 ≤ Firm Size < 10k
Fatih Guvenen Myths vs. Facts 18 / 66
Fact #1B: Mega Firms (10,000+ FTE)
0.5
11
.5L
og
Ch
an
ge
Sin
ce 1
98
1
1980 1990 2000 2010Year
Highest−Paid Empl
5th−Highest Paid
10th−Highest Paid
25th−Highest Paid
50th−Highest Paid
100th−Highest Paid
Median at Firm
Subgroup: 10000 ≤ Firm Size
Fatih Guvenen Myths vs. Facts 19 / 66
Fact #1C: Rise in Inequality
0.2
.4.6
.8L
og
Ch
an
ge
, 1
98
2−
20
12
0 20 40 60 80 100Percentile of Indv Total Wage
Indv Total Wage
Fatih Guvenen Myths vs. Facts 20 / 66
Rise in Inequality Without Top Executives
0.2
.4.6
.8L
og
Ch
an
ge
, 1
98
2−
20
12
0 20 40 60 80 100Percentile of Indv Total Wage
Indv Total Wage
Indv Total Wage (Non−Top 1 Employees)
Fatih Guvenen Myths vs. Facts 21 / 66
Rise in Inequality Without Top Executives
0.2
.4.6
.8L
og
Ch
an
ge
, 1
98
2−
20
12
0 20 40 60 80 100Percentile of Indv Total Wage
Indv Total Wage
Indv Total Wage (Non−Top 1 Employees)
Indv Total Wage (Non−Top 5 Employees)
Fatih Guvenen Myths vs. Facts 22 / 66
Rise in Inequality: 1000+ FTE
−.2
0.2
.4.6
Lo
g C
ha
ng
e,
19
82
−2
01
2
0 20 40 60 80 100Percentile of Indv Total Wage
Indv Total Wage
Indv Total Wage (Non−Top 1 Employees)
Indv Total Wage (Non−Top 5 Employees)
Fatih Guvenen Myths vs. Facts 23 / 66
Top 1% Inequality: Baseline
.6.8
11
.21
.41
.6L
og
Ch
an
ge
, 1
98
2−
20
12
99 99.2 99.4 99.6 99.8 100Percentile of Indv Total Wage
Indv Total Wage
Indv Total Wage (Non−Top 1 Employees)
Indv Total Wage (Non−Top 5 Employees)
Fatih Guvenen Myths vs. Facts 24 / 66
Top 1% Inequality: 1000+ FTE
.6.8
11
.21
.41
.6L
og
Ch
an
ge
, 1
98
2−
20
12
99 99.2 99.4 99.6 99.8 100Percentile of Indv Total Wage
Indv Total Wage
Indv Total Wage (Non−Top 1 Employees)
Indv Total Wage (Non−Top 5 Employees)
Fatih Guvenen Myths vs. Facts 25 / 66
Robustness
I This pattern is pervasive. It holds within
– most industries (44 of 49 Fama-French industries)
– US regions (Census regions, counties)
– across firms of different sizes
Fatih Guvenen Myths vs. Facts 26 / 66
Trends in Income Risk
Myth #2:
The volatility of income shocks...
has increased significantly over the past 40 years.
Fatih Guvenen Myths vs. Facts 27 / 66
Myth #2: Upward Trend in Income Risk
I This conclusion has been reached by virtually all papers that usePSID data.
I Moffitt and Gottschalk (1995) documented it first in anow-famous paper, and it has been confirmed by a largesubsequent literature.
I Opening quote from Ljungqvist and Sargent (2008, ECMA):
A growing body of evidence points to the fact that theworld economy is more variable and less predictabletoday than it was 30 years ago... [There is] morevariability and unpredictability in economic life
Heckman (2003).
Fatih Guvenen Myths vs. Facts 28 / 66
Myth #2: Upward Trend in Income Risk
I This conclusion has been reached by virtually all papers that usePSID data.
I Moffitt and Gottschalk (1995) documented it first in anow-famous paper, and it has been confirmed by a largesubsequent literature.
I Opening quote from Ljungqvist and Sargent (2008, ECMA):
A growing body of evidence points to the fact that theworld economy is more variable and less predictabletoday than it was 30 years ago... [There is] morevariability and unpredictability in economic life
Heckman (2003).
Fatih Guvenen Myths vs. Facts 28 / 66
Myth #2: Upward Trend in Income Risk
I This conclusion has been reached by virtually all papers that usePSID data.
I Moffitt and Gottschalk (1995) documented it first in anow-famous paper, and it has been confirmed by a largesubsequent literature.
I Opening quote from Ljungqvist and Sargent (2008, ECMA):
A growing body of evidence points to the fact that theworld economy is more variable and less predictabletoday than it was 30 years ago... [There is] morevariability and unpredictability in economic life
Heckman (2003).
Fatih Guvenen Myths vs. Facts 28 / 66
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
0.45
1970
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
Source: Moffitt and Gottschalk (2012)
Fact #2: No Upward Trend in Volatility
I Administrative data: the opposite conclusion emerges robustly
I See, e.g., Congressional Budget Office (2007); Sabelhaus andSong (2010); Guvenen et al. (2014)
I In fact, volatility of earnings changes has been declining withinmost
– industries– age groups– gender groups– U.S. regions– etc.
Fatih Guvenen Myths vs. Facts 30 / 66
Fact #2: No Upward Trend in Volatility
I Administrative data: the opposite conclusion emerges robustly
I See, e.g., Congressional Budget Office (2007); Sabelhaus andSong (2010); Guvenen et al. (2014)
I In fact, volatility of earnings changes has been declining withinmost
– industries– age groups– gender groups– U.S. regions– etc.
Fatih Guvenen Myths vs. Facts 30 / 66
Fact #2: No Upward Trend in Volatility
.8.9
11.
11.
21.
3P9
010
1980 1985 1990 1995 2000 2005 2010Year
All Male Female
Cross Sectional Dispersion by Sex (25 to 65 yrs)
Fatih Guvenen Myths vs. Facts 31 / 66
Fact #2: No Upward Trend in Volatility
.6.8
11.
21.
4P9
010
1980 1985 1990 1995 2000 2005 2010Year
All Male Female
Cross Sectional Dispersion by Sex (30 to 50 yrs)
Fatih Guvenen Myths vs. Facts 32 / 66
Robustness
I Declining wage volatility holds within every private industry, withthe exception of agriculture (2% of employment).
I It is also robust to alternative measures of dispersion (top end:P90-50, bottom end, P50-10, and so on)
Fatih Guvenen Myths vs. Facts 33 / 66
Risk and Inequality Over the
Business Cycle
Business Cycle Variation in Shocks
Myth #3:
The variance of idiosyncratic shocks
rises substantially during recessions.
Fatih Guvenen Myths vs. Facts 35 / 66
Myth #3: 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
Fatih Guvenen Myths vs. Facts 36 / 66
Countercyclical Variance
I Constantinides and Duffie (1996): countercyclical variance cangenerate interesting and plausible asset pricing behavior.
I Existing indirect parametric estimates find a tripling of thevariance of persistent innovations during recessions (e.g.,Storesletten et al (2004)).
I Our direct and non-parametric estimates show no change invariance over the cycle.
Fatih Guvenen Myths vs. Facts 37 / 66
Countercyclical Variance
I Constantinides and Duffie (1996): countercyclical variance cangenerate interesting and plausible asset pricing behavior.
I Existing indirect parametric estimates find a tripling of thevariance of persistent innovations during recessions (e.g.,Storesletten et al (2004)).
I Our direct and non-parametric estimates show no change invariance over the cycle.
Fatih Guvenen Myths vs. Facts 37 / 66
Countercyclical Variance
I Constantinides and Duffie (1996): countercyclical variance cangenerate interesting and plausible asset pricing behavior.
I Existing indirect parametric estimates find a tripling of thevariance of persistent innovations during recessions (e.g.,Storesletten et al (2004)).
I Our direct and non-parametric estimates show no change invariance over the cycle.
Fatih Guvenen Myths vs. Facts 37 / 66
Fact #3: 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
Std. dev. ratio, 5-year change
Storesletten et al (2004)'s benchmark estimate: 1.75
Fatih Guvenen Myths vs. Facts 38 / 66
Fact #3: Procyclical 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
Fatih Guvenen Myths vs. Facts 39 / 66
Fact #3: Procyclical Skewness
0 10 20 30 40 50 60 70 80 90 100-0.4
-0.3
-0.2
-0.1
0
0.1ExpansionRecession
Fatih Guvenen Myths vs. Facts 40 / 66
Fact #3: Procyclical Skewness: Longer Series
−.3
−.15
0.1
5.3
Kelly
Ske
wne
ss
1953 1963 1973 1983 1993 2003Years
Men Women
Kelly Skewness Dispersion of Wage Growth (1yr)
Fatih Guvenen Myths vs. Facts 41 / 66
How About in Europe? Robustness
I We find exactly the same patterns for Sweden, Germany, andFrance:
– flat shock variance, procyclical skewness (Busch, Domeij,Guvenen and Madera, 2016; and Busch, Fialho, Guvenen, 2016).
I Moving from individual to household income, as well asincorporating government policy has little effect oncountercyclical left-skewness in the US.
I Gov’t policy more effective in Germany and Sweden
I ) Procyclical skewness of income shocks is a commonfeature of modern business cycles.
Fatih Guvenen Myths vs. Facts 42 / 66
Firm-level Data
I Salgado, Guvenen, Bloom (2016): examine firm level variables ina panel of firms covering 44 countries:
– growth rate of sales, profits, employment, inventories– stock prices
I Robust evidence of procyclical skewness for all variables in 90%of the countries.
I Kehrig (2016): estimates firm-level TFP for US firms and finds nocyclicality in variance, but procyclical skewness.
Fatih Guvenen Myths vs. Facts 43 / 66
Firm Variables: Procyclical Skewness
-2-1
01
2
Skew
ness
: Kel
ly(n
orm
aliz
ed to
mea
n 0,
SD
1)
1 2 3 4 5 6 7 8 9 10GDP growth deciles
Sales Employment Stock Returns
Fatih Guvenen Myths vs. Facts 44 / 66
Firm Variables: Slightly Countercyclical Dispersion
−10
12
3
Dis
pers
ion:
P90−P
10(n
orm
aliz
ed to
mea
n 0,
SD
1)
1 2 3 4 5 6 7 8 9 10GDP growth deciles
Micro Sales Micro ReturnsMacro GDP Macro Returns
Fatih Guvenen Myths vs. Facts 45 / 66
Is Business Cycle Risk Predictable?
Myth #4:
Business cycle risk is mostly ex-post risk
Fatih Guvenen Myths vs. Facts 46 / 66
Fact #4: 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)
MeanLogIn
comeChangeDuringRecession
1979-83
1990-92
2000-02
2007-10
Fatih Guvenen Myths vs. Facts 47 / 66
Business Cycle Risk for Top 1%
Myth #4:
The top 1% are largely immune
to the pain of business cycles.
Fatih Guvenen Myths vs. Facts 48 / 66
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)
MeanLogIn
comeChangeDuringRecession
1979-83
1990-92
2000-02
2007-10
Fatih Guvenen Myths vs. Facts 49 / 66
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
Fatih Guvenen Myths vs. Facts 50 / 66
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%
Fatih Guvenen Myths vs. Facts 51 / 66
Risk and Inequality Over the
Life Cycle
Distribution of Income Shocks
Myth #5:
It is OK to model income growth...
...as a lognormal distribution
=) it is OK to assume...
...zero skewness and no excess kurtosis
Fatih Guvenen Myths vs. Facts 53 / 66
Distribution of Income Shocks
Myth #5:
It is OK to model income growth...
...as a lognormal distribution
=) it is OK to assume...
...zero skewness and no excess kurtosis
yt = zit + "i
t "it ⇠ N (0,�2
" )
zit = ⇢zi
t + ⌘it ⌘i
t ⇠ N (0,�2⌘)
Fatih Guvenen Myths vs. Facts 53 / 66
Kurtosis
Myth #5: 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
Density
N(0,0.432)
Fatih Guvenen Myths vs. Facts 55 / 66
Fact #5: 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
Density
N(0,0.432)
US Data, Ages 35-54, P90 of Y
Kurtosis: 28.5
Kurtosis: 3.0
Fatih Guvenen Myths vs. Facts 56 / 66
Fact #5: 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
Fatih Guvenen Myths vs. Facts 57 / 66
Fact #5: 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
Fatih Guvenen Myths vs. Facts 58 / 66
Skewness
Fact #5: 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
Skewness
of(y
t+1−
yt)
Age=25-34
Age=35-44
Age=45-49
Age=50-54
Fatih Guvenen Myths vs. Facts 60 / 66
Double Pareto Tails of Earnings Growth
-4 -2 0 2 4yt+1 − yt
-10
-8
-6
-4
-2
0
2LogDen
sity
US Data
N (0, 0.512)
Slope: 1.40
Slope: −2.18
Fatih Guvenen Myths vs. Facts 61 / 66
Do Higher-Order Moments Matter?
I Guvenen-Karahan-Ozkan-Song (2016):– the welfare costs of idiosyncratic fluctuations are 25-40% of
lifetime consumption compared to 10-12% with Gaussian shocks.(RRA=2)
I Constantinides-Ghosh (2015, JF), Golosov-Troshkin-Tsyvinski(2016, AER), Schmidt (2016), Kaplan-Moll-Violante (2016) findsubstantially different results when higher-order moments aretaken into account.
Fatih Guvenen Myths vs. Facts 62 / 66
Do Higher-Order Moments Matter?
I Guvenen-Karahan-Ozkan-Song (2016):– the welfare costs of idiosyncratic fluctuations are 25-40% of
lifetime consumption compared to 10-12% with Gaussian shocks.(RRA=2)
I Constantinides-Ghosh (2015, JF), Golosov-Troshkin-Tsyvinski(2016, AER), Schmidt (2016), Kaplan-Moll-Violante (2016) findsubstantially different results when higher-order moments aretaken into account.
Fatih Guvenen Myths vs. Facts 62 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Myth #1: Rising income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up strongly in the past40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Myth #1: Rising income inequality partly (or largely) driven by
rising within-firm inequality (e.g., CEO pay)
– Myth #2: Income risk has been trending up strongly in the past40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Myth #2: Income risk has been trending up strongly in the past40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Myth #2: Income risk has been trending up strongly in the past40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Myth #3: Business cycles are... mostly about countercyclical
variance of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Myth #4: Top 1% are immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Fact #4: Top 1% are NOT immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Fact #4: Top 1% are NOT immune to business cycle risk.
3. Life-cycle:
–
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Fact #4: Top 1% are NOT immune to business cycle risk.
3. Life-cycle:
– Myth #5: Income shocks can be modeled fairly well as Gaussian
Fatih Guvenen Myths vs. Facts 63 / 66
Recap: Five Myths vs Five Facts
1. Long-run trends:– Fact #1: Rising income inequality mostly driven by rising
between-firm inequality
– Fact #2: Income risk has been trending DOWN strongly in thepast 40 years.
2. Business cycle:– Fact #3: Business cycles are... mostly about procyclical
skewness of shocks.
– Fact #4: Top 1% are NOT immune to business cycle risk.
3. Life-cycle:
– Fact #5: Income shocks are very non-Gaussian
Fatih Guvenen Myths vs. Facts 63 / 66
Final Thoughts
I Public funding for collecting micro panel data for researchpurposes is woefully inadequate.
I To provide perspective:– NASA’s annual budget: ~20 Billion dollars
– International Space Station total cost: ~150 Billion dollars.
– All worthy efforts. Now consider this:
– US gov’t transfer payments in 2014: ~1.9 trillion dollars.
I For micro research on distributional issues, PSID’s annual budget(only US panel with consumption data): ~3 million dollars!
I Increased public funding for good quality data is essential forgood quality economic research.
Fatih Guvenen Myths vs. Facts 64 / 66
Final Thoughts, cont’d
I We have played the “blind men and the elephant” for too long.
I There is hope: fantastic new datasets becoming accessible:
– Earnings: from IRS, SSA, and LEHD through various calls forproposals.
– Administrative data for Europe is especially impressive.
I Challenges: Data on consumption.. still very limited.
– Still there is hope: Private companies (Mint.com, Credit agencies)and research products (Michigan-Berkeley project) are becomingmore useful for researchers.
I I hope these new facts will feed back into theory and policy work.
Fatih Guvenen Myths vs. Facts 65 / 66
Final Thoughts, cont’d
I We have played the “blind men and the elephant” for too long.
I There is hope: fantastic new datasets becoming accessible:
– Earnings: from IRS, SSA, and LEHD through various calls forproposals.
– Administrative data for Europe is especially impressive.
I Challenges: Data on consumption.. still very limited.
– Still there is hope: Private companies (Mint.com, Credit agencies)and research products (Michigan-Berkeley project) are becomingmore useful for researchers.
I I hope these new facts will feed back into theory and policy work.
Fatih Guvenen Myths vs. Facts 65 / 66
Final Thoughts, cont’d
I We have played the “blind men and the elephant” for too long.
I There is hope: fantastic new datasets becoming accessible:
– Earnings: from IRS, SSA, and LEHD through various calls forproposals.
– Administrative data for Europe is especially impressive.
I Challenges: Data on consumption.. still very limited.
– Still there is hope: Private companies (Mint.com, Credit agencies)and research products (Michigan-Berkeley project) are becomingmore useful for researchers.
I I hope these new facts will feed back into theory and policy work.
Fatih Guvenen Myths vs. Facts 65 / 66
Final Thoughts, cont’d
I We have played the “blind men and the elephant” for too long.
I There is hope: fantastic new datasets becoming accessible:
– Earnings: from IRS, SSA, and LEHD through various calls forproposals.
– Administrative data for Europe is especially impressive.
I Challenges: Data on consumption.. still very limited.
– Still there is hope: Private companies (Mint.com, Credit agencies)and research products (Michigan-Berkeley project) are becomingmore useful for researchers.
I I hope these new facts will feed back into theory and policy work.
Fatih Guvenen Myths vs. Facts 65 / 66
References
Congressional Budget Office, “Trends in Earnings Variability overthe Past 20 Years,” Technical Report, Congressional Budget Office2007.
Guvenen, Fatih, Serdar Ozkan, and Jae Song, “The Nature ofCountercyclical Income 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 Relationto Earnings Mobility,” Boston College Working Papers inEconomics 444, 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.
Fatih Guvenen Myths vs. Facts 66 / 66
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