Get Rich or Die Tryin

Transcription

Get Rich or Die Tryin’
Determinants of Wealth Accumulation in Europe
S. Humer — M. Moser
INEQ @ WU Vienna
September 30, 2015
1/1
Overview
How is wealth structured?
How do socioeconomic characteristics correlate w/ wealth?
How are rich households different?
How to get rich (in different countries)?
This talk analyzes
Sources of wealth inequality: Capital income
Structure of wealth inequality: Education, Employment, ...
The roots of wealth inequality: Income vs. Inheritance
2/1
HFCS Data
Household Finance and Consumption Survey 2010
Ex ante harmonized household survey
Underreporting, especially at the upper tail
Analysis of non-response, collection of metadata,
interviewer-effects
Measurement unit: private households
↖↘
Socioeconomic characteristics at the individual level
3/1
“...20 years of hard
work are enough to
live off the interest...”
Frank Stronach (2012)
4/1
Labor vs. capital income
5/1
Figure: Joint Distribution of Income and Wealth in Austria
6/1
Distribution of wealth in the Euro area (Gini)
Source: Sierminska and Medgyesi (2013)
6/1
Employment status
1.00
Category:
Employee
Self-employed
Transfer benef.
Retiree
Other
Composition
0.75
0.50
0.25
0.00
0
10
20
30
40
50
60
70
80
90
100
Percentiles
7/1
Economic sectors of business wealth
1.00
Composition
0.75
Category:
Primary
Secondary
Tertiary
0.50
0.25
0.00
0
10
20
30
40
50
60
70
80
90
100
Percentiles
8/1
Results for Education (∅ per 1.000 Euro)
Education
Primary
Secondary I
Secondary II
Tertiary
Share
Main Residence
Tangibles
Real Estate
Business
0,00
0,16
0,68
0,15
65,7
99,9
124,4
148,7
7,3
8,0
29,4
66,3
0,0
24,3
76,3
88,2
Primary
Secondary I
Secondary II
Tertiary
Finan. Wealth
Safe
Risky
Tot. Wealth
Gross
Net
22,6
13,1
34,5
54,9
102,5
154,1
290,3
408,2
2,8
2,3
9,9
24,7
59,3
146,3
273,2
382,6
9/1
Results for Labour Status (∅ per 1.000 Euro)
Labour Status
Employed
Self-employed I
Self-employed II
Family workers
Share
0,43
0,04
0,06
0,00
Employed
Self-employed I
Self-employed II
Family workers
Main Residence
104,9
231,9
264,3
270,3
Tangibles
Real Estate
17,6
107,3
129,0
9,6
Finan. Wealth
Safe
Risky
Tot. Wealth
Gross
Net
31,4
83,6
51,9
48,5
204,7
982,9
891,0
814,1
9,4
18,8
9,7
0,0
Business
28,9
471,9
408,3
427,6
181,4
931,9
860,7
670,2
10 / 1
Who are the Millionaires?
HH ≥ 1 Mil. Euro
Employed
0.44
Self-emp. I
4.91
Self-emp. II
4.51
Total
1.00
Education
Secondary I
Secondary II
Tertiary
0.89
0.42
0.33
0.00
4.69
5.26
5.79
4.91
1.88
0.81
0.93
1.56
HH Size
1 Person
2 Persons
3 Persons
4 Persons
5 Persons
6 Persons
7 and more
0.15
0.33
1.00
0.24
1.26
2.13
0.00
2.31
3.82
4.76
6.53
11.21
12.18
0.84
4.39
6.77
7.06
5.18
16.12
17.29
0.31
0.98
1.79
1.36
2.99
4.96
2.61
Business
Agriculture
Industry
Services
None
8.62
2.97
2.30
0.21
10.81
6.36
7.67
2.04
11.88
3.85
2.40
1.64
11.68
4.76
4.96
0.36
11 / 1
Multivariate Approach: Quantile Regressions
Estimation approach: address skewness of the distribution
Koenker and Bassett, 1978: regress on quantiles of the CDF
Given the empirical quantile function
Q(τ ) = F−1 (τ ) = inf(y : F(y) ≥ τ )
the τ th quantile can be calculated by
min
ξ∈ℜ
n
∑
ρτ (yi − ξ)
i=1
where ρτ is a ‘check–function’
ρτ = τ · I(yi > ξ) + (1 − τ ) · I(yi < ξ)
...which weighs the errors according to the chosen quantile
∑
Followingly we compute β̂(τ ) = arg min ni=1 ρτ (yi − x′i β)
12 / 1
Controlling for Household Structure
For the univariate analysis we rely on one reference value
e.g. sex of the main respondent
Another, more comprehensive approach is given by Fessler,
Lindner, and Segalla, 2013:
Choose grouped classification variables (age, gender)
Classify households by strings of values
3132: Man and woman both 35–64 years old
Using four age groups, sex (m/f/children) we are left with 151
HH types
35 of the cover 90% of the sample
Included as dummy variables in the regression setup
13 / 1
Quantile Regression
Intercept
Female (MR)
Age (MR)
Tert. Edu. (MR)
Couples
Single parent
Families
Liabilities
Inc. empl. (HH)
Inc. self. (HH)
Inc. pens. (HH)
Inc. trans (HH)
Main residence
Business
Inheritance
OLS
Mean
P10
15.30
-1.35
0.14
6.71
6.98
3.08
8.88
-0.07
0.12
0.13
0.08
-0.19
35.30
18.34
7.96
2.30
-0.92
0.04
5.31
3.00
0.83
3.72
-0.12
0.09
0.09
0.07
-0.31
40.98
21.17
2.78
Quantile regression
P25
P50
P75
7.35
-1.71
0.05
6.57
5.60
0.30
6.29
-0.10
0.12
0.08
0.09
-0.36
40.12
19.49
5.37
14.14
-1.77
0.11
6.61
8.36
4.38
9.07
-0.07
0.14
0.12
0.11
-0.21
37.87
18.87
7.02
23.29
-0.47
0.17
6.16
7.49
4.54
12.30
-0.03
0.16
0.14
0.15
-0.05
35.20
17.80
8.41
P90
33.09
-0.79
0.19
5.37
6.57
2.38
10.08
-0.03
0.13
0.19
0.16
0.04
32.06
15.22
9.03
Source: HFCS, own calculations.
14 / 1
Main findings I
Wealth concentration very distinctive
Analysis of gross vs. net wealth is relevant for the
bottom 5%
Compared to the rest of the distribution, the lowest 30%
hold hardly any wealth
are significantly less indebted
An increase in the relative wealth postion
increases both the value of single wealth categories as well as
the participation in other asset classes
Especially pronounced: estate & business wealth inequality
15 / 1
Main findings II
Household size & composition
Size correlated with wealth
Older couples & families at the top
Occupation
Managers, Academics & Farmers
Employment status
Self employment almost entirely in top decile
Average wealth approx. 5 times as large as average wealth
of employed
Business wealth is concentrated at the top
Inheritances (number & type) correlate significantly with
wealth postion
16 / 1
Main findings III
Conditional on other characteristics..
Neg. correlation of WP and female RP within lower 30%
Pos. correlation of WP and employed RP around the median
Pos. correlation of WP and HH-size BUT neg. correlation
with number of kids in the upper half
Neg. but diminishing correlation of WP and indebtedness
Constant negligible effect of income: 1 percentile each
10.000 e
Most influential indicators for wealth postion
Ownership of main residence
Business wealth
Received inheritances
17 / 1
“...Money meant
Power...”
Get Rich or Die Tryin’ (2005)
How do income and inheritances
affect the wealth position?
18 / 1
Next steps..
Switch focus to Europe
Based on the findings for Austria take a closer look at
Europe’s Top 1% and answer
who they are
where they live
how they made their fortune
19 / 1
Caveat: “Super Rich” not included I
Source: Vermeulen (2014)
20 / 1
Caveat: “Super Rich” not included II
Figure: How rich are the top 1% really? — Q: ECB, Bloomberg
21 / 1
Equal Distribution vs. Actual: Top 10%
Wealth Share of Top 10%
0
1000000
3000000
Share if equally distributed
Actual share
AT
BE
CY
DE
ES
FI
FR
GR
IT
LU
MT
NL
PT
SI
SK
22 / 1
Population share of Top 10%
0
1000000
3000000
Actual share
AT
BE
DE
ES
FR
GR
LU
NL
PT
SI
SK
23 / 1
0
500000
1500000
Actual share
AT
BE
DE
ES
FR
GR
LU
NL
PT
SI
SK
24 / 1
400000
0
200000
Actual share
AT
BE
DE
ES
FR
GR
LU
NL
PT
SI
SK
25 / 1
Equal Distribution vs. Actual: Top 0.1%
Population share of Top 0.1%
0
20000
40000
Actual share
AT
BE
DE
ES
FR
GR
LU
NL
PT
SI
SK
26 / 1
Quantify the influence of bequests
Fessler and Schürz (2015) assess the relationship between
income, inheritance and wealth
Regressions on relative wealth position
displays social status
more robust to measurement error
CDFwnet =β0 + β1 Inheritance + β2 CDFinc + β3 Age + β4 Age2
+ β5 Tert. Edu. + β5 Retired + β7 Entrepreneur + ϵ
(1)
⇒ bequests increase rank by about 14 percentiles
⇒ three percentiles in the income distribution lead to one
percentile in the wealth distribution
27 / 1
Regression results: Income (CDF)
0.6
●
Estimate
●
●
●
0.4
●
●
AT
BE
DE
●
●
●
●
●
●
●
●
ES
FR
●
GR
LU
●
●
●
0.2
NL
PT
●
●
SI
SK
0.25
0.50
0.75
Quantile
28 / 1
Regression results: Has Received Inheritance
25
●
●
20
●
●
●
Estimate
●
15
●
●
BE
DE
●
●
ES
●
●
10
FR
●
●
●
GR
●
LU
●
NL
●
5
AT
●
PT
●
●
SI
SK
●
0
0.25
0.50
0.75
Quantile
29 / 1
Ratio Inheritance/Income (Percentile Gain)
●
BE
150
Ratio Percentile Gain
AT
DE
ES
FR
100
GR
LU
50
●
●
●
●
●
●
●
●
NL
●
●
●
●
●
PT
●
●
●
●
●
●
SI
SK
●
●
0
0.25
0.50
0.75
Quantile
30 / 1
...more to come...
→ wu.ac.at/ineq
31 / 1

Get Rich or Die Tryin

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