LWS Final Conference, Roma, Italia, July 5-7th , 2007“The Luxembourg Wealth Study: Enhancing Comparative Research

on Household Finance

Introduction Motivation Past Empirical Evidence Methods Decomposition

Figure 1: Net Worth¹ by Martial Status and Gender, Germany 2002

0

20000

40000

60000

80000

100000

120000

140000

160000

male female male female male female male female male female male female

total married cohabiting divorced/sep. widowed single

Marital Status / Gender

Net

wo

rth

(E

uro

)

Figure 1: Net Worth¹ by Martial Status and Gender, Germany 2002

¹Estimates derived from multiply imputed data together with a 95% confidence interval

Source: German SOEP 2002; authors’ calculations.

Simple model of accumulation of assets in period t+1 can be expressed in the following way

r - gross rate of return on investments Yt - income in period t

Ct - consumption in period t.

Differences in wealth accumulation could result from:› households differ in the amount they save (Y-C)› households enter the period with different stocks of assets (A) – perhaps due to

inheritance.› households receive different rates of return due to differences in portfolio

allocation reflecting individual variation in risk preference.

))(1(1 tttt CYArA

Gender differences in wealth accumulation could result from:› households differ in the amount they save (Y-C)

Position in life-cycle (age) Income ( labor market participation (Warren et al 2001); Earnings (for

ex. Blau & Kahn 1997, 2000) Differences in precautionary savings due to liquidity constraints

› households enter the period with different stocks of assets (A) – perhaps due to inheritance. Differences in probability of owning a home; discrimination in

mortgage lending (Ladd 1998); differences by family type (Sedo & Kossoudji 2004)

› households receive different rates of return due to differences in portfolio allocation reflecting individual variation in risk preference. Women invest more conservatively (eg. Jiankokopolos & Bernasek

1998)

Additionally:› Gender differences in family type e.g. marriage patterns (Zagorsky 1999).

Male

TOTAL

Male married Male single -

never

married

Female

TOTAL

Female

married

Female

single - never

married

Demographics

Age (in years) 47.1 53.3 30.5 49.4 50.2 32.0

Income

Equiv. Annual Post-Govt. Income (mean) 20,788 21,877 18,712 18,915 21,355 17,091Relative post-govt. income position 105 111 95 96 108 86

Individual Annual Labor Income (mean) 22,952 26,139 15,975 10,019 9,827 11,711Relative labor income position 143 163 99 62 61 73

Education

low (isced=0,1,2) 17.6 13.1 32.1 26.1 22.0 33.2middle (isced=3) 47.9 47.7 44.9 47.9 51.2 40.9

(higher) vocational (isced=4,5) 13.1 13.7 10.5 11.4 11.2 12.2higher eduation (isced=6) 21.4 25.5 12.4 14.6 15.7 13.7

Labor market status

FT employed 42.6 44.9 37.4 20.6 17.0 29.4

PT employed 2.0 1.5 3.5 13.5 19.3 4.6self employed 7.3 7.7 4.8 2.7 3.1 2.6

not employed 25.7 33.0 6.6 42.5 46.0 13.3unemployed 6.6 5.1 7.8 5.7 4.8 6.4

  Male TOTAL

Male married

Male single - never

married

Female TOTAL

Female married

Female single - never

married

     

% recent inheritance (since 1997) 4.1 4.0 3.9 4.8 4.6 4.6

Amt recent inheritance (median, in €) 15,400 15,400 35,800 12,800 12,800 12,800

% expected inheritance 15.4 12.9 21.9 11.8 12.3 15.3

Gender specific Population Share in % 100.0 58.5 23.5 100.0 52.1 17.2

Table 3: Relative Gender Wealth Gap (Men/Women) based on average wealth holdings by Marital Status, Germany 2002

Wealth Component TOTAL Married Cohabiting Single - divorced/ separated

Single - widowed

Single - never

married

Housing 1.09 1.14 1.17 1.39 1.22 1.03

Other Property 1.46 1.54 2.75 1.73 0.57 1.34

Financial assets 1.36 1.54 0.96 2.19 1.34 1.22

Insurance/ Private pensions 2.01 1.84 1.95 2.58 2.53 1.98

Business assets 5.52 5.10 8.78 10.00 1.10 7.52

Tangible assets 1.39 1.43 2.04 1.38 0.85 1.35

Debt 1.35 1.23 1.43 1.86 0.45 1.43

Total 1.45 1.56 1.74 1.88 1.18 1.40

Shaded cells indicate significant deviation (p<=0,05).

Note: Calculations are based on multiply imputed data

Source: SOEP 2002.

Focus on married and couple households

F>M: Woman's wealth exceeds man's wealth in a couple household

F=M: Equal sharing among partners in a couple household

F<M: Male-in-charge: Man's wealth exceeds woman's wealth in a couple household

NO: No wealth in a couple household

Top/bottom code net worth

SOEP Wealth module in 2002 LWS (household level)

Individual level (all HH members >16): n=23.900 (12.500 HH) Multiple Imputation (5 replicates)

• Own Property• Other Property• Financial Assets (>2.500 €) Total Assets • Private Pensions• Business Assets• Tangible Assets (>2.500 €) Net Worth

• Main Property Debt• Other Property Debt Total Debt• Consumer Debt (>2.500 €)

Not included: cars, public pension entitlements, durables

Table 4a. Distribution of different type of sharing couple households by wealth quantiles.

Wealth quantile Sharing Type  

  F>M Eq M>F No Total1 22.1 8.8 22.0 47.1 1002 30.7 14.8 54.5  1003 28.6 20.5 50.9  1004 25.8 17.4 56.8  1005 25.0 14.4 60.7  100

Total 26.4 15.1 48.8 9.7 100

Table 4b. Average wealth gap in couple household by different type of sharing couple households and wealth quantiles.

Wealth quantile

Sharing Type  

F>M Eq M>F Total

1 -16039 0 10396 -1261

2 -9370 0 12836 4111

3 -38344 0 43569 11209

4 -67742 0 80748 28374

5 -160458 0 227005 97689

Total -56799 0 88113 27967

Table 4e. Prevalence of the self employed by different type of sharing couple households and wealth quantiles.

Wealth quantile

Sharing Type  

F>M Eq M>F Total

1 0.05 0.02 0.03 0.03

2 0.02 0.01 0.06 0.04

3 0.04 0.02 0.12 0.08

4 0.05 0.03 0.12 0.08

5 0.11 0.11 0.24 0.19

Total 0.05 0.04 0.13 0.08

Table 4f. Prevalence of homeowners by different type of sharing couple households and wealth quantiles.

Wealth quantile

Sharing Type  

F>M Eq M>F Total

1 0.08 0.07 0.07 0.04

2 0.09 0.22 0.10 0.11

3 0.44 0.77 0.64 0.61

4 0.63 0.99 0.90 0.85

5 0.70 0.97 0.92 0.87

Total 0.38 0.67 0.60 0.50

Decompose the gender wealth gap

Mean difference› Into portions attributable to differences in the distribution of

endowments and differences in the return to these endowments (wealth function) or way in which the endowments are transformed into wealth (Blinder-Oaxaca)

Juhn, Murphy, Pierce (1991) method› Decomposes the component due to differences in coefficients

into two parts: unobserved ability and differences in the wealth function (discrimination)

Women   MenVariables Coef.   Mean     Coef.   Mean

 migback -42523* 0.14  -45950* 0.14partner -25520* 0.12  -13341** 0.11loc89east -47858* 0.21  -53839* 0.20badhlth -12711* 0.18  -14289* 0.18goodhlth 11011* 0.44  5468 0.46kids04 -10494** 0.12  -10710** 0.12_Iedu_2 22981* 0.51  13075* 0.48_Iedu_3 35848* 0.12  30978* 0.14_Iedu_4 51965* 0.14  54493* 0.22notlabor 1846* 18.49  2441* 11.16over65 1262* 1.06  531 1.61expft02 1718* 12.73  2929* 25.78exppt02 2585* 4.41  566 0.45expue02 -67 0.64  -1062 0.67expmiss 74214* 0.01  71378* 0.01selfemp 71177* 0.04  151277* 0.08autonom 16203* 0.08  11471** 0.24hiedu_f 30886* 0.06  6588 0.07hiedu_m -3582 0.02  -7719 0.02hiedu_p -13807 0.01  25391 0.01inheri1 49425* 0.08  55280* 0.08inheri2 46541* 0.06  63263* 0.08Ind tot inc dec1 15162* 0.19  25343*** 0.01Ind tot inc dec2 -160 0.18  30085** 0.02Ind tot inc dec3 -5024 0.13  12522 0.05Ind tot inc dec4 -219 0.10  15916** 0.06Ind tot inc dec6 3301 0.07  2989 0.12Ind tot inc dec7 2966 0.08  22390* 0.13Ind tot inc dec8 -451 0.07  38292* 0.14Ind tot inc dec9 7771 0.06  43372* 0.16Ind tot inc dec10 39847* 0.03  86535* 0.21_cons -17822**       -65737*  N 7803   7803Adj R-squared 0.1913        0.2858   

 Note * denotes statistical significance at the 1% level; ** at the 5% level; and *** at the 10% level

 

Wealth gap

 

Average wealth

 

Amount of gap explained with differences in

characteristics

 

Women's av. wealth if male characteristic

s

 

Amount of gap explained with differences in coefficients

 Women's av.

wealth if male's characteristics

and wealth function

 

Interaction

 

Average wealth

 

   

Women 74247 90982 73250 102446 Men

  28199      16735     -17732      29196      

st err 2249 2218 5675 5716

 

Wealth gap

Average wealth

Amount of gap explained with differences in coefficients

Men's av. wealth if

women's wealth function

Amount of gap explained with differences in

characteristics

Men's av. wealth if

women's wealth function and

characteristics

InteractionAverage wealth

 

   

Men 102446 120179 103444 74247Women

  -28199      17732      -16735      -29196     

st.err 2249 5675 2218 5716

 

Wealth gap

 

Average wealth

 

Amount of gap explained with differences in characteristics

 

Women's av. wealth if male characteristics

 

Amoount of gap explained with differences in coefficients

 Women's av.

wealth if male's characteristics

and wealth function

 

Unexplained

 

Average wealth

 

   

Women 74247 91094 102558 102446 Men

JMP 28199 16847 11464 -112

Women 74247 90982 73250 102446 Men

Oaxaca 28199      16735       -17732       29196    

Note: Women as reference group

Table 8. Wealth decomposition results across the wealth distribution (Juhn-Murphy-Pierce).

                  

Wealth gap

 Amount of gap explained with differences in

characteristics

 Amount of gap explained with differences in coefficients

 

Unexplained

 

 

 

10th 0 3503 -2631 -872

25th 3950 18438 -2086 -12402100 467 -53 -314

50th 18250 22264 9160 -13175100 122 50 -72

75th 32500 14426 19819 -1745100 44 61 -5

90th 67959 20806 32938 14215100 31 48 21

 

Wealth gap

 Amount of gap explained with differences in

characteristics

 Amount of gap explained with differences in coefficients

 

Unexplained

 

 

P90-P10 67959 17303 35569 15087

100 25 52 22

P90-P50 49709 -1459 23778 27390

100 -3 48 55

P50-P10 18250 18761 11791 -12302

100 103 65 -67

P75-P25 28550 -4012 21905 10656

  63  -14  77   

Oaxaca decomposition suggests that half of the gap is due to differences in characteristics between men and women and half due to differences in coefficients

JMP takes a closer look at the gap across the wealth distribution› Bottom: large part of the gap is due to differences in characteristics

(diminishes)› Bottom: the way women accumulate wealth has a reducing effect on

the gap› This is not the case past the median

Consistent result that a big portion of the gap is due to characteristics

Quantile regressions Robustness checks for equal sharing assumption marriage patterns of men vs women Life-time work histories Control for differences within couples DiNardo, Fortin , Lemieux decomposition partition wealth

vector: Labor market experience, education level, intergenerational

characteristics and demographic characteristics

Future work