The Impact of Individual Investment Behavior for Retirement Welfare: Evidence from the United States and Germany



Table 3: Determinants for Share of Risky Assets (Tobit Regression); Dependent

Variable: Percent Risky 2 (for definition of variables, see Appendix B)

______SCF USA_______

______EVS Germany______

coef

std. err.

coef

std. err.

Age

0.0143

0.0040 ***

-0.0020

0.0018

Age2

-0.0001

0.00004 ***

-0.00003

0.00002

Gender

0.0533

0.0246 **

-0.0372

0.0097 ***

Education Low

0.0852

0.0368 **

-0.0343

0.0199 *

Education High

-0.0521

0.0283 *

0.0096

0.0101

Occupation Retired

-0.0087

0.0371

0.0276

0.0149 *

Ln(Labor Income)

-0.0160

0.0165

-0.0275

0.0088 ***

Ln(Net worth)

0.0735

0.0103 ***

0.2550

0.0033 ***

Labor Income / Net worth

-0.0487

0.0083 ***

0.0001

0.00002 ***

Constant

-0.4316

0.1564 ***

-1.9020

0.0959 ***

Pseudo R2

0.4040

0.3731

* significant at 10%, ** significant at 5%, *** significant at 1% level

Table 4: Joint Distribution of Labor Income and Net Worth for the United

States and Germany and Indication (Shaded Area) of Potentially Large Welfare
Losses *

SCF USA                                   Net worth Quantile

0-25         25-50         50-75        75-100

0-25

g ≡       25-50

I g ≡       50-75

j « O     75-100

9

%         7

%^^M7
%^^M9
%^^M

%

7%        1%

11%        2%

0%        7%

8%        15%

EVS Germany

Net worth Quantile

0-25

25-50

50-75

75-100

0-25

13%

4%

4%

3%

g       25-50

7%

5%

6%

S о ≡       50-75

ι-*-√ ɔ f

4%

8%

6%

JZj Ξ O       75-100

1%

6%

8%

10%

*

Note, the distribution shown here refers to the subpopulation aged 50. Preference assumptions and
indication of potentially large welfare losses are based on Figure 3 and Figure 8.



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