Thomas PostThe impact of gender-specific investment behavior … ARIA Annual Meeting Quebec, 2007 August 6, 2007- 1 - Dr. Wolfgang Schieren Chair for Insurance

Thomas Post The impact of gender-specific investment behavior …

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Dr. Wolfgang Schieren Chair for Insurance and Risk Management

The impact of gender-specific investment behavior for retirement welfare: Evidence from the U.S. and Germany

Helmut Gründl*Thomas Post*Joan T. Schmit**Anja Zimmer*

* Humboldt-Universität zu Berlin ** University of Wisconsin-Madison


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1 Motivation (1)

Demographic shift, with low birth rates and increasing

longevity

Strain placed on public and private pension systems

Concern over the adequacy of retirement accumulations


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Research shows differences in investment behavior

according to

Age, gender, education, wealth, income, culture, risk attitude

Usually, women are found to be more risk-averse

Research questions

What differences in retirement wealth result from different investment decisions?

- Monetary benchmark (€, $)

What differences in retirement welfare result from different investment decisions?

- Utility benchmark

Special focus on gender-specific effects

1 Motivation (2)


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2 Literature OverviewPositive perspective

- How do people invest?- What factors drive investment behavior?- How do people differ in risk attitude?Barsky et al., 1997; Halek and Eisenhauer, 2001; Poterba and Samwick, 2001; Bertaut and Starr-McCluer, 2002; Eymann and Börsch-Supan, 2002; Ameriks and Zeldes, 2004 …

Normative perspective

- How should people invest, given preferences, endowments, and opportunity sets?

Perfect market solutions: Phelps, 1962; Yaari, 1965; Mossin, 1968; Hakansson, 1969, 1970; Merton, 1969, 1971; Samuelson, 1969; Richard, 1975

More realistic conditions: Zeldes, 1989; Deaton, 1991; …; Cocco et al., 2005; …

Combination of both approaches

- What are the welfare/utility consequences of investing suboptimally?

Dammon et al., 2004: tax optimal asset location vs. commonly observed location choicesCocco et al., 2005: optimal asset allocation vs. common investment advisers’ recommendationsYao and Zhang, 2005: optimal renting / owning a house strategy vs. only renting or housing

Our contribution: optimal asset allocation vs. in the data observed behavior


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3 Research Steps

Econometric Analysis Specification and Calibration ofNormative Model

Actual Decisions& Drivers of

Actual Decisions

Simulation of Retirement Wealth

Optimal Decisions

Welfare Analysis

„Are there important differences in retirement wealth?“

„Are the differences in retirement wealth really important

(from an utility perspective)?“


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Today

Econometric Analysis

Actual Decisions& Drivers of

Actual Decisions

Specification and Calibration ofNormative Model

Simulation of Retirement Wealth

Optimal Decisions

Welfare Analysis

„Are there important differences in retirement wealth?“

„Are the differences in retirement wealth really important

(from an utility perspective)?“


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5 Econometric Analysis

Data

Singles (neither married or living with partner)

- Clear indication of who makes decisions (vs. analysis on household level) and his or her characteristics

- Normative optimization model kept tractable (vs. interactions in the household)

U.S. data: Survey of Consumer Finances (SCF) 2004

- 1,393 singles

German data: Income and Expenditure Survey (EVS) 2003

- 12,631 singles


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5.1.1 Descriptives SCF

Male (36%)

Female (64%)

Independent sample t-Test

Male (52%)

Female (54%)


Male (48%)

Female (46%)


Mean Mean Mean Mean Mean Mean Age 47.91 53.53 *** 48.89 54.73 *** 47.12 51.98 ***# Children 0.21 0.53 *** 0.26 0.62 *** 0.17 0.43 ***Education **

High School 0.30 0.30 0.35 0.34 0.25 0.25Some College 0.21 0.23 0.21 0.20 0.20 0.27College 0.37 0.30 0.22 0.21 0.49 0.43No high school degree 0.13 0.17 0.22 0.25 0.06 0.05

Ocupation *** **Employed 0.58 0.55 0.57 0.49 0.59 0.64Self-employed 0.13 0.05 0.04 0.03 0.20 0.08Retired 0.24 0.34 0.34 0.42 0.16 0.24Unemployed 0.05 0.06 0.05 0.07 0.04 0.04

Income 44,934.92 32,305.45 *** 26,961.59 21,176.38 *** 59,293.56 46,717.56 *Own Hose 0.64 0.61 0.55 0.50 0.70 0.74House Value 160,100.69 116,177.16 ** 90,349.42 63,291.78 215,859.54 184,688.32Assets 345,474.99 202,838.09 ** 121,681.48 77,421.38 524,313.42 365,304.05% Risky Assets 0.19 0.12 *** 0.34 0.28 ***In Debt 0.70 0.70 0.63 0.66 0.75 0.74Debt 50,229.61 36,621.44 *** 24,028.12 21,940.54 71,174.94 55,629.52 **Networth 310,235.84 176,548.58 ** 106,246.48 61,124.21 473,241.91 326,077.15* significant at 10%, ** significant at 5%, *** significant at 1% level

pooled dataSCF U.S.A. SCF U.S.A. SCF U.S.A.

individual has no risky individual has risky


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5.1.2 Descriptives EVS

Male(34%)

female(66%)


Male (54%)

Female (67%)


Male (46%)

Female (33%)


Mean Mean Mean Mean Mean Mean Age 45.81 51.13 *** 47.07 51.72 *** 44.37 49.92 ***# Children 0.27 0.36 *** 0.28 0.38 *** 0.26 0.32 ***Education *** *** ***

Apprentice 0.40 0.46 0.45 0.49 0.33 0.41Some College 0.04 0.03 0.05 0.03 0.04 0.02College 0.53 0.43 0.46 0.39 0.60 0.51No high school degree 0.03 0.08 0.04 0.09 0.02 0.06

Ocupation *** *** ***Employed 0.59 0.51 0.52 0.47 0.67 0.58Self-employed 0.07 0.03 0.07 0.02 0.08 0.05Retired 0.27 0.39 0.31 0.43 0.22 0.33Unemployed 0.07 0.07 0.10 0.08 0.04 0.04

Income 48,626.84 36,961.71 *** 39,386.94 32,481.11 *** 59,267.57 46,222.60 ***Own Hose 0.42 0.35 *** 0.37 0.31 *** 0.48 0.42 ***House Value 82,758.51 61,601.60 *** 65,586.44 53,088.79 *** 102,533.99 79,196.60 ***Assets 121,201.69 88,519.10 *** 87,867.68 71,278.38 *** 159,589.36 124,153.73 ***% Risky 0.11 0.08 *** 0.24 0.24In Debt 0.39 0.29 *** 0.38 0.29 *** 0.41 0.31 ***Debt 23,786.86 13,890.75 *** 17,902.30 12,343.34 *** 30,563.56 17,089.07 ***Networth 97,414.83 74,628.35 *** 69,965.38 58,935.04 *** 129,025.80 107,064.66 **** significant at 10%, ** significant at 5%, *** significant at 1% level

EVS Germany EVS Germanyindividual has no risky individual has riskypooled Data

EVS Germany


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5.1.3 Summary of Descriptive Results

Pooled data: significant divergence in wealth distribution

between females and males in both countries

Risky asset holdings: greater for males and females who, on

average,

are younger, more educated, earn substantially more income, and have substantially more networth

Investment behavior seems to differ between both countries

On average, Americans invest a higher share in risky assets

Share of risky assets given investment > 0

- higher for males in the U.S.

- does not differ between German males and females


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5.2 Regression Analysis

Two approaches

Tobit: Decision to own risky assets and share made

simultaneously

Probit/Truncated OLS: Decision to own risky assets may be

independent from the share finally invested


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5.2.1 Regression Analysis SCF

SCF 2004pooled male female pooled male female pooled male female

coef coef coef coef coef coef coef coef coef

Gender -0.0173 0.0828 -0.0350Age 0.0099 0.0181 0.0047 0.0189 0.0480 -0.0009 0.0112 0.0123 0.0108Age2 -0.0001 -0.0002 -7.46E-05 -0.0003 -0.0005 -0.0002 -9.75E-05 -0.0001 -9.07E-05Children -0.0454 -0.0352 -0.0430 -0.2188 -0.3207 -0.1868 0.0023 0.0301 -0.0108High School 0.0249 -0.1456 0.1556 0.2089 -0.1316 0.5208 -0.0847 -0.2251 -0.0042College 0.0297 -0.1025 0.1391 0.3543 0.1510 0.5994 -0.0936 -0.2237 -0.0081Some College 0.0758 -0.0666 0.1840 0.3575 0.1665 0.5673 -0.0682 -0.1922 0.0146Self-employed 0.1638 0.2159 0.1137 0.5588 0.9544 0.2117 0.1738 0.1627 0.1744Retired -0.0638 -0.0112 -0.0901 -0.2222 -0.0346 -0.3170 0.0352 0.0560 0.0149Unemployed -0.0319 0.0311 -0.0788 -0.0543 -0.0045 -0.1447 0.0233 0.0508 -0.0111Ln(Income) 0.0133 0.0180 0.0060 0.1583 0.1630 0.1479 0.0157 0.0178 -0.0019Own House -0.5613 -0.5233 -0.5911 -1.1884 -0.9573 -1.2900 -0.4249 -0.4422 -0.4071Sqrt(House) -0.0002 -0.0002 -0.0002 -0.0008 -0.0008 -0.0012 -0.0001 -0.0001 -0.0002Ln(Assets) 0.1597 0.1388 0.1772 0.5217 0.4310 0.6144 0.0409 0.0579 0.0446In Debt -0.0125 0.0475 -0.0519 -0.1835 0.0350 -0.3214 -0.0262 0.0149 -0.0554Sqrt(Debt) -3.97E-05 -2.63E-05 -0.0001 3.53E-04 0.0006 0.0002 1.61E-05 1.08E-05 -4.27E-05Networth 1.20E-09 2.60E-09 -1.00E-09 3.61E-08 3.59E-08 1.68E-07 5.80E-09 4.50E-09 7.80E-09Income/Assets 2.60E-05 2.92E-05 -0.0002 1.02E-04 9.05E-05 7.97E-05 0.0005 0.0005 0.0036Constant -1.5834 -1.5591 -1.6005 -6.3973 -6.3789 -6.5582 -0.1864 -0.3080 -0.1255Adj. R2 or Pseudo R2 0.3880 0.3723 0.4024 0.3806 0.3659 0.3940 0.3631 0.3467 0.3777significant at 10% , significant at 5%, significant at 1% level

Tobit (share) Probit (ownership) Truncated OLS (share)


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5.2.2 Regression Analysis EVS

EVS 2003pooled male female pooled male female pooled male female

coef coef coef coef coef coef coef coef coef(std.err.) (std.err.) (std.err.) (std.err.) (std.err.) (std.err.) (std.err.) (std.err.) (std.err.)

Gender -0.0601 -0.1545 -0.0308Age -0.0059 -0.0106 -0.0033 -0.0153 -0.0269 -0.0093 -0.0017 -0.0038 0.0003Age2 2.24E-05 0.0001 8.39E-07 1.27E-05 0.0001 -2.94E-05 3.35E-05 4.75E-05 2.02E-05Children -0.0477 -0.0493 -0.0462 -0.1465 -0.1458 -0.1431 -0.0088 -0.0204 -0.0015Apprentice 0.0175 0.0498 0.0153 0.0476 0.2035 0.0341 -0.0028 -0.0303 0.0120College 0.0742 0.1209 0.0678 0.1892 0.3947 0.1593 0.0227 0.0058 0.0324Some College 0.1776 0.1949 0.1735 0.3696 0.5350 0.3198 0.0536 0.0089 0.0968Self-employed 0.0052 -0.0074 0.0242 -0.0242 -0.1435 0.1179 0.0207 0.0313 0.0104Retired 0.0267 0.0293 0.0214 0.0417 0.0693 0.0212 0.0384 0.0339 0.0394Unemployed -0.0125 -0.0585 0.0134 -0.0248 -0.1567 0.0483 0.0097 0.0033 0.0129Ln(Income) 0.0894 0.0728 0.1001 0.3273 0.3195 0.3277 -0.0038 -0.0054 -0.0036Own House -0.0314 -0.0331 -0.0260 0.1385 0.1652 0.1381 0.0149 0.0012 0.0243Sqrt(House) -0.0012 -0.0013 -0.0012 -0.0037 -0.0042 -0.0035 -0.0007 -0.0008 -0.0007Ln(Assets) 0.0809 0.0675 0.0880 0.2993 0.2780 0.3106 -0.0804 -0.0695 -0.0905In Debt -0.0103 -0.0159 -0.0048 -0.0815 -0.1825 -0.0059 -0.0396 -0.0175 -0.0580Sqrt(Debt) 3.04E-04 5.41E-04 0.0001 1.30E-03 0.0025 0.0005 4.78E-04 4.42E-04 5.61E-04Networth 9.00E-07 9.86E-07 9.20E-07 3.23E-06 3.84E-06 3.01E-06 8.35E-07 8.21E-07 8.98E-07Income/Assets -3.89E-05 -5.60E-05 0.0000 -3.34E-06 -1.78E-05 3.63E-07 0.0002 0.0002 0.0003Constant -1.5804 -1.1736 -1.9049 -5.8986 -5.4484 -6.3317 1.1588 1.1576 1.1438adj R2 or Pseudo R2 0.1374 0.1549 0.1161 0.1601 0.1716 0.1403 0.3778 0.3864 0.3755significant at 10% , significant at 5%, significant at 1% level

Tobit (share) Probit (ownership) Truncated OLS (share)


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5.2.3 Discussion of Regression Results (1)

U.S.

Dummy variable for gender not significant

Differences in investment behavior seem to vanish after controlling for education, employment status, income, assets…

But: coefficients in separate male/female regressions differ

Differences still present for slope of regression equation

For example: for any level of assets/income (considering the simultaneous effect on income, assets, networth) females invest more into the risky asset (Tobit/Truncated OLS)

Interpretation under CRRA utility (see, Haliassos, and Michaelides, 2003): women are less (relative) risk averse


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Germany

Dummy variable for gender significant and negative in all regressions

Females seem to be more risk-averse: smaller likelihood to hold the risky asset and lower share invested

Equal investment share, given investment > 0 (descriptives) = result of lower assets value of females (negative coefficient in truncated OLS multiplied with smaller asset value)

Again: coefficients in separate male/female regressions differ

Differences also present for slope of regression equation

For example: here, for any level of assets/income (considering the simultaneous effect on income, assets, networth) females invest less into the risky asset (Tobit/Truncated OLS)

Interpretation under CRRA utility: women are more (relative) risk averse


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Other results

Age has a hump-shaped effect on the share invested in risky assets in the U.S. but an U-shaped effect in Germany

Owning a house has a much stronger (-) effect on risky asset holding (share/ownership) in the U.S.

R2 much better for Tobit and Probit model for U.S. data (38%) than for German data (15%)

…


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Summary

Magnitude, sometimes also the sign, of coefficients for holding risky assets and risky share differ between both countries, males and females

Gender effect for Germany, women invest less riskily

For the U.S. no gender effect (intercept) / ambiguous (slope)

Implications

Differences in investment behavior between males and females, Germany and the U.S.

Often driven by different financial and demographic characteristic

But also by differences in risk attitudes


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6 Specification and Calibration of Normative Model / Welfare Analysis (1)

More conservative investment strategies lead on average

to lower retirement wealth

But: if differences in risk aversion drive the investment

strategy, accepting lower retirement wealth may be

perfectly optimal from the individuals standpoint

Therefore, non-monetary evaluation of investment

behavior necessary


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Benchmark Model: realistically calibrated intertemporal

expected utility framework

CRRA utility

Borrowing (?) and short-selling constraints

Stochastic

- Labor/pension income

- Life-span

- Asset returns (risky asset, real estate (?))

Decisions on: consumption, saving, debt (?), asset allocation: risk-free, risky, annuities (?), housing (?)


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Calibration issues

Coefficient of relative risk aversion of each respondent?

Asset allocation over the life-cycle of each respondent?

- Constant as observed now

- Constant gap (as observed now) to optimal decision

- As predicted by regression (depending on age, …)

Housing, debt decisions?


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7 Conclusions and Outlook for Further Research

Regression results indicate differences in investment

behavior between males and females, Germany and the

U.S.

Retirement wealth likely to vary by gender

Policy implications cannot be derived at present

Observed differences in risk-aversion make utility based evaluation necessary

Documents

Thomas PostThe impact of gender-specific investment behavior … ARIA Annual Meeting Quebec, 2007 August 6, 2007- 1 - Dr. Wolfgang Schieren Chair for Insurance