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Racial/Ethnic Disparities in Risky Asset Ownership: A Decomposition Analysis1
Sherman D. Hanna and Cong Wang, Ohio State University
In 2004, only 27% of Hispanic households and 33% of Black households had any risky,
high return investments, compared to 64% of White households. This paper examines the
disparities of risky asset ownership between Whites and Blacks and between Whites and
Hispanics, using the 2004 Survey of Consumer Finances. Logistic models show that differences
in risk tolerance levels and household characteristics contribute to the differences in risky asset
ownership between households with White respondents and those with Black and Hispanic
respondents. This study presents the first application of the Oaxaca decomposition technique to
racial/ethnic differences in risky asset ownership, and provides estimates of the relative
importance of these factors in accounting for the gaps. Differences in net worth, income, risk
tolerance, and education account for a large part of these disparities.
Introduction
A number of studies have examined households’ disparities in wealth distribution
different race/ethnic groups in the U.S., with most studies examining the gaps between Blacks
and Whites (Altonji, Ulrich, & Segal, 2000; Barsky, Bound, Kervin, & Lupton, 2002; Blau &
Graham, 1990; Smith, 1995; Sharmila, 2002; Wolff, 1998). Investment in risky, high return
assets is an important factor in future economic well-being of households, especially in terms of
potential retirement adequacy. White households have much higher stock investment ownership
rates than minority groups, even after controlling for income and other factors (Zhong & Xiao,
1
This publication was made possible by a generous grant from the NASD Investor Education Foundation.
1
1995; Schooley & Worden, 1996; Wang & Hanna, 2006). However, little is known about to what
extent the racial/ethnic wealth gaps are due to differences in income, and household situation,
versus differences in risk tolerance levels. In addition to stock investment ownership, we also
include investment real estate and business ownership in the category of owning risky, high
return assets. Unlike stock investments, it is difficult to estimate the rate of return and standard
deviation of investment real estate, though during the period of 1975 to 2005, publicly traded
Real Estate Investment trusts are estimated to have had slightly higher rates of return than the
S&P 500 stock index (National Association of Real Estate Investment Trusts, 2007). Business
ownership might plausibly assumed to have a higher expected return than stocks of publicly
traded small companies, with correspondingly higher risk levels (Lai & Hanna, 2004). Bond
investments are sometimes assumed to be risky investments, based on having higher mean
returns and standard deviations than cash equivalent investments. However, based on the much
lower inflation-adjusted mean arithmetic return of long-term corporate bonds, 3.3% versus 9.2%
for large company stocks, and much lower standard deviations, 9.8% versus 20.4% for large
company stocks (Ibbotson Associates, 2005), it seems reasonable to exclude bond investments
from the risky asset category. During the 1926 to 2004 period, bonds were substantially inferior
to stock investments for building wealth, as $1 invested as of the beginning of 1926 would by the
end of 2004 have turned into an inflation-adjusted value of $239 in a hypothetical large stock
fund, $1,222 in a small stock fund, $9 in a corporate bond fund, and $6 in an intermediate
government bond fund (Ibbotson Associates, 2005).
In this study, we use a technique proposed by Fairlie (2005), which is an extension of
Blinder and Oaxaca’s (1973) method, to examine the relative importance of compositional
factors in explaining two racial/ethnic differentials in risky asset ownership — between Whites
2
and Blacks and between Whites and Hispanics, and investigate what portion of the observed
differences in risky asset can be explained by their household and economic characteristics.
Literature Review
Overview
Households with similar demographic and financial resources might behave quite
differently in the allocation of investment portfolios. For financial investments for long-term
goals, because of the relationship between risk and return, the portfolio allocation in risky assets
will make a substantial difference in the projected accumulation. In the sections below, we first
briefly summarize some important literature relevant to the racial differences in terms of stock
and other risky asset holdings and wealth accumulation, and then examine what factors affect the
choice of holding risky assets. We then explore to what extent those factors contribute the racial
disparities between White and Black households and between White and Hispanic households.
Investment behavior can be influenced by preferences. Ogden, Ogden and Schau (2004)
suggest that subculture, which may be represented by race or ethnicity, might impact
preferences. Previous studies have shown that the households who are more willing to take risk
are more likely to have stock investments (Wang & Hanna, 2006).
Most studies find that Blacks are less willing to take investment risk than Whites (Yao,
Gutter, & Hanna, 2005). Based on the SCF investment risk tolerance measure, Sung and Hanna
(1996) find no significant differences between Blacks and Whites in willingness to take some
investment risk after controlling for other variables, for a sample of employed households in
1989, although they find that Hispanics are significantly less likely than otherwise similar Whites
to be willing to take some risks. Yao, Gutter and Hanna (2005) analyze a combination of the
3
1983 to 2001 SCF datasets, and report that Blacks and Hispanics are less willing to take some
investment risk than otherwise similar White households, although more willing to take
substantial investment risk than Whites. Coleman (2004) analyzes the 1998 SCF models to
compare the risk tolerance levels of Whites, Blacks, and Hispanics. In a logistic regression
controlling for racial/ethnic group, gender, marital status, education, age, and family size, she
finds that Blacks and Hispanics are more likely to be unwilling to take any risk compared to
otherwise similar Whites. However, when she also controls for net worth, the predicted net
worth difference between Blacks and Whites is not significant. She obtains similar results with a
tobit analysis of the risky asset proportion, in that Blacks and Hispanics are have a significantly
lower risky asset proportion than otherwise similar Whites when net worth is not controlled, but
when net worth controlled, the predicted difference between Blacks and Whites is not
significant.
A consumer’s access to information and related services in financial markets might also
affect its financial behavior. If a consumer can obtain more information and service for financial
investment, they probably will be more willing to participate in financial markets. For example,
more educated households may be more willing to participate in financial market than less
educated people. Haurin and Morrow-Jones (2007) conclude that differences in knowledge of
markets might contribute to lower homeownership rates of Black households, so it is plausible
that similar factors may contribute to lower risky asset ownership rates.
Wealth, Investment and Race
The gap between non-Hispanic White households and households with respondents in
other racial/ethnic groups narrowed between 1995 and 2001, but then became much wider in
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2004 (Aizcorbe, Kennickell, & Moore, 2003; Bucks, Kennickell, & Moore, 2006; Kennickell,
Starr-McCluer, & Sundén, 1997; Kennickell, Starr-McCluer, & Surette, 2000).
Smith (1995) compares the racial and ethnic differences in wealth through 1992 HRS
(Health and Retirement Study) and concludes that income is an important reason for racial and
ethnic deficits, but that income-conditioned wealth disparities in asset remain large. Differences
in risky asset investment ownership may contribute to the wealth differences. Previous
researchers all find a racial wealth gap, using a variety of data sets, but have inconsistent results
about the influence of various demographic and financial characteristics on the gap. Altonji, et
al. (2003) use a decomposition method and find that most or all of the race gap in the wealth
level for single men and single women and a substantial portion of the gap for married couples
would disappear if Blacks and Whites have the same distribution of income and demographic
variables and if the slope coefficients of the White wealth equation hold for Blacks. For instance,
they found that single Black men would have 108% of the wealth of single White men if they
have the same income and demographics as White men when using the estimated coefficients of
the wealth model for Whites. Kaufman (1983) uses a structural decomposition of Black-White
earning differentials based on a regression standardization approach and concludes that
eliminating all Black-White differences within labor market divisions would still leave a
significant earnings gap between Blacks and Whites due to the structure of the labor market.
Blau and Graham (1990), using data from the 1976 and 1978 National Longitudinal Surveys of
young men and young women (NLSY) to examine the Black-White differences in wealth and
asset composition among younger families, find that as much as 75% of racial differences remain
unexplained by income and other demographic and locational characteristics. Barsky et al.
(2002) use a non-parametric approach to analyze racial differences in earnings and compares the
5
result with the traditional decomposition method. After re-weighting observations to give a
sample of Whites a similar earnings distribution as that for Blacks, they found that differences in
characteristics explain only 64% of the wealth gap, compared with 97% when the standard
(linear) regression decomposition approach is used (with White coefficients).
Given the importance of investments in the risky assets in contributing to wealth
differences between different racial/ethnic groups, many studies focus on the ownership of risky
assets and its relationship with race (Haliassos & Bertaut, 1995; Plath & Stevenson, 2000;
Coleman, 2003). For instance, using the 1992 HRS, Choudhury (2002) demonstrates that Whites,
Blacks and Hispanics were different in saving behavior, and minority households were notably
more disinclined to invest in riskier, higher-yielding financial assets. Gutter, Fox and Montalto
(1999) analyze Black/ White racial difference in the likelihood of owing risky assets based on
Jackson and Lindley (1989)’s method by using 1995 SCF and conclude that differences in risky
asset ownership between Black households and White households are due to racial
characteristics in the individual determinants of risky asset ownership, not to race in and of itself.
Stevenson and Plath (2006) compare financial service consumption patterns of Hispanics with
those of non-Hispanic white, using the 1998 SCF, and find that Hispanics differ markedly from
their non-Hispanic White counterparts in terms of financial product preferences and investment
asset portfolio composition. Further, Hispanics trail their non-Hispanic White counterparts in
terms of breath and depth of financial holdings, particularly in the area of more risky but
historically higher return asset categories.
An important part of racial/ethnic differences in risky asset holdings is risky asset
holdings. While previous researchers' methods vary in analyzing the distribution of wealth, there
is agreement that the distribution of wealth in the United States is very unequal and that
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inequality has worsened in recent decades. Keister and Moller (2000) note that participation in
stock and real estate investments markets have very important influences on the unequal
distribution of wealth. Wang and Hanna (2006) report that even after controlling for risk
tolerance levels and other variables, Blacks and Hispanics are less likely to directly or indirectly
hold stock investments than Whites. Hurst, Luoh and Stafford (1998) find that the lower rate of
stock ownership among Black families prevent them from benefiting as much as White families
from the economic of the 1990s. Therefore, it is important to explore the causes of low risky
asset investment ownership by minority households. Gutter and Fontes (2006) found that risky
asset ownership was the key to racial differences in portfolio choices, as Black and Hispanic
households that owned any risky asset were not significantly different from similar White
households in risky asset proportions. Therefore, in this study, we focus on differences in risky
asset ownership.
Decomposition Method
The typical approach to decomposing racial differences in wages, employment, and
wealth is to use some extension of what is known as the Blinder-Oaxaca technique, which was
first developed by Oaxaca (1973) and Blinder (1973). The Blinder-Oaxaca decomposition
technique is widely used to identify and quantify the separate contributions of group differences
in measurable characteristics. Fairlie (1999) and Fairlie (2005) note that a linear regression
implies that the slope of an racial effect with respect to all other covariates is the same for all
races, and only the intercept of the function is shifted up or down. Given this restriction, it is
more appropriate to employ non-linear decomposition to give a robust estimate of the
contribution of racial effects. Fairlie (1999) and Fairlie (2005) propose an extended non-linear
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decomposition technique which uses the coefficients directly from a logit or probit model when
the outcome is binary. In this study we use a variation of the Blinder-Oaxaca decomposition
method as described by Fairlie (2005) to examine how racial/ethnic groups are different in terms
of the risky asset ownership and how much of these disparities are due to the observed
differences in household characteristics.
Oaxaca and Ransom (1994) suggest a way to circumvent the index number problem,
whereby the pooled coefficient vector-pooled for the two races being compared-is taken as the
non-discriminatory unemployment structure. We illustrate the process of decomposition of the
Black-White gap in risky asset ownership as an example of the technique we adopt. In general,
the disparity of ownership rate between Blacks and Whites can be expressed as below:
∧ ⎞
∧ ⎞⎤ ⎡
∧ ⎞
∧ ⎞⎤
⎡
⎛
⎛
⎛
⎛
B w ⎟⎥ ⎢
B w⎟
B B ⎟⎥
⎜
⎜
⎜
⎢ F ⎜ X iW β w ⎟
F X β
F X β
F X β
⎟ NB ⎜ i
⎟⎥
⎟ NB ⎜ i
⎟ ⎥ ⎢N B ⎜ i
⎢N w ⎜
W
B
⎝
⎠
⎝
⎠⎥
⎝
⎠
⎝
⎠
⎥+⎢
Y −Y = ⎢
−
−
B
B
w
B
⎥
⎢
⎥
⎢ i =1
N
N
N
N
i =1
i =1
⎥
⎥ ⎢ i =1
⎢
⎥
⎥ ⎢
⎢
⎦
⎦ ⎣
⎣
∑
Y
W
∑
∑
∑
B
and Y represents the average predicted probability of ownership for White and Black
groups respectively. F (.) is the cumulative distribution function from the logistic distribution and
N represents the sample size in different groups. X W and X B are row vectors of average value
∧
w
∧
B
for the individual characteristics of Whites and Blacks respectively. β and β are the vector of
coefficient estimates for Whites and Blacks respectively. For a linear regression, the standard
Blinder-Oaxaca decomposition of the White/Black gap in the average of the dependent variable,
Y is expressed as: Y
W
−Y
B
∧ ⎤ ⎡
∧ ⎞⎤
⎡ W
⎛ ∧
B
B
= ⎢⎛⎜ X − X ⎞⎟ β w ⎥ + ⎢ X ⎜ β w − β B ⎟⎥ . As Fairlie (2005) illustrates,
⎟⎥
⎜
⎠
⎢⎝
⎥ ⎢
⎣
⎠⎦
⎦ ⎣
⎝
this equation is a special case of the equation we employ in our study.
8
The racial gap in this respect can be divided into two parts: one part of explained
difference (expressed as the first term in the equation) due to the differences in household
characteristics we include in the model, and another part of unexplained difference due to the
inability to include unmeasureable variables (expressed as the second term in the equation). The
contribution of each variable to the gap is equal to the change in the average predicted
probability from replacing the Black distribution with the White distribution of that variable
while holding the distributions of the other variable constant.
Given that our White sample size is much larger than that of Hispanic and Black samples,
we implement the average prediction decomposition method by following the suggestion of
Fairlie (2005) to randomly select a White sample to match the Black sample size for the analysis.
The Blacks and sampled Whites are then ranked by race and matched based on their predicted
stockownership outcomes. This sampling process is repeated 1,000 times, and calculated
statistics that are computed with the alternative weights are averaged. In this way, we can largely
avoid selection bias from sample differences between different racial groups.
In our study, the sample weights are used to estimate the mean outcomes but not the logit
regressions. There is no reason for us to prefer the Black or White estimates in this equation,
therefore, we follow the common approach of presenting both sets of estimates and also added
another set of overall sample estimates as proposed by Oaxaca and Ransom (1994). In this way,
we can test the sensitivity and consistency of decomposition specifications from estimates.
Therefore, in our study models are estimated separately—overall sample with racial/ethnic
controlled, White sample only, Black sample only, and Hispanic sample only, as shown in Table
1. These estimates include the same explanatory variables, with the exception that the race/ethnic
coefficients are dropped when they are applied in decomposition specification (1) as shown in
9
Table 4 and Table 5. By incorporating the coefficients from logistic results, decomposition
results can show the relative contribution to differences in risky asset ownership from household
characteristics such as age, education, and health status.
Methods
Data and Variables
We use the 2004 Survey of Consumer Finances (SCF) to study the White-Black and
White-Hispanic gap in risky asset ownership because the SCF is the best source of information
on the wealth or financial assets holdings and characteristics of American households (Bucks, et
al., 2006). The SCF is a triennial interview survey of U.S families sponsored by the Board of
Governors of the Federal Reserve System with the cooperation of the U.S. Department of the
Treasury. The race and ethnicity of a household in the SCF are classified according to the
respondent to the SCF interview. The surveys before 2004 include one race/ethnic question, with
White and Hispanic presented as different categories (Yao, et al., 2005). A few respondents also
indicate a second category of racial/ethnic identity, but in the public use datasets before 2004, it
is impossible to identify respondents who choose Hispanic as a second category. Starting from
the 2004 SCF, there is now a separate question about Hispanic status: whether respondents
consider themselves to be Hispanic or Latino in culture or origin. Combining that answer with
the primary question gives a higher proportion of Hispanics. However, the results are similar
when using the one question racial/ethnic variable, so for consistency with research using
previous SCF datasets, we use the one question categorization.
The 2004 SCF survey includes 4,519 households, with five implicates for each household
(Bucks, et al., 2006). The coding for the race/ethnic status variable (X6809) is different across
10
implicates for 13 households, so those households are excluded from the analyses. Of the
remaining 4,506 households, there are 3,511 households with White respondents, 482 with Black
respondents, 347 with Hispanic respondents, and 166 with respondents choosing some other
racial/ethnic group. (The SCF does not provide detailed breakdowns of this last group in the
public dataset.) In analyses of all households, we include the “other” households, but do not
present separate analyses. Using the SCF population weight, 73.6% of the households have
White respondents, 13.6% have Black respondents, 9.2% have Hispanic respondents, and 3.7%
have “other” respondents.
We base our multivariate analyses on previous research and on the idea that a
household’s decision to hold risky asset investments is related to its risk tolerance, its investment
horizon, and its desire to invest for future goals, especially retirement. The appropriateness of
risky asset investments for savings goals is strongly related to the investment horizon, given the
volatility of risky assets compared to alternate investments such as cash equivalents and shorter
term government bonds. Campbell and Viceira (2002) show that the optimal stock proportion of
a portfolio should be related to age, for each level of risk aversion. Lower income households
may decide to not save much for retirement because of the higher replacement rate for Social
Security pensions, so putting funds in any higher return investment should be strongly related to
household income. Liquidity concerns may lead some households with low net worth to avoid
putting money in high return investments. The focus on much research and normative analysis
in finance is on the ratio of risky assets to the total portfolio (e.g., Coleman, 2005). However,
most Black and Hispanic households hold no risky assets, so a direct comparison of the risky
asset ratio of minority households with White households does not provide much insight,
because most of the racial/ethnic differences are due to differences in ownership of risky assets.
11
Our basic models for the multivariate analysis are:
For the overall sample:
Choice of holding any risky investments = f (race/ethnic group, X)
(1)
Where X is a vector of household characteristics and risk tolerance.
For each of the individual subsamples (White, Black and Hispanic):
Choice of holding any risky asset investments = f (X)
(2)
Where X is a vector of household characteristics and risk tolerance.
The dependent variable is dichotomous, and equals 1 if the household holds any risky
asset investments, including in mutual funds inside of defined contribution retirement accounts.
The explanatory variables in this study are age, age squared, household respondents’ education
level, health situation, risk tolerance, household income, net worth, and presence of children age
under 19, homeownership, business ownership and gender of respondents (Appendix 1.) Net
worth might be endogenous, since over time, households who choose risky assets will tend to
accumulate more net worth. Our logistic regression and decomposition analysis results are
similar to those presented in this paper when net worth is not included, so if there is an
endogeneity bias from using net worth as an independent variable, it does not seem to be large.
Analysis
Figure 1 shows the aggregate allocation of risky assets. Stock investments, including
stocks in mutual funds, account for 38% of all risky assets, business investments account for
37%, and investment real estate assets account for 25%. Figure 2 shows the allocation for
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households with a total amount of risky assets less than one million dollars. Stock investments
account for 52% of all risky assets, business investments account for 19%, and investment real
estate assets account for 29%.
Households with White respondents are older, more educated, in better health, have
higher income, have higher net worth, and are less likely to say they are unwilling to take any
risk with investments than are households with Black or Hispanic respondents (Table 1).
Only
27% of Hispanic households and 33% of Black households have any type of risky asset
investments, compared to 64% of White households (Table 2). The patterns are similar for the
new racial/ethnic categories taking into account the separate Hispanic question, so for
comparability with previous SCF datasets, we use the older categories. Figure 3 shows the
actual pattern of risky asset ownership, as well as the predicted pattern, discussed below.
The dependent variable in our model is an indicator of whether or not households hold
risky directly and/or indirectly. The explanatory variables included in the study are age, age
squared, household heads’ education level, health situation, risk tolerance, household income, net
worth, and presence of children aged under 19, homeownership, business ownership and gender
of respondents.
Decomposition Specifications of Racial Disparities in Risky Asset Ownership
The logit empirical analyses (Table 3) show that many household characteristics are
related to risky asset investment ownership, and there are some consistent significant factors
across three kinds of samples. The logit for the overall sample had a concordance ratio of 92.5%,
meaning that the logit correctly classifies over 92% of the households in terms of risky asset
investment ownership. Even after controlling for income, net worth, education, risk tolerance,
13
and other variables, Blacks, Hispanics, and those in one of the other categories (largely Asian)
have much lower predicted risky asset investment ownership rates than White households.
Figure 3 shows the actual and predicted risky asset rates. The gaps between White households
and Black, Hispanic households narrow after controlling for the independent variables, though
there still substantial differences. In the following section, we examine the fraction of the gaps
that can be explained by those characteristics.
Black-White Disparity.
The difference between White and Black risky asset rates is 31 percentage points. Table
4 shows the decomposition analyses for White-Black differences. Household net worth is the
biggest contributor to the racial disparity in risky asset ownership, accounting for 50% of the
differences in the full sample, 48% in the Black sample, and 50% in the White sample. Risk
tolerance contributes about 19% of the Black-White disparity in estimates based on three
samples. Black risky asset investment ownership would be much higher if Black households had
the same net worth or the same risk tolerance as White households. The other important
contributors are household income and education. Our result indicates that the gap between
Black and White risky asset investment ownership is almost completely determined by
differences in education, income, net worth, risk tolerance, and other household characteristics.
The overall explained difference is 106.5% for the full sample, which implies that if Black
households had the same characteristics and risk tolerance as White households, they would have
higher risky asset ownership than White households. Figure 4 shows the relative contributions of
factors that are related to lower Black risky asset investment ownership, based on the analysis of
the overall sample shown in Table 4.
14
Hispanic-White Disparity.
The Hispanic-White decomposition shows a pattern similar to the Black-White
decomposition. The difference between Hispanic and White households in risky asset investment
ownership rates is 37 percentage points. Net worth, risk tolerance, education and income are the
most important contributors to the gap. Risk tolerance’s contribution is very close in three
specifications and education is the next important contributor, based on the overall sample.
Figure 5 shows the relative contributions of factors that are related to lower Hispanic risky asset
investment ownership, based on the analysis of the overall sample shown in Table 5. If Hispanic
households had the same wealth, risk tolerance, income, education, and other characteristics as
White households, their risky asset ownership would be almost as high as White households
based on the Hispanic only estimates and close to the White rate based on the full sample and the
White-only sample.
Conclusions
Differences in demographic and economic characteristics explain virtually all of the total
differential between the Black and White risky asset investment ownership, and explain more
than 80% of the total differential between the Hispanic and White risky asset. Household
economic characteristics, net worth and income play the most significant role in causing the
racial gap in risky asset investment ownership between both Black-White and Hispanic-White
disparity. Obviously, improving those conditions of minority groups may improve their situation
in risky asset ownership, but risk tolerance also plays an important role in explaining both sets of
racial/ethnic disparities in risky asset ownership. Therefore, education targeted at increasing the
risk tolerance of minorities should increase their likelihood of owning risky assets.
15
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17
Table 1
Means and Proportions of Household Characteristics by Racial/Ethnic Groups, 2004
Variable
White
Black
Hispanic
Own stocks directly and/or indirectly
0.57
0.26
0.19
Own investment real estate
0.20
0.12
0.12
Own business assets
0.14
0.05
0.04
Own one or more risky assets
0.64
0.33
0.27
Age
50.39
47.2
41.82
Income ($)
76,958
37,781
38,395
Net worth ($)
553,363
109,718
126,129
Education of respondent
Less than high school degree
0.17
0.26
0.50
High school degree
0.20
0.24
0.20
>12 years education without degree
0.22
0.25
0.18
2 years degree
0.06
0.06
0.02
Bachelor degree
0.21
0.12
0.06
Post bachelor degree
0.13
0.08
0.03
Health of respondent
Poor health
0.06
0.10
0.09
Fair health
0.16
0.21
0.28
Good health
0.48
0.43
0.41
Excellent health
0.30
0.27
0.23
Risk tolerance level
Not willing to take risk
0.37
0.57
0.65
Average risk tolerance
0.43
0.28
0.20
Above average risk tolerance
0.18
0.09
0.10
Substantial risk tolerance
0.03
0.05
0.05
Couple household
0.55
0.26
0.50
Own home
0.76
0.50
0.48
Presence of child aged under 19 at home
0.40
0.48
0.62
Female respondent
0.54
0.68
0.52
Actual (unweighted) number of households
3,511
482
347
Weighted percent of sample
73.61%
13.56%
9.18%
Calculated by authors, weighted analysis of the 2004 SCF, with 13 households deleted that had different
responses to racial/ethnic group in different implicates. Households with respondents coded as “Other
racial/ethnic group” are included in the overall analyses but the results are not presented here.
18
Table 2
Risky Asset Ownership by Race/Ethnic Category, 2004
Percent
Distribution
Original SCF Race/Ethnic Categorization
White
73.60%
Black
13.56%
Hispanic
9.17%
Other, including Asian
3.67%
New SCF Race/Ethnic Categorization
White Non-Hispanic
71.82%
Black Non-Hispanic
13.41%
Hispanic
11.17%
Other, including Asian
3.61%
Own Stocks
Directly and/or
Indirectly
Own Any Risky
Asset
56.69%BC
25.51%A_
18.65%AD
45.92%C_
63.66%BC_
32.63%AD_
26.74%AD_
60.55%BC_
57.32% BC_
25.62%A__
20.53% AD
46.66%C__
64.33%BC
32.67%AD
28.21%AD
61.52%BC
Based on authors’ calculations, weighted analyses of 2004 Survey of Consumer Finances datasets, using RII
technique with all implicates.
A
Significantly different from White at 0.05 level or better
B
Significantly different from Black at 0.05 level or better
C
Significantly different from Hispanic at 0.05 level or better
D
Significantly different from Asian/Other at 0.05 level or better
19
Table 3
Logistic results of racial difference in risky asset ownership in different samples
Samples
Full Sample
Black
Parameters
coefficient
p-value
coefficient
Intercept
-8.6597
.000
-7.1268
Racial/ethnic groups: reference category= White
Black
-0.6184
.000
Hispanic
-0.6684
.000
Asian/other groups
-0.1709
.4726
Age of respondent/100
7.6228
.000
10.1196
Age/100 squared
-7.0924
.000
-9.3816
Education of respondent: reference category=less than high school
High school degree
0.3991
.0082
0.2034
>12 years but no
degree
0.8065
.000
1.1657
2 year degree
0.8713
.000
1.4644
Bachelor degree
1.3638
.000
0.7319
Post BS
1.5029
.000
2.0730
Health condition of the respondent: reference category=poor health
Excellent health
0.6568
.0040
0.2404
Good health
0.8182
.0002
0.7488
Fair health
0.1866
.4193
-0.2345
Expect to inherit wealth
0.3187
.0310
0.4775
Risk tolerance: reference category=not willing to take any risk
Average
1.3170
.000
1.2835
Above average
1.7521
.000
1.1265
Substantial
1.7828
.000
1.0763
Log of income
0.2656
.000
0.0612
Log of net worth
0.2152
.000
0.1910
Couple (versus
noncouple)
0.3568
.0009
0.0812
Homeowner (versus
rent)
0.2469
.0429
0.1163
Child<19
-0.2838
.0085
-0.1335
Gender of respondent: reference category=male respondent
Female respondent
-0.0470
.6462
-0.1247
Likelihood ratio
2749.6882
.000
207.4501
Concordance
92.5
86.6
p-value
.000
White
coefficient
p-value
- 8.7103
.000
Hispanic
coefficient
p-value
-21.5262
.000
.0480
.0701
7.2218
-6.6944
.000
.000
4.8335
-4.7065
.5736
.6123
.6319
0.3507
.0528
0.8626
.0889
.0038
.0124
.1207
.0004
0. 6726
0. 7092
1.5795
1.4273
.000
.0057
.000
.000
1.9793
1.1491
1.4726
0.6649
.000
.3784
.0358
.5074
.6808
.1715
.6983
.3166
0.6743
0.7865
0.1963
0.2994
.0108
.0017
.4631
.0675
0.7256
1.2158
1.0717
0.2352
.4933
.2285
.2978
.8018
.000
.0062
.0519
.5053
.000
1.2981
1.9475
2.1087
0.2629
0.2260
.000
.000
.000
.000
.000
1.3824
1.3146
1.5843
1.5074
0.1457
.0015
.0209
.0148
.000
.0133
.7880
0.3696
.0035
0.5641
.1802
.7031
.6253
0.2879
-0.2758
.0529
.0333
0. 5162
-1.0112
.2728
.0182
.6465
.000
0.00684
1806.6933
92.1
.9555
.000
-0.1952
221.7992
93.3
.6318
.000
20
Table 4: Decompositions of risky asset ownership of Black versus White Households
Specifications based on the coefficients of three types of samples
(1)
(2)
(3)
Full sample
Black
White
White risky asset
ownership
0.6370831
0.6370831
0.6370831
Black risky asset
ownership
0.3266508
0.3266508
0.3266508
Black-White gap
0.3104322
0.3104322
0.3104322
Contributions of individual variable
age
0.0027733
0.0082362
0.0027766
percentage
0.89%
2.65%
0.89%
Standard error
0.0018351
0.0093460
0.0021691
Education of respondent
percentage
Standard error
0.0276147
8.90%
0.0029920
0.0447952
14.43%
0.0137480
0.0291764
9.40%
0.0034564
Health condition of
respondent
percentage
Standard error
0.0111366
3.59%
0.0023057
0.0090538
2.92%
0.0073665
0.0110680
3.57%
0.0027328
Expectation of
inheritance
percentage
Standard error
0.0035342
1.14%
0.0015886
0.0065488
2.11%
0.0062766
0.0031479
1.01%
0.0016731
Risk tolerance
percentage
Standard error
0.0576447
18.57%
0.0043576
0.0576961
18.59%
0.0134937
0.0592377
19.08%
0.0051424
Income
percentage
Standard error
0.0349686
11.26%
0.0048381
0.0121048
3.90%
0.0179528
0.0335846
10.82%
0.0051597
Net worth
percentage
Standard error
0.1540481
49.62%
0.0087262
0.1487488
47.92%
0.0219598
0.1554906
50.09%
0.0100102
Couple households
percentage
Standard error
0.0198953
6.41%
0.0060243
0.0045715
1.47%
0.0170822
0.0203280
6.55%
0.0070221
Home ownership
percentage
0.0149084
4.80%
0.0066333
2.14%
0.0170365
5.5%
21
Specifications based on the coefficients of three types of samples
(1)
(2)
(3)
Full sample
Black
White
Standard error
0.0074987
0.0175929
0.0089652
Presence of child aged
under 19
percentage
Standard error
0.0027312
0.88%
0.0010406
0.0013111
0.42%
0.0027014
0.0026247
0.85%
0.0012289
Female respondent
percentage
Standard error
0.0015048
0.48%
0.0032787
0.0045265
1.46%
0.0098935
-0.000215550
-0.07%
0.0038631
Overall explained
difference
percentage
0.3307599
106.54%
0.3042261
98.01%
0.3342555
107.63%
Unexplained difference
percentage
-0.0203277
-6.54%
-0.0062061
-0.0199%
-0.0238233
-7.63%
22
Table 5: Decompositions of risky asset ownership of Hispanic versus White Households
Specifications based on the coefficients of three types of samples
(1)
(2)
(3)
Full sample
Hispanic
White
White risky asset
ownership
0.6370831
0.6370831
0.6370831
Hispanic risky asset
ownership
0.2676478
0.2676478
0.2676478
Hispanic-White gap
0.3694353
0.3694353
0.3694353
Contributions of individual variable
age
0.0042616
0.000473932
0.0043972
percentage
1.15%
0.13%
1.19%
Standard error
0.0037545
0.0162458
0.0044477
Education of head
percentage
Standard error
0.0526597
14.25%
0.0065400
0.0610312
16.52%
0.0198262
0.0510064
13.81%
0.0075833
Health condition of
respondent
percentage
Standard error
0.0138003
3.74%
0.0029418
-0.000313840
-0.08%
0.0074368
0.0123291
3.34%
0.0032969
Expectation of
inheritance
percentage
Standard error
0.0042915
1.16%
0.0019285
0.0024777
0.67%
0.0097057
0.0037292
1.01%
0.0019750
Risk tolerance
percentage
Standard error
0.0727156
19.68%
0.0052961
0.0543840
14.72%
0.0160104
0.0750290
20.31%
0.0062484
Income
percentage
Standard error
0.0287046
7.77%
0.0041315
0.1230444
33.31%
0.0251098
0.0285574
7.73%
0.0044918
Net worth
percentage
Standard error
0.1108654
30.01%
0.0068908
0.0568416
15.39%
0.0211263
0.1124444
30.44%
0.0079513
Couple households
percentage
Standard error
0.0062095
1.68%
0.0018733
0.0071903
1.95%
0.0053581
0.0065067
1.76%
0.0022014
Home ownership
percentage
0.0114741
3.11%
0.0173686
4.70%
0.0129651
3.51%
23
Specifications based on the coefficients of three types of samples
(1)
(2)
(3)
Full sample
Hispanic
White
Standard error
0.0057310
0.0162501
0.0067769
Presence of child aged
under 19
percentage
Standard error
0.0108484
2.94%
0.0041112
0.0262617
7.11%
0.0114747
0.0104848
2.84%
0.0049212
Female respondent
percentage
Standard error
0.000488929
0.13%
0.0010657
0.0020071
0.54%
0.0042118
-0.000069568
-0.02%
0.0012464
Overall explained
difference
percentage
0.316319629
85.62%
0.350766692
94.96%
0.31737932
85.92%
Unexplained difference
percentage
0.05311567
14.38%
0.018668608
0.04%
0.052055568
14.08%
24
Figure 1
Risky Investment Assets, Household Allocation, All Households, 2004 SCF.
real estate other than
primary residence,
25%
(24.94%)
stock,
38%
(37.88%)
business ownership,
37%
(37.18%)
25
Figure 2
Risky Investment Assets, Household Allocation, Among Households with Risky Assets <
$1,000,000, 2004 SCF
real estate other than
primary residence,
29%
(29.02%)
stock, 52%
(51.92%)
(19.06%)
business
ownership,
19%
26
Figure 3
Risky Ownership Rates for Blacks, Whites, Hispanics, and Others, 2004 Survey of Consumer
Finances.
70%
Risky Investment Ownership Rate
60.5%
60%
50%
59.6% 63.7%
55.5%
44.3%
43.1%
40%
32.7%
26.8%
30%
20%
10%
0%
Black
Hispanic
Other
Race/Ethnic Group of Respondent
predicted
White
actual
Calculated by authors, weighted analysis of the 2004 Survey of Consumer Finances.
Predicted rates are calculated from the logistic regression results for the full sample in Table 3, at
the mean values of other variables, and adjusted so that at the mean of all variables, the predicted
ownership rate equals the overall sample ownership rate.
27
Figure 4
Decomposition of Black-White Risky Asset Ownership Differences in 2004 Based on Overall
Sample. Factors Related to Lower Risky Ownership Rate of Blacks.
expect inheritance, 1%
female,
inher
income,
11%
Income
female
child<19,
1%
Child
age, 1%
Age
health,
Health
couple,
6%
Couple
net
worth, 50%
NW
own home,
5%
Own
Hm
education,Educ
9%
risk tolerance,Risk
19%Tol
Calculated by authors, based on Table 4, Overall Sample
28
Figure 5
Decomposition of Hispanic-White Risky Asset Ownership Differences Based on Overall
Sample. Factors Related to Lower Risky Asset Ownership Rate of Hispanics.
income,
8%
Income
female,
inher
0.1%
female
expect inheritance, 1%
child<19,
3%
Child
age, Age
1%
net worth, 30%
health,
Health
NW
couple,
2%
Couple
own home,
3%
Own Hm
education, Educ
14%
Risk Tol
risk tolerance, 20%
Calculated by authors, based on Table 5, Overall Sample
29
Appendix 1: Description of Explanatory Variables
Variables
Description
Demographic variables
Age
Age of respondent
Age-squared
Squared age of respondent
Race/ethnic self-identification of the respondent (based on variable X6809)
White
Black
Hispanic
Other (including Asian)
Respondent’s education
Less than high school
1 if years of education <12, 0 otherwise
High school
1 if years of education =12, 0 otherwise
More than 12 years’ education without degree
1 if years of education>12 and no degree
2 year degree
1 if get a 2 year’s degree
Bachelor degree
1 if get a bachelor degree
Post bachelor degree
1 if get a degree higher than bachelor
Respondent self-assessment of health
Poor health
1 if the respondent said she or he had a generally poor health; 0
otherwise
Fair health
1 if the respondent said she or he had a generally fair health; 0
otherwise
Good health
Excellent health
Respondent’s investment risk tolerance
Not willing
Average risk tolerance
Above average risk tolerance
Substantial risk tolerance
Business ownership
Home ownership
Married couple
Female respondent
Presence of child aged under 19
Economic variables
Log of Income
Log of Net worth
1 if the respondent said she or he had a generally good health; 0
otherwise
1 if the respondent said she or he had a generally excellent health; 0
otherwise
1 if not willing to take any risk, 0 otherwise
1 if not willing to take average risk, 0 otherwise
1 if not willing to take above average risk, 0 otherwise
1 if not willing to take substantial risk, 0 otherwise
1 if the respondent said the household owned business, 0 otherwise
1 if the respondent said the household owned home, 0 otherwise
1 if the respondent is married to household member, 0 otherwise
1 if the sex of the respondent is female, 0 otherwise
1 if there is a child aged under 19 living in the household, 0
otherwise
Log of household’ annual income (if income ≤ 0, use ln (0.01)
Log of household’s net worth (if net worth ≤ 0, use ln (0.01)
30