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Race differences in middle class lifestyle: The role of home ownership
SOCIAL
SCIENCE
RESEARCH
8,63-78
(1979)
Race Differences in Middle Class Lifestyle: The Role of Home Ownership JOHN C. HENRETTA University of Florida This paper examines race differences in two aspects of “middle class” lifestyle: home ownership and net worth. Home ownership indicates stability; and for older persons net worth is an important part of economic status. Data from the NLS studies of older men are analyzed. The major findings are: (1) while whites at any earnings level are very likely to own homes by ages 50-64, only at relatively high earning levels do blacks begin to approach the home ownership rates of whites; (b) the net worth of blacks is substantially lower than that of whites after adjusting for variables in a standard status attainment model; and (c) however, among home owners the race difference as well as effects of other variables are much smaller than for renters. This is attributed to forced saving through home ownership. The paper concludes with a discussion of possible sources of low home ownership rates and low net worth of blacks and the implication of these findings for the study of middle class status.
Recent stratification research on race differences has concentrated on labor market processes as determinants of earnings and occupation (e.g., Duncan, 1%8; Jencks, Smith, Acland, Bane, Cohen, Ginhs, Heyns, and Michelson, 1972; Stolzenberg, 1975). Both segmented market theories and human capital or status attainment approaches have emphasized labor markets or returns to investment, and have ignored other aspects of the stratification process which are consistent with these approaches and which have traditionally been of interest to sociologists. Race differences in class-related lifestyle comprise one of these recently neglected areas of study. This paper examines two related aspects of “middle class” lifeSupport for the preparation of this paper was provided by a grant from the Administration on Aging (Training Grant 90-A-834 (1)) to’the Center for Gerontological Studies and Programs, University of Florida and by a grant from the Administration on Aging (Grant 90-A-1048) to the Data Archive Project, Center for the Study of Aging and Human Development, Duke University. I wish to thank Angela O’Rand, Marshall Pomer, Richard Campbell, and Richard Cohn for their comments on an earlier version of this paper. Requests for reprints should be sent to John C. Henretta, Department of Sociology, University of Florida, Gainesville, FL 32611. 63 0049089W79/010063-16$02.00/0 Copyright @ 1979 by Academic Press. Inc. All rights of reproduction in any fom reserved.
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JOHN
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style, home ownership and the accumulation of savings, and compares the process of their attainment for blacks and whites. The concept of the middle class lifestyle is usually defined for both blacks and whites by consumption patterns. There is little agreement on one definition of the middle class, but the themes found in the stratification literature emphasize security, home ownership, deferred gratification, and saving (Dobriner, 1963, p. 48; Kronus, 1971, pp. 23-27: Rossides, 1976, pp. 23-28). These themes are also found in the older literature on the black middle class (Davis and Dollard, 1940) even though it has been simultaneously argued by some that black middle class lifestyle included conspicuous consumption (e.g., Frazier, 1957.)’ While they are not the only possible measures of lifestyle, home ownership and savings are important attainments. Home ownership provides stability, security, and a sense of belonging to a community. Home owners are much more likely to be registered voters than are renters (Gallup, 1972, p. 1227); and home owning provides freedom from interference by landlords and neighbors. As such, it can provide an entree to a middle class iifestyle for those with relatively small incomes.” Owning not only allows a higher level of current consumption (e.g., more space) than rental housing available for the same price, but it is also an important form of forced savings (Kain and Quigley, 1973, pp. 272-276). Overall, it would be difficult to overestimate the importance of home ownership in American society. It is an anchor to the community, a source of psychological satisfaction, and a good economic investment. This significance of housing for status was recognized in early stratification indices which incorporated place of residence and dwelling type (though not home ownership) in status measures (Warner, 1949; Hollingshead and Redlich, 1958). Equally important to the attainment of security is the accumulation of savings. For most persons home equity is the most important part of total savings: but for those who do not purchase a home, the accumulation of net worth in the form of savings or other investments is necessary to insure one’s future living standard (Weisbrod and Hansen, 1968; Miller and Roby, 1970). This is particularly true for older persons who have relatively few years remaining in the labor force and for whom labor market attainments will provide relatively little future return. In old age, savings not only provide added income, but also provide security and confidence in the face of unexpected events. Net worth, including home ’ (Frazier, 1957, p. 76) quotes Booker T. Washington: “A man never begins to have self-respect until he owns a home.” and “Art and music to people who live in rented houses and with no bank account are not the most important subjects to which attention can be given . .” 2 See, for example, Sennett and Cobb’s (1972, p. 48) discussion of the meaning of home ownership to a “middle class” man who currently held a manual job.
RACE
DIFFERENCES
AND
HOME
OWNERSHIP
65
equity, can be used to purchase services from nursing homes or retirement communities. Economists have elaborated theories of consumption and saving (e.g., Friedman, 1957), but relatively little sociological attention has been given to them. A consumption-based measure such as home ownership or net worth is useful in sociology because it can be related to the current status attainment literature which emphasizes socioeconomic attainments as well as to the stratification literature which emphasizes class related lifestyle. Such measures are particularly useful since they allow an examination of race differences in the effects of labor market returns on lifestyle. The causal model implicit in this discussion relating net worth and home ownership to earlier attainments is:3 Home ownership
= a + b -X + u
Net Worth = a + b -(Home ownership)
+ b *X + v
(1) (2)
Where X = race, father’s education and occupation, respondent’s age, education, occupation, marital status, household size, average weeks unemployed, earnings, and pension coverage. Father’s education and occupation measure intergenerational transmission of status. Ideally, one should have a measure of wealth of the family of orientation, but these data are not available. Respondent’s age measures the effect of longer labor force participation on accumulation of net worth; there is also a cohort effect resulting from earnings levels associated with cohorts, but given the small range of ages in the sample (ages 50-64), this should not predominate. The age range for this sample is theoretically appropriate because net worth and home ownership are more important for the status of older persons, since at later ages physical capital is increasingly important compared to human capital (Henretta and Campbell, 1978). The age range also helps in disentangling the direction of causation between net worth and home ownership. Does home ownership lead to greater net worth accumulation, or does net worth allow one to purchase a home? By ages 50-64 it is not unreasonable to expect that increased net worth is a function of home ownership not only because of the growth of home equity but also because of the continuing savings attributable to home ownership.4 Respondent’s education and occupation measure early attainments of B The estimation of Equations 1 and 2 would allow straightforward computation of direct and indirect effects through home ownership. However, since the effect of status attainment variables on net worth varies over levels of home ownership (but not over race). Equation 2 is estimated separately for owners and renters. 4 For a discussion of this problem, see Kain and Quigley (1975, pp. 151-152).
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JOHN C. HENRETTA
the respondent as is standard procedure in models of this kind. Respondent’s marital status and household size measure financial commitments of the respondent. These factors both affect ability to accumulate net worth. The remaining variables measure economic attainments. Earnings are average yearly earnings for family members for four years. Weeks unemployed is average number of weeks the respondent has been unemployed in the previous five years. This is included because a common explanation of race differences in net worth or in yearly saving is greater instability of blacks’ earnings (e.g., Terrell, 1971). A dummy for pension coverage is included to allow for the effect of forced saving. City size and region are also included in the model. A description of the data source, the NLS study of older men, and a discussion of coding of the variables in the analysis are included as an appendix. Race and Home Ownership
Table 1 presents logit estimates, for Equation 1, the determinants of home ownership.5 The antilogs of the coefficients are predicted proportional changes in the odds ratio per unit change in the independent variable. These predicted changes are shown in the third column of the table. Most attainment variables have very small effects on likelihood of home ownership. However, both the marital status contrasts have very large effects: the odds ratio of divorced persons is 13% that of married persons: the odds ratio predicted for persons in the other category is 16% of that of married persons. This should be expected since persons who are not married have less use for owned housing. Among the economic attainments, pension coverage has a large effect. The predicted odds ratio for persons with a pension is 34% higher than persons without pension coverage; jobs with pension coverage may pros The logit model was estimated using a version of the Nerlove and Press (1973) program modified and maintained by the Economics Department at North Carolina State University. The procedure allows for the computation of maximum likelihood estimates of logits for models with continuous independent variables. For a discussion of the logit’model, see Hanushek and Jackson (1977). The form chosen for the equation restricts all coefficients except earnings to have the same slope across race. Freeing other coefficients did not improve the fit significantly. Kain and Quigley (1973) estimate a similar model for home ownership using GLS and restrict all coefficients to be the same across race. However, the choice of the present model would be the same using this logit procedure or a least squares procedure. The chi square value for the difference between the earnings restricted and the earnings free model for the logit model is approximately 20 with 1 df. In the parallel OLS model, the F-ratio for the increment to R* when the earnings coefticient is freed is 22. The two models may not differ as much as they seem to at first glance since, as discussed in the text, there are few blacks in the region where the regression slopes intersect. Yet, as noted, the existence of the income interaction is theoretically significant.
RACE DIFFERENCES
AND HOME OWNERSHIP
61
TABLE 1 Logit Estimates of Home Ownership b
Father’s education Father’s SE1 Respondent’s age Respondent’s education Respondent’s SE1 Divorce Other marital status Household size Weeks unemployed Pension Earnings (blacks) Earnings (whites)’ Race (white) South City size:* 250,000 + 25,000-250,OclO 2500-25,000 Constant
.0034 - .0046 .0055 .0139 .0114 -2.001 -1.788 - .0186 - .ooo9 .2953 .1746 .0411 1.4%
seb
(.0174) (.0028) (.0126) (.01&Q) (.0030) (.2738) (.1684) (.0296) (.0054) (.1084) (.0324) (.0326) (.2390)
Predicted proportional change 1.003 9954 1.005 1.013 1.011 .1352 .1672 .9815 .9991 1.343 1.190 1.039 4.46
.3191 (.1244) - .388 - .0265 .3309 - 1.098
(.1300) (.1680) (.1844) (.74%)
1 t-test for differences of earnings coefficients = 4.14. Earnings coefficient is for $1000 increment in earnings. 9 The omitted category is rural.
vide more stability and longer tenure for their incumbents and thus encourage home buying. However, the most interesting results are for earnings. While for blacks each additional $1000 of family earnings increases the predicted odds ratio l%, the predicted change for whites is only 3.9%. Given the lower intercept for blacks, the predicted odds ratios for blacks and whites are equal at about $11,000. This is misleading, however, since fewer than 6% of the blacks in this sample have average four-year family earnings that high. At higher earnings levels, black home ownership rates approach those of whites; as income decreases, blacks are increasingly less likely to own homes compared to whites net of other characteristics. These findings suggest important aspects of the process of home ownership. By the time they reach late middle age, whites are quite likely to own a home regardless of earnings. For blacks this is not true; only at higher incomes do they approach the same likelihood of home ownership as whites. The earnings levels which allow whites to achieve “middle class” status are lower than those required for blacks. It is not possible in these data to go further in explaining the difference, but there is relevant
68
JOHN C. HENRETTA
external evidence of housing discrimination, residential segregation. and refusal to sell to blacks which bear on this point. The home ownership patterns of this cohort are, to a great extent, the result of conditions in the period 193 l- 1956 when members of the cohort were at ages when they were probably buying first homes.” Past housing discrimination is not difficult to demonstrate: it was only in 1948, when these persons were aged 27-41, that the United States Supreme Court forbad state courts to enforce racially restrictive covenants on homes (Simpson and Yinger, 1965, p. 33 1). The analysis of home ownership data suggests that it is low income blacks who, in comparison with whites at the same earnings level, were most unlikely to purchase homes. If this is a result of discrimination, it does not require a pattern of unequal treatment as blatant as the pre1948 law allowed. There is also some evidence that this difference in home ownership does not represent the preference of blacks. First, to the extent that other variables in the model measure preferences, these are already adjusted for. Second, if there were a constant difference between blacks and whites, preferences might be a reasonable explanation. While the presence of an interaction does not rule this out, the crucial role of earnings is also compatible with the explanation that only at higher earnings levels are blacks able to realize their preferences. This explanation is bolstered by Kain and Quigley (1975, pp. 63-82) who argue that housing is more expensive in glack ghetto areas than in white areas. The additional cost of housing in the limited housing market available to blacks may well explain why earnings level is more important for black ownership than for white. Additional evidence against the preference hypothesis explanation comes from Kain and Quigley’s (1973) analysis of housing markets in several metropolitan areas. By computing expected black home ownership rates based on income and age specific white ownership rates, they conclude that the difference between expected and actual rates is greatest in cities where there is a great deal of residential segregation and in which there is little single family housing available in black residential areas. The implications of home owning are increased by a consideration of its effect on the accumulation of net worth. Since blacks are less likely to own homes, more are located in the renter category; and the only path to financial security open to them is other saving. Thus an examination of the effect of owning a home on total net worth is necessary to trace the entire effect of limitations on black home ownership, (Kain and Quigley, 1975, pp. 148-152). @This period is arbitrary and approximate. Since life cycle events are timed differently for different individuals it is not possible to pinpoint home buying years. In 1931 the oldest members of the cohort being considered were age 24; in 1956 the youngest members were age 3.5. This seems a good approximation of likely home buying years.
RACE DIFFERENCES
AND HOME OWNERSHIP
69
Race and Net Worth Table 2 presents regression models for log net worth separately for home owners and renters.7 Within each category, one model suffices for blacks and whites.* The left panel of Table 2 presents results for home owners. Most of the effect of parental status is mediated by respondent’s education and occupation. The effects of parental status in Equation 4, the full equation, are small; but father’s occupation is still three times its standard error. The effect of age is similar in all equations. In the full equation, each year is associated with a predicted 1.6% increase in assets. This age effect is expected from the theoretical relation of age to saving: if average saving is positive, older persons should have greater savings. A cohort effect which would lead to the opposite finding is probably weaker. Respondent’s attainments have significant effects on net worth. Each percent increase in education is associated with an increase of .207% in net worth in Equation 4. Of the total education effect in Equation 2, about one-third is mediated by respondent’s family and economic status measures. Each 10 points of respondent’s SE1 is associated with a predicted 5.1% increase in net worth; slightly over half of the total effect in Equation 2 (11.6%) is mediated by economic status. Net of earnings and other predictors, divorced persons have predicted net worth 54% that of married persons.g Among home owners, those in the other marital status category have higher assets than married persons. This coefficient increases from Equation 3 to 4 suggesting that while overall these persons may not have high assets, their net worth is higher than would be expected from their economic attainments. ’ The log transformation reduces the effect of positive outliers and stabilizes the variance. Theoretical justification for this is that saving is for later consumption and the marginal utility of an additional dollar of net worth is likely to be a log function of net worth, not a linear one. That is, each additional dollar saved adds less to later status. On practical grounds, the extreme skewness of the new worth distributions and the influence of outliers on regression equations makes working with untransformed net worth impractical. Regression coefficients when the dependent variable is in the semilog form can be interpreted as follows: the antilog of the coefficient is the predicted percentage change in the dependent variable when the independent variable changes one unit (Theil, 1967, 1974). While in the case of cross-sectional data this does not yield a percentage change in the arithmetic mean, it does predict a change in the geometric mean. In the skewed net worth distribution, the geometric mean is a better guide to central tendency than is the mean. Prior to logging, those persons with negative assets were coded one. These persons constituted about 3% of the sample, and alternative treatment would be unlikely to change the results. 8 The F-ratio for homogeneity of coefficients across race for owners is 2.20; for renters it is 2.06. These are of borderline statistical significance. In contrast, the F- ratio for homogeneity of owner and renter models is 41.2. This pattern, as well as the pattern of the coefficients themselves, suggest there is one model for owners and one for renters. 9 No doubt some of the owners have at one time been divorced and have subsequently remarried and begin to accrue home equity again. The lack of marital history makes exploring this process with present data impossible.
Earnings3
Weeks unemployed
Pension
Household size
Other marital status
Divorce2
R’s SE1
R’s education (LN)
Respondent’s age
Father’s SE1
Father’s education
(Z) .014 uw
.022 t.006)
I
-.ool (J-w .005 (.ow .021 ww .324 (.@@I .Oll (.ow
2
Homeowners
.OOl (.ow .005 C.001) ,014 (.@w .291 Low .Oll (.ool) -.769 (.142) .I01 (.112) -.063 (.013)
3
.065 (JO3)
(.ow
- .003 c.0f-m .003 (.@Jl) .016 (.ow .207 W5) .oos (.Wl) -.621 (.132) .225 (.104) -.065 (.012) -.I80 (.037) .OOl
4 .115 (.043) .036 ww -.Oll (.031)
1 .008
(442) .014 (.007) .006 (.030) 1.14 (.227) .051 (.007) -.585 (.352) - .432 (.356) -.161 (.cw
.014 .015 (.007) .019 (.029) 1.11 (.228) .053 (.007)
3
(442)
2
Renters
TABLE 2 Reduced and Full Form Equations Department Variable: LN Assets’
,002 (.@w .009 (.007) .036 (.029) .895 (.222) .030 (.007) -.230 (.340) .318 (.353) -.I37 (.063) 2394 (.272) - .020 (.009) .166 (.027)
4
5.63
2.92
6.08
1.56
.30
1.08
.19
f-value for bo-6, = 0
.l% (051) -.012 (.ow .I24 (.067) 8.74 (.261) .252
- .783 (.057) .211 (.@w
.067 (.%3) 7.49 (.271) .348
(.062)
-.113
(348)
.087
-519 (.056) .194 t.044) .097 (.048) -.115 (.W .053 (.%3) 8.13 (.2%) .365 1849
-.465 (.056) .189 ww .045 (.@w -.108 (.057) .078 (.058) 7.98 (.276) .457
- .424 (.052) .155 t.0411
1.42 (.511) 2.78 (1.67) .303
(442)
.868 (.328) .501
-2.09 (.323) 1.75 (.323) .312 (.314) .007 (.417) 1.01 C.484) -1.06 (1.65) ,389
-1.27 (.314) 1.43 (.311)
’ Standard errors (not corrected for sampling efficiency) in parentheses. Metric coefficients are reported. * Each marital status category is a contrast with married, spouse present. 3 Increment of $looO. ’ The omitted category is rural.
R2 N
Intercept
250&25,000
25,000-250,000
City size:’ 250,000 +
Nonsouth
Race (black)
.246 (.315) -.012 (.418) .913 (.484) .369 (1.73) .395
-1.15 (.317) 1.39 (.311) -.237 (.313) - .250 (.403) .548 (467) 1.19 (1.W 448 712
-.994 (.305) 1.17 (.3OQ
1.59
.54
1.37
5.23
2.71
P m
5 u
c i2 u G m” fi 3 E
JOHN C. HENRETTA
72
TABLE 3 Summary of Race Differences in LN Net Worth
All respondents Homeowners Renters Race difference net of region und city size All respondents Homeowners Renters Race difference net of region, city size and attainment variables Homeowners Renters
Race difference
Black predicted NW as proportion of white
3.22 ,998 3.678
,039 ,368 ,025
3.03 .82 3.40
,048 ,440 ,033
.424 ,994
,654 ,370
Pension has a negative effect on net worth. Earnings has a positive effect: each thousand dollars of average earnings over a four-year period is associated with a 6.7% increase in net worth.‘” Most of the effects of region and city size are absorbed by adjusting for status variables; however, home owners in large cities and those living outside the south have some advantage. Net of region and city size, black home owners have predicted net worth 44% that of whites (see Table 3). After adjusting for status attainment and family variables, black net worth predicted in Equation 4 is 65.4% that of whites (an increase of about one-third the total difference. These race differences are discussed extensively below. In the equation for renters in the right panel of Table 2, almost all coefficients are larger. Though the patterns of mediation are similar, all attainments as well as one’s race, seem to make far more difference. The last column of Table 2 presents t-ratios for the test of equality of coefficients in the owner and renter models. The education coefficient is about four times larger in the equation for renters; each percent increase in education is associated with a .8950/o increase in predicted net worth instead of .207. Savings are much more affected by economic status for renters. Each $1000 of earnings is associated with an 18% increase in net worth, over two and one-half times its effect in the owner equation. Pension has a large positive effect on net ‘” Terrell(1971) logs both income and net worth and therefore argues that the relationship is linear in the double log form. Though his dependent variable is not the same, Friedman (1957) argues that the proper specification of the relationship between income and saving in any one year is the double log. In the present data, the regression of log net worth on family earnings is quite linear for blacks and whites, and so the semilog specification has been retained. The double log fits these data less well. See Henretta and Campbell (1978).
RACE DIFFERENCES
AND HOME OWNERSHIP
73
worth compared to a negative effect in the owner equation. Each week per year unemployed costs about 2% in assets. As with owners, race makes a large difference in net worth. However, for renters the difference is substantially larger. Net of region and city size blacks have predicted net worth 2.5% that of whites (see Table 3). Net of status and family variables, black predicted net worth is 37% that of whites. The reason for these lower coefficients for race and attainment variables is that for home owners, particularly those of low status, monthly mortgage payments are a form of forced savings which is not affected by events such as unemployment. In addition, capital gains from home ownership are probably the only ones available to persons of low status. The result of these processes is that, in proportional terms, there is less difference between high and low status home owners on net worth than there is among renters for whom there is less likely to be forced saving. Home ownership blunts the effect of high status or race on net worth when differences are expressed in proportional terms.” Why Are There Race Differences in Net Worth? Because the proper models for net worth vary over home ownership status there is not just one race difference but two. Table 2 presents race differences coefficients for several equations including some not presented in Table 1. Net of region and city size the predicted assets of blacks are 4.8% those of whites. Among homeowners, black predicted assets are 44% of those of whites net of region and city size; for renters predicted black assets are only 3.3% of white assets. After adjusting for attainment variables, black predicted assets are 65.4% and 37% of those of whites, respectively. Clearly there is a large residual race difference. One possible explanation is Terrell’s (1971): the current earnings of blacks do not represent very well their earning histories. The better measures used here (including ‘I A reader has suggested that the proper comparison is between renters and owners using only nonhome net worth for home owners. While this changes the nature of the analysis (i.e., no longer is net worth a comprehensive measure of security) the model was estimated for home owners using only nonhome net worth. Generally the results were similar to the home owner results using the full definition; however, the coefficient for race was large and negative as in the renter equation. It is difficult to interpret these findings because the way one divides net worth into various kinds of saving is dependent on race and home ownership. First, for owners, home ownership is an integral part of net worth. Kain and Quigley (1973) report as discussed in the text, that home ownership can result in lower housing costs after many years and therefore in a chance to accumulate nonhome net worth. More important, Terrell (1971) reports that blacks have a larger proportion of their net worth in their homes. Thus subtracting home value from net worth should lead to a larger race coefficient. Data presented by Kain and Quigley (1975) suggest this may result from blacks’ having to pay more for housing.
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JOHN C. HENRETTA
four year average earnings and unemployment experience over five years) still may not solve the problem. And, the residual race difference is greatest where status variables make the most difference: among renters. Equally as interesting are the differences which can be explained: owning a home is a potent factor in raising black assets relative to those of whites. Those blacks who own a home have much higher assets compared to whites (proportionally) than those who do not. This probably has the same interpretation as do steeper slopes for renters: low status characteristics are less of a liability in the face of forced saving. An additional implication flows from the number of cases in each category. While 78% of the whites in the sample are buying or own a home, only 54% of blacks are. Blacks are more likely than whites to end up in the renter category, a category which penalizes them more because of their race and their status characteristics. Conclusion: Becoming Middle Class
Blacks have lower net worth than whites for three reasons that can be traced: first, they have lower attainments in education, occupation, and measured earnings. Second, they are more likely to be divorced and to have large households. A third reason for low black net worth is that, especially at low earnings levels, blacks are less likely to own homes net of their other socioeconomic characteristics. This means that blacks are more likely to be in the renter category where their generally low status characteristics are particularly disadvantageous. All of this could be a result of measured earnings’ not being a good proxy for lifetime earnings; yet the findings have implications for the attainment of middle class status as a social process. If we adjust for various measured labor market returns, blacks arrive at retirement with less security than whites and less of a middle class lifestyle. A key element in this process is home ownership and its effect on net worth accumulation. These findings are consistent with the literature on the black middle class discussed earlier in that a middle class lifestyle has always been more difficult for blacks to attain and has been seen as a more distinctive accomplishment for blacks. (Frazier, 1957; Drake and Cayton, 1%2). This paper advances that literature by suggesting quantitative indicators of middle class status and by suggesting the social process leading to their attainment. APPENDIX
Data for the analysis are from the National Longitudinal middle aged men (Pames, 1970). (See Table Al.)
Studies of
Variables Father’s education and occupation are measured in years and Duncan SE1 scores. Age is measured in years.
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DIFFERENCES
AND
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OWNERSHIP
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Respondent’s education and occupation are measured by years completed and Duncan SE1 scores. Education has a nonlinear effect on net worth, and this is modeled in the net worth equations by the log of education. This describes a relationship, assuming the dependent variable is also logged, which increases at a diminishing rate. Since the dependent variable in this analysis is LN net worth, the resulting coefficient can be interpreted as an elasticity; the coefficient is the predicted percent change in wealth associated with a percent change in education. Two family measures are included in the model: marital status and household size. Marital status is trichotomized: married, divorced or separated, and other, including never married or widowed. There were not enough cases for further breakdown of the marital status categories. Household size, as a proxy for number of children, measures consumption needs. Three measures of current economic attainments and activity are included in the model. The earnings variable is the average of husband’s and wife’s earnings for the years 1965, 1966, 1968, and 1970, years the NLS collected earning data. A full accounting of net worth attainment requires development of a family model. This requires better data on wife’s occupational history than are available in NLS. The unemployment variable measures the annual average number of weeks the respondent has been unemployed during the years 1965 to 1970. The pension variable indicates whether there is pension coverage on the respondent’s current job. All equations also include variables indicating region and city size. Region is measured by a south-nonsouth dichotomy. The use of more detailed categories did not increase variance explained. City size is coded as follows: the omitted category is rural. The other categories are, in order: cities larger than 250,000; cities 25,000-250,000; and cities 250025,000. Though these variables have little effect in income or net worth models (Jencks, 1973; Henretta and Campbell, 1978), it is possible they may have an effect in models dealing with home ownership. Anyone who currently owns or is buying a house is coded as a home owner. Net worth is the sum of estimated home value, savings, business assets and real estate value. For persons who did not report liquid savings, about 20% of the final sample, savings was estimated using education and race specific medians. Education categories for savings replacement were: O-4, 5-8, 9-11, 12, 13-15, and 16+. After replacement, the final sample included 77% of white and 70% of black 1971 respondents to the NLS survey. While home value is reported in survey data with little bias (Kish and Lansing, 1954; Robins and West, 1977), nonhome net worth reports are subject to bias: those who have a great deal are likely not to report their holdings (Ferber, 1966). Since underreporting is correlated with true score, underreporting will bias effects downward.
Father’s education Father’s occupation Age R’s Education (LN) R’s SE1 Divorce Other marital status Household size Pension Weeks unemployed Earnings South L Net worth Net worth Net worth w/o home Home equity N
5.25 15.55 56.38 1.79 21.76 .048 .036 3.66 ,504 4.31 6457 ,666 9.09 13597 3717 9764 525
Mean
Black
2.92 12.79 4.09 ,692 17.82 ,216 ,188 2.03 .500 9.75 3548 .472 .957 21279 17288 6847
SD
Homeowners
Means
7.65 29.41 55.98 2.31 41.74 .012 .029 3.04 .611 3.19 10664 .263 10.145 43359 22062 19198 1324
Mean
White
3.47 22.94 4.21 ,382 24.06 ,109 ,168 I .45 ,487 7.79 5896 ,440 .974 72893 58454 23040
SD
TABLE Al and Standard Deviations
332
4.83 14.18 56.58 1.59 15.85 ,242 ,156 3.46 ,295 8.20 4155 .677 2.16 1045
Mean
Black
2.91 9.14 4.10 ,760 12.57 .429 .363 2.51 ,457 14.78 2964 .468 3.27 4417
SD
Renters
380
7.29 27.09 56.23 2.18 31.35 .139 .I59 2.79 .497 4.84 7966 ,209 5.77 12904
Mean
White
.5on 10.85 6742 ,407 4.165 43394
.347 ,367 I.65
5
2
0
23.15
5 is
3.39 20.81 4.30 ,503
SD
RACE DIFFERENCES
AND HOME OWNERSHIP
77
REFERENCES Davis, A., and Dollard, J. (1940), Children of Bondage, American Council on Education, Washington. Drake, St. C., and Cayton, H. R. (1%2), [1945] Black Metropolis, Harper & Row, New York. Dobriner, W. M. (1963), Class in Suburbia, Prentice-Hall, Englewood Cliffs, NJ. Duncan, 0. D. (1968), “Inheritance of Poverty or Inheritance of Race,” in On Understanding Poverty: Perspectives from the Social Sciences (ed. by D. P. Moynihan), Basic, New York, pp. 85-l 10. Ferber, R. (1%6), The Reliability of Consumer Reports of Financial Assets and Debts. University of Illinois, Bureau of Economic and Business Research, Urbana, IL. Frazier, E. F. (1957), Black Bourgeoisie, The Free Press, Glencoe. Friedman, M. (1957). A Theory of the Consumption Function, Princeton University Press, Princeton, NJ. Gallup, G. H. (1972), The Gallup Poll: Public Opinion 1935-1971, Random House, New York. Hanushek, E., and Jackson, J. (1977). Statistical Methods for Social Scientists, Academic Press, New York. Henretta, J. C., and Campbell, R. T. (1978). Net Worth as an Aspect of Status, American Journal of Sociology, 83: 5 (March), 1204-1223. Hollingshead. A. B., and Redlich, F. (1958), Social Class and Mental Illness, Wiley, New York. Jencks, C., Smith, M., Acland, H., Bane, M. J., Cohen, D., Gintis, H., Heyns, B., and Michelson, S. (1972), Inequality: A Reassessment of the Effect of Family and Schooling in America, Basic Books, New York. Jencks, C. (1973) “Inequality in Retrospect,” Harvard Educational Review 43 (February), 138-165. Kain, J. F., and Quigley, J. M. (1973), “Housing Market Discrimination, Home Ownership, and Savings Behavior,” American Economic Review, 62: 3 June, 263-277. Kain, J. F., and Quigley, J. M. (1975). Housing Markets and Racial Discrimination: A Microeconomic Analysis, National Bureau of Economic Research, New York. Kish, L.. and Lansing, J. B. (1954), “Response Error in Estimating the Value of Homes,” Journal of the American Statistical Association 49 (September), 520-538. Kronus, S. (1971), The Black Middle Class, Charles E. Merrill, Columbus. Miller, S. M., and Roby, P. (1970), The Future of Inequality, Basic Books, New York. Nerlove, M., and Press, S. J. (1973), “Univariate and Multivariate Log-linear and Logistic Models,” R-1306-EDA/NIH, The Rand Corporation, Santa Monica. Parnes, H. S. (1970), “The Pre-retirement Years: A Longitudinal Study of the Labor Force Experience of Men,” Manpower Research Monograph No. 15 (2 ~01s.) U.S. Department of Labor, Washington, DC. Robins, P. K., and West, R. W. (1977), “Measurement Errors in the Estimation of Home Value,” Journal of the American Statistical Association 72 (June), 290-294. Rossides, D. W. (1976), The American Class System: An Introduction to Social Stratification, Houghton-Mifflin, Boston. Sennett, R., and Cobb, J. (1972), The Hidden Injuries of Class, Vintage, New York. Simpson, G., and Yinger, J. M. (1965). Racial and Cultural Minorities, (3rd ed.), Harper and Row, New York. Stolzenberg, R. M. (1975), “Education, Occupation, and Wage Differences Between White and Black Men,” American Journal of Sociology 81 (June), 299-323. Terre]], H. S. (1971), “Wealth Accumulation of Black and White Families: The Empirical Evidence,” Journal of Finance 26: 3, 363-377.
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Theil, H. (1967). Economics and Information Theory, Rand McNally, Chicago. Theil, H. (1974), Principles of Econometrics, Wiley, New York. Warner, W. L., Meeker, M., and Eells, K. (1949), Social Class in America, Science Research Associates, Chicago. Weisbrod, B. A., and Hansen, W. L. (1%8), “An Income-Net Worth Approach to Measuring Economic Welfare,” American Economic Review 58 (December), 1315-1329.
SCIENCE
RESEARCH
8,63-78
(1979)
Race Differences in Middle Class Lifestyle: The Role of Home Ownership JOHN C. HENRETTA University of Florida This paper examines race differences in two aspects of “middle class” lifestyle: home ownership and net worth. Home ownership indicates stability; and for older persons net worth is an important part of economic status. Data from the NLS studies of older men are analyzed. The major findings are: (1) while whites at any earnings level are very likely to own homes by ages 50-64, only at relatively high earning levels do blacks begin to approach the home ownership rates of whites; (b) the net worth of blacks is substantially lower than that of whites after adjusting for variables in a standard status attainment model; and (c) however, among home owners the race difference as well as effects of other variables are much smaller than for renters. This is attributed to forced saving through home ownership. The paper concludes with a discussion of possible sources of low home ownership rates and low net worth of blacks and the implication of these findings for the study of middle class status.
Recent stratification research on race differences has concentrated on labor market processes as determinants of earnings and occupation (e.g., Duncan, 1%8; Jencks, Smith, Acland, Bane, Cohen, Ginhs, Heyns, and Michelson, 1972; Stolzenberg, 1975). Both segmented market theories and human capital or status attainment approaches have emphasized labor markets or returns to investment, and have ignored other aspects of the stratification process which are consistent with these approaches and which have traditionally been of interest to sociologists. Race differences in class-related lifestyle comprise one of these recently neglected areas of study. This paper examines two related aspects of “middle class” lifeSupport for the preparation of this paper was provided by a grant from the Administration on Aging (Training Grant 90-A-834 (1)) to’the Center for Gerontological Studies and Programs, University of Florida and by a grant from the Administration on Aging (Grant 90-A-1048) to the Data Archive Project, Center for the Study of Aging and Human Development, Duke University. I wish to thank Angela O’Rand, Marshall Pomer, Richard Campbell, and Richard Cohn for their comments on an earlier version of this paper. Requests for reprints should be sent to John C. Henretta, Department of Sociology, University of Florida, Gainesville, FL 32611. 63 0049089W79/010063-16$02.00/0 Copyright @ 1979 by Academic Press. Inc. All rights of reproduction in any fom reserved.
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style, home ownership and the accumulation of savings, and compares the process of their attainment for blacks and whites. The concept of the middle class lifestyle is usually defined for both blacks and whites by consumption patterns. There is little agreement on one definition of the middle class, but the themes found in the stratification literature emphasize security, home ownership, deferred gratification, and saving (Dobriner, 1963, p. 48; Kronus, 1971, pp. 23-27: Rossides, 1976, pp. 23-28). These themes are also found in the older literature on the black middle class (Davis and Dollard, 1940) even though it has been simultaneously argued by some that black middle class lifestyle included conspicuous consumption (e.g., Frazier, 1957.)’ While they are not the only possible measures of lifestyle, home ownership and savings are important attainments. Home ownership provides stability, security, and a sense of belonging to a community. Home owners are much more likely to be registered voters than are renters (Gallup, 1972, p. 1227); and home owning provides freedom from interference by landlords and neighbors. As such, it can provide an entree to a middle class iifestyle for those with relatively small incomes.” Owning not only allows a higher level of current consumption (e.g., more space) than rental housing available for the same price, but it is also an important form of forced savings (Kain and Quigley, 1973, pp. 272-276). Overall, it would be difficult to overestimate the importance of home ownership in American society. It is an anchor to the community, a source of psychological satisfaction, and a good economic investment. This significance of housing for status was recognized in early stratification indices which incorporated place of residence and dwelling type (though not home ownership) in status measures (Warner, 1949; Hollingshead and Redlich, 1958). Equally important to the attainment of security is the accumulation of savings. For most persons home equity is the most important part of total savings: but for those who do not purchase a home, the accumulation of net worth in the form of savings or other investments is necessary to insure one’s future living standard (Weisbrod and Hansen, 1968; Miller and Roby, 1970). This is particularly true for older persons who have relatively few years remaining in the labor force and for whom labor market attainments will provide relatively little future return. In old age, savings not only provide added income, but also provide security and confidence in the face of unexpected events. Net worth, including home ’ (Frazier, 1957, p. 76) quotes Booker T. Washington: “A man never begins to have self-respect until he owns a home.” and “Art and music to people who live in rented houses and with no bank account are not the most important subjects to which attention can be given . .” 2 See, for example, Sennett and Cobb’s (1972, p. 48) discussion of the meaning of home ownership to a “middle class” man who currently held a manual job.
RACE
DIFFERENCES
AND
HOME
OWNERSHIP
65
equity, can be used to purchase services from nursing homes or retirement communities. Economists have elaborated theories of consumption and saving (e.g., Friedman, 1957), but relatively little sociological attention has been given to them. A consumption-based measure such as home ownership or net worth is useful in sociology because it can be related to the current status attainment literature which emphasizes socioeconomic attainments as well as to the stratification literature which emphasizes class related lifestyle. Such measures are particularly useful since they allow an examination of race differences in the effects of labor market returns on lifestyle. The causal model implicit in this discussion relating net worth and home ownership to earlier attainments is:3 Home ownership
= a + b -X + u
Net Worth = a + b -(Home ownership)
+ b *X + v
(1) (2)
Where X = race, father’s education and occupation, respondent’s age, education, occupation, marital status, household size, average weeks unemployed, earnings, and pension coverage. Father’s education and occupation measure intergenerational transmission of status. Ideally, one should have a measure of wealth of the family of orientation, but these data are not available. Respondent’s age measures the effect of longer labor force participation on accumulation of net worth; there is also a cohort effect resulting from earnings levels associated with cohorts, but given the small range of ages in the sample (ages 50-64), this should not predominate. The age range for this sample is theoretically appropriate because net worth and home ownership are more important for the status of older persons, since at later ages physical capital is increasingly important compared to human capital (Henretta and Campbell, 1978). The age range also helps in disentangling the direction of causation between net worth and home ownership. Does home ownership lead to greater net worth accumulation, or does net worth allow one to purchase a home? By ages 50-64 it is not unreasonable to expect that increased net worth is a function of home ownership not only because of the growth of home equity but also because of the continuing savings attributable to home ownership.4 Respondent’s education and occupation measure early attainments of B The estimation of Equations 1 and 2 would allow straightforward computation of direct and indirect effects through home ownership. However, since the effect of status attainment variables on net worth varies over levels of home ownership (but not over race). Equation 2 is estimated separately for owners and renters. 4 For a discussion of this problem, see Kain and Quigley (1975, pp. 151-152).
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JOHN C. HENRETTA
the respondent as is standard procedure in models of this kind. Respondent’s marital status and household size measure financial commitments of the respondent. These factors both affect ability to accumulate net worth. The remaining variables measure economic attainments. Earnings are average yearly earnings for family members for four years. Weeks unemployed is average number of weeks the respondent has been unemployed in the previous five years. This is included because a common explanation of race differences in net worth or in yearly saving is greater instability of blacks’ earnings (e.g., Terrell, 1971). A dummy for pension coverage is included to allow for the effect of forced saving. City size and region are also included in the model. A description of the data source, the NLS study of older men, and a discussion of coding of the variables in the analysis are included as an appendix. Race and Home Ownership
Table 1 presents logit estimates, for Equation 1, the determinants of home ownership.5 The antilogs of the coefficients are predicted proportional changes in the odds ratio per unit change in the independent variable. These predicted changes are shown in the third column of the table. Most attainment variables have very small effects on likelihood of home ownership. However, both the marital status contrasts have very large effects: the odds ratio of divorced persons is 13% that of married persons: the odds ratio predicted for persons in the other category is 16% of that of married persons. This should be expected since persons who are not married have less use for owned housing. Among the economic attainments, pension coverage has a large effect. The predicted odds ratio for persons with a pension is 34% higher than persons without pension coverage; jobs with pension coverage may pros The logit model was estimated using a version of the Nerlove and Press (1973) program modified and maintained by the Economics Department at North Carolina State University. The procedure allows for the computation of maximum likelihood estimates of logits for models with continuous independent variables. For a discussion of the logit’model, see Hanushek and Jackson (1977). The form chosen for the equation restricts all coefficients except earnings to have the same slope across race. Freeing other coefficients did not improve the fit significantly. Kain and Quigley (1973) estimate a similar model for home ownership using GLS and restrict all coefficients to be the same across race. However, the choice of the present model would be the same using this logit procedure or a least squares procedure. The chi square value for the difference between the earnings restricted and the earnings free model for the logit model is approximately 20 with 1 df. In the parallel OLS model, the F-ratio for the increment to R* when the earnings coefticient is freed is 22. The two models may not differ as much as they seem to at first glance since, as discussed in the text, there are few blacks in the region where the regression slopes intersect. Yet, as noted, the existence of the income interaction is theoretically significant.
RACE DIFFERENCES
AND HOME OWNERSHIP
61
TABLE 1 Logit Estimates of Home Ownership b
Father’s education Father’s SE1 Respondent’s age Respondent’s education Respondent’s SE1 Divorce Other marital status Household size Weeks unemployed Pension Earnings (blacks) Earnings (whites)’ Race (white) South City size:* 250,000 + 25,000-250,OclO 2500-25,000 Constant
.0034 - .0046 .0055 .0139 .0114 -2.001 -1.788 - .0186 - .ooo9 .2953 .1746 .0411 1.4%
seb
(.0174) (.0028) (.0126) (.01&Q) (.0030) (.2738) (.1684) (.0296) (.0054) (.1084) (.0324) (.0326) (.2390)
Predicted proportional change 1.003 9954 1.005 1.013 1.011 .1352 .1672 .9815 .9991 1.343 1.190 1.039 4.46
.3191 (.1244) - .388 - .0265 .3309 - 1.098
(.1300) (.1680) (.1844) (.74%)
1 t-test for differences of earnings coefficients = 4.14. Earnings coefficient is for $1000 increment in earnings. 9 The omitted category is rural.
vide more stability and longer tenure for their incumbents and thus encourage home buying. However, the most interesting results are for earnings. While for blacks each additional $1000 of family earnings increases the predicted odds ratio l%, the predicted change for whites is only 3.9%. Given the lower intercept for blacks, the predicted odds ratios for blacks and whites are equal at about $11,000. This is misleading, however, since fewer than 6% of the blacks in this sample have average four-year family earnings that high. At higher earnings levels, black home ownership rates approach those of whites; as income decreases, blacks are increasingly less likely to own homes compared to whites net of other characteristics. These findings suggest important aspects of the process of home ownership. By the time they reach late middle age, whites are quite likely to own a home regardless of earnings. For blacks this is not true; only at higher incomes do they approach the same likelihood of home ownership as whites. The earnings levels which allow whites to achieve “middle class” status are lower than those required for blacks. It is not possible in these data to go further in explaining the difference, but there is relevant
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JOHN C. HENRETTA
external evidence of housing discrimination, residential segregation. and refusal to sell to blacks which bear on this point. The home ownership patterns of this cohort are, to a great extent, the result of conditions in the period 193 l- 1956 when members of the cohort were at ages when they were probably buying first homes.” Past housing discrimination is not difficult to demonstrate: it was only in 1948, when these persons were aged 27-41, that the United States Supreme Court forbad state courts to enforce racially restrictive covenants on homes (Simpson and Yinger, 1965, p. 33 1). The analysis of home ownership data suggests that it is low income blacks who, in comparison with whites at the same earnings level, were most unlikely to purchase homes. If this is a result of discrimination, it does not require a pattern of unequal treatment as blatant as the pre1948 law allowed. There is also some evidence that this difference in home ownership does not represent the preference of blacks. First, to the extent that other variables in the model measure preferences, these are already adjusted for. Second, if there were a constant difference between blacks and whites, preferences might be a reasonable explanation. While the presence of an interaction does not rule this out, the crucial role of earnings is also compatible with the explanation that only at higher earnings levels are blacks able to realize their preferences. This explanation is bolstered by Kain and Quigley (1975, pp. 63-82) who argue that housing is more expensive in glack ghetto areas than in white areas. The additional cost of housing in the limited housing market available to blacks may well explain why earnings level is more important for black ownership than for white. Additional evidence against the preference hypothesis explanation comes from Kain and Quigley’s (1973) analysis of housing markets in several metropolitan areas. By computing expected black home ownership rates based on income and age specific white ownership rates, they conclude that the difference between expected and actual rates is greatest in cities where there is a great deal of residential segregation and in which there is little single family housing available in black residential areas. The implications of home owning are increased by a consideration of its effect on the accumulation of net worth. Since blacks are less likely to own homes, more are located in the renter category; and the only path to financial security open to them is other saving. Thus an examination of the effect of owning a home on total net worth is necessary to trace the entire effect of limitations on black home ownership, (Kain and Quigley, 1975, pp. 148-152). @This period is arbitrary and approximate. Since life cycle events are timed differently for different individuals it is not possible to pinpoint home buying years. In 1931 the oldest members of the cohort being considered were age 24; in 1956 the youngest members were age 3.5. This seems a good approximation of likely home buying years.
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AND HOME OWNERSHIP
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Race and Net Worth Table 2 presents regression models for log net worth separately for home owners and renters.7 Within each category, one model suffices for blacks and whites.* The left panel of Table 2 presents results for home owners. Most of the effect of parental status is mediated by respondent’s education and occupation. The effects of parental status in Equation 4, the full equation, are small; but father’s occupation is still three times its standard error. The effect of age is similar in all equations. In the full equation, each year is associated with a predicted 1.6% increase in assets. This age effect is expected from the theoretical relation of age to saving: if average saving is positive, older persons should have greater savings. A cohort effect which would lead to the opposite finding is probably weaker. Respondent’s attainments have significant effects on net worth. Each percent increase in education is associated with an increase of .207% in net worth in Equation 4. Of the total education effect in Equation 2, about one-third is mediated by respondent’s family and economic status measures. Each 10 points of respondent’s SE1 is associated with a predicted 5.1% increase in net worth; slightly over half of the total effect in Equation 2 (11.6%) is mediated by economic status. Net of earnings and other predictors, divorced persons have predicted net worth 54% that of married persons.g Among home owners, those in the other marital status category have higher assets than married persons. This coefficient increases from Equation 3 to 4 suggesting that while overall these persons may not have high assets, their net worth is higher than would be expected from their economic attainments. ’ The log transformation reduces the effect of positive outliers and stabilizes the variance. Theoretical justification for this is that saving is for later consumption and the marginal utility of an additional dollar of net worth is likely to be a log function of net worth, not a linear one. That is, each additional dollar saved adds less to later status. On practical grounds, the extreme skewness of the new worth distributions and the influence of outliers on regression equations makes working with untransformed net worth impractical. Regression coefficients when the dependent variable is in the semilog form can be interpreted as follows: the antilog of the coefficient is the predicted percentage change in the dependent variable when the independent variable changes one unit (Theil, 1967, 1974). While in the case of cross-sectional data this does not yield a percentage change in the arithmetic mean, it does predict a change in the geometric mean. In the skewed net worth distribution, the geometric mean is a better guide to central tendency than is the mean. Prior to logging, those persons with negative assets were coded one. These persons constituted about 3% of the sample, and alternative treatment would be unlikely to change the results. 8 The F-ratio for homogeneity of coefficients across race for owners is 2.20; for renters it is 2.06. These are of borderline statistical significance. In contrast, the F- ratio for homogeneity of owner and renter models is 41.2. This pattern, as well as the pattern of the coefficients themselves, suggest there is one model for owners and one for renters. 9 No doubt some of the owners have at one time been divorced and have subsequently remarried and begin to accrue home equity again. The lack of marital history makes exploring this process with present data impossible.
Earnings3
Weeks unemployed
Pension
Household size
Other marital status
Divorce2
R’s SE1
R’s education (LN)
Respondent’s age
Father’s SE1
Father’s education
(Z) .014 uw
.022 t.006)
I
-.ool (J-w .005 (.ow .021 ww .324 (.@@I .Oll (.ow
2
Homeowners
.OOl (.ow .005 C.001) ,014 (.@w .291 Low .Oll (.ool) -.769 (.142) .I01 (.112) -.063 (.013)
3
.065 (JO3)
(.ow
- .003 c.0f-m .003 (.@Jl) .016 (.ow .207 W5) .oos (.Wl) -.621 (.132) .225 (.104) -.065 (.012) -.I80 (.037) .OOl
4 .115 (.043) .036 ww -.Oll (.031)
1 .008
(442) .014 (.007) .006 (.030) 1.14 (.227) .051 (.007) -.585 (.352) - .432 (.356) -.161 (.cw
.014 .015 (.007) .019 (.029) 1.11 (.228) .053 (.007)
3
(442)
2
Renters
TABLE 2 Reduced and Full Form Equations Department Variable: LN Assets’
,002 (.@w .009 (.007) .036 (.029) .895 (.222) .030 (.007) -.230 (.340) .318 (.353) -.I37 (.063) 2394 (.272) - .020 (.009) .166 (.027)
4
5.63
2.92
6.08
1.56
.30
1.08
.19
f-value for bo-6, = 0
.l% (051) -.012 (.ow .I24 (.067) 8.74 (.261) .252
- .783 (.057) .211 (.@w
.067 (.%3) 7.49 (.271) .348
(.062)
-.113
(348)
.087
-519 (.056) .194 t.044) .097 (.048) -.115 (.W .053 (.%3) 8.13 (.2%) .365 1849
-.465 (.056) .189 ww .045 (.@w -.108 (.057) .078 (.058) 7.98 (.276) .457
- .424 (.052) .155 t.0411
1.42 (.511) 2.78 (1.67) .303
(442)
.868 (.328) .501
-2.09 (.323) 1.75 (.323) .312 (.314) .007 (.417) 1.01 C.484) -1.06 (1.65) ,389
-1.27 (.314) 1.43 (.311)
’ Standard errors (not corrected for sampling efficiency) in parentheses. Metric coefficients are reported. * Each marital status category is a contrast with married, spouse present. 3 Increment of $looO. ’ The omitted category is rural.
R2 N
Intercept
250&25,000
25,000-250,000
City size:’ 250,000 +
Nonsouth
Race (black)
.246 (.315) -.012 (.418) .913 (.484) .369 (1.73) .395
-1.15 (.317) 1.39 (.311) -.237 (.313) - .250 (.403) .548 (467) 1.19 (1.W 448 712
-.994 (.305) 1.17 (.3OQ
1.59
.54
1.37
5.23
2.71
P m
5 u
c i2 u G m” fi 3 E
JOHN C. HENRETTA
72
TABLE 3 Summary of Race Differences in LN Net Worth
All respondents Homeowners Renters Race difference net of region und city size All respondents Homeowners Renters Race difference net of region, city size and attainment variables Homeowners Renters
Race difference
Black predicted NW as proportion of white
3.22 ,998 3.678
,039 ,368 ,025
3.03 .82 3.40
,048 ,440 ,033
.424 ,994
,654 ,370
Pension has a negative effect on net worth. Earnings has a positive effect: each thousand dollars of average earnings over a four-year period is associated with a 6.7% increase in net worth.‘” Most of the effects of region and city size are absorbed by adjusting for status variables; however, home owners in large cities and those living outside the south have some advantage. Net of region and city size, black home owners have predicted net worth 44% that of whites (see Table 3). After adjusting for status attainment and family variables, black net worth predicted in Equation 4 is 65.4% that of whites (an increase of about one-third the total difference. These race differences are discussed extensively below. In the equation for renters in the right panel of Table 2, almost all coefficients are larger. Though the patterns of mediation are similar, all attainments as well as one’s race, seem to make far more difference. The last column of Table 2 presents t-ratios for the test of equality of coefficients in the owner and renter models. The education coefficient is about four times larger in the equation for renters; each percent increase in education is associated with a .8950/o increase in predicted net worth instead of .207. Savings are much more affected by economic status for renters. Each $1000 of earnings is associated with an 18% increase in net worth, over two and one-half times its effect in the owner equation. Pension has a large positive effect on net ‘” Terrell(1971) logs both income and net worth and therefore argues that the relationship is linear in the double log form. Though his dependent variable is not the same, Friedman (1957) argues that the proper specification of the relationship between income and saving in any one year is the double log. In the present data, the regression of log net worth on family earnings is quite linear for blacks and whites, and so the semilog specification has been retained. The double log fits these data less well. See Henretta and Campbell (1978).
RACE DIFFERENCES
AND HOME OWNERSHIP
73
worth compared to a negative effect in the owner equation. Each week per year unemployed costs about 2% in assets. As with owners, race makes a large difference in net worth. However, for renters the difference is substantially larger. Net of region and city size blacks have predicted net worth 2.5% that of whites (see Table 3). Net of status and family variables, black predicted net worth is 37% that of whites. The reason for these lower coefficients for race and attainment variables is that for home owners, particularly those of low status, monthly mortgage payments are a form of forced savings which is not affected by events such as unemployment. In addition, capital gains from home ownership are probably the only ones available to persons of low status. The result of these processes is that, in proportional terms, there is less difference between high and low status home owners on net worth than there is among renters for whom there is less likely to be forced saving. Home ownership blunts the effect of high status or race on net worth when differences are expressed in proportional terms.” Why Are There Race Differences in Net Worth? Because the proper models for net worth vary over home ownership status there is not just one race difference but two. Table 2 presents race differences coefficients for several equations including some not presented in Table 1. Net of region and city size the predicted assets of blacks are 4.8% those of whites. Among homeowners, black predicted assets are 44% of those of whites net of region and city size; for renters predicted black assets are only 3.3% of white assets. After adjusting for attainment variables, black predicted assets are 65.4% and 37% of those of whites, respectively. Clearly there is a large residual race difference. One possible explanation is Terrell’s (1971): the current earnings of blacks do not represent very well their earning histories. The better measures used here (including ‘I A reader has suggested that the proper comparison is between renters and owners using only nonhome net worth for home owners. While this changes the nature of the analysis (i.e., no longer is net worth a comprehensive measure of security) the model was estimated for home owners using only nonhome net worth. Generally the results were similar to the home owner results using the full definition; however, the coefficient for race was large and negative as in the renter equation. It is difficult to interpret these findings because the way one divides net worth into various kinds of saving is dependent on race and home ownership. First, for owners, home ownership is an integral part of net worth. Kain and Quigley (1973) report as discussed in the text, that home ownership can result in lower housing costs after many years and therefore in a chance to accumulate nonhome net worth. More important, Terrell (1971) reports that blacks have a larger proportion of their net worth in their homes. Thus subtracting home value from net worth should lead to a larger race coefficient. Data presented by Kain and Quigley (1975) suggest this may result from blacks’ having to pay more for housing.
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JOHN C. HENRETTA
four year average earnings and unemployment experience over five years) still may not solve the problem. And, the residual race difference is greatest where status variables make the most difference: among renters. Equally as interesting are the differences which can be explained: owning a home is a potent factor in raising black assets relative to those of whites. Those blacks who own a home have much higher assets compared to whites (proportionally) than those who do not. This probably has the same interpretation as do steeper slopes for renters: low status characteristics are less of a liability in the face of forced saving. An additional implication flows from the number of cases in each category. While 78% of the whites in the sample are buying or own a home, only 54% of blacks are. Blacks are more likely than whites to end up in the renter category, a category which penalizes them more because of their race and their status characteristics. Conclusion: Becoming Middle Class
Blacks have lower net worth than whites for three reasons that can be traced: first, they have lower attainments in education, occupation, and measured earnings. Second, they are more likely to be divorced and to have large households. A third reason for low black net worth is that, especially at low earnings levels, blacks are less likely to own homes net of their other socioeconomic characteristics. This means that blacks are more likely to be in the renter category where their generally low status characteristics are particularly disadvantageous. All of this could be a result of measured earnings’ not being a good proxy for lifetime earnings; yet the findings have implications for the attainment of middle class status as a social process. If we adjust for various measured labor market returns, blacks arrive at retirement with less security than whites and less of a middle class lifestyle. A key element in this process is home ownership and its effect on net worth accumulation. These findings are consistent with the literature on the black middle class discussed earlier in that a middle class lifestyle has always been more difficult for blacks to attain and has been seen as a more distinctive accomplishment for blacks. (Frazier, 1957; Drake and Cayton, 1%2). This paper advances that literature by suggesting quantitative indicators of middle class status and by suggesting the social process leading to their attainment. APPENDIX
Data for the analysis are from the National Longitudinal middle aged men (Pames, 1970). (See Table Al.)
Studies of
Variables Father’s education and occupation are measured in years and Duncan SE1 scores. Age is measured in years.
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OWNERSHIP
75
Respondent’s education and occupation are measured by years completed and Duncan SE1 scores. Education has a nonlinear effect on net worth, and this is modeled in the net worth equations by the log of education. This describes a relationship, assuming the dependent variable is also logged, which increases at a diminishing rate. Since the dependent variable in this analysis is LN net worth, the resulting coefficient can be interpreted as an elasticity; the coefficient is the predicted percent change in wealth associated with a percent change in education. Two family measures are included in the model: marital status and household size. Marital status is trichotomized: married, divorced or separated, and other, including never married or widowed. There were not enough cases for further breakdown of the marital status categories. Household size, as a proxy for number of children, measures consumption needs. Three measures of current economic attainments and activity are included in the model. The earnings variable is the average of husband’s and wife’s earnings for the years 1965, 1966, 1968, and 1970, years the NLS collected earning data. A full accounting of net worth attainment requires development of a family model. This requires better data on wife’s occupational history than are available in NLS. The unemployment variable measures the annual average number of weeks the respondent has been unemployed during the years 1965 to 1970. The pension variable indicates whether there is pension coverage on the respondent’s current job. All equations also include variables indicating region and city size. Region is measured by a south-nonsouth dichotomy. The use of more detailed categories did not increase variance explained. City size is coded as follows: the omitted category is rural. The other categories are, in order: cities larger than 250,000; cities 25,000-250,000; and cities 250025,000. Though these variables have little effect in income or net worth models (Jencks, 1973; Henretta and Campbell, 1978), it is possible they may have an effect in models dealing with home ownership. Anyone who currently owns or is buying a house is coded as a home owner. Net worth is the sum of estimated home value, savings, business assets and real estate value. For persons who did not report liquid savings, about 20% of the final sample, savings was estimated using education and race specific medians. Education categories for savings replacement were: O-4, 5-8, 9-11, 12, 13-15, and 16+. After replacement, the final sample included 77% of white and 70% of black 1971 respondents to the NLS survey. While home value is reported in survey data with little bias (Kish and Lansing, 1954; Robins and West, 1977), nonhome net worth reports are subject to bias: those who have a great deal are likely not to report their holdings (Ferber, 1966). Since underreporting is correlated with true score, underreporting will bias effects downward.
Father’s education Father’s occupation Age R’s Education (LN) R’s SE1 Divorce Other marital status Household size Pension Weeks unemployed Earnings South L Net worth Net worth Net worth w/o home Home equity N
5.25 15.55 56.38 1.79 21.76 .048 .036 3.66 ,504 4.31 6457 ,666 9.09 13597 3717 9764 525
Mean
Black
2.92 12.79 4.09 ,692 17.82 ,216 ,188 2.03 .500 9.75 3548 .472 .957 21279 17288 6847
SD
Homeowners
Means
7.65 29.41 55.98 2.31 41.74 .012 .029 3.04 .611 3.19 10664 .263 10.145 43359 22062 19198 1324
Mean
White
3.47 22.94 4.21 ,382 24.06 ,109 ,168 I .45 ,487 7.79 5896 ,440 .974 72893 58454 23040
SD
TABLE Al and Standard Deviations
332
4.83 14.18 56.58 1.59 15.85 ,242 ,156 3.46 ,295 8.20 4155 .677 2.16 1045
Mean
Black
2.91 9.14 4.10 ,760 12.57 .429 .363 2.51 ,457 14.78 2964 .468 3.27 4417
SD
Renters
380
7.29 27.09 56.23 2.18 31.35 .139 .I59 2.79 .497 4.84 7966 ,209 5.77 12904
Mean
White
.5on 10.85 6742 ,407 4.165 43394
.347 ,367 I.65
5
2
0
23.15
5 is
3.39 20.81 4.30 ,503
SD
RACE DIFFERENCES
AND HOME OWNERSHIP
77
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78
JOHN C. HENRETTA
Theil, H. (1967). Economics and Information Theory, Rand McNally, Chicago. Theil, H. (1974), Principles of Econometrics, Wiley, New York. Warner, W. L., Meeker, M., and Eells, K. (1949), Social Class in America, Science Research Associates, Chicago. Weisbrod, B. A., and Hansen, W. L. (1%8), “An Income-Net Worth Approach to Measuring Economic Welfare,” American Economic Review 58 (December), 1315-1329.