Take the Money and Run : Economic Segregation in U . S . Metropolitan Areas

Compared to racial segregation, economic segregation has received little attention in recent empirical literature. Yet a heated debate has arisen concerning Wilson's hypothesis (1987) that increasing economic segregation plays a role in the formation of urban ghettos. This paper presents a methodological critique of the measure of economic segregation used by Massey and Eggers (1990) and finds that it confounds changes in the income distribution with spatial changes. I develop a "pure" measure of economic segregation and present findings on all U.S. metropolitan areas from 1970 to 1990. There have been steady increases in economic segregation for whites, blacks, and Hispanics in both the 1970s and 1980s, but the increases have been particularly large and widespread for blacks and Hispanics in the 1980s. The causes of these changes are explored in a reduced form, fixed-effects model. Social distance theory and structural economic transformations do affect economic segregation, but the large increases in economic segregation among minorities in the 1980s cannot be fully explained within the model. These rapid increases in economic segregation, especially in the context of recent, albeit small, declines in racial segregation, have important implications for urban policy, poverty policy, and the stability of urban communities. Take the Money and Run: Economic Segregation in U.S. Metropolitan Areas Racial segregation in the United States has been declining since 1970, but the decreases have been so slow that segregation of African Americans is still very high (Farley and Frey 1994). At the same time, economic inequality has been increasing since 1979, if not before (U.S. Bureau of the Census 1992, 1993). Both phenomena have been extensively documented. A third dimension of socioeconomic differentiation has received much less attention in recent years: economic segregation, i.e., the spatial segregation of households by income or social class. The relative dearth of recent studies in this area is surprising, in light of the importance ascribed to economic segregation by William Julius Wilson in his classic work, The Truly Disadvantaged (1987). In this paper I document the fact that economic segregation has been increasing, not only among blacks, as Wilson alleges, but also within all major racial and ethnic groups, although the increases were largest for blacks and Hispanics in the 1980s. Moreover, the phenomenon is incredibly widespread, affecting virtually every metropolitan area in the United States. I also develop a causal model which, though clearly incomplete, suggests that the rapid increases in economic segregation among minorities represent a dramatic break from the pattern of previous decades. In other words, the factors which explain the changes in economic segregation for all groups over the 1970 to 1980 period consistently underpredict the changes for minorities in the 1980 to 1990 period. The breakdown of the 1970–1980 model represents a fundamental change in the processes that generate economic segregation. Understanding the causes of these changes, I would argue, has become a prerequisite to effective analysis of urban policies and programs. 2 ECONOMIC SEGREGATION AND ASSIMILATION A rich literature exists on the interrelationship of racial and economic segregation. Much of this literature can be traced to the classic paper by Park, who predicted that "successful individuals [of minority groups] move out and eventually find their places" so that "changes of economic and social status . . . tend to be registered in changes of location" (1926: 9). More generally, Park argued that "social relations are . . . inevitably correlated with spatial relations; . . . physical distances . . . are, or seem to be, the indexes of social distances" (18). Subsequent research has followed up on two related but conceptually distinct hypotheses stemming from this work: the first is the "social distance" hypothesis and the second is the "assimilation" hypothesis (Massey 1981: 642–643). According to the social distance hypothesis, racial or ethnic segregation is "positively associated with differences between groups" (642). The assimilation hypothesis states that better-off strata within a minority group will translate individual gains into spatial assimilation. If the assimilation hypothesis is correct, then the degree of racial segregation should differ among social classes within a minority group. And it follows that higher-status members of the minority group will become more segregated from lower-status members of their own group as they become assimilated. Erbe (1975) addressed the issue of the socioeconomic segregation by income within racial groups. Responding to Kantrowitz, who found that "the better off black is as separated from the poorer black residentially as the better off white is from the poor white" (Kantrowitz 1973: v), Erbe argued that Kantrowitz's use of the index of dissimilarity (D) to measure economic 1 segregation is inadequate because the index is insensitive to the relative proportions of lower-, middle-, and upper-class individuals in each group. She argued that "intergroup contact" is "a function both of residential segregation of populations and their relative size in the total population" (1975: 803). Erbe favored the use of a measure of "asymmetric intergroup contact," more commonly known as the exposure index (P ), which for two groups gives the average probability of contact of members of * 3 one group with members of the other. Using Chicago census data, Erbe found that the indices of dissimilarity by various measures of social class within racial groups differed little between whites and blacks. In contrast, exposure measures showed that higher-status blacks were far more likely to come into contact with lower-status members of their racial group than whites. Her conclusion: Middle-class blacks are not randomly distributed throughout the ghetto and thus are segregated from the black lower class in this sense. Nevertheless they live in much closer propinquity with the lower class than do middle-class whites, simply because the black lower class is proportionally larger than the white lower class. To the extent that neighborhoods are the functional locus of many institutions, most especially schools, this is of great consequence.... In particular, it may be one factor in accounting for the low degree of occupational inheritance between high-status black fathers and their sons and the high degree of intergenerational downward mobility among blacks compared to whites (1975: 803). How strange this quote sounds today, when the concern has been turned on its head: Wilson (1987) argues that the out-migration of the black middle class has resulted in the social isolation of the innercity black poor, with attendant "concentration effects." Erbe's results demonstrated that one must distinguish between measures of economic segregation that are conditioned on income distribution and those that are not. Each type of measure has a value, but they answer different questions. If the incomes of all black households were to rise by a fixed amount, and nobody moved, the P* index measuring contact with persons in poverty would change a great deal because there would be less black poverty to be exposed to. Assuming no changes in the classifications of families by social class, the index of dissimilarity would not change, since no one had moved. Thus, the exposure measure would accurately indicate that blacks were less likely to 2 come into residential contact with poor persons, while the index of dissimilarity would correctly indicate that the degree of sorting by social class had not changed. Farley (1977) also examined the interactions of racial and economic segregation. In part of his analysis, Farley applied the index of dissimilarity to years of education completed. He broke education into ten categories and calculated the 45 pair-wise indices of dissimilarity among them. He also 4 calculated the dissimilarity of each group from all other groups. Farley found that the level of social class segregation was generally low relative to racial segregation, that the pattern of social class segregation among blacks and whites was quite similar, and that racial segregation was nearly constant across levels of social class. Taken together, these findings imply that racial segregation is not a product of economic segregation. Massey summarized the large body of empirical work on the social distance and assimilation hypotheses as follows: "In general, social distance is highly correlated with residential dissimilarity between particular ethnic groups [citations omitted]. In contrast, the assimilation hypothesis has not received consistent support" (1981: 643). Massey attributed some of this lack of support for the assimilation hypothesis to the use of "indirect standardization" in much of the literature. He argued that direct standardization, i.e., examining the index of dissimilarity between ethnic groups at fixed levels of social status, is preferable when possible. Studies employing the latter technique, such as Farley's study cited above, are more supportive of assimilation for Hispanics, but not for blacks (Massey 1981: Figure 1). While much more could be said, for the purposes of this study there are several substantive conclusions to be drawn from the literature. First, racial segregation is far more complete than social class segregation; neighborhoods tend to be heterogeneous by income even as they are highly segregated by race and ethnicity. Second, although social distance is useful in predicting the crosssectional pattern of segregation, the prediction of residential assimilation following from economic advancement has not been supported for blacks, again highlighting the persistence of racial segregation. Finally, racial segregation is not an artifact of economic segregation: the primary organizing principle of the metropolis is race/ethnicity, not social class. Kain concluded that "virtually every systematic study has concluded that black and white differences in income and other socioeconomic 5 variables account for very little of the current and past patterns of racial segregation" (1986: 114). Hence, it makes sense to study economic segregation within racial groups; otherwise, some of the observed economic segregation will actually be an artifact of the underlying racial basis of residence patterns. MEASUREMENT OF ECONOMIC SEGREGATION To measure economic segregation within racial groups, Erbe and most of the pre-1987 research used the index of residential dissimilarity applied to broad occupational groupings. However, the social meaning and economic status of various occupational categories have changed in ways that make it difficult to compare occupation-based measures over time. One must choose either large numbers of pair-wise dissimilarity indices or a few very broad (perhaps meaningless) groupings of occupations. Farley's work applied the index of dissimilarity to groupings by years of education completed. (He also used the correlation ratio in the same article, which I discuss below.) More recent work has attempted to use income as the indicator of social class. A prominent example is Massey and Eggers (1990). Census tract data, the basis of almost all segregation studies, report counts of households by income brackets rather than the values for individual households. Massey and Eggers adjusted the 1970 income bracket amounts by the CPI. Since the 1969–79 CPI is 1.98, or nearly double, these adjusted income categories nearly "lined up" the 1980 income brackets. 4 They reduced the twelve income categories to four social classes: Poverty Incomes $0–$7,499; Lower Middle Class $7,500–$14,999; Upper Middle Class $15,000–$29,999; Affluent $30,000 and up. They then computed the six pair-wise indices of dissimilarity. To come up with a final aggregate measure, the average of these six measures is computed (D ). Massey and Eggers concluded that I 5 6 interclass segregation among blacks "increased over the 1970's often quite sharply" (1990: 1170). 6 Whites, Hispanics, and Asians, however, had declines in interclass segregation by this measure. While ingenious, this method of measuring interclass segregation has a number of problems. First, the cutoff points between the income classes are arbitrary. Second, income is a continuous variable, and collapsing it into only four categories inevitably loses information. Third, and most important, a measure of dissimilarity based on fixed income cutoffs is not independent of the mean and variance of household income. As the mean and variance change, the class categories "cut" the income distribution at different points. In other words, like P , the index of dissimilarity used in this way may * confound changes in the underlying income distribution with changes in spatial organization. A simulation will illustrate the problem. Imagine a metropolitan area with 100 neighborhoods. Each neighborhood has 100 households. There is a modest degree of economic segregation. An 7 increase in the mean of the income distribution, with no change in the variance of income (inequality), will shift households from one class to another, even though they have not moved from one neighborhood to another. If the variance of household income increases, with no change in the mean, some households will shift up in class and others down, again with no spatial movement. Table 1 shows the effect of such shifts on the D measure. Holding constant the mean level of I income, increases in inequality cause the measure of economic segregation to fall even though no household has moved and all households stand in the same relative position in the income distribution. Increases in the mean, holding the standard deviation constant, cause D to rise, though less so at I 7 TABLE 1 Simulation of Values of D , Under Different Assumptions about the Mean and Variance I of the Income Distribution Standard Deviation of Household Income Mean Household Income 10,000 15,000 20,000 25,000 $20,000 0.57 0.49 0.44 0.41 $25,000 0.58 0.50 0.45 0.42 $30,000 0.61 0.50 0.45 0.42 $35,000 0.67 0.52 0.47 0.43 Note: See text for derivation of values. 8 higher income levels. Thus, the index of dissimilarity applied to the income distribution confounds monotonic changes in the income distribution with actual changes in spatial organization. The major source of this problem is that the index of dissimilarity is a measure built around the idea of two welldefined groups. Several authors have developed variations of the index of dissimilarity that can account for more than two groups, but the groups are still expected to be finite in number and well-defined (Sakoda 1981; Morgan and Norbury 1981). James (1986) developed a "generalized segregation index," which extends the reach of exposure-based measures to more than two groups. Income, however, is a continuous variable. Collapsing the distribution into categories throws away information and creates problems of maintaining comparable categories over time. A counterargument is that income is merely an indicator of social class, which is a polytomous discrete variable. However, the class structure in the United States is too ill-defined and fluid for that really to be true. Moreover, indicators of social class, such as years of education and occupation, change their meaning over time. While income, especially annual income, does have its limitations, it at least measures the relative success of individuals in the labor market and their standing in terms of purchasing power. "Money isn't the only thing," as the saying goes, "but it's pretty far ahead of whatever is in second place." A segregation measure that is suitable for use with continuous measures is referred to as the "correlation ratio." Farley applied the correlation ratio to years of education to get a "succinct measure" of social-class segregation. He described his method as follows: Each individual in the sample was assigned a number equaling the years of schooling he had completed. Using these data, we calculated an estimate of the variance of the educational attainment of blacks and whites in the entire urbanized area.... [U]sing the attainment of each person in a tract and the tract mean, we developed a within-tract estimate of the variance in attainment. The correlation ratio, sometimes referred to as eta squared, equals one minus the within tract variance divided by the estimate of variance developed from the total sample (citations omitted). This may be thought of as a one-way analysis of variance model in which the overall variance in socioeconomic status is divided into within census tract and between census tract variances (1977: 503).