Race and the value of owner-occupied housing, 1940–1990

Regional Science and Urban Economics 33 (2003) 255–286 www.elsevier.com / locate / econbase

Race and the value of owner-occupied housing, 1940–1990 William J. Collins*, Robert A. Margo Department of Economics, Box 1819 -B, Vanderbilt University, Nashville, TN 37235, USA Received 1 March 2001; received in revised form 15 April 2002; accepted 8 May 2002

Abstract We use data from the Integrated Public Use Microdata Series (IPUMS) to document racial convergence in the value of owner-occupied housing from 1940 to 1990. Most of this convergence occurred before 1970, as black and white-owned units became more similar in terms of housing characteristics and as black and white homeowners became more similar in terms of their household characteristics. During the 1970s, convergence in values stalled despite continued convergence in housing and household characteristics; thus, the ‘unexplained’ or residual portion of the racial value gap increased. Because changes within metropolitan areas are central to the post-1970 story, we go on to explore the changing empirical connection between urban residential segregation and the racial value gap.  2002 Elsevier Science B.V. All rights reserved. Keywords: Race; Housing; Segregation JEL classification: R20; J15; N30

1. Introduction In the United States, racial differences in wealth are large relative to racial differences in income (Blau and Graham, 1990; Oliver and Shapiro, 1995).1 Such *Corresponding author. Tel.: 11-615-322-3428; fax: 11-615-343-8495. E-mail address: [email protected] (W.J. Collins). 1 According to one recent study, the black / white ratio of mean household wealth in 1995 was 0.18, compared with a black / white ratio of mean household income of 0.64 (Wolff, 1998). See Higgs (1982) and Margo (1984) for historical perspective. 0166-0462 / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved. doi:10.1016/S0166-0462(02)00027-3

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gaps exist across all types of assets, but those related to housing are particularly significant because housing equity is a major component of household wealth, particularly for African Americans.2 There are three proximate causes of the racial gap in housing equity: differences in rates of home ownership, differences in the value of owner-occupied housing, and differences in the ratio of housing values to housing equity, or equivalently, differences in debt–equity ratios (Long and Caudill, 1992; Wolff, 1998). This paper offers new, long-run insights into the evolution of the second proximate cause: differences in the value of owneroccupied housing. In addition to its connection with the racial gap in wealth, the racial gap in property values reflects differences in well-being derived from housing services. The asset value of housing depends on the flow of services associated with residing there. These services include not only shelter and comfort, but also access to schools, employment, transportation, retail establishments, security, and various public goods. Thus, a great deal of information about households’ economic quality of life is embedded in the housing values we examine here. Despite the issue’s potential importance, economists and economic historians have not written much about the long-run trends in racial differences in housing market outcomes. Our previous work uses the Integrated Public Use Microdata Series (IPUMS) of the federal population censuses to study the evolution of racial differences in the rate of home ownership among male household heads from 1900 to 1990 (Collins and Margo, 2001). This paper documents and analyzes racial differences in the value of owner-occupied housing over the period 1940 to 1990, again focusing on male household heads, though with some discussion of the significance of rising rates of female headship. Our primary methodological approach is simply to decompose the racial gap in mean housing values into two parts. One component captures racial differences in observable housing characteristics (for example, the number of rooms) that are strongly correlated with housing values. The other is a ‘residual gap’ which reflects the unobserved correlates of race, including unobserved differences in housing and neighborhood quality. Long and Caudill (1992), which examines racial differences in housing values in 1970 and 1980, is perhaps the most closely related paper to this one.3 Our study differs from theirs in that we observe and discuss a much longer time frame, and we explore the connection between residential segregation and the racial value gap in metropolitan areas.

2

For example, in the sample of household heads analyzed by Blau and Graham (1990, p. 324) black household heads held 62 percent of their net assets in the form of housing equity, compared with 47 percent among white household heads. 3 Other recent studies examining racial differences in housing values include Ihlanfeldt and MartinezVazquez (1986), and Boehm and Hoffler (1987). Unlike ours, these studies focus on data from one or a small number of metropolitan areas. See also Bianchi et al. (1982), Krivo (1981), Villemez (1980), and Wilson (1979).

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Some of the results of this paper will be familiar to students of racial differences in housing market outcomes, but other results are more surprising and provocative. Between 1940 and 1990, the average value of black-owned homes increased substantially relative to that of whites, primarily due to convergence in housing characteristics that are strongly correlated with housing values. But there was also a decline in the residual gap. Remarkably, most of the post-1940 increase in the black / white ratio, and all of the post-1940 narrowing of the residual gap, occurred before 1970. We focus our investigation of the post-1970 experience on metropolitan areas. In particular, we link our investigation with recent research which has revived scholarly interest in the economic effects of persistently high levels of residential segregation (Massey and Denton, 1993; Cutler and Glaeser, 1997). We find that on the eve of World War II, the racial gap in home values was smaller in more segregated cities, but over time, the correlation between segregation and the relative value of blacks’ housing turned strongly negative. One interpretation of this phenomenon emphasizes adverse changes in blacks’ relative neighborhood quality, particularly in central cities, but additional research, including detailed city-level case studies, will be required to isolate and identify such neighborhood effects. The paper is organized as follows. In Section 2, we describe the data and document the changes in the racial gap in housing values over time by region and metropolitan status. In Section 3, we decompose the sources of racial convergence in housing values, initially based on OLS regressions and samples of homeowners, but then allowing for selection into the homeowner category according to household characteristics. In Section 4, we explore the connection between residential segregation and the racial housing value gap; we note how dramatically that connection has changed over time; and we briefly discuss some hypotheses and evidence related to that change. We present our conclusions and suggest some extensions for future research in Section 5.

2. Trends in the racial housing value gap

2.1. The IPUMS data With the exception of 1950, each IPUMS sample since 1900 contains information on home ownership. The census classified dwellings as owner-occupied if the owner lived there, though it did not explicitly identify who within the household actually owned the home. Following census convention, we assume that if the home was owned, it was owned by the household head. Starting in 1940, information is available on property values for owner-occupied homes, and beginning in 1960, various housing characteristics are also reported. Our base samples in each year consist of all black and white male household heads from the

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ages of 20 to 64 who were not in school and who were homeowners. The implications of including female household heads and distinguishing Hispanic household heads (who are typically counted as white) from others are discussed briefly below. Some care must be taken in interpreting these data because the method of value estimation and the universe of coverage changed over time.4 First, in 1940, property values were estimated by homeowners in consultation with the census enumerator, but from 1960 onwards, values were estimated by homeowners alone. Evidence on the accuracy of self-appraisals is mixed. Kain and Quigley (1972) argue that self-appraisals are trustworthy, whereas Ihlanfeldt and Martinez-Vazquez (1986) claim that white homeowners tended to overestimate values relative to black homeowners in Atlanta. The racial bias uncovered by Inlanfedlt and Martinez-Vazquez was small, however, and its relevance to other cities and other time periods is unknown. As long as the bias did not change over time, the trends we identify here should be unaffected. Second, since 1960 a small portion of the distribution of house and property values has been top-coded. In our samples, approximately the top three percent of households in 1960 (above $35,000), 1970 (above $50,000), and 1990 (above $400,000) are top-coded, and the top one percent in 1980 (above $200,000) is top-coded. However, the value distribution was not top-coded in 1940, and we use the top-end of that distribution to estimate the mean value of the top-coded categories in other years.5 A related issue is that from 1960 onward, the housing value data are organized into intervals, with each household assigned the midpoint value of its interval (see Ruggles and Sobek, 1997 for details). The broadness of the intervals, in terms of how much of the value distribution is contained within each, changes from decade to decade. We have attempted to check our results for robustness to these interval changes by constructing broader intervals for some years for the sake of comparison. Third, there are changes in the range of properties for which value data were recorded. For the sake of comparability over time, we exclude farms, condominiums, trailers, properties used for commercial purposes, properties with 10 acres or more, and owner-occupied units in multifamily buildings from the samples of homeowners. Later in the paper, when allowing for selection into the ‘owner’ sample, we expand the sample to cover renters, including those in multi-family buildings. Finally, the geographic coverage of the IPUMS ‘metropolitan status’ variable changes over time, sometimes omitting entire states (1960 and 1970). Also, the IPUMS categorizes a changing proportion of metropolitan residents into an ‘in 4

For details of coverage see Ruggles and Sobek (1997). For example, the average value of the top-coded category in 1960 is estimated by multiplying the top-code by the ratio of the average value of homes in the top three percent in 1940 to the value of homes at the 97th percentile (a factor of approximately 1.44). 5

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metro area, central-city status unknown’ category (as opposed to a definite identification of ‘central-city’ versus ‘outside central-city’ status). The figures in Table 1 do not attempt to standardize the coverage with respect to the metro status variable, and in fact, perfectly standardized coverage is infeasible. Nonetheless, we have attempted to check the sensitivity of the trends to changes in coverage, and when significant differences emerge, we note them below.6

2.2. Trends Table 1 reports the average value of black-owned homes relative to whiteowned homes for each census region and for the entire U.S. from 1940 to 1990, the last year for which IPUMS data are currently available. Within each region, the table also reports ratios for residents of metropolitan areas, residents of central cities (a subset of metropolitan residents), and residents of metro areas who are not in central cities (which we refer to as suburbs). For the full sample of the entire country, there has a been a strong upward movement in the black / white ratio over time. In 1940, the ratio stood at 0.37. By 1990, the black / white ratio (0.65) was still far below unity, but the increase over the previous half-century was impressive nevertheless. Clearly, however, the increase in the black / white ratio was neither continuous over time nor even across geographic locations. For example, the ratio’s leap between 1940 and 1960 was large in comparison with subsequent increases, and it occurred despite the prevalence of overt racial biases in mortgage and housing markets (Massey and Denton, 1993). Then, despite the adoption of policies intended to discourage discrimination in housing markets, the aggregate black / white ratio stagnated in the 1970s before resuming a slight upward trend during the 1980s. The aggregate stagnation of the 1970s masks a decline in the national black / white ratio in central cities, a decline that continued into the 1980s, especially in the Northeast and Midwest regions.7 The post-1970 central-city collapse was a severe drag on overall racial convergence in housing values, largely offsetting the significant racial convergence occurring in suburban and non-metro areas. If property values among central-city homeowners had converged at the same pace as in the suburbs and non-metro 6 We have taken two approaches to checking the trends. First, for 1980 and 1990 we recalculated figures while omitting a set of states for which metro status was not available in 1960 and 1970. Second, we identified an overlapping set of metropolitan areas in which no households were classified as ‘metro area, central-city status unknown’ in 1980 and 1990; that is, all were categorized as central-city or not central-city residents. The idea is to track a fixed set of cities which are free from the ‘central-city status unknown’ ambiguity. We cannot continue backward in this manner because of restrictions on the metro area identifications in 1970 and 1960. 7 The apparent decline in the 1980s in the Midwest disappears if we restrict the 1980 and 1990 metro samples to only those cities with no residents in the ‘central-city status unknown’. This is, however, a severe sample restriction which retains only 40 metro areas nationally.

260 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 Table 1 Black / white value of owner-occupied housing, by region 1940

1960

1970

1980

1990

Northeast Full sample Non-metro areas Metro areas Central cities Suburbs

0.64 0.54 0.63 0.67 0.58

0.62 0.69 0.59 0.66 0.62

0.66 0.86 0.62 0.71 0.71

0.65 0.85 0.62 0.69 0.73

0.72 1.03 0.68 0.64 0.82

Midwest Full sample Non-Metro Areas Metro areas Central cities Suburbs

0.55 0.49 0.51 0.59 0.35

0.69 0.60 0.63 0.72 0.52

0.71 0.63 0.66 0.77 0.65

0.62 0.72 0.58 0.65 0.68

0.68 0.80 0.59 0.60 0.71

South Full sample Non-metro areas Metro areas Central cities Suburbs

0.34 0.33 0.39 0.46 0.25

0.52 0.45 0.57 0.62 0.46

0.60 0.56 0.62 0.67 0.58

0.63 0.65 0.63 0.60 0.70

0.67 0.67 0.66 0.62 0.77

West Full sample Non-metro areas Metro areas Central cities Suburbs

0.60 0.56 0.58 0.56 0.62

0.75 0.59 0.71 0.73 0.67

0.78 0.52 0.74 0.74 0.76

0.78 0.70 0.74 0.69 0.78

0.92 0.99 0.84 0.69 0.87

All U.S. Full sample Non-metro areas Metro areas Central cities Suburbs Black observations White observations

0.37 0.33 0.44 0.51 0.34 3,231 75,544

0.55 0.43 0.58 0.65 0.52 7,349 139,635

0.62 0.54 0.62 0.69 0.64 12,510 206,284

0.62 0.61 0.61 0.58 0.70 15,647 233,247

0.65 0.66 0.63 0.53 0.75 15,448 260,253

Notes: Samples consist of black and white male household heads between 20 and 64 years of age who are not in school. Since 1960, house and property values have been top-coded. Approximately, the top three percent of households in 1960 (above $35,000), 1970 (above $50,000), and 1990 (above $400,000) are topcoded. The top one percent in 1980 (above $200,000) are top-coded, but values are not top-coded in 1940. The average value of the top-coded category in 1960 is estimated by multiplying the top-code by the ratio of the average value of homes in the top three percent in 1940 to the value of homes at the 97th percentile. Similar multiples are formed for 1970, 1980, and 1990 on the basis of 1940s data. Farms, condominiums, trailers, properties used for commercial purposes, properties with 10 acres or more, and owner-occupied units in multifamily buildings are excluded for comparability over time. Some observations are included here but not in the regressions below due to missing information on household or housing characteristics. Those with ‘in metro area, central-city status unknown’ are included in the ‘metro area’ averages but not the ‘central-city’ or ‘suburb’ averages. The coverage of the IPUMS metro status variable changes over time, but we have not attempted to standardize the coverage above. Observations are weighted using household weights from the IPUMS. Source: Data are from the IPUMS (Ruggles and Sobek, 1997).

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areas, the national black / white value ratio would have been markedly higher by 1990. In fact, outside central cities (and excluding ‘in metro area, central-city status unknown’ observations), the black / white value ratio increased from 0.56 to 0.73 between 1970 and 1990. If the ratio within central cities had also increased to 0.73, the national black / white ratio would have been approximately 0.75 in 1990 rather than 0.66.8 At the regional level, the most important center of change, not surprisingly, has been the South. The South had, by far, the lowest black / white ratio in 1940. Moreover, average property values in the South, white and black, were below property values elsewhere in the country. Over time, the convergence of southern property values on those in the rest of the country, increases in the black / white ratio within the South, and the regional redistribution of black homeowners, have all served to raise the national black / white ratio. But convergence within the South itself had an especially strong impact because of the geographic concentration of black homeowners in that region: In 1940, 73 percent of the sample’s black homeowners lived in the South, and in 1990, 57 percent still did. For perspective, raising the 1940 southern black property values so that the 1940 southern black / white ratio matches the 1990 ratio would raise the 1940 national black / white ratio from 0.37 to 0.56.9 In contrast, given the average value of black-owned nonfarm homes across regions in 1940, redistributing black homeowners in 1940 to match the 1990 regional distribution would only raise the national black / white ratio from 0.37 to 0.42. The samples’ focus on male household heads simplifies the analysis, but it might also deliver a misleading general characterization of the racial value gap because it neglects differential changes in the proportion of female-headed households across race categories. Likewise, the rising tide of Hispanic immigration could confound our interpretation of the trends apparent in Table 1. However, it turns out that female headship and Hispanic immigration actually had relatively small, though growing, impacts on the observed racial gap in owner-occupied housing values (Collins and Margo, forthcoming). In national samples of all household heads (including females), the black / white value ratio is slightly lower than for samples of males only: by 1990, the difference between the samples is about four percentage points. Similarly, excluding Hispanic male homeowners

8 These figures are a bit different from those in Table 1 because we exclude the ‘metro area, central-city status unknown’ observations here. The counterfactual black central-city value is calculated relative to the actual white value in 1990. The counterfactual value for black central-city homeowners is then averaged with the actual non-central-city black property values using appropriate weights. Finally, this weighted average is expressed relative to the actual white average value. 9 In 1940, 73.38 percent of nonfarm black homeowners lived in the South. The average southern black home was valued at $1039.75; the average southern white home was valued at $3028.80. Raise the average southern black value to $1997.12 so that the black / white ratio is 0.6594 (as in 1990). The national average white home was valued at $3659.00. Therefore, raising the southern black value will raise the national ratio of values by [0.73383(1997.12–1039.75)] / 3659.0050.19.

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(typically white) from the samples has a slightly depressing impact on the black / white ratio in 1990, but only by about one percentage point. Differential trends in the proportion of female-headed households had a larger impact on the racial home ownership gap than on the value gap, and they had a significant impact on the proportion of children under ten years of age residing in owneroccupied housing.

3. Housing values and housing characteristics Although Table 1 sheds some light on when and where there were changes in the black / white value ratio, considerably more insight can be derived from regression analyses of the IPUMS samples. For simplicity, we first analyze the samples of homeowners described above. Later in this section, recognizing that home ownership is not randomly distributed across household heads, we allow for selection into ownership on the basis of several household characteristics. Supposing that the value of a property can be expressed as a linear combination of the values of its component characteristics (Rosen, 1974), we estimate equations of the following general form: ln V 5 XB 1 e where V is the value of the home, X is a list of observable housing characteristics, B is a set of regression coefficients reflecting the market valuation of those characteristics, and e is a random error term. In 1940, we can observe the location of housing units, but no information is available in the IPUMS on the housing unit itself. From 1960 to 1980, additional information is available on the number of rooms (entered in the regression quadratically, with a dummy for the topcode), the number of bathrooms (a series of indicators), age of building (a series of indicators), type of heating system (a series of indicators), and attachment status of houses (an indicator). Unfortunately, information on two of these characteristics (bathrooms and heating systems) is not reported in the 1990 data. After estimating separate regression equations for black and white home values, we decompose the overall racial gap into two parts. At any point in time, the racial gap at the sample means can be expressed as: ln VW 2 ln VB 5 (XW 2 XB )Bmean 1 (BW 2 BB )Xmean where W and B subscripts denote ‘white’ and ‘black’, and Bmean and Xmean are simple averages of the coefficient and characteristic vectors across race categories (Oaxaca, 1973; Goodman and Thibodeau, 1998). The first term on the right side of the equation reflects the importance of racial differences in observed housing characteristics in determining the size of the housing value gap when black and white-owned homes are valued using a common set of hedonic prices. The second term captures racial differences in the market valuation of housing

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attributes. These differences could reflect persistently high levels of residential segregation to the extent that blacks and whites purchased housing in separate sub-markets. Alternatively, differences in household characteristics (like income) or neighborhood characteristics that are correlated with race might imply differences in housing quality that are not captured entirely by our observed housing characteristics variables. If the correlation between race and unobserved housing and neighborhood characteristics declines, so should this residual term. More direct information about the households will enter into later sets of decompositions which control for selection into the homeowners sample. The top row of Table 2 reports the total (log) racial gap in owner-occupied housing values. Subsequent rows report how much of that gap is accounted for by racial differences in observed housing characteristics (elements of the ‘first term’ described above). The last row reveals how much of the total gap is left unexplained by the observed housing characteristics (the ‘second term’ described above). Because the list of observable housing characteristics changed over time, we report two sets of decompositions for 1960 and 1980 to allow better comparisons. For example, column 1980(A) is comparable to the 1970 specification, whereas 1980(B) is comparable to the 1990 specification which necessarily omits bathroom and heating system information. Table 2 Housing values and housing characteristics

Total value gap Rooms and bathrooms Age of building Attachment status Heating system Central-city residence Suburban residence Midwest South West Gap accounted for by characteristics Residual gap

1940

1960(A)

1960(B)

1970

1980(A)

1980(B)

1990

1.149 – – – – 0.040 0.084 20.053 0.351 20.039 0.384 0.765

0.642 – – – – 20.119 0.114 0.001 0.083 0.014 0.095 0.547

0.642 0.193 0.059 0.013 0.144 20.064 0.059 0.013 20.005 0.022 0.435 0.207

0.495 0.143 0.042 0.012 0.091 20.070 0.071 0.001 0.030 0.016 0.337 0.159

0.501 0.095 0.027 0.009 0.036 20.064 0.076 0.001 0.004 0.046 0.229 0.272

0.501 0.046* 0.043 0.008 – 20.110 0.106 0.000 0.017 0.046 0.156 0.345

0.415 0.050* 0.027 0.008 – 20.137 0.127 20.035 0.090 0.027 0.157 0.257

Note: Decompositions are based on OLS regressions of log housing value on housing characteristics and location. The regression samples are subject to the restrictions on housing types described in text, and for comparability with Table 3’s samples, the regressions exclude those with non-positive family income and indeterminate migrant status. Observations are weighted by household weights. Specifications for columns 1960(B), 1970, and 1980(A) include a full array of housing characteristics; columns 1940(A) and 1960(A) lack housing characteristics other than location; and columns 1980(B) and 1990 lack variables for bathrooms and heating systems. The asterisks (*) on the ‘rooms and bathrooms’ component for 1980(B) and 1990 indicate that only ‘rooms’ are actually included in the specification. See the notes to Table A.2 for more information. The regression results by race which underlie these decompositions are reported in the tables in Appendix A. Source: Data are from Ruggles and Sobek (1997).

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Clearly, racial differences in housing characteristics and location have been strong proximate causes of racial differences in housing values. In column 1960(B), for instance, fully two-thirds of the total value gap can be accounted for by housing characteristics and location. Even in 1940, when only location is known, one-third of the value gap reflects blacks’ relative concentration in the South. Between 1940 and 1960(A), the decline in the empirical significance of blacks’ southern concentration is driven by a decline in the magnitude of the southern regression coefficient and by a declining share of black homeowners in the South. Expressed relative to the Northeast category, the coefficient on southern residence increased from 20.66 in 1940 to 20.26 in 1960(A).10 Multiplied by the relative concentration of blacks in the South in 1940 (0.7320.2050.53), this coefficient change would narrow the gap by 0.21. At the same time, the proportion of black homeowners in the South fell from 0.73 to 0.56, while the share of white homeowners in the South increased from 0.20 to 0.24. Multiplied by the 1940 coefficient on southern residence, this relative redistribution would narrow the gap by 0.14.11 Over the same period, there is also a large decline in the residual gap from 0.77 to 0.55, which must reflect considerable convergence in unobserved housing and neighborhood characteristics. Racial differences in observed housing characteristics such as rooms and bathrooms, building age, heating system, and attachment status contributed proportionately less over time to the overall racial gap in housing values, accounting for 0.41 log points of the gap in 1960(B) but only 0.17 log points in 1980(A). Importantly, between 1960 and 1970 the convergence in housing characteristics was complemented by convergence in the residual term—that is, both observed and unobserved factors were driving the value gap toward zero. This concordance came apart during the 1970s. The overall gap changed little between 1970 and 1980(A) even though the underlying observed differences in housing characteristics offered less support for the gap, implying a widening residual portion. Restricting the geographic coverage of the 1980 sample, as described in the previous section, to form a closer comparison group with 1970 does not alter these trends.12 Also, recoding the 1980 housing value data with

10 Keep in mind that the ‘coefficients’ are actually averages of the coefficients from separate regressions on white and black samples. In this case, both white and black southern coefficients increased from 1940 to 1960, but the black increase was especially strong. 11 The two calculations offered in the text sum to more than the actual decline in the southern contribution to the value gap. Technically, to perform the decomposition over time in way that ‘adds up’, we would either have to weight the coefficient change by the 1960 regional distribution, or weight the change in distribution with the 1960 southern coefficient. Such a choice is arbitrary. In the text, we use 1940 weights for both the coefficient and distributional changes. Adjusting the 1940 sample to reflect the narrower geographic coverage in 1960 has a small effect on these results. 12 Under such restrictions, the explained portion of the gap is 0.233 (rather than 0.229).

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wider intervals, thereby making it more like the 1970 data, does not alter the trends reported in Table 2.13 On net, racial differences in location accounted for a small portion of the value gap throughout the 1960 to 1980 period. By 1960, blacks were concentrated relative to whites in central cities, whereas whites were relatively concentrated in suburban areas. Ceteris paribus, houses in central cities and suburbs were both more valuable than those in non-metro areas. Therefore, in Table 2, blacks’ concentration in central cities tended to narrow the overall housing value gap, whereas whites’ concentration in the suburbs tended to widen the gap by approximately the same amount. Whites’ relative concentration in the West and blacks’ concentration in the South tended to widen the value gap, but the magnitude of the regional contribution is small compared to the overall gap. The range of observable housing characteristics narrows in the 1990 IPUMS, and so for comparison, we decomposed the 1980 sample without using the bathroom and heating system variables in column 1980(B). Consequently, the apparent sizable decline in the residual component during the 1980s may reflect, in part, continuing but unobserved racial convergence in the bathroom and heating characteristics. Houses in the Midwest and South lost value relative to those in the Northeast during the 1980s, the upshot of which is a sizable increase in the contribution of southern residence to the racial value gap between 1980 and 1990.

3.1. Selection and ownership To this point, we have ignored the potential econometric problem of selection into the home owning sample of household heads. This is a concern to the extent that the nature or strength of the selection process changed over time, potentially confounding the interpretation of trends in Tables 1 and 2. For example, suppose that nothing were to change except that new anti-discrimination legislation makes it easier for lower-income households, and blacks in particular, to buy houses. Also suppose that lower-income home buyers tend to purchase houses that are less valuable than other houses with similar observable (to us) characteristics. Then, we might observe a widening residual gap because the market value of black-owned homes, accounting for observable housing characteristics, would be declining. This would be a very different story from one in which there was no change in the nature of selection into home ownership. In that case, a widening residual gap might reflect a decline in the relative quality of the neighborhoods in which blacks

13 The IPUMS 1980 value data are assembled with more intervals than the 1970 data. To check the impact of this, we bundled together groups from the 1980 data so that each one covered roughly the same proportion of the distribution as 1970s value groups. Then we recalculated the 1980 decomposition from Table 2. The effect on the results was small.

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reside. The stories are not mutually exclusive, but using common selectioncorrection techniques, we can go some distance in discerning between them. Following Heckman (1979), we re-estimate the value regressions in a two-step procedure and then re-compute the decompositions accordingly.14 The first step is a probit model of ownership based on household head characteristics including marital status (dummy variable), size of family (series of dummies), log family income, age (quartic), education (quadratic), migrant status (dummies for the foreign born and for natives residing in a region different from that of birth), and location (region and metro status). From this stage, estimates of the inverse Mills ratio (that is, estimates of the nonselection hazard, or the hazard of not owning a home) are formed for each homeowner. In the second step, the inverse Mills ratio is included as an independent variable in an OLS regression of log housing values on housing characteristics. The presumption underlying the analysis is that the impact of household characteristics on house values is captured by the selection term. As specified, the model itself makes no presumption about the sign of the selection term’s coefficient. However, on economic grounds, it seems likely that the coefficient will be negative. That is, homeowners with relatively high nonselection hazards will tend to own homes which are less valuable than observationally equivalent housing units owned by those with relatively low nonselection hazards.15 Table 3 incorporates this selection-correction into the decomposition framework from 1940 to 1990. The results are very similar to those reported in Table 2. Over time, underlying racial differences in observed housing characteristics supported a declining racial value gap, but there was a sizable increase in the residual component between 1970 and 1980. Interestingly, between 1960 and 1970, the residual gap in Table 3 declines by less than it does in Table 2, implying that the inclusion of the Mills ratio helps account for the ‘unobserved forces’ working in favor of blacks’ relative housing values during that decade, including considerable income convergence (Donohue and Heckman, 1991). As noted already, from 1970 to 1980, the selection correction does not diminish the sizable increase in the

14

The Tobit approach can be recast as a special, and highly restricted, version of the Heckman approach. In particular, the Tobit approach would assume that the variables and parameter values characterizing selection are identical to those characterizing housing values. In the context of housing, these are difficult assumption to justify. See Johnston and DiNardo (1997) for a discussion. 15 For example, throughout the century, lower-income households were less likely to own homes (Collins and Margo, 2001). Imagine, however, that a particular low-income household head was a homeowner, and that he chose a house that was of lower quality in ways not reflected in observed housing characteristics. The value of the non-selection hazard for this individual would be high, but the value of his house would be lower than expected (on the basis of observed housing characteristics) producing a negative correlation between the Mills ratio term and house value. In an earlier version of this paper, we entered the household characteristics directly into the same OLS value regression. Essentially, the difference here is that the household characteristics are first related to ownership, and then bundled together into the inverse Mills ratio estimate in the OLS stage.

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Table 3 Housing values and housing characteristics, allowing for selection

Total value gap Rooms and bathrooms Age of building Attachment status Heating system Central-city residence Suburban residence Midwest South West Inverse mills ratio Gap accounted for by characteristics Residual gap

1940

1960(A)

1960(B)

1970

1980(A)

1980(B)

1990

1.149 – – – – 0.048 0.069 20.098 0.431 20.077 0.240 0.614 0.535

0.642 – – – – 20.146 0.089 20.012 0.118 0.006 0.247 0.303 0.339

0.642 0.179 0.062 0.012 0.137 20.079 0.050 0.007 0.018 0.018 0.106 0.509 0.133

0.495 0.136 0.043 0.012 0.087 20.079 0.065 20.002 0.041 0.013 0.062 0.379 0.116

0.501 0.090 0.028 0.008 0.035 20.074 0.075 20.002 0.011 0.044 0.045 0.260 0.241

0.501 0.041* 0.044 0.007 – 20.122 0.103 20.003 0.028 0.044 0.069 0.209 0.292

0.415 0.042* 0.029 0.006 – 20.157 0.124 20.039 0.100 0.027 0.093 0.224 0.190

Note: The decompositions are based on two-step Heckman selection regressions, consisting of a probit for ownership on household characteristics in the first step (which provides an estimate of the inverse Mills ratio) and OLS regressions of log value on house characteristics and the inverse Mills ratio in the second step. The regressions by race which underlie these decompositions are reported in the tables in Appendix A. The regression samples are subject to the restrictions on housing types described in text, and due to the selection equation, the regressions exclude those with non-positive family income and indeterminate migrant status. The asterisks (*) on the ‘rooms and bathrooms’ component for 1980(B) and 1990 indicate that only rooms are actually included in the specification. Source: Data are from Ruggles and Sobek (1997).

residual gap, suggesting that the widening residual gap was not driven by the movement of relatively high nonselection hazard black household heads into the ownership category. In fact, just the opposite occurred as the average inverse Mills ratio for blacks declined relative to that of whites. Between 1980 and 1990, the Heckman approach did not substantially diminish the decline in the residual gap, suggesting that the unobserved forces of convergence were not working through relative changes in household characteristics. As expected, the negative sign on the Mills ratio coefficient indicates that homeowners with high non-selection hazards tended to own less valuable homes, ceteris paribus, than other homeowners (see tables in Appendix A). Because black homeowners had larger non-selection hazards than whites on average, this term helps account for some of the racial housing value gap, but that portion declined from 1960 to 1980 as blacks’ household characteristics converged on those of whites. The increase in the contribution of the Mills ratio to the gap between 1980(B) and 1990 is driven by an increase in the magnitude of the Mills ratio coefficient in the value regressions rather than by a change in the relative values of the average black and white Mills ratios themselves. Thus far, we have shown that the value of black-owned housing increased significantly relative to the value of white-owned housing between 1940 and 1970.

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Most of this increase occurred because of racial convergence in the characteristics of the households and of the housing itself. But this story of relative improvement took a surprising turn in the 1970s. The overall value gap did not narrow even as observable housing characteristics continued to converge, implying a widening residual gap. In Table 1, it appears that metropolitan areas, and central cities especially, were at the heart of the post-1970 stagnation. To extend this line of inquiry, the paper’s next section focuses on the racial value gap within metropolitan areas, and in particular, on the changing role of residential segregation in influencing the value gap.

4. Residential segregation and the housing value gap A high degree of racial residential segregation has long been one of the most striking features of American cities (Cutler et al., 1999). To a large extent, throughout the twentieth century, white and black families have lived in separate parts of cities. In thinking about the racial gap in property values within metro areas, therefore, the potential significance of residential segregation cannot be ignored. This section documents an important shift from 1940 to 1990 in the empirical connection between segregation levels and the size of the racial gap in housing values. It also offers an interpretation of that shift which suggests that economic shocks to urban areas may have had uneven racial impacts which were amplified by the extent of residential segregation. Over the course of the twentieth century, millions of African Americans migrated from the rural South to predominantly black neighborhoods in central cities. As blacks poured into these metropolitan areas, especially from 1940 to 1970, the pressure to expand the geographic scope of black neighborhoods intensified. At the same time, white families were moving rapidly to new, racially exclusive, suburban developments which were strongly favored by Federal Housing Administration policy relative to existing central-city neighborhoods (Jackson, 1985; Margo, 1992; Massey and Denton, 1993). Reflecting these divergent flows, African Americans were about half as likely as whites to live in central cities in 1910, but by 1970, African Americans were about twice as likely to reside in central cities. Despite the high level of racial segregation in metropolitan areas and continued racial discrimination in housing markets, both the rate of black home ownership and the value of black-owned homes relative to white-owned homes rose through the 1960s. In part, the rise in relative value occurred because black homeowners were increasingly able to occupy higher quality housing, often the stock left behind by whites moving to the suburbs. With the benefit of hindsight, however, both the high level of racial segregation in metropolitan areas and the concentration of black home ownership in central cities left black home values vulnerable to adverse economic shocks that disproportionately affected urban areas

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(for example, see Sugrue (1996) on Detroit). Recent work by Massey and Denton (1993) and Cutler and Glaeser (1997) has re-awakened scholarly interest in how such shocks might be magnified in the context of highly segregated housing markets (see also Wilson, 1987 and Becker and Murphy, 2001). Massey and Denton (1993, pp. 118–130) construct a variety of simulations showing that, under high levels of racial segregation, the effects of any economic shock that adversely affects blacks (for example, a decline in the demand for labor in central cities) will be concentrated geographically, thereby enhancing, in their words, the ‘social problems associated with income deprivation (e.g., crime, housing abandonment, unstable families, poor schools, etc.)’ (1993, p. 122). Using census data for 1990, Cutler and Glaeser (1997) demonstrate that the likelihoods of a variety of adverse economic and social outcomes among African Americans in metropolitan areas (low income, non-participation in the labor market, the incidence of teen-age motherhood) were strongly and positively related to the level of segregation. As they point out, however, in theory, it is not necessarily the case that ghettos are always ‘bad’. And in fact, they might not always have been ‘bad’. Elsewhere, we have shown that the adverse effects of segregation, so evident in the 1990 census data, were not evident in the 1970 census data (Collins and Margo, 2000; see also Vigdor, forthcoming). The logic of Massey and Denton’s (1993) simulations (and Cutler and Glaeser, 1997) suggests an economic link between segregation and property values. If ghettos are ‘bad’, then a higher level of racial segregation will be associated with lower relative quality of black neighborhoods and, therefore, a lower relative value of black-owned homes even after controlling for the observable characteristics of the housing. Moreover, given the timing of the emergence of ‘bad’ ghettos, we would expect to observe an increase in the magnitude of (any) adverse effect of segregation on the relative value of black-owned homes after 1970. To examine this hypothesis, we incorporate a ‘dissimilarity index’ of racial segregation in metropolitan areas into housing value regressions and interact it with a race dummy variable. The segregation indices were constructed from census tract information by Cutler et al. (1999).16 The regressions also include metropolitan area fixed effects to absorb any unobserved city-specific factors that might otherwise influence the correlation between segregation and housing values. Thus, we estimate: ln Vij 5 d 3 Black ij 1 l 3 Black ij 3 Segregation j 1 Xij B 1 uj 1 e ij 16

The dissimilarity index is based on the distribution of people across census tracts within a city or metropolitan area. The index used is based on city-level information for 1940 and metro-area-level information for 1970–1990. The measure lies between zero and one, and higher values correspond to higher levels of segregation. More precisely, dissimilarity51 / 2 o Ni 51 u(black i /black t ) 2 (nonblack i 2 nonblack t )u, where black i is the number of blacks in a particular census tracts, black t is the number of blacks in the metro area, and N is the total number of tracts in the area. See Cutler and Glaeser (1997), or Cutler et al. (1999) for more details.

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where i indexes individuals, j indexes cities, X is the same set of housing characteristics as above, and u is a set of metropolitan area fixed effects. Including the city fixed effects implies that a coefficient on the segregation index itself cannot be identified (because it does not vary within cities), but the coefficient on the interaction of the index with the race dummy ( l) is identified. Essentially, l tells us whether the racial value gap was wider (or narrower) in more segregated places after controlling for differences in housing characteristics and city-specific influences.17 The implicit identifying assumption is that there were no unobserved city-specific factors that differentially affected black and white property values and were correlated with the level of residential segregation. Unfortunately, such regressions cannot be estimated in a fully consistent manner across all of the IPUMS samples. As noted earlier in the paper, metro areas are not identified in the 1960 IPUMS, and so we cannot match the segregation index with the IPUMS observations. For 1970, metro areas are identified but only at the expense of using a sample without information on suburban versus central-city residence. Consequently, in Table 4 we examine metropolitan areas without making distinctions between central-city and suburban residents. Later in the paper, we explore that distinction extensively using the 1980 data. A changing number of metro areas are identified in each IPUMS sample, and so for the sake of comparison, Table 4 includes supplementary regressions run on samples which match earlier samples’ metro areas as closely as possible. For example, column 1970(B) reports results from a sample that includes 119 metro areas; column 1980(A1) reports results from a sample that includes 118 of those same metro areas; and column 1980(A2) reports results for the full set of available metro areas in 1980.18 Those three specifications are grouped together in the table because the full set of housing characteristics are available in those years but not in others. The 1940–1970(A2) group (the first three columns) and the 1980(B)–1990 group (the last three columns) are based on specifications which lack some (or all) housing characteristics. These limitations aside, the basic findings reported in Table 4 are clear and compelling. In 1940, there is no evidence that a higher level of segregation was associated with a larger value gap. In fact, higher levels of segregation are associated with smaller gaps in 1940.19 In column 1970(A1), we estimate the same

17 Even with the metro area fixed effects, it might be argued that segregation is endogenous. However, in a similar specification, Cutler and Glaeser (1997) show that OLS and instrumental variable estimates are virtually identical. Further, given that our interest is in change over time, only a changing OLS bias (relative to IV) would confound our interpretation of the results. 18 The lost metro area is Wilkes-Barre / Hazelton, Pennsylvania. 19 Cutler et al. (1999) report a similar finding, which they attribute to the effectiveness of restrictive covenants and other devices in limiting black access to housing of similar quality to that occupied by whites.

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Table 4 Housing values and residential segregation in metropolitan areas

Black Black3Seg. Observations Mean ln val Mean metro seg. Metro areas

1940

1970(A1)

1970(A2)

1970(B)

1980(A1)

1980(A2)

1980(B)

1990(A1)

1990(A2)

21.310 (0.265) 0.730 (0.351) 34,958 8.159 0.77 47

20.733 (0.088) 0.363 (0.106) 86,937 9.957 0.83 45

20.642 (0.066) 0.244 (0.081) 130,064 9.910 0.80 119

20.008 (0.054) 20.243 (0.066) 130,064 9.910 0.80 119

0.296 (0.034) 20.876 (0.046) 146,511 10.946 0.71 118

0.181 (0.027) 20.720 (0.038) 181,266 10.913 0.69 259

20.009 (0.029) 20.536 (0.041) 181,266 10.913 0.69 259

0.207 (0.029) 20.871 (0.046) 191,805 11.525 0.63 278

0.362 (0.035) 21.091 (0.054) 145,428 11.545 0.65 118

Notes: The dependent variable is the log of housing value. Robust standard errors are in parentheses. The IPUMS data do not report SMA codes for 1960, and so unfortunately, we cannot examine the connection between segregation and housing values in that year. Observations located in places without a segregation measure are automatically excluded from the regressions. Within each set of three columns, a similar specification is estimated: in the first three columns, no housing characteristics are used; in the next three columns, the full set of housing characteristics is used; in the last three columns, all housing characteristics except heating system and bathrooms are used. Samples are subject to the restrictions on housing types described in the text. The full regressions results are reported in Table A.6. Sources: Housing data are from the IPUMS (Ruggles and Sobek, 1997). Segregation data are from Cutler et al. (1999).

sparse specification as for 1940 (without housing characteristics) on a similar set of metropolitan areas, and find that the l coefficient had declined by half by that time. The additional metro areas in column 1970(A2) have a relatively small impact on the coefficients of interest, but with the addition of housing characteristics to the regression specification in column 1970(B), the slope of the racesegregation relationship changes sharply. The implication is that in more segregated metro areas (such as those of the Midwest), black-owned houses had better characteristics relative to those of whites than black-owned houses in less segregated metro areas.20 In other words, the segregation level is positively correlated with blacks’ relative housing characteristics in 1970, and the omission of those characteristics from the regression allows the l coefficient (in column 1970(A2)) to reflect that positive correlation. Once the characteristics of housing are accounted for, the relationship between segregation and the racial value gap

20

This can be seen directly by hedonically pricing all housing according to its characteristics and a common set of prices and then splitting the sample into high and low segregation metro areas (with the cutoff at a dissimilarity index value of 0.8). Without metro area fixed effects, the racial gap is 0.22 log points in the high segregation areas compared to 0.31 in the low segregation areas. With fixed effects, the gaps are 0.22 and 0.29 respectively.

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turns negative. Importantly, this relationship becomes more strongly negative by 1980 and continues to increase in magnitude through 1990.21 The changing empirical connection between segregation and the racial value gap is clear from Table 4, but the economic significance of the coefficients is rather less apparent. One way to size up the importance of this changing relationship is to focus on columns 1970(B) and 1980(A1) which cover a similar set of metro areas and have the full set of housing characteristics reported. Then, we can impose the 1970 relationship between race and segregation on the 1980 data, and we can calculate what the racial value gap would have been in the absence of the post-1970 changes in d and l.22 In 1980, the actual racial value gap in the relevant metro areas is approximately 0.55 log points; the counterfactual gap, with the 1970 race-segregation relationship imposed, is only 0.39. In other words, ceteris paribus, the racial gap would have been about 17 percent smaller. What forces drove this reorientation of the segregation-housing value relationship? One controversial hypothesis, attributable to Wilson (1987), is that in the wake of the Civil Rights Movement, middle and upper class blacks were able to move more easily to suburban neighborhoods, whereas before their choice of housing had been artificially constrained to predominantly black central-city neighborhoods. Middle and upper class blacks might have provided a viable economic base and positive ‘role model’ effects in such neighborhoods, and therefore their departure might have contributed to a decline in neighborhood quality for those who remained. The IPUMS data are not sufficient to offer a clear evaluation of this hypothesis, though it is apparent that among metro area residents, the correlation between income and suburban residence became increasingly strong over time, especially for blacks.23 Another possibility is that rising crime rates in the 1970s in the 1980s drove the change, but the extent to which rising crime was a cause or a symptom of ‘ghettos-gone-bad’ is unclear.24 Perhaps the answer, as Massey and Denton (1993) suggest, lies in the superimposition of the unique events of the 1960s on a (much) longer term process of ‘urban decline’, reinforced by slow macroeconomic growth in the 1970s. The riots of the 1960s may have accelerated the process of inner-city economic decline, but population (and employment) decentralization within 21 In a sparse specification for 118 metro areas in 1980, similar to that reported in column 1970(A2), the l coefficient is 20.874. Thus, the strong negative coefficient in 1980 in Table 4 does not depend on the inclusion of housing characteristics. 22 Arithmetically, we subtract (d1980 3 Black ij 1 l1980 3 Black ij 3 Segregation j ) and we add (d1970 3 Black ij 1 l1970 3 Black ij 3 Segregation j ) to ln V for each observation in 1980. 23 In samples of metro area residents, probit regressions of central-city residence on income and other household characteristics reveal an increasingly negative correlation of income and central-city residence for blacks relative to the correlation for whites. 24 That is, rising crime may be a symptom, rather than a cause, of ‘bad’ ghettos. Grogger and Willis (1998) argue that the introduction of crack cocaine caused a rise in urban crime in the 1980s but, based on the cities where crack use became prevalent, it is difficult to believe that its introduction was independent of ‘bad’ ghettos in the first place.

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metropolitan areas was well underway before the riots occurred (Margo, 1992).25 Commuting times for blacks appear to have increased since 1970, which may have been a factor contributing to rising rates of idleness (Cutler and Glaeser, 1997, p. 863). Similarly, long-term shifts of employment away from manufacturing had a disproportionately negative effect on the earnings and employment prospects of black men, especially those with a high school degree or less. Declines in the relative demand for less-educated black labor continued through the 1980s, as a consequence of skill-biased technological change (Bound and Holzer, 2000; Madden, 2000). In light of Massey and Denton’s simulations, such declines may have been particularly potent in their ability to generate negative externalities in black neighborhoods; and consequently, declining relative property values.26 Up to this point, we have examined the correlation between metro-wide segregation and the metro-wide racial value gap. However, it may be that the segregation level that truly matters is essentially ‘local’; that is, a high degree of segregation within central cities might widen the value gap there but have little or no impact on the value gap in the suburbs. Similarly, low levels of segregation in the suburbs might be associated with a smaller racial value gap in the suburbs, but be unrelated to the central-city value gap. Furthermore, as the discussion above suggests, if residential segregation facilitates potentially adverse, intra-group, interpersonal spillovers, then we might expect a stronger connection between segregation and the racial value gap in central cities than in suburbs because central cities tend to be more densely populated and more conducive to interpersonal spillovers and / or because central cities were harder hit by exogenous adverse socioeconomic trends (such as de-industrialization and riots). That is, both the ‘impulse’ and the ‘propagation mechanism’ could have been stronger in central cities than suburbs. Table 5 reports the results from two regressions using a fixed set of metropolitan areas for which separate ‘within-central-city’ and ‘within-suburb’ segregation levels can be calculated for 1980.27 In the first column, we regress housing values 25 For 1970 and 1980, we ran regressions similar to those in Table 4, but we separated the sample into two groups: metro areas that experienced at least one race-related civil disturbance in the 1960s and metro areas that did not. Data on the incidence of riots were culled from the U.S. Senate (1967), Lemberg Center for the Study of Violence (1968, 1969), and Spilerman (1970). Between 1970 and 1980, the l coefficient became more strongly negative in the cities with riots than in the cities without riots. We emphasize that these regressions are not designed to measure a ‘treatment effect’ of the riots, but they do suggest that the connections between riots, segregation, and property values are worthy of further investigation. 26 Cutler and Glaeser (1997, p. 862) show that an index of ‘education exposure’ for blacks (the index is greater than zero if blacks are residentially concentrated among more educated people of either race, and negative if the reverse were true) in 1990 was lower in cities with high levels of segregation. 27 Unfortunately, the 1970 tract-level data underlying the Cutler et al. (1999) segregation indices does not appear to reveal central-city versus suburban location of census tracts, making the calculation of separate indices impossible. The 1960 IPUMS data do not include metro area identifiers, and the 1940 census tract data pertain to cities only (not metro areas). We gratefully acknowledge Jacob Vigdor’s advice regarding this part of the empirical exploration.

274 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 Table 5 Segregation and housing values, central cities and suburbs, 1980

Black Black3CC seg. Black3Suburb seg. Observations Mean ln val Mean cc seg. Mean sub. seg. Metro areas

Central cities

Suburbs

0.071 (0.042) 20.834 (0.065) 0.347 (0.065) 42,859 10.782 0.67 0.58 121

20.008 (0.054) 20.097 (0.088) 20.314 (0.079) 103,712 11.029 0.70 0.62 121

Notes: The dependent variable is log of housing value. Robust standard errors are in parentheses. The regressions include controls for housing characteristics and metro-area fixed effects. The samples are subject to the restrictions on housing types described in the text. The full regression results are reported in Table A.7. Sources: Housing data are from the IPUMS (Ruggles and Sobek, 1997). Separate segregation data for central city and suburban areas are from personal communication with Jacob Vigdor.

of central-city residents on housing characteristics, city fixed effects, race, and race interacted with both the central-city and suburban segregation indices. Column 2 estimates the same specification for suburban residents. Consistent with our metro-wide results, among both central-city and suburban residents, we find that higher levels of ‘local’ segregation (i.e., within central cities for central-city residents, or within suburbs for suburban residents) were associated with wider racial value gaps. We also find that the negative correlation was stronger in the central-city sample than in the suburban sample.28 This is consistent with the hypothesis that the adverse effects of segregation operated with more force in central cities than in the suburbs, and it is inconsistent with the notion that intra-metro area mobility equalizes the effects of segregation on housing values between suburban and central-city neighborhoods. Interestingly, in the sample of central-city residents, we find that higher levels of suburban segregation did not adversely affect the racial value gap; if anything, the gap is narrower in central cities surrounded by highly segregated suburbs. Though verification is far beyond the reach of these data, one speculative interpretation, in line with Wilson (1987), would be that in metro areas where (presumably relatively well-off) blacks did not successfully integrate suburban neighborhoods, black-owned central-city property values fared better relative to white-owned properties than elsewhere, perhaps because central-city black neighborhood quality 28 This is also true when we do not include the suburban segregation index in the central-city regression and vice versa.

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deteriorated less. Among suburban residents, after controlling for the level of suburban segregation, we find little evidence of a spillover effect from central-city segregation on the racial value gap; the coefficient is negative but statistically insignificant.29

5. Conclusion The value of a home is directly linked to the flow of consumption services enjoyed by its residents, and therefore housing values convey useful information about households’ economic well-being. Furthermore, housing values are a piece of a larger and more complex story about wealth holding and wealth creation in the United States. Housing equity has long been a significant component of household wealth, and at least since the Depression, home-owning households have, on average, benefited from rising property values. For many households, highly leveraged housing investments, facilitated by modern mortgage financing and treated favorably by the tax code, have paid off handsomely. Thus, racial differences in home ownership and housing values have significant implications for racial differences in well-being derived from housing services and in wealth formation. This paper documents significant racial convergence in the value of owneroccupied housing, beginning at least as early as 1940. Over the same period, Collins and Margo (2001) found convergence in the rate of home ownership. However, the process of convergence in housing market outcomes has been neither smooth over time nor evenly spread across places. In fact, most of the convergence in housing values occurred before 1970. Both observable characteristics and unobservable forces (reflected in movement of the residual term) pushed strongly toward equality in the value of owner-occupied housing up to 1970. The post-1970 story is rather different. During the 1970s, despite continuing relative improvements in observable housing characteristics, overall convergence in housing values stalled and the residual value gap widened. In metropolitan areas, this residual widening was accompanied by a sharp change in the steepness of the relationship between residential segregation and the value gap. In the absence of this change, the racial value gap would have been narrower in 1980 than it actually was. That is, in the language of Cutler and Glaeser (1997) and from a variety of socioeconomic viewpoints (Collins and Margo, 2000) ghettos 29 Because the segregation indices are calculated ‘within’ central-city and suburban areas, they do not capture the influence of segregation ‘between’ central cities and suburbs. A simple way to bring the ‘between’ influence into the regressions is to include the overall metro area level of segregation. When this is done, the impact of the ‘within’ component is still larger in magnitude in the central-city sample than in the suburban sample; however, in the central city regression, the coefficient on the interaction between race and the suburban segregation index becomes statistically insignificant.

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became increasingly ‘bad’, and the impact was manifested in downward pressure on the relative value of black-owned homes in the 1970s, pressure that continued in central cites in the 1980s. The analysis in this paper could be profitably extended in several directions. First, it would be useful to continue to probe the factors underlying the changing relationship between relative black home values and segregation in the 1970s. Detailed city-level studies may prove valuable for identifying the timing and mechanisms of this process. Second, the roles of federal, state, and local antidiscrimination policies in housing markets have remained in the background of this paper, but a direct assessment of the policies’ impacts would be helpful, especially if the policies significantly increased the pace of higher-income blacks’ out-migration from central cities. Third, school desegregation efforts and households’ responses to those efforts have not been directly addressed in this paper but may be important to the post-1970 location decisions of households and the evolution of the racial value gap. Fourth, as noted above, housing values are a piece of a larger story regarding racial differences in wealth. However, the link between housing values and wealth is complicated by racial differences in portfolio allocation across asset types and by racial differences in the proportion of housing equity to housing value (Blau and Graham, 1990; Oliver and Shapiro, 1995). A full understanding of the racial gap in wealth will require a precise mapping of such underlying factors and their evolution over time.

Acknowledgements Collins is Assistant Professor of Economics at Vanderbilt University and Faculty Research Fellow at the National Bureau of Economic Research; Margo is Professor of Economics and History at Vanderbilt University and Research Associated at the National Bureau of Economic Research. We are grateful to two anonymous referees, Stanley Engerman, Edward Glaeser, Matthew Kahn, Jens Ludwig, Peyton McCrary, Gary Solon, and workshop / conference participants at the 2000 ASSA meetings in Boston, Columbia University, Lehigh University, University of Michigan, University of Mississippi, the NBER, and Yale University for helpful comments. We are especially grateful to Jacob Vigdor for assistance with the residential segregation data.

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Appendix A Table A.1 Regressions for 1940 decompositions Table 2 White Value equation midwest south west cent. city suburb constant inv. mills Selection equation age age 2 age 3 age 4 educ educ 2 fam2 fam3 fam46 fam7 married ln faminc ir migrant fb migrant midwest south west cent. city suburb constant R2 Total observations (Owners) Mean ln val

Table 3 Black

White

Black

20.1676

20.3669

20.3685

20.6197

20.4409 20.3436 0.8882 0.8004 7.4359 –

20.8701 20.2964 1.0034 0.4908 6.9532 –

20.5099 20.5549 1.0373 0.6510 8.5572 21.0987

21.0981 20.7092 1.2515 0.4058 8.0827 20.8278

– – – – – – – – – – – – – – – – – – – – 0.19 49,426

– – – – – – – – – – – – – – – – – – – – 0.26 2379

20.1623 0.0090 20.0002 9.51 e207 0.0340 20.0019 0.4334 0.5508 0.5995 0.4833 20.0979 0.2353 20.1858 0.0156 0.3619 0.2920 0.5292 20.4129 0.0968 22.8422

20.0834 0.0048 20.0001 3.77 e207 0.0760 20.0014 0.5398 0.6525 0.6399 0.6454 20.1365 0.1663 20.2257 20.0070 0.5226 0.5942 0.9627 20.6383 0.1097 23.5798

7.764

6.615

136,816 (49,426) 7.764

11,320 (2379) 6.615

Notes: The log of housing value is the dependent variable. ‘Table 2’ columns are OLS regressions. ‘Table 3’ columns are Heckman regressions allowing for selection into the home owner category. See the notes to Table A.2 for definitions of all the variables. Source: Ruggles and Sobek (1997).

278 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 Table A.2 Regressions for 1960 decompositions Table 2

Table 3

1960(A) White Value equation rooms rooms 2 room top ] built yr 2 builtyr 3 ] builtyr 4 ] builtyr 5 ] builtyr 6 ] attached baths 2 ] baths 3 ] baths 4 ] heat 2 ] heat 3 ] heat 4 ] heat 5 ] midwest south west cent. city suburb constant inv. mills

1960(B) Black

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 20.0073 0.0315 20.2088 20.3067 0.0928 0.2843 0.3466 0.6022 0.4485 0.4334 9.1752 8.5391 – –

Selection equation age – age 2 – age 3 – age 4 – educ – educ 2 – fam2 – fam3 – fam46 – fam7 – married – ln faminc – ir migrant – fb mig. – midwest – south – west – cent. city –

– – – – – – – – – – – – – – – – – –

1960(A)

White

Black

0.2283 20.0101 0.1420 0.0227 20.0030 20.1109 20.2067 20.4377 20.2197 0.5578 0.7975 0.9762 0.5725 0.4355 0.3475 0.2543 0.0866 20.0218 0.1722 0.1922 0.2030 7.4349 –

0.1620 20.0070 0.0121 20.0474 20.0883 20.2037 20.2664 20.3716 20.1125 0.3898 0.6264 0.7095 0.5859 0.3581 0.3437 0.2341 0.1324 0.0539 0.4149 0.3153 0.2505 7.5709 –

– – – – – – – – – – – – – – – – – –

– – – – – – – – – – – – – – – – – –

White

1960(B) Black

White

Black

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 20.1465 20.0481 20.3073 20.4248 0.0069 0.1509 0.4841 0.6790 0.3244 0.3622 9.6829 9.0439 20.7388 20.4533

0.1795 0.1263 20.0069 20.0047 0.1278 0.0001 20.0046 20.0574 20.0499 20.1097 20.1631 20.2357 20.2538 20.2936 20.4818 20.4014 20.2014 20.1016 0.5230 0.3683 0.7453 0.5922 0.9140 0.6671 0.5441 0.5664 0.4121 0.3435 0.3309 0.3303 0.2429 0.2217 0.0192 0.0905 20.0852 20.0257 0.1238 0.3465 0.2608 0.3680 0.1601 0.2224 7.9207 7.9763 20.3067 20.2058

0.5547 0.2912 20.0128 20.0046 0.0001 0.0000 0.0000 0.0000 0.0747 0.0375 20.0027 0.0002 0.8665 0.5308 1.0494 0.6786 1.2298 0.7071 1.0931 0.6597 0.0501 0.0531 0.4040 0.3554 20.2674 20.1708 20.3094 20.5175 0.4252 0.2921 0.4200 0.4322 0.3687 0.5395 20.2567 20.4858

0.5547 0.2912 20.0128 20.0046 0.0001 0.0000 0.0000 0.0000 0.0747 0.0375 20.0027 0.0002 0.8665 0.5308 1.0494 0.6786 1.2298 0.7071 1.0931 0.6597 0.0501 0.0531 0.4040 0.3554 20.2674 20.1708 20.3094 20.5175 0.4252 0.2921 0.4200 0.4322 0.3687 0.5395 20.2567 20.4858

W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 suburb constant R2 Observations (Owners) Mean ln val

– – 0.13 115,604

– – 0.3 6066

9.431

8.789

– – 0.58 115,604

– – 0.54 6066

9.431

8.789

20.7111 0.0965 213.1945 29.8423 – – 200,833 19,517 (115,604) (6066) 9.431 8.789

279

20.7111 0.0965 213.1945 29.8423 – – 200,833 19,517 (115,604) (6066) 9.431 8.789

Notes: The log of housing values is the dependent variable. ‘Table 2’ columns are OLS regressions. ‘Table 3’ columns are Heckman regressions allowing for selection into the home owner category. Generally, we include as much detail as possible in the specifications, with some compromises to maintain comparability over time as mentioned in the text. See the IPUMS documentation for full details (Ruggles and Sobek, 1997). The age of the building is summarized by the series of ‘builtyr’ dummies (0–1 year old is omitted category; dummies are: 2–5 years; 6–10 years; 11–20 years; 21–30 years; 31years and over). The number of bathrooms is summarized by the series of ‘baths’ dummies (0–1 / 2 bath is omitted category; dummies are: 1 full bath; 1.5 bathrooms; and 2 or more full bathrooms). The heating system is described by the ‘heating’ series of dummies (not heated or ‘other’ (e.g., room heater) is the omitted category; dummies are: steam or hot water; central warm-air furnace; built-in electric unit; and floor, wall, or pipeless furnace). Family size is entered as a series of dummies (1 is omitted category; dummies are: 2, 3, 4 to 6, and 7 and over). Migrant status is described by two dummies (native born, residing in region of birth is the omitted category; dummies are: ‘ir migrant’ for native born residing in region different from birth, and ‘fb migrant’ for foreign-born). Source: Ruggles and Sobek (1997). Table A.3 Regressions for 1970 decompositions Table 2

Value equation rooms rooms 2 room top ] built yr 2 builtyr 3 ] builtyr 4 ] builtyr 5 ] builtyr 6 ] attached baths 2 ] baths 3 ] baths 4 ] heat 2 ] heat 3 ] heat 4 ] heat 5 ] midwest south west cent. city suburb constant

Table 3

White

Black

0.1412 20.0021 0.1135 20.0593 20.1238 20.1408 20.2424 20.4911 20.2603 0.4695 0.6970 0.8742 0.5165 0.3674 0.3387 0.2162 0.0023 20.1633 0.1134 0.1753 0.2185 8.1865

0.1591 20.0073 20.0177 20.0733 20.1472 20.2293 20.3159 20.4811 20.1696 0.4985 0.7456 0.8384 0.4379 0.2934 0.2598 0.1696 0.0238 20.0743 0.3623 0.2648 0.2697 8.0614

White 0.1142 20.0005 0.1066 20.0756 20.1516 20.1784 20.2792 20.5227 20.2492 0.4521 0.6685 0.8371 0.5008 0.3550 0.3321 0.2112 20.0274 20.1864 0.1046 0.2085 0.2064 8.4583

Black 0.1369 20.0060 20.0253 20.0867 20.1698 20.2563 20.3433 20.5084 20.1645 0.4810 0.7182 0.8040 0.4235 0.2809 0.2547 0.1638 20.0272 20.1371 0.3058 0.2890 0.2430 8.3680

280 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 inv. mills

–

Selection equation age age 2 age 3 age 4 educ educ 2 fam2 fam3 fam46 fam7 married ln faminc ir mig. fb mig. midwest south west cent. city suburb constant R2 Observations (Owners) Mean ln val

–

– – – – – – – – – – – – – – – – – – – – 0.57 173,163

– – – – – – – – – – – – – – – – – – – – 0.48 10,466

9.827

9.332

20.2083

20.1738

0.6610 20.0172 0.0002 0.0000 0.1045 20.0037 0.7890 1.0131 1.2342 1.1315 0.1359 0.3795 20.2454 20.4676 0.3590 0.3348 0.2276 20.3656 0.1166 214.3986 – 260,630 (173,163) 9.827

20.0835 0.0082 20.0002 0.0000 0.0196 0.0012 0.5615 0.6981 0.8114 0.7276 0.0774 0.4180 20.0538 20.4869 0.4759 0.6022 0.5260 20.2791 0.1932 26.8270 – 24,618 (10,466) 9.332

Notes: The log of housing value is the dependent variable. ‘Table 2’ columns are OLS regressions. ‘Table 3’ columns are Heckman regressions allowing for selection into the home owner category. See the notes to Table A.2 for definitions of all the variables. Source: Ruggles and Sobek (1997).

Table A.4 Regressions for 1980 decompositions Table 2

Table 3

1980(A)

Value equation rooms rooms 2 room top ] built yr 2 builtyr 3 ] builtyr 4 ] builtyr 5 ] builtyr 6 ]

1980(B)

1980(A)

White

Black

White

Black

0.0777 0.0029 0.0659 20.0533 20.1320 20.1600 20.1800 20.3594

0.0405 0.0038 20.0077 20.0912 20.2112 20.2566 20.3222 20.4954

0.2045 20.0014 0.0499 20.0680 20.1734 20.2296 20.3179 20.5526

0.0892 0.0048 20.0163 20.0999 20.2773 20.3918 20.5261 20.7415

White

0.0415 0.0051 0.0570 20.0660 20.1544 20.1921 20.2149 20.3851

1980(B) Black

0.0177 0.0050 20.0083 20.0979 20.2282 20.2835 20.3516 20.5238

White

0.1393 0.0023 0.0380 20.0864 20.2049 20.2738 20.3604 20.5766

Black

0.0524 0.0066 20.0157 20.1099 20.3003 20.4270 20.5614 20.7727

W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 attached baths 2 ] baths 3 ] baths 4 ] heat 2 ] heat 3 ] heat 4 ] heat 5 ] midwest south west cent. city suburb constant inv. mills

20.1486 20.1372 20.1553 20.1007 – 0.5693 0.4113 – – 0.7832 0.6541 – – 0.9854 0.7816 – 0.3526 0.4255 – – 0.2582 0.3258 – – 0.2539 0.2694 – – 0.1737 0.2296 – – 0.0280 20.0063 0.0143 20.0120 20.0765 0.0406 20.0906 20.0556 0.3854 0.6374 0.4048 0.6341 0.1968 0.2039 0.2807 0.4018 0.2809 0.3474 0.3538 0.5236 9.1553 9.1973 9.6109 9.7334 – – – –

20.1351 0.5445 0.7451 0.9386 0.3372 0.2456 0.2442 0.1703 0.0055 20.0901 0.3838 0.2364 0.2799 9.4470 20.2050

20.1300 0.3972 0.6280 0.7515 0.4153 0.3171 0.2674 0.2215 20.0502 20.0055 0.6013 0.2263 0.3428 9.4628 20.1532

20.1344 – – – – – – – 20.0200 20.1105 0.4005 0.3344 0.3463 10.0296 20.2050

281

20.0914 – – – – – – – 20.0795 20.1224 0.5766 0.4255 0.5072 10.1146 20.1532

Selection equation age – – – – 0.7511 0.4126 0.7511 0.4126 age 2 – – – – 20.0222 20.0104 20.0222 20.0104 age 3 – – – – 0.0003 0.0001 0.0003 0.0001 age 4 – – – – 21.47 e-06 26.19 e-07 21.47 e-06 26.19 e-07 educ – – – – 0.0891 0.0446 0.0891 0.0446 educ 2 – – – – 20.0023 20.0003 20.0023 20.0003 fam2 – – – – 0.6690 0.6285 0.6690 0.6285 fam3 – – – – 0.8510 0.8067 0.8510 0.8067 fam46 – – – – 1.0126 0.9385 1.0126 0.9385 fam7 – – – – 0.8302 0.8249 0.8302 0.8249 married – – – – 0.1952 0.0898 0.1952 0.0898 ln faminc – – – – 0.4580 0.4028 0.4580 0.4028 ir mig. – – – – 20.2368 20.0692 20.2368 20.0692 fb mig. – – – – 20.5086 20.5273 20.5086 20.5273 midwest – – – – 0.3446 0.5172 0.3446 0.5172 south – – – – 0.2661 0.5161 0.2661 0.5161 west – – – – 0.1741 0.3804 0.1741 0.3804 cent. city – – – – 20.4231 20.2989 20.4231 20.2989 suburb – – – – 20.0155 0.0159 20.0155 0.0159 constant – – – – 215.2713 211.6845 215.2713 211.6845 R2 0.52 0.45 0.44 0.36 – – – – Observations 197,239 13,712 197,239 13,712 289,471 28,950 289,471 28,950 (Owners) (197,239) (13,712) (197,239) (13,712) Mean ln val 10.8574 10.3563 10.8574 10.3563 10.8574 10.3563 10.8574 10.3563 Notes: The log of housing value is the dependent variable. ‘Table 2’ columns are OLS regressions. ‘Table 3’ columns are Heckman regressions allowing for selection into the home owner category. See the notes to Table A.2 for definitions of all the variables. Source: Ruggles and Sobek (1997).

282 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 Table A.5 Regressions for 1990 decompositions Table 2

Value equation rooms rooms 2 room top ] built yr 2 builtyr 3 ] builtyr 4 ] builtyr 5 ] builtyr 6 ] attached midwest south west cent. city suburb constant inv. mills Selection equation age age 2 age 3 age 4 educ educ 2 fam2 fam3 fam46 fam7 married ln faminc ir mig. fb mig. midwest south west cent. city suburb constant R2 Observations (Owners) Mean ln val

Table 3

White

Black

0.0665 0.0089 0.0152 20.0366 20.1781 20.2557 20.2763 20.4168 20.1068 20.4466 20.3997 0.1905 0.5438 0.6278 10.6595 –

20.0710 0.0166 0.0173 20.0676 20.2239 20.3182 20.3793 20.5254 20.1589 20.5640 20.3928 0.4025 0.4093 0.7138 11.0255 –

– – – – – – – – – – – – – – – – – – – – 0.47 179,351

– – – – – – – – – – – – – – – – – – – – 0.44 11,102

11.442

11.028

White 20.0330 0.0145 20.0040 20.0544 20.2085 20.3084 20.3361 20.4509 20.0712 20.4748 20.4111 0.2177 0.6374 0.6094 11.2900 20.4628

0.5830 20.0157 0.0002 28.61 e-07 0.0579 20.0017 0.4711 0.5889 0.7101 0.4900 0.1975 0.5091 20.2473 20.4971 0.2597 0.1820 20.0225 20.4624 20.0106 214.1938 – 265,524 (179,351) 11.442

Black 20.1027 0.0173 0.0243 20.1011 20.2801 20.4002 20.4615 20.5924 20.1359 20.6471 20.4698 0.3610 0.4552 0.7027 11.5270 20.3452

20.2835 0.0145 20.0003 1.53 e-06 0.0134 0.0012 0.5361 0.7659 0.8440 0.7492 0.0187 0.4051 20.1072 20.4437 0.4801 0.4721 0.1359 20.4025 20.1624 24.6881 – 22,095 (11,102) 11.028

Notes: The log of housing value is the dependent variable. ‘Table 2’ columns are OLS regressions. ‘Table 3’ columns are Heckman regressions allowing for selection into the home owner category. See the notes to Table A.2 for definitions of all the variables. Source: Ruggles and Sobek (1997).

W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286

283

Table A.6 Segregation regressions underlying Table 4

Black Black*Seg. rooms

1940

1970(A1)

1970(A2)

1970(B)

1980(A1)

1980(A2)

1980(B)

1990(A1)

1990(A2)

21.3101 (0.265) 0.7295 (0.351) –

20.7333 (0.088) 0.3626 (0.106) –

20.6420 (0.066) 0.2440 (0.081) –

20.0077 (0.054) 20.2425 (0.066) 0.0876 (0.010)

0.2964 (0.034) 20.8758 (0.046) 0.0374 (0.010)

0.1809 (0.027) 20.7199 (0.038) 0.0512 (0.009)

20.0094 (0.029) 20.5358 (0.041) 0.1413 (0.010)

0.2072 (0.029) 20.8705 (0.046) 0.0408 (0.009)

0.3623 (0.035) 21.0913 (0.054) 0.0071 (0.009)

0.0018 (0.001) 0.1095 (0.009) 20.0491 (0.006) 20.1187 (0.006) 20.1611 (0.006) 20.2453 (0.006) 20.4482 (0.006) 20.2519 (0.006) 0.3675 (0.018) 0.5497 (0.018) 0.7124 (0.018) 0.3376 (0.006) 0.2755 (0.005) 0.2418 (0.008) 0.1321 (0.006) 8.7777 (0.034) 0.60 130,064 119 yes 9.9098 0.80

0.0059 (0.001) 0.0704 (0.007) 20.0526 (0.006) 20.1481 (0.006) 20.1918 (0.006) 20.2294 (0.006) 20.3807 (0.006) 20.2500 (0.007) 0.4214 (0.034) 0.6042 (0.034) 0.7840 (0.034) 0.2631 (0.007) 0.2383 (0.006) 0.2461 (0.007) 0.1034 (0.008) 9.8546 (0.046) 0.60 146,511 118 yes 10.9459 0.71

0.0047 (0.001) 0.0743 (0.006) 20.0485 (0.005) 20.1414 (0.005) 20.1863 (0.005) 20.2259 (0.005) 20.3779 (0.006) 20.2398 (0.007) 0.4513 (0.029) 0.6338 (0.029) 0.8172 (0.029) 0.2627 (0.006) 0.2388 (0.005) 0.2531 (0.006) 0.0975 (0.007) 9.7482 (0.040) 0.60 181,266 259 yes 10.9125 0.69

0.0025 (0.001) 0.0521 (0.007) 20.0608 (0.006) 20.1834 (0.006) 20.2578 (0.005) 20.3632 (0.005) 20.5610 (0.006) 20.2442 (0.007) –

0.0098 (0.001) 0.0494 (0.007) 20.0529 (0.008) 20.1592 (0.008) 20.2674 (0.007) 20.3560 (0.007) 20.4823 (0.007) 20.2740 (0.007) –

0.0125 (0.001) 0.0351 (0.007) 20.0490 (0.009) 20.1567 (0.009) 20.2690 (0.008) 20.3551 (0.009) 20.4748 (0.009) 20.2888 (0.008) –

rooms 2

–

–

–

room top ]

–

–

–

built yr 2

–

–

–

builtyr 3 ]

–

–

–

builtyr 4 ]

–

–

–

builtyr 5 ]

–

–

–

builtyr 6 ]

–

–

–

attached

–

–

–

baths 2 ]

–

–

–

baths 3 ]

–

–

–

baths 4 ]

–

–

–

heat 2 ]

–

–

–

heat 3 ]

–

–

–

heat 4 ]

–

–

–

heat 5 ]

–

–

–

constant R2 Observations Metro areas Fixed effects Mean ln val Mean metro seg

8.1811 (0.004) 0.09 34,958 47 yes 8.1591 0.77

9.9878 (0.002) 0.19 86,937 45 yes 9.9567 0.83

Notes and Sources: See Table 4 in text.

9.9385 (0.001) 0.21 130,064 119 yes 9.9098 0.80

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

–

10.2421 (0.031) 0.54 181,266 259 yes 10.9125 0.69

11.1589 (0.027) 0.65 191,805 278 yes 11.5249 0.63

11.2786 (0.029) 0.63 145,428 118 yes 11.5445 0.65

284 W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286 Table A.7 Segregation regressions underlying Table 5 1980 Black Black*CC seg. Black*Suburb seg. rooms rooms 2 room top ] built yr 2 builtyr 3 ] builtyr 4 ] builtyr 5 ] builtyr 6 ] attached baths 2 ] baths 3 ] baths 4 ] heat 2 ] heat 3 ] heat 4 ] heat 5 ] constant R2 Observations Metro areas Fixed effects Mean ln val Mean cc seg. Mean sub. seg.

Central cities

Suburbs

0.0708 (0.0420) 20.8339 (0.065) 0.3468 (0.065) 0.0392 (0.018) 0.0058 (0.001) 0.0847 (0.015) 20.0522 (0.015) 20.1810 (0.015) 20.2175 (0.015) 20.2554 (0.015) 20.4220 (0.015) 20.0970 (0.012) 0.2613 (0.065) 0.4663 (0.065) 0.6278 (0.065) 0.3146 (0.013) 0.2939 (0.011) 0.2723 (0.013) 0.1408 (0.013) 9.9248 (0.085) 0.63 42,859 121 yes 10.7817 0.67 0.58

20.0082 (0.0536) 20.0971 (0.088) 20.3144 (0.079) 0.0452 (0.012) 0.0053 (0.001) 0.0653 (0.008) 20.0521 (0.006) 20.1445 (0.006) 20.1914 (0.006) 20.2264 (0.006) 20.3364 (0.007) 20.1960 (0.009) 0.5032 (0.040) 0.6704 (0.040) 0.8559 (0.040) 0.2425 (0.008) 0.2125 (0.007) 0.2257 (0.008) 0.0786 (0.010) 9.7977 (0.054) 0.59 103,712 121 yes 11.0290 0.70 0.62

Notes and Sources: See Table 5 in text.

W. J. Collins, R. A. Margo / Regional Science and Urban Economics 33 (2003) 255–286

285

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