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Mesure des inégalités scolaires

The Geo-Spatial Distribution of Educational Attainment: Cultural Capital and Uneven Development in Metropolitan Kansas City, 1960-1980

La répartition spatiale des niveaux de formation. Capital culturel et développement inégal dans la métropole de Kansas City
John L. Rury et Sanae Akaba
p. 219-248


Cet article examine l’évolution des inégalités spatiales en matière de formation, en mettant l’accent sur l’éducation des adultes, dans le cadre spécifique de la métropole de Kansas City. Les variations du niveau d’éducation sont interprétées selon le concept bourdieusien de capital culturel. L’analyse statistique, par la méthode de régression des moindres carrés ordinaires (MCO), des données du recensement suggère que les niveaux d’éducation étaient modérément indépendants des autres caractéristiques sociales en 1960 et en 1980. Historiquement, les « zones d’éducation » ont été mises en place avant que le conflit sur la déségrégation n’ait éclaté. Les différences de niveau des élèves sont aussi examinées à partir des données individuelles, ainsi que l’influence des pairs sur la réussite scolaire. En définitive, l’article montre que l’éducation est devenue une dimension importante de l’inégalité spatiale dans la vie métropolitaine, indépendemment des controverses sur la race et la « fuite des blancs ».

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  • 1   R. Sampson, 2012, Part 1; A. Bryk et al., 2010, p. 169.

1Education in the United States is organized in locally controlled school districts, historically supported by property taxes. This has become a source of considerable unevenness or inequality in educational development, contributing especially to central city decline and turmoil that has unfolded for more than fifty years. As a number of studies have demonstrated, school problems are often associated with other forms of social disorder, including crime, violence and economic deprivation. Together, these factors shape public perceptions of the metropolitan landscape, labeling some neighborhoods or communities “good” and others “bad.”1

  • 2   D. Harding et al., 2011, p. 278.

2This is an issue that has garnered increased attention lately. Sociologist David Harding and his colleagues have pointed to a growing polarization in the geography of socio-economic status. “Changes in income inequality,” they write, “have particularly increased the spatial segregation of the affluent from middle class and lower class families.” They suggest, moreover, that this has important implications for education: “These trends suggest that diverging educational outcomes by family income… may be due in part to the increasing numbers of youths growing up in neighborhoods characterized either by concentrated poverty or concentrated affluence.”2 In short, the educational landscape is being divided more sharply along lines of social and economic status. Although this development has been noticed recently, little is known about its history.

3This paper explores historical evidence of metropolitan inequities in education and related indices of community standing, focusing on greater Kansas City. It addresses the question of spatial inequality in education and social status in a particular metropolitan setting. Specifically, we consider inequality in adult education levels, focusing on differences from one school district or area to another. As suggested below, this is an especially revealing indicator of social and cultural resources critical to success in school. We also examine geographic differences in student attainment, controlling for socio-economic background. Unlike much prior research on such questions, this analysis extends beyond the city to the larger metropolitan setting, utilizing quantitative data drawn from US census reports.

  • 3   R. Sampson, 2012, p. 21.

4As Robert J. Sampson has put forward in a recent study of Chicago, perceptions contribute directly to “enduring neighborhood inequality” on a variety of social and economic indices. The same could be said of additional American communities, and as R.J. Sampson suggests, in other countries too.3 These spatial patterns of inequity are aggravated—if not often caused—by longstanding ethnic and racial bigotry, especially in the US. But as this paper notes, such prejudice and intolerance was not a necessary condition for socio-economic differentiation of this sort. Clear patterns of inequity appear to have predated major conflicts over school integration and other racially-charged issues in Kansas City. Insofar as geo-social status differentiation affected education, however, it helped to reproduce disadvantage across generations and thereby deepen inequality. Changes in key characteristics of particular localities—such as adult education levels—can be measured over time, helping to pinpoint the shifting spatial ordering of metropolitan inequality.

1. Adult Educational Attainment as a Focal Point

  • 4   J. Coleman, 1969, p. 9-24.
  • 5   V. Roscigno & J. Ainsworth-Darnell, 1999, p. 158; E. Epps, 1995, p. 593-608.
  • 6   A. Lareau, 1987, p. 83.
  • 7   R. Seginer, 1983, p. 1-23; D. Entwisle et al., 1997, Ch. 1.

5A telling facet of metropolitan development that has not been given due consideration is the uneven distribution of educated adults. Since the Coleman Report in 1966, researchers have recognized that family background factors are the most critical determinants of educational success among children, far more than school resources.4 Subsequent studies built on this insight by demonstrating that children of highly educated parents have superior levels of vocabulary and reading ability and perform better on other educational indicators. Educated parents also have higher aspirations for their children, take a more active interest in their schooling, and demand that teachers meet their needs.5 Annette Lareau has argued that parent education levels are a critical determinant of the “home advantage” enjoyed by upper middle class families regarding their children’s education. “The resources tied directly to social class (e.g., education, prestige, income),” she wrote, “and certain patterns of family life (e.g., kinship ties, socialization patterns, leisure activities) seem to play a large role in facilitating the participation of parents in schools.” In her narrative, however, educational differences loomed largest, even if children without such traits succeeded at times.6 This view has been corroborated by numerous quantitative studies that demonstrate the importance of parental education—usually expressed in terms of attainment—on various measures of educational success.7 Given this large body of evidence, there can be little doubt about the importance of such parental qualities in educational inequality.

  • 8   N. Grubb, 2009, p. 92.
  • 9Ibidem, p. 95.
  • 10   R. Mickelson, 1990, p. 44.

6This line of work was summarized recently in The Money Myth, by economist Norton Grubb. He pointed out that among the most important resources that schools possess are attributes that students bring to the educational process, especially characteristics of their families. As N. Grubb notes, “virtually every examination of schooling performance has found substantial advantages to families of higher standing, whether measured by income, occupation, parental education, or some other dimension.”8 Writing about the statistical effects of different family background factors on educational outcomes, N. Grubb points out that “the most consistent and powerful effects are those of maternal education,” noting that parents with a college degree are especially influential in their children’s success.9 He argues that parental occupation and income, by contrast, are considerably weaker predictors of educational outcomes, although they do account for some variation in achievement. Parental aspirations for their children are also important, but the effect of such goals often depends on other characteristics, including parent education and peers.10

  • 11   V. Roscigno et al., 2008, p. 2121-2145.
  • 12   N. Grubb, 2009, Ch. 4.

7As N. Grubb also notes, such parental attributes often go unacknowledged in studies examining resource differences across schools, but clearly can represent a critical element of inequality between schools. More typically, research on spatial inequity has focused on school funding, occasionally with estimates of changes required to make educational outcomes more equitable.11 N. Grubb demonstrates that viewing key parental characteristics as an additional resource would make such studies more inclusive of elements shaping the educational process.12

  • 13   J. Epstein, 1986, p. 277.
  • 14   P. Bourdieu, 1986, p. 243.

8Finally, it is important to acknowledge the significance of adult education as a facet of schooling and socio-cultural inequality. Parents with higher levels of education have a comprehension of school procedures and expectations that others do not.13 This is a vital asset for their children in the competitive ethos of institutionalized learning. As Pierre Bourdieu asserted, it is a critical dimension of cultural capital, or non economic forms of advantage in status attainment. In a celebrated essay The Forms of Capital, he suggests that such distinctions “may be institutionalized in the form of educational qualifications.”14 With regard to student success, parental educational experiences can enhance knowledge of academic processes, expectations and standards, a form of embodied cultural capital that benefits certain children but not others.

  • 15   P. Bourdieu, 1989, p. 16.
  • 16Ibidem, p. 17.

9P. Bourdieu also noted that “one can observe almost everywhere a tendency toward spatial segregation, people who are close together in social space tending to find themselves, by choice or by necessity, close to one another in geographic space.”15 As suggested above, this is a widely recognized phenomenon, readily evident in current patterns of socio-economic inequality. P. Bourdieu adds, however, that “agents are distributed in the overall social space, in the first dimension, according to the overall volume of capital they possess and, in the second dimension, according to the structure of their capital, that is, the relative weight of the different species of capital, economic and cultural, in the total volume of their assets.”16 In this light, the level of adult education in a given community can be viewed as a potent form of capital, a local sum of assets. Such a cultural formation can function as a resource to children who are provided access to it. For those who lack such access, conversely, educational experiences are often correspondingly impoverished. This is a rarely acknowledged facet of geo-spatial educational inequality.

  • 17   D. Harding et al., 2011, p. 277; A. Saatcioglu & J. Rury, 2012, p. 21.

10These are important insights that have only recently emerged in the social science literature regarding education and inequality, but what do they mean for history? One challenge for historians is clearly documenting these momentous and enduring forms of spatial disparity as they existed in the past. This raises a number of questions. Did geo-spatial patterns of inequality develop in recent years, as suggested by some analysts?17 If not, when did they become salient, and under what conditions? Answering such queries calls for a careful examination of the social and economic landscape in the past, utilizing a variety of data sources. In this paper we undertake this with statistical data on population characteristics and neighborhood differences, at the individual level (using IPUMS), census tracts, and counties. A more detailed account of the data and methods is provided below.

2. Kansas City as a Case Study

  • 18   J. Shortridge, 2012, Ch. 5.

11Metropolitan Kansas City is a representative setting for the examination of these issues. In 1960 it was among the twenty largest American metropolitan areas. Straddling two states, it is characteristic of the vast “Midwest,” with residents from the Northeast and South regions. It also has a long history of racial division and conflict. Like many other American metropolises, it grew as a center of regional trade and manufacturing and experienced massive suburban “sprawl” following World War Two.18 This makes it appropriate for examining the changing geographic distribution of educational inequality.

  • 19   M. Gottdiener, 1987, Ch. 3.

12In greater Kansas City, as in many other American urban centers, educational inequality came to be expressed in rather clear geographic terms. This represented what Mark Gottdiener has described as the “social production of urban space.” Drawing upon Henri Lefebvre’s conception of social space as productive of class differentiation and conflict, M. Gottdiener argued that it is essential to studying social change.19 As Kansas City demonstrates, the American metropolis became increasingly fragmented in geo-spatial terms between 1950 and 1980. This was a concomitant of suburbanization, and education was a key component of it.

  • 20   J. Shortridge, 2012, p. 102-104; K. Gotham, 2002, p. 14-22.

13This was a period of immense changes in American cities, as millions left the urban core to settle in suburbs. In some settings, such as Kansas City and many western municipalities, cities attempted to forestall population loss by annexing neighboring territory. This strategy was less viable in the East, as older cities were constrained by mature suburbs that opposed annexation. But even in Kansas City, which could expand geographically, school districts could not follow suit. As in many such cases, outer districts within new city boundaries continued to function as suburban school systems, technically within municipal limits but separate in financial and cultural terms. This was a process that eventually came to be associated with race, but social class differences divided many parts of the expanded municipality too. With geographic growth by annexation, Kansas City demonstrated how suburban sprawl could be evident within the city limits if critical geo-social distinctions operated there as well.20

  • 21   J. Shortridge, 2012, p. 80-85.
  • 22   W. Worley, 1990, Ch. 10.

14Despite annexation, metropolitan Kansas City did undergo extensive formal suburban growth in the postwar era. Much of this occurred on the Missouri side of the state line, as older suburban communities such as Independence, Liberty and Lee’s Summit grew in the postwar era. Other suburbs such as Raytown and Grandview developed to the south from unincorporated areas, largely in response to annexation threats. The other major direction of suburban development was to the southwest. Famous local developer J. C. Nichols established affluent residential tracts there in the early twentieth century, first in Kansas City, Missouri and then in nearby Kansas. The area embracing his developments in Johnson County, Kansas eventually became well known for its public schools.21 J. C. Nichols was dedicated to insuring that his communities would retain property values and appeal to the most fashionable residents. He was a pioneer in using restrictive deed covenants and other measures to exclude “undesirable” people. By 1950 many of the area’s most influential families lived in these communities, along with thousands of other affluent residents.22

  • 23   K. Gotham, 2002, p. 40-47; J. Shortridge, 2012, p. 122-129.

15At the heart of the educational geography of greater Kansas City was the public school district of Kansas City, Missouri (KCMSD). For many decades it was the region’s premier school system, widely seen as the area’s very best. By contrast, even as late as the 1940, suburban schools were seen as inferior, due to their small size and limited curricular offerings. Better teachers went to the city, where pay was higher and opportunities to specialize were better. This changed dramatically following 1950. As middle class, white families left the city or settled in the suburbs, schools there gained a better reputation. Indeed, some suburban districts became known for educational excellence, and this proved a major advantage in their development. In Kansas City it was quite clear which schools developed in this fashion, especially institutions in the Shawnee Mission School District in northeast Johnson County, serving communities built by J. C. Nichols and other fashionable developers.23 As indicated below, their rise was linked to observable differences in wealth and cultural capital.

  • 24   J. Dunn, 2008, p. 2-7.
  • 25   P. Moran, 2004, Ch. 3; J. Shortridge, 2012, p. 139-144.

16Like many other American cities, Kansas City experienced considerable controversy and social unrest due to school desegregation in the sixties and seventies. It was the site of one of the nation’s most sweeping desegregation law suits, Jenkins v. Missouri, which resulted in nearly two billion dollars in improvements to city schools between 1985 and 1995.24 But conflict over racial segregation did not occur right away. In fact, after voluntarily ending legal requirements for segregation in 1956, Kansas City was widely praised for its open and liberal policies regarding school integration. But in 1960 all but three of the city’s nineteen high schools remained majority white, and most African Americans were concentrated in an area of several square miles near the downtown business district. When the black population spread southward in the city as it grew, “white flight” developed gradually and schools remained majority white until 1970. Unlike other large cities, a sense of urgency regarding race and education was relatively slow to develop.25

  • 26   K. Gotham, 2002, p. 99-103; J. Shortridge, 2012, p. 190.

17Controversy over desegregation eventually flared in the latter sixties and white flight in Kansas City accelerated as the African American population continued to expand. Even so, the school board ignored calls for system-wide integration. It was not until the latter seventies that a clear desegregation plan was adopted, and African American students began to be bussed in large numbers to schools across the city. Among the last schools to be integrated was Southwest High, located near the historic “Country Club” residential district established by J. C. Nichols. Southwest was known as the best secondary school in the region and among the best in the country, but the arrival of African American students was widely seen as lowering its quality. Affluent white families sent their children to nearby private schools or moved across the state line to the Shawnee Mission School District, which also had become known for academic distinction.26 It was the end of an era, and henceforth the city’s schools would widely be judged inferior to their suburban counterparts.

18Kansas City thus offers an example of metropolitan development similar to other urban settings in the United States during this period. Its timing may have differed from places where controversy and conflict over desegregation occurred earlier, but the broad trajectory of events was comparable to most other large cities. As such, it can serve as a useful site to examine changing patterns of educational inequality, particularly with regard to geo-spatial differences. As we have suggested, some parts of the area acquired reputations for excellent schools, others just the opposite. As we demonstrate below, however, these differences were also associated with other conditions that affected children’s academic success, including the educational levels of proximate adults. As P. Bourdieu and other commentators have noted, this is a particularly critical resource in shaping educational outcomes.

3. Mapping Adult Educational Inequality: Data and Method

19Considering the spatial ordering of educational attainment or any other dimension of status requires data that can be identified geographically. In United States and other countries the most commonplace such entity is the census tract. Beginning in 1910, the United States Census has used this unit as a more or less consistent point of comparison, particularly to study changes in urban areas. US census tracks generally average around four thousand people, with considerable variability in dimensions. New tracts were drawn in smaller geographic spaces as population grew. Over time, a wide range of information about the characteristics of people living in these areas has become available. For our purposes, consequently, the census tract offers an appropriate means of examining variation in education and other facets of social status across the metropolitan landscape, and considering how it changed.

  • 27   US Census Bureau, 1971, passim; and US Census Bureau,1983, passim.

20Data used in this portion of the study were drawn from the published Census of Population and Housing, which provide data in tract reports issued for 1960 and 1980.27 We selected these years because they represented different points in the historical development of schooling in metropolitan Kansas City, and offered comparable data for geospatial analysis. 1960 was the first year in which reliable data on household income and wealth were available for most respondents, offering a good baseline. As indicated above, 1960 also preceded major local conflict and controversy over desegregation and large-scale white flight. On the other hand, 1980 came immediately after the city’s first comprehensive school integration plan and more than a decade of declining white enrollment in KCMSD. Comparing patterns of spatial differentiation across this time-span, consequently, should help identify patterns associated with the advent of racial conflict over education and growth in suburban areas.

21Metropolitan Kansas City, including Johnson County, Kansas, is our principal point of interest. We have thus assembled a dataset to examine the distribution of educated adults across Jackson County in Missouri, which includes much of Kansas City and its southern suburbs, part of Clay County, Missouri, and Johnson County, Kansas. This enables us to assess the extent of geo-spatial inequality on a number of dimensions, with the census tract as the principal unit of analysis. Within the municipal boundaries of Kansas City, tracts one through ninety six were generally consistent in 1960 and 1980 census reports. Tracts outside the city that did not generally remain the same were adjusted to create comparable units across time. We divided the tracts into five groups, corresponding to certain school district boundaries and other identifiable neighborhoods. These include the following: 1) Kansas City Missouri School District (KCMSD); 2) The Country Club Area; 3) The Shawnee Mission School District (SMSD) in Johnson County, Kansas; 4) the Raytown, Missouri School District; and 5) the North Kansas City Missouri School District (Clay County). Other tracts in Jackson County comprise a comparison group. Assessing characteristics of these areas can provide a profile of the variation in social, economic and educational conditions that existed across the metropolitan area.

22In constructing a geo-spatial data set we have selected a number of variables to identify the association between family economic status and other geographic, racial, and social factors that could be associated with educational attainment. These included total tract population, African American population, adult education levels, home ownership, the value of homes, and the number of young adults in each tract. Definitions are provided in Appendix 1.

  • 28   S. Ruggles et al., 2010, passim.

23We have also assembled a five percent sample of seventeen year-old residents of Jackson, Clay and Johnson Counties in 1980, using US Census data from the Integrated Public Use Microdata Series (IPUMS) at the University of Minnesota.28 The sample is described in greater detail below, but was designed to measure success in public high schools. These data permit us to examine the relationships of a range of factors to educational attainment, including residence in an affluent suburban area and the central city. Together with the tract-level data, this helps to provide a statistical portrait of how educational attainment was distributed unequally across the metropolitan area.

4. Uneven Development and Educational Attainment

24Educational differences that existed across the metropolitan area were clearly evident in both the years we have selected for analysis. With respect to adult educational attainment, the highest levels were in the Country Club Area of Kansas City and in adjacent suburban Johnson County, which also featured the area’s highest levels of per capita wealth and income for much of this period. This is apparent in figures 1 and 2, which shows broad differences across the metropolitan region. The areas are numbered in the order of their presentation above. The one with the lowest level of adult attainment was the remainder of Kansas City, which largely represented KCMSD. Suburban Jackson County, outside the city limits, exhibited a slightly higher rate. These differences were emblematic of a historical process of social and economic differentiation that had transformed the metropolitan landscape. Indeed, as suggested in comparing the maps, differences grew more dramatic with time.

  • 29   N. Grubb, 2009, Ch. 4.

25We have conducted an analysis for each year to examine the association between residential area and adult educational inequality. Percentages of residents with at least one year of college for 1960 and college graduates for 1980 were designated outcome variables. We selected these levels of education because having attended college in 1960 was roughly equivalent to four years or more of college in 1980 as a share of the area’s adult population (20% and 17% respectively). In this respect, these factors represent similar stocks of cultural capital for each point in time, a level of formal education achieved by one in five or six people. As N. Grubb has noted, having some college was (and is) a key parental attribute in predicting the educational success of students, and this too is a relative rather than an absolute value.29 To minimize distortions due to tract size differences, we weighted all analyses by total population.

26Several patterns are evident in descriptive statistics for the two years in question, displayed in Tables 1 and 2. It is clear that most of the area’s Black and low income population lived within KCMSD as early as 1960, even though the district was still widely viewed as effective and some respects excellent at the time. Not surprisingly, home values were lower there on average, as was overall adult attainment. The principal exception to this, of course, was the Country Club Area on the city’s southwest side, which had very high home values, few black and poor residents, and a high level of adult education. The suburban communities exhibited a good deal of variation as well. The SMSD area of Johnson County, Kansas was wealthier and better educated than the Missouri suburbs of Raytown in Jackson County and North Kansas City (a part of Kansas City, MO, but not KCMSD). Raytown was somewhat similar to SMSD in home values, but was a much smaller community. Few African Americans lived in suburban areas, although the SMSD area had more than either of the Missouri suburban districts.

Table 1. Descriptive statistics, 1960: Five Geo-Spatial Areas (weighted averages of tract data)

Table 1. Descriptive statistics, 1960: Five Geo-Spatial Areas (weighted averages of tract data)

Table 2. Descriptive statistics, 1980: Five Geo-Spatial Areas (weighted averages of tract data)

Table 2. Descriptive statistics, 1980: Five Geo-Spatial Areas (weighted averages of tract data)

27By 1980 the city’s black population had roughly doubled, spreading southward and encroaching upon the Country Club Area and the suburbs. Overall levels of attainment had gone up everywhere, but the Country Club Area still had the highest levels of adult education. Home ownership had also grown, and SMSD commanded the highest property values with Raytown close behind. With desegregation then underway in Kansas City, extending to the celebrated Country Club Area, Johnson County was a logical destination for affluent whites in search of good schools unlikely to be racially integrated. Many of those wanting to remain in Missouri headed south to Raytown or other Jackson County suburbs, but overall levels of education remained lower there.

28These patterns are evident in figures 1 and 2, which exhibit levels of adult education across the metropolitan area in 1960 and 1980. As mentioned earlier, the 1960 map displays the percentage of adults with at least one year of college, and darker shades indicate a higher proportion with this level of education. It is clear that a substantially greater concentration of college educated adults lived in the geographically adjacent areas of Southwest Kansas City and Johnson County, Kansas, mostly developed by J. C. Nichols. Several tracts recorded more than sixty percent, nearly double the level of any other tract on the Missouri side of the border. Altogether, more than half of the adult population of SMSD had at least one year of college in 1960, a level more than three times greater than the rest of the metropolitan region.

Figure 1. Map of 1960 some college, including graduates (Extracted from Social Explorer Plus)

Figure 1. Map of 1960 some college, including graduates (Extracted from Social Explorer Plus)

Darkest shade=60%-75% with collegiate education; Lightest shade, 5%-10%

Figure 2. Map of 1980 four-years of college or more (Extracted from Social Explorer Plus)

Figure 2. Map of 1980 four-years of college or more (Extracted from Social Explorer Plus)

Darkest shade=60%-75% with four-years of college education; Lightest, 5%-10%

29This, no doubt, was a major factor in the high performance and strong academic reputation of schools in the area. If adult education is among the most important determinants of school success, it is little wonder that students in SMSD did so well. They lived in a community defined to a large degree by higher education.

  • 30   K. Gotham, 2002, p. 91-99; J. Shortridge, 2012, p. 97-99.

30The 1980 map, which shows the distribution of adults with four or more years of college, is strikingly similar, although the differences between city and suburbs are more clearly drawn. Here too, Johnson County, Kansas and the adjacent Country Club Area stand out. Two tracts, one on either side of the state line, counted more than sixty percent of adults with four or more years of college. These were the wealthy J. C. Nichols developments of Mission Hills and Sunset Hills, where many of Kansas City’s most elite families owned homes. Other tracts on both sides of the border featured nearly half of the adult population with college degrees, a level unmatched elsewhere in the region. To the north and east, within the boundaries of KCMSD, it is possible to see the infamous “Troost Wall,” a vertical line that separated the less educated black population to the east from better educated white neighborhoods to the west. In Kansas the area of high attainment extended south and west into the relatively new city of Overland Park, which was rapidly becoming a center for corporate headquarters and telecommunications. A few tracts of somewhat lower attainment appeared in the northern section of SMSD, as older housing stock there attracted buyers from Wyandotte County to the North. The newly reconstituted Blue Valley School District served children in the southern part of the county, and would soon emerge as the area’s premier educational system.30 Once again, children in this part of the metro area undoubtedly benefitted from the concentration of highly educated adults evident in figure 2, despite changes in certain tracts in the north and west. On the other hand, these maps also highlight dramatic changes that occurred in Kansas City during this period. At the same time that college educated adults were clustering in SMSD and other suburban settings, relatively fewer of them were evident in the urban core area of KCMSD. While attainment levels had increased everywhere, by 1980 the new standard of “college educated” meant having four-years of college, not merely having attended. Using this metric, KCMSD showed a visible decline in educated adults across this period.

  • 31   P. Moran, 2004, Ch. 5; K. Gotham, 2002, p. 99-103.

31This was emblematic of major changes in the district during these years: white flight, growing poverty and a declining tax base to support local public schools. Perhaps even more telling, however, was the absence of educated adults, a critical indicator of cultural capital. This meant that children living in much of the city lacked vital resources in the quest for an education equivalent to that available in suburban districts. Given these circumstances, it is little wonder that the schools in these parts of KCMSD came to be seen as failing.31

32Altogether, these patterns point to widening inequality on this dimension of social status across the metro area. If there was a distinctive “higher education sector” in the region, it was located in the southwestern quadrant of greater Kansas City. The rest of KCMSD and suburban Jackson County featured fewer resources of this sort. In the central city, KCMSD had clearly fallen behind with respect to the cultural capital (adult education) resources represented on the 1980 map. The Missouri suburbs, including NKCSD, were higher, but still lagged the higher education profile of the Country Club area and Johnson County, Kansas. Like many large American metropolitan regions, greater Kansas City was divided by race and social class, but it was also sharply divided by the parental educational resources available to children. It is little wonder, in that case, that the schools with the best academic reputations were in Kansas and Southwest Kansas City. It was the result of a process of social and economic differentiation that had unfolded over several decades.

5. Regression Analysis of Census Tract Data

33As the maps discussed above indicate, educated adults were distributed quite unevenly across the metropolitan Kansas City landscape in both 1960 and 1980. There can be little doubt, however, that these patterns were highly correlated with wealth, race and a number of other factors. As P. Bourdieu noted, material and cultural capital often occupied the same social and geographic spaces, so it is important to distinguish educational inequality from these other forms of distinction. To do this, we explore the spatial dissimilarities identified above with the use of ordinary least square (OLS) regression.

34To identify the spatial organization of adult educational attainment in each of these years, we created fixed dummy (binary categorical, 0-1) variables to represent the principal geographic units discussed above. We ran separate cross sectional regressions for each year, with the percentage of college educated adults (with any college in 1960 and at least four-years of college in 1980) as dependent variables. We then added a number of additional factors to control for the effects of race and the age structure of the adult population, and home ownership and wealth (property values) across all one hundred and twenty census tracts in each year. The results are presented in tables 3 and 4.

Table 3. Regression Analysis, Adult Education Levels in 1960 (Census Tract Data)

Table 3. Regression Analysis, Adult Education Levels in 1960 (Census Tract Data)

Dependent variable: Percent of adults over twenty five with any college attendance. ** significant at 0.005 level; * significant at 0.05 level.

Table 4. Regression Analysis, Adult Education Levels in 1980 (Census Tract Data)

Table 4. Regression Analysis, Adult Education Levels in 1980 (Census Tract Data)

Dependent variable: Percent of adults over twenty five with four-years or more of college. ** significant at 0.005 level; * significant at 0.05 level.

35In conducting this analysis, we ran three models in stepwise fashion for each point in time, displayed from left to right in tables 3 and 4. The first features just the five geographic areas discussed above, which roughly correspond to four school districts and the Country Club Area established by J. C. Nichols in southwest Kansas City. Suburban Jackson County, excluding Raytown, serves as the comparison category. All results are expressed in the form of standardized regression coefficients (Beta) with tolerance coefficients (Tol) to indicate inflation of standard errors. As can be seen in both years, this analysis reflects patterns readily evident in figures 1 and 2 above. In 1960 adult attainment levels were lowest in KCMSD, even before desegregation controversies had occurred there and well before it had become majority African American. Adult attainment was highest, on the other hand, in the Country Club Area and SMSD, areas developed by J. C. Nichols and associated with highly regarded secondary schools. The coefficients are positive, modestly robust and highly significant. This was the “higher education sector” identified on the maps above, and it is clearly evident in the first models in both tables 3 and 4. The two other suburban districts in these models, on the other hand, are not appreciably different from the comparison Missouri suburban tracts, failing to achieve significance in either table. Altogether, these fixed geo-spatial dummy variables account for about half of the variation in adult attainment in both years, though slightly less in 1980, and all tolerance coefficients are satisfactory.

36The picture changes in models 2 and 3 in both tables, when additional variables are included. Controlling for race (proportion black in each tract) and adult age structure (adults aged twenty five to thirty five) increases the explained variance somewhat, but only race is significant in both years. Not surprisingly, the sign is negative and the effect size is about twice as large in 1980, when the African American population was greater and resided in more tracts. Including race in the analysis also reduced the negative coefficient for KCMSD in both tables, more appreciably in 1980, when it became insignificant. This reflects changes that had occurred in the region’s urban core, such as white flight, declining property values and growing perceptions of school problems, all associated with race. Controlling for race made adult attainment levels in KCMSD statistically similar to suburban Jackson County in 1980; in other words, it was race that accounted for most of its negative association with attainment levels in model 1. This is clear evidence of the impact that “white flight,” growing numbers of poor minority students and other issues associated with desegregation had on this dimension of educational resources in the urban core.

37Tellingly, the other geo-spatial dummy variables in these analyses were affected much less dramatically by the inclusion of race in model 2, particularly those for the Country Club Area and SMSD. They represented areas, of course, with much smaller African American populations. Coefficients for the latter areas were reduced substantially, on the other hand, by the inclusion of a wealth variable (home values) in model 3 for both years. This too is hardly a surprise, as these areas had relatively high property values, especially the prestigious Country Club Area. Controlling for home values reduced the coefficient for this area more substantially in 1960 than in 1980, and the reverse was true for SMSD. As noted earlier, there had been turnover in certain census tracts in northern Johnson County, which was still growing faster than the older neighborhoods of southwest Kansas City. This may have accounted for somewhat less change in the college educated adult population on the Kansas side of the border. It is revealing on this count, that the raw (uncontrolled) variable for SMSD is slightly less robust than the coefficient for the Country Club Area in 1980, whereas the reverse had been true in 1960. This suggests that there was a higher tendency for college educated adults to settle on the Missouri side of the border by 1980, at least in this part of the metro area.

  • 32   P. Bourdieu, 1989, p. 17.

38It is noteworthy, of course, that the coefficients for both these wealthy areas, one inside the city limits and the other an adjoining suburb, remained positive and significant after controlling for race and property values, along with other factors. This was true for both years, although the magnitude of this independent effect varied, as noted above. Both areas—together comprising one contiguous sector divided by the state line—exhibited high levels of adult educational attainment, even controlling for economic status and key demographic factors. Such circumstances point to P. Bourdieu’s conceptualization of “the relative weight of the different species of capital, economic and cultural.”32 This part of the metropolitan area was wealthy, to be sure, but its residents were also highly educated, beyond the level generally associated with their “economic capital.” In short, they were better educated than their property values suggested.

  • 33   J. Shortridge, 2012, p. 121.

39A bit further to the east, the rapidly growing community of Raytown offers a telling point of comparison. As noted earlier in table 2, property values were relatively high there in 1980, but adult education levels were considerably lower than SMSD and median income was less too. Raytown’s all white schools were known as good academically, but not as exemplary as the SMSD schools, which received national recognition. Raytown was a largely blue-collar town, solidly middle class but not nearly as fashionable or sophisticated as some of the neighborhoods in SMSD.33 Its inhabitants may have possessed considerable economic capital, reflected in the value of their homes, but they lacked the cultural capital of SMSD and Country Club residents to the west. In this respect, the legacy of J. C. Nichols and his success as a developer was clear; his communities continued to be associated with a degree of refinement and status that other communities lacked.

  • 34   P. Bourdieu, 1989, p. 16.

40Other factors that contributed to these differences are not altogether clear. Perhaps it was the allure of high quality schools in the vicinity, particularly two high schools rated among the best in the country in the latter fifties, one on either side of the border. On the other hand, perhaps it was a more subtle form of influence, individuals and families deciding to settle in these areas because of people with similar tastes and backgrounds already lived there. Whatever the reason, the result is plain to see in model 3 in both tables 3 and 4. In addition to considerable economic wealth, this quadrant of greater Kansas City clearly contained its most educated segment of the population. As P. Bourdieu noted, “people who are close together in social space tending to find themselves, by choice or by necessity, close to one another in geographic space.”34 This was nowhere more evident than in this part of metropolitan Kansas City.

41As a final point, it is worth noting that the coefficient for the “Young Adult Males” factor in model 3 for the 1980 analysis is positive and statistically significant. This suggests that areas with larger numbers of these individuals and their young families exhibited higher levels of adult education. The areas with the highest numbers of residents in this category were SMSD and the Country Club Area, with levels about 25% higher than the rest of the metropolitan area. This group represented the leading edge of the “baby boom” generation, born between 1945 and 1955, and they were starting families during the seventies. The fact that they were settling in larger numbers in Johnson County and the adjacent Country Club Area is evidence of the attraction this “higher education sector” held for families in search of educational opportunity. This group of younger adults also would be most likely to exhibit higher four-year college levels of attainment than older adults. This was evidence that the next generation of collegiate adults was choosing the very best educated neighborhoods to settle and raise their children.

42The sign on the KCMSD variable in model 3 is positive, although it failed to achieve significance. This may be due to a portion of the urban core that exhibited higher levels of educational attainment than normally associated with the relatively low stocks of economic capital (property wealth) that existed there. This was certainly evident in the higher levels of adult education exhibited to the west of the notorious “Troost Wall” in Westport and other neighborhoods in its immediate vicinity. Much of this area was undergoing the early stages of gentrification in the latter 1970s, and this may have accounted for some of this effect. In short, once race and property values were held constant, there were parts of Kansas City, Missouri outside of the Country Club Area that exhibited levels of adult education similar to that seen to the south and west in the metropolitan area. These were beginning signs of urban revival in the city.

6. A Look at Adolescent Attainment

43It is clear from the foregoing that considerable unevenness existed in the distribution of highly educated adults in metropolitan Kansas City during this era, but what did this mean for the education of students? In the discussion to follow, we consider this question by examining the attainment of seventeen year-old secondary students across much of the area in 1980. This final step in the analysis will enable us to assess just how these geo-spatial patterns of adult education influenced the school experiences of children and youth.

44As indicated earlier, addressing this question requires a different dataset: individual-level data drawn from the US Census via IPUMS. Using this source, we created a 5% sample of seventeen year old youth from the Kansas City metropolitan area in 1980, featuring a range of demographic, educational and household characteristics. We excluded those living in group quarters, such as orphan asylums, other residential institutions or jails, and those living in Wyandotte County, which included Kansas City, Kansas. This means the analysis below is limited to Jackson and Clay Counties in Missouri (including nearly all of Kansas City) and Johnson County in Kansas. We have also excluded those attending private schools, which included many living in the Country Club Area, home to the area’s most elite non-public institutions. The resulting sample included 958 individual cases, weighted to represent local population parameters.

45This sample enables us to examine youth attending public schools in three broad geo-spatial groupings identifiable in the dataset: the city of Kansas City, Missouri, suburban communities in Jackson and Clay Counties, and suburban Johnson County. It was not possible to isolate KCMSD in IPUMS data, so the entire city is utilized as a geo-spatial entity, including annexed areas served by other schools. In the analysis below, Kansas City and Johnson County are included as fixed geo-spatial dummy (binary categorical, 0-1) variables, with suburban Jackson and Clay County residents serving as the comparison group. Insufficient between-level variance existed for a hierarchical analysis.

  • 35   A. Saatcioglu & J. Rury, 2012, p 38.

46Our focal point in this analysis is individual student educational attainment. In particular, we are interested in progress that each of the youth in our sample had made toward high school graduation. The dependent variable is whether or not each seventeen year-old was still in school and had reached at least eleventh grade, the junior or penultimate year of school. It is necessary to use this measure because the census only recorded household characteristics for youth residing at home. The permissible age for dropping out was sixteen in both Kansas and Missouri, and the census was taken in April, toward the end of the academic year. Most students who were still in school and juniors then were quite likely to graduate, as relatively few pursued General Equivalency Diplomas (GED) or other alternatives. The variable is coded as a binary measure, with one for enrollment in grade eleven or twelve or graduated, and zero for enrollment in a lower grade or out of school without a diploma. It is a broad measure of success in school, or attainment of a fixed standard of accomplishment. At the state level, it is correlated at 0.9 with nineteen year-old high school graduation rates, and it has been used in a number of other studies as a proxy for secondary graduation.35

47In addition to the large geo-spatial categories discussed above, we draw upon a range of social and demographic characteristics available in the IPUMS dataset. These are factors widely used in studies of educational attainment and status attainment research, including race, gender, economic status, wealth, family structure and parental education. We discuss the definition of these variables below, but are especially concerned with ways that they interacted with the geo-spatial factors specified above. The point of our analysis, after all, is to explore how the uneven distribution of educated adults affected the educational experiences of youth. Within the limitations posed by the IPUMS data, it is possible to identify general patterns of attainment that reflect the larger profile of geo-spatial inequality discussed earlier.

48Because our dependent variable is dichotomous, we use binary logistic regression to assess the likelihood of high school success for individuals in our sample. This technique produces odds ratios to express the chances of such outcomes, net of other factors in the model. These are expressed as coefficients by exponentiation to produce values ranging between zero and one, with positive and negative signs for odds greater or less than even. Each coefficient or odds ratio is an expression of the likelihood that a given characteristic is associated with the outcome in question, controlling for other specified factors. In this case, we are interested in odds of school success for seventeen year-olds in Johnson County, Kansas and urban and suburban settings in metropolitan Kansas City, Missouri.

49To address this question we have conducted the analysis in a stepwise fashion with four models, similar in certain respects to the approach in tables 3 and 4 above. Model 1 includes the two geo-spatial dummy variables described above, and other factors are added in successive stages. The final model includes all variables in the analysis, and exhibits odds ratios for each of them. This approach is useful for identifying how various factors affect one another, and particularly their interaction with the geo-spatial variables in model 1. The results are presented in table 5.

Table 5. Binary Logistic Regression: Junior Year Status or Higher, Seventeen Year-olds in 1980

Table 5. Binary Logistic Regression: Junior Year Status or Higher, Seventeen Year-olds in 1980

*Significant at 0.05 level; ** Significant at 0.01 level; ***Significant at 0.001 level

50To begin, model 1 leaves little question that major differences in school success distinguish youth in these places. Seventeen year-olds in Johnson County were seventy percent more likely to have reached their junior year in high school or better than their Missouri suburban counterparts, and the same was true of their success compared to youth in Kansas City, despite the fact that the latter group included many students attending suburban schools. There appears to have been relatively little difference in the likelihood success between Kansas City students and those in suburban Jackson and Clay County, perhaps because of the expansive Kansas City sample. Both areas were alike, however, in highlighting the success of students in Johnson County, the highest attainment level in the region. This, of course, is a pattern broadly similar to that observed in our analysis of adult attainment levels earlier.

51Subsequent models in the analysis introduce additional factors, all of them characteristics of students or their families. In model 2, race and gender are added, with binary categorical variables for being African American or female. Not surprisingly, being Black was associated with nearly twenty five percent lower odds of school success in 1980, and controlling for this improved the odds of attainment in Kansas City to almost ten percent greater odds than suburban Missouri residents. Since more than ninety percent of African American youth in the sample lived in Kansas City, their lower odds of success clearly affected overall city attainment levels. The inclusion of these variables, on the other hand, had no meaningful effect on the odds of school success in Johnson County, where few Blacks lived at the time.

52The picture changed somewhat in models 3 and 4, however, where socio-economic, educational and family structure factors were introduced, each in binary categorical (0-1) form. Here too, the results conform closely to findings of other studies of educational attainment in the postwar era and beyond. Living in a home owned by the family more than doubled the odds of school success for youth in the sample, while living in a household below the poverty level lowered the odds of attainment by nearly fifty percent. These are very robust effects, and controlling for them reversed the negative sign on the African American variable and reduced it to insignificance, along with boosting the fixed Kansas City dummy. Controlling for poverty and home ownership, African American youth were as likely to experience school success as whites, and Kansas City residents were nearly thirteen percent more likely than their Missouri suburban counterparts. The effect of being female was enhanced as well, with girls exhibiting a nearly seventeen percent advantage over boys. Controlling for these factors also reduced the advantage exhibited by Johnson County youth to about forty four percent greater odds of school success, especially the home ownership variable, which accounted for much of this change alone. Part of the Johnson County advantage in attainment likelihood was attributable to the area’s high level of home ownership: ninety three percent of student there lived in family owned homes compared to seventy five percent in Kansas City and seventy eight percent in the Missouri suburbs (calculated from IPUMS data).

53In the final step of the analysis, model 4, the picture changes once again with the introduction of categorical variables for living in a single parent, female-headed household and having at least one parent with a college degree. These too are very robust factors; living in a single parent family was associated with a forty five percent reduction in the likelihood of school success and having a college educated parent increased the odds of attainment by more than a multiple of three (three hundred percent). The latter variable, adult collegiate education, was the factor of interest in the geo-spatial OLS regression analysis above. In this analysis, it exhibited the strongest relationship to student educational attainment, with a wide range of additional variables controlled. Its inclusion dropped the Johnson County fixed dummy substantially, reducing the advantage of its resident students over their Missouri suburban counterparts to less than fourteen percent greater likelihood of school success. The family structure variable in model 4 had very little effect on the Johnson County dummy, although its inclusion did make the Black categorical variable both positive and significant. The effect of home ownership was reduced as well, along with Kansas City residence. From the standpoint of this analysis, however, the interaction of this factor with the Johnson County dummy helps to demonstrate the manner in which parental education affected the high levels of educational success attained by local students. Within the IPUMS sample, fully forty percent of Johnson County youth had at least one parent with a college degree, as opposed to twelve percent in Kansas City and nine percent in suburban Missouri. In short, students in Johnson County were more than three times more likely than their peers elsewhere in metropolitan Kansas City to have college-educated parents, and this was a major advantage regarding success in school.

54Altogether, the analysis presented in table 5 demonstrates the importance of parental education levels to student success in recent history. It also points to the implications of this for geo-spatial differences in educational attainment in the Kansas City metropolitan area. Johnson County, Kansas was clearly part of the “higher education zone” identified earlier. Students who lived there were seventy percent more likely to experience success in public secondary schools than their counterparts in the region, and much of this was associated with the area’s high levels of home ownership and adult education. Its schools may have been better, as Johnson County students were thirteen percent more likely to succeed even after these factors were controlled, or perhaps students benefitted there from highly motivated peers. But much of the observable variation in attainment appears to have been linked to these family background factors, a finding consistent with decades of research on such questions. Poverty, single parent families, lower parental education and racial diversity affected attainment levels in other parts of the region. For students living in Johnson County, however, their families’ stocks of physical (property) and cultural (education) capital were resources that appear to have contributed a great deal to their educational accomplishments.

7. A Note on Peer Effects

  • 36   A. Saatcioglu, 2010, p. 1391; J. Angrist & K. Lang, 2004, p. 1613; M. Hallinan & R. Williams, 199 (...)

55As suggested earlier, there remains a question of whether the effect of the geo-spatial variables in the foregoing analysis were partly reflective of an accumulation of advantage in a given area. Put somewhat differently, we might ask whether students in non-elite households derived a benefit from living in proximity to the high levels of adult education evident in Johnson County, Kansas in 1980. The effect of such peer effects has received a good deal of attention in recent years, particularly with respect to the impact of school desegregation. A significant body of research has suggested, for example, that poor African American students may have benefitted from attending school with more affluent whites.36 Could a similar dynamic be operating in this study?

56One way to consider such a question would be to compare the likelihood of graduation for youth without college educated parents in different parts of metropolitan Kansas City. In our 1980 sample, students in Johnson County whose parents had not attended college were only slightly more likely to persist in public high school than their peers in Jackson County, and the difference was not statistically significant (F=1.043). There was a bigger and statistically significant difference when youth whose parents had not graduated from college were compared (F=19.870), but it was reduced to insignificance when controls for homeownership and parents with some college were introduced. Youth with college educated parents were about equally likely to succeed in public high schools in all parts of the metro area. This suggests that there was little discernible independent effect that could be possibly attributed to peers, at least in terms of influencing the school success or persistence of these groups of youth in Johnson County.

  • 37   K. Choi et al., 2008, p. 846; C. Hoxby, 2000, p. 1.
  • 38   J. Shortridge, 2012, p. 122-129.

57In many respects this is hardly an unexpected finding. Studies that have examined peers effects focus on the school or classroom levels, where students are likely to have regular and sustained contact with one another.37 This study has utilized data from much larger spatial units of analysis—census tracts and counties—where relatively large numbers of people could live without having much everyday interaction. In Johnson County there were a number of communities that counted many fewer college graduate adults than the most elite J. C. Nichols communities, and it is doubtful that the high concentrations of well educated families in the latter communities exerted much influence on youth in the formers ones.38 In short, there was enough social and economic differentiation even within rather well educated Johnson County to make the detection of peer effects unfeasible at this level of aggregation.


58Given the patterns of educational inequality described above, there can be little doubt that critical geo-spatial disparities marked the distribution of educational resources in Greater Kansas City in the decades prior to 1980. Adults with collegiate education were clustered in a distinctive “higher education zone” that was also noted for its wealth and style, attributes that certainly must have proven attractive to parents seeking the very best educational opportunities for their children. As Mark Gottdiener has suggested, these spatial distinctions were socially produced and maintained, and our analysis has demonstrated their durability during a period of considerable social change in American history. If some areas in metropolitan Kansas City were seen as good or desirable, SMSD and the Country Club Area were surely at the top of the list. A major part of this was the accumulation of educational resources in these areas, chief among them the attainment levels of the adult population. Simply put, higher status people lived there, and collegiate education was an increasingly important mark of social standing. As P. Bourdieu has suggested, cultural capital came to be expressed in academic credentials.

  • 39Ibidem, p. 148-153.

59Our analysis has demonstrated that the appearance of this geo-spatial differentiation in education, and by extension cultural distinction, largely preceded the emergence of race as a major issue in education at this time in Kansas City. Although some residents of Johnson County, Kansas eventually decided to settle there out of fear or anger over racial issues—along with outright racism—the area’s association with higher educational attainment was well established by the end of the 1950s. Indeed, there is considerable evidence that most “white flight” from KCMSD during the sixties and seventies went south to Raytown and other suburban districts rather than Kansas.39 Instead, the cultural and educational attributes of the former J. C. Nichols developments, along with the area’s highly regarded schools, appear to have proven quite attractive to families of a certain economic and social standing. And if James Coleman’s analysis is germane, as our analysis suggests, this more than anything else accounted for the academic success of its youth. Meanwhile, the departure of middle class whites from much of KCMSD signaled the identification of that part of the city as problematic, leading to its eventual decline.

60As indicated at the outset, considerable attention has been given recently to widening social and economic inequality in the United States, and particularly to the geo-spatial partitioning of different status groups. Our analysis suggests that this is a process that extends back more than fifty years in history, and has been an integral aspect of metropolitan development at least since the postwar period. Suburbanization has long been recognized as a process of social and economic differentiation; our analysis has demonstrated that educational attainment was also a community characteristic that came to distinguish some parts of metropolitan regions from others. The American tradition of independent school districts certainly contributed to the identification of discrete geographic parameters with desirable educational attributes. As P. Bourdieu notes, cultural forms of capital often operate as a telling form of distinction, and this seems to have been the case in Kansas City. N. Grubb has pointed out, moreover, that such distinctions can in turn become a vital resource in the quest for status maintenance. The Kansas City experience demonstrates how these social and cultural forces have operated in recent American history. We leave it to other historians to examine additional cities to ascertain if patterns such as these were evident elsewhere as well.

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Angrist, Joshua D. & Lang, Kevin, “Does School Integration Generate Peer Effects? Evidence from Boston’s METCO Program”, American Economic Review, 94, 2004, p. 1613–1634.

Bourdieu, Pierre, “Social Space and Symbolic Power,” Sociological Theory, 7 (1), 1986, p. 14-25.

–, “The Forms of Capital,” in John G. Richardson (ed.), Handbook of Theory and Research in the Sociology of Education, Westport, CT, Greenwood, 1986, p. 241-258.

Bryk, Anthony, Bender Sebring, Penny, Allensworth, Elaine & Luppescu Stuart, Organizing Schools for Improvement: Lessons from Chicago, Chicago, University of Chicago Press, 2010.

Choi, Kate H., Raley, R. Kelly, Muller, Chandra & Riegle-Crumb, Catherine, “Class Composition, Socioeconomic Characteristics of Coursemates and College Enrollment”, Social Science Quarterly, 89 (4), 2008, p. 846-866.

Coleman, James S., “‘The Concept of Equality of Educational Opportunity,’ in Harvard Educational Review”, Equal Educational Opportunity, Cambridge, MA, Harvard University Press, 1969, p. 9-24.

Dunn, Joshua, Complex Justice: The Case of Missouri v. Jenkins, Chapel Hill, NC, University of North Carolina Press, 2008.

Entwisle, Doris R., Alexander, Karl L. & Steffel Olson, Linda, Children, Schools, and Inequality, Boulder, CO, Westview, 1997.

Epps, Edgar G., “Race, Class, and Educational Opportunity: Trends in the Sociology of Education,” Sociological Forum, 10 (4), 1995, p. 593-608.

Epstein, Joyce L., “Parents’ Reactions to Teacher Practices of Parent Involvement”, The Elementary School Journal, 86 (3), 1986, p. 277-294.

Garner, Catherine L. & Raudenbush, Stephen W., “Neighborhood Effects on Educational Attainment: A Multilevel Analysis”, Sociology of Education, 64 (2), 1991, p. 251-262.

Gotham, Kevin F., Race, Real Estate and Uneven Development: The Kansas City Experience, 1900-2000. Albany, NY, SUNY Press, 2002.

Gottdiener, Mark, The Social Production of Urban Space, Austin, TX, University of Texas Press, 1987.

Grubb, Norton, The Money Myth: Schools, Resources, Outcomes and Equity, New York, Russell Sage Foundation, 2009.

Hallinan, Maureen T. & Williams, Richard A., “Students’ Characteristics and the Peer-Influence Process”, Sociology of Education, 63 (1), 1990, p. 122–132.

Harding, David, Gennetian, Lisa, Winship, Christopher, Sanbonmatsu, Lisa & Kling, Jeffrey, “Unpacking Neighborhood Influences on Educational Outcomes: Setting the Stage for Future Research”, in Greg Duncan & Richard Murnane (eds.), Wither Opportunity: Rising Inequality, Schools and Children’s Life Chances, New York, Russell Sage Foundation, 2011, p. 277-298.

Hoxby, Caroline M., Peer Effects in the Classroom: Learning from Gender and Race Variation (Working Paper No. 7867), Cambridge, MA, National Bureau of Economic Research, 2000.

Lareau, Annette, “Social Class Differences in Family-School Relationships: The Importance of Cultural Capital”, Sociology of Education, 60 (2), 1987, p. 73-85.

Mickelson, Roslyn Arlin, “The Attitude-Achievement Paradox Among Black Adolescents”, Sociology of Education, 63 (1), 1990, p. 44-61.

Moran, Peter W., Race, Law and the Desegregation of Public Schools, New York, LFB Scholarly Publishing, 2004.

Roscigno, Vincent J. & Ainsworth-Darnell, James W., “Race, Cultural Capital, and Educational Resources: Persistent Inequalities and Achievement Returns”, Sociology of Education, 72 (3), 1999, p. 158-178.

Roscigno, Vincent J., Tomaskovic-Devey, Donald & Crowley, Martha, “Education and the Inequalities of Place”, Social Forces, 84 (4), 2008, p. 2121-2145.

Ruggles Steven, Alexander, J. Trent, Genadek, Katie, Goeken, Ronald, Schroeder, Matthew B. & Sobek, Matthew, Integrated Public Use Microdata Series: Version 5.0, Minneapolis, University of Minnesota, 2010.

Saatcioglu, Argun, “Disentangling School and Student Level Effects of Desegregation and Resegregation on the Dropout Problem in Urban Schools: Evidence from the Cleveland Municipal School District, 1877-1998”, Teachers College Record, 112 (5), 2010, p. 1391-1442.

Saatcioglu, Argun & Rury, John L., “Education and the Changing Metropolitan Organization of Inequality: A Multilevel Analysis of Secondary Attainment in the United States, 1940–1980”, Historical Methods, 45 (1), 2012, p. 21-40.

Sampson, Robert J., Great American City: Chicago and the Enduring Neighborhood Effect, Chicago, University of Chicago Press, 2012.

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1   R. Sampson, 2012, Part 1; A. Bryk et al., 2010, p. 169.

2   D. Harding et al., 2011, p. 278.

3   R. Sampson, 2012, p. 21.

4   J. Coleman, 1969, p. 9-24.

5   V. Roscigno & J. Ainsworth-Darnell, 1999, p. 158; E. Epps, 1995, p. 593-608.

6   A. Lareau, 1987, p. 83.

7   R. Seginer, 1983, p. 1-23; D. Entwisle et al., 1997, Ch. 1.

8   N. Grubb, 2009, p. 92.

9Ibidem, p. 95.

10   R. Mickelson, 1990, p. 44.

11   V. Roscigno et al., 2008, p. 2121-2145.

12   N. Grubb, 2009, Ch. 4.

13   J. Epstein, 1986, p. 277.

14   P. Bourdieu, 1986, p. 243.

15   P. Bourdieu, 1989, p. 16.

16Ibidem, p. 17.

17   D. Harding et al., 2011, p. 277; A. Saatcioglu & J. Rury, 2012, p. 21.

18   J. Shortridge, 2012, Ch. 5.

19   M. Gottdiener, 1987, Ch. 3.

20   J. Shortridge, 2012, p. 102-104; K. Gotham, 2002, p. 14-22.

21   J. Shortridge, 2012, p. 80-85.

22   W. Worley, 1990, Ch. 10.

23   K. Gotham, 2002, p. 40-47; J. Shortridge, 2012, p. 122-129.

24   J. Dunn, 2008, p. 2-7.

25   P. Moran, 2004, Ch. 3; J. Shortridge, 2012, p. 139-144.

26   K. Gotham, 2002, p. 99-103; J. Shortridge, 2012, p. 190.

27   US Census Bureau, 1971, passim; and US Census Bureau,1983, passim.

28   S. Ruggles et al., 2010, passim.

29   N. Grubb, 2009, Ch. 4.

30   K. Gotham, 2002, p. 91-99; J. Shortridge, 2012, p. 97-99.

31   P. Moran, 2004, Ch. 5; K. Gotham, 2002, p. 99-103.

32   P. Bourdieu, 1989, p. 17.

33   J. Shortridge, 2012, p. 121.

34   P. Bourdieu, 1989, p. 16.

35   A. Saatcioglu & J. Rury, 2012, p 38.

36   A. Saatcioglu, 2010, p. 1391; J. Angrist & K. Lang, 2004, p. 1613; M. Hallinan & R. Williams, 1990, p. 122.

37   K. Choi et al., 2008, p. 846; C. Hoxby, 2000, p. 1.

38   J. Shortridge, 2012, p. 122-129.

39Ibidem, p. 148-153.

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Table des illustrations

Titre Table 1. Descriptive statistics, 1960: Five Geo-Spatial Areas (weighted averages of tract data)
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Titre Table 2. Descriptive statistics, 1980: Five Geo-Spatial Areas (weighted averages of tract data)
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Titre Figure 1. Map of 1960 some college, including graduates (Extracted from Social Explorer Plus)
Légende Darkest shade=60%-75% with collegiate education; Lightest shade, 5%-10%
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Titre Figure 2. Map of 1980 four-years of college or more (Extracted from Social Explorer Plus)
Légende Darkest shade=60%-75% with four-years of college education; Lightest, 5%-10%
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Titre Table 3. Regression Analysis, Adult Education Levels in 1960 (Census Tract Data)
Légende Dependent variable: Percent of adults over twenty five with any college attendance. ** significant at 0.005 level; * significant at 0.05 level.
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Titre Table 4. Regression Analysis, Adult Education Levels in 1980 (Census Tract Data)
Légende Dependent variable: Percent of adults over twenty five with four-years or more of college. ** significant at 0.005 level; * significant at 0.05 level.
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Titre Table 5. Binary Logistic Regression: Junior Year Status or Higher, Seventeen Year-olds in 1980
Légende *Significant at 0.05 level; ** Significant at 0.01 level; ***Significant at 0.001 level
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John L. Rury et Sanae Akaba, « The Geo-Spatial Distribution of Educational Attainment: Cultural Capital and Uneven Development in Metropolitan Kansas City, 1960-1980 »Histoire & mesure, XXIX-1 | 2014, 219-248.

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John L. Rury et Sanae Akaba, « The Geo-Spatial Distribution of Educational Attainment: Cultural Capital and Uneven Development in Metropolitan Kansas City, 1960-1980 »Histoire & mesure [En ligne], XXIX-1 | 2014, mis en ligne le 30 juin 2017, consulté le 14 avril 2024. URL : ; DOI :

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John L. Rury

University of Kansas, 423 JR Pearson Hall, 1122 West Campus Rd, Lawrence, KS 66045-3101, USA. E-mails:

Sanae Akaba

University of Kansas, 423 JR Pearson Hall, 1122 West Campus Rd, Lawrence, KS 66045-3101, USA. E-mails:

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