- Email: [email protected]
Segregation and school disorder
The Social Science Journal 42 (2005) 405–420
Segregation and school disorder Paul B. Stretesky ∗ , Michael J. Hogan Department of Sociology, Colorado State University, Clark Building, Fort Collins, CO 80523, USA
Abstract This investigation extends research on racial and ethnic segregation, poverty, and crime rates to schools. We study 371 high schools in Florida to determine whether poverty mediates the relationship between segregation and rates of school disorder. The data for this work come from the Florida Department of Education and the National Center for Educational Statistics. Consistent with previous studies of racial segregation and crime, we find that school segregation is strongly associated with levels of school disorder. Unlike previous research, however, poverty completely mediates the segregation–disorder relationship. Nevertheless, our findings are highly consistent with contemporary theories of racial segregation and violence. © 2005 Elsevier Inc. All rights reserved.
1. Introduction Massey (2001, p. 334) argues that “segregation is a fundamental factor behind crime.” Studies that investigate the association between residential segregation and crime rates find evidence consistent with Massey’s assertion (Krivo & Peterson, 1996; Logan & Messner, 1987; Shihadeh & Flynn, 1996). However, research that specifically examines the association between school segregation and school crime has yet to be conducted. The lack of focus on schools is surprising for three reasons. First, research has found that community crime rates and school crime rates are not necessarily associated. For instance, Welsh, Stokes, and Greene (2000, p. 269) maintain that “[s]implistic assumptions that communities with high crime rates automatically commit a school to a high rate of disorder are untenable.” Second, research on school segregation has generally found that residential segregation does not necessarily correspond with the segregation of schools (Reardon, Yun, & Eitle, 2000). Third, research
∗ Corresponding author. Tel.: +1 970 491 6825/6044; fax: +1 970 491 2191. E-mail address: [email protected] (P.B. Stretesky).
0362-3319/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.soscij.2005.06.007
406
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
examining the topic of school crime and school segregation has attracted a considerable amount of attention as separate areas of study in the social science literature. The purpose of our research is to study the relationship between school segregation, school poverty, and school disorder. Specifically, we test whether poverty is likely to be the generative mechanism through which segregation influences rates of school disorder. We suspect that if the racial and economic composition of neighborhoods influences crime rates, a high school’s economic, ethnic, and racial status should also be associated with rates of school disorder—especially because high school is such an important part of the social lives of many students (Ballantine, 2001).
2. Background School researchers such as Ballantine (2001, p. 229) believe “that each school has a culture of its own, like a miniature society” which encompasses the beliefs, values, and attitudes of students. In this section, we build upon Ballantine’s (2001) arguments to describe the reasons that school segregation and school poverty may be associated with rates of school disorder. 2.1. School segregation There is good reason to suspect that school segregation is related to school disorder. Massey and Denton (1993), for example, describe how segregation produces an “oppositional culture” that may lead to violence (see also Anderson, 1999; Massey, 2001). Empirical evidence supports the oppositional culture hypothesis among schools since victimization rates for robbery, theft, and violence are much higher in Black schools than in White schools (So, 1992). Research conducted on crime rates and residential segregation suggests that segregation creates a disjunction between cultural values and social structural arrangements and produces high rates of criminal violence because community controls that prevent violence are weakened by a lack of respect for social norms (Logan & Messner, 1987). Research on school segregation is consistent with arguments in the criminological literature. For instance, there is evidence that African Americans attending desegregated schools have much higher occupational aspirations than African Americans attending segregated schools, suggesting a greater commitment in conventional culture (Wells, 1995). Also, Bankston and Caldas (1996) discovered that African Americans attending segregated schools scored much lower on school achievement tests than African Americans attending integrated schools. Finally, Wells (1995) reports that African Americans who attend segregated schools have aspirations that are not compatible with their educational background. This condition may lead to high levels of frustration. Many of the negative outcomes described in the school segregation literature are also related to poverty. Massey (1994, p. 480) argues that racial segregation concentrates poverty and “anything that is correlated with poverty: crime, drug, abuse, welfare dependency, single parenthood, and education difficulties.” It is well documented that African Americans who attend desegregated schools have better reading comprehension, are more likely to
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
407
attend college, will obtain better jobs, and have a greater probability of getting promoted at work than African Americans who attend segregated schools (Ballantine, 2001; Wells, 1995). Our observations concerning educational outcomes lead us to question whether levels of poverty may be the mechanism that links school segregation to rates of school disorder. 2.2. Poverty There is strong theoretical support for the position that levels of school poverty are positively related to rates of school disorder. We expand upon the theoretical arguments presented in the work of Ainsworth (2002; see also Wilson, 1987) to argue that there are four interrelated mechanisms through which levels of school poverty might influence rates of school disorder. We argue that these mechanisms provide the theoretical basis for the association between levels of school poverty and rates of school disorder. Schools with a large percentage of poor students are likely to be faced with (1) weak amounts of collective socialization to prosocial behavior; (2) less access to social capital; (3) lower levels of social control; (4) and high levels of social strain resulting from differential opportunities (see Ainsworth, 2002, p. 118). One way that high levels of school poverty may lead to high rates of school disorganization is through a lack of collective socialization. This is likely because schools with high levels of school poverty have fewer students who come from advantaged neighborhoods where conventional culture is emphasized (Wilson, 1987). Thus, students who attend schools with high levels of poverty are more likely than those students attending affluent schools to come from neighborhoods that exhibit behavior that is in opposition to conventional values. This behavior results from social isolation—a structural condition brought on by the lack of exposure to working adults who serve as conventional role models to neighborhood youth (Wilson, 1987). The result of this social isolation is an oppositional culture that is more accepting of crime and violence. For example, Anderson (1994, p. 82) observes that by the fourth grade the “code of the street”—a set of informal rules “governing interpersonal public behavior, including violence” begins to filter into the school culture and poor schools become a staging area for campaigns for respect. According to Anderson, campaigns for respect often involve the use of violence. Even those students whose home lives reflect mainstream values must be able to handle themselves in a street-oriented environment that is pervasive in low-income schools (Anderson, 1999, p. 33). Thus, the higher the level of school poverty, the more pervasive oppositional values become since there is a greater proportion of students who must adopt the code of the street just to negotiate daily life in school. Unlike students attending high poverty schools, students attending affluent schools are more likely to come from affluent neighborhoods and are therefore more likely to be socialized into conventional norms (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993; Morenoff, Sampson, & Raudenbush, 2001). These students do not need to use violence since they attend schools where there is little or no oppositional culture. A second way that increased levels of poverty may be related to school disorder is through decreased social capital (Sampson & Groves, 1989; Wilson, 1987). Social capital is simply the number and quality of social networks that develop. These relationships and networks are
408
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
typically part of the normative structure and are highly important in schools. Coleman (1990) argues, for instance, that social capital prevents crime though the creation of effective norms that inhibit crime. Social networks provide information that is important in establishing a stake in conventional behavior (Granovetter, 1973). Students attending high poverty schools, for instance, may have less access to college guidance counselors and/or older brothers and sisters who have information about college and are willing to come and talk with students in school. These informational sessions aid in creating a strong attachment to education and help orient students to conventional norms. Moreover, many of the jobs and job contacts high school students receive are from teachers, friends, and friend’s parents who have important connections in the community. Students attending poorer schools are not likely to have the same number and quality of contacts regardless of their own economic status. Thus, students attending poor schools are the least likely to have developed networks that will give them access to more education and meaningful employment (Wilson, 1987). For instance, Kaufman and Roesnbaum (1992) found that low-income students who moved to more affluent suburbs were more likely to finish high school and go on to four-year colleges than students attending school where a majority of the students are poor. Moreover, Kaufman and Roesnbaum (1992) report that students and parents spoke of the advantages of attending high schools where networks and ties to education were available. Informal ties also may extend to student’s peers, who may share information, resources, attitudes toward education, and motivation. Peers are extremely important in schools where high levels of poverty may alter the culture of a school (Coleman, 1990). In short, this suggests that there is a difference between a student who is poor but attends a school with students who are not poor than a student who is poor and attends a school with other poor students. Bankson and Caldas (1996, p. 536) argue that “[p]arents who send their children to good schools provide their children with the advantages of associating with good students, advantages that may outweigh those of superior school facilities and event those of quality of teaching.” This may include intellectual capital, collective motivation, as well as access to the resources of peers such as books, computers, and the Internet (Wells, 1995). Schools with an overwhelming number of poor students may change the school culture in such a way that normative structure discourages or disrupts important social networks. There is considerable empirical evidence in support of this position. For instance, studies find that poor students do much better in school when their peers are affluent as opposed to poor (Wells, 1995). In more affluent schools, there is also a greater amount of parent involvement and parents may serve as an important source of social capital. Students attending schools that lack these informal networks are likely to have less involvement in conventional activities and are therefore—according to many criminological theories—more likely to engage in crime and violence (see Hirschi, 1969; Wiatrowski, Griswold, & Roberts 1981). Social control is a third mechanism that may link levels of school poverty and school disorder rates. Social control is practices that schools use or have available that encourage or promote conformity to conventional behavior. Social control may extend from simple forms of faculty monitoring of students to more important forms of internalization. More affluent schools often have extensive networks which include adults in the community that have the time to engage in school functions and aid in organizing school activities such as sporting events, arts programs,
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
409
social clubs. In fact, schools are often the focal point of youth functions and provide a forum for collective socialization, attainment of social capital, and the promotion of social control because they provide direct constraints on time and behavior. It should be pointed out that in poor schools where there is a lack of effective collective socialization, even a large number of teachers may not be able to effectively provide the level of social control that is needed to prevent high levels of school disorder. The fourth mechanism that is likely to link levels of school poverty to rates of school disorder is differential occupational opportunity. Most students are taught that if they work hard enough in school they will become successful. However, in high poverty schools notions of success are offset by experience. Students in poor schools see that working hard in school is not likely to improve life chances, especially since most students that graduate or leave high poverty highschools do not get well paying conventional jobs (Ballantine, 2001). According to Wilson (1987), many residents in high poverty conditions adopt important elements of mainstream culture, including the desire to achieve the American dream. However, students attending high poverty schools also see limited opportunities for success. This may lead students to reject the idea that schools provide a means to achieve success. This frustration may, in turn, lead to elevated levels of crime and violence, some of which may be directed—out of frustration—at conventional institutions (Merton, 1938).
3. Data and methods In this work, we study high schools in Florida to determine whether poverty levels mediate the relationship between Black and/or Hispanic segregation and school disorder. We propose the following two hypotheses based on our review of existing literature on poverty and school segregation. Hypothesis 1 Poverty levels mediate the relationship between Black segregation and school disorder. Hypothesis 2 Poverty levels mediate the relationship between Hispanic segregation and school disorder. Our data on school disorder were obtained from the Florida Department of Education’s Florida Schools Indicator Report (FSIR) database (2002), which includes information on the total number of incidents of crime and violence reported by schools from the 1996–1997 through the 2000–2001 school years. The FSIR database indicates that during the 1998–1999 school year, there were 397 high schools in existence that were not also classified as special education, adult education, vocational training, or juvenile justice facilities. The latter types of schools tend to have few students and do not report data on crime and violence. We also use data obtained from the National Center for Education Statistics’ “Common Core Data” (2002), which includes information on the racial, ethnic, and economic composition of individual schools. The data obtained from these two sources were merged to construct the variables used in this analysis. We were able to obtain complete information for 371 high schools (approximately 94% of all Florida high schools).
410
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
3.1. Rates of school disorder Florida schools are required by state law to report to the Florida Department of Education incidents of crime and violence that are discovered to have occurred on school grounds, in school transportation, or at school-sponsored events. The principal of each Florida school is required to report incidents of crime and violence through the use of a standardized incident report that is then forwarded to the Florida Department of Education, which verifies the accuracy of the information. Incidents that are reported to the Florida Department of Education include violent acts against persons; possession of alcohol, tobacco, and other drugs; property offenses; fighting and harassment; weapons possession; other nonviolent offenses; and disorderly conduct (Bureau of Equity, Safety, and School Support, 2002). Data on crime and violence are not broken down by category for all years, so we examine total incidents of crime and violence only. Statewide data from 1998 to 2001 indicate that fighting is by far the most frequent incident, comprising approximately 43% of the total reported incidents (Bureau of Equity, Safety, and School Support, 2002). Our dependent variable, then, is a general measure of school disorder and is computed by summing the total incidents of crime and violence for the 1998–1999, 1999–2000, and 2000–2001 school years, dividing by the total student population during that same time period, and then multiplying by 1,000. We average high school disorder rates over three years to minimize potential measurement error produced by misclassification and/or instability resulting from yearly fluctuations in reporting to the Department of Education (see also Williams & Flewelling, 1987). 3.2. Black/White school segregation scores Black/White school segregation scores are computed for each high school in Florida. School segregation scores are calculated by taking the difference between the proportion of Florida’s Black population attending each high school from the 1997–1998 through the 1999–2000 school years and the proportion of the state’s White population attending the high school during the same time frame. School segregation scores may range from −1 (all the White students in the state attend one school that no Black students attend) to +1 (all the Blacks in the state attend one school that no White students attend). School segregation scores are approximately normally distributed. The minimum Black/White school segregation score is −0.008 (average of 23 Black students and 3,084 White students per year for the years 1998–2000). The maximum Black/White school segregation score during the same time period is 0.019 (2,739 Black students and 23 White students). 3.3. Hispanic/White segregation scores While Massey and Denton (1993) argue that Hispanics are not segregated to the extent that Blacks are, other researchers find that Hispanics are becoming increasingly segregated (Clotfelter, 2001; Reardon et al., 2000). Thus, we also include Hispanic/White segregation scores in our analysis. Hispanic/White school segregation scores are computed for each high
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
411
school in Florida by taking the difference between the proportion of Florida’s Hispanic population attending each high school from the 1997–1998 through the 1999–2000 school years and the proportion of the state’s White population attending the high school during the same time frame. Hispanic/White high school segregation scores are also approximately normally distributed. The minimum Hispanic/White school segregation score is −0.007 (average of 91 Hispanic students and 3,084 White students per year for the years 1998–2000). The maximum Hispanic/White school segregation score during the same time period is 0.037 (4,238 Hispanic students and 508 White students). 3.4. Poverty levels Poverty is likely to influence school disorder rates. To assess the impact of poverty’s effects on the relationship between school segregation and school disorder, we use the percentage of a school’s students eligible for free lunch under the National School Lunch Act (NSLA). The NSLA provides cash subsidies for free lunches to students based on family size and income. In our data, 45.5% of all Black high school students in Florida attend a school where at least 30% of the students receive free or reduced-price lunches, while 24.3% of all White high school students in Florida attend such schools. These results are highly consistent with residential studies looking at poverty levels by race (Massey, 2001; Wilson, 1987). 3.5. Control variables 3.5.1. Size School size as measured through total student enrollment and classroom size has been found to be an important variable in some school effect studies (Gottfredson & Gottfredson, 1985). In general, however, the effect of school size on school disorder rates is inconsistent (Gottfredson, 2001; Welsh et al., 2000). School size should matter, since it is often harder to monitor students in large schools. According to Welsh et al. (2000, p. 250), in larger schools “[t]here is more ground to cover, many spaces not easily subject to surveillance, and many interpersonal interactions that increase the opportunities for conflict” (see also Gottfredson & Gottfredson, 1985; McPartland & McDill, 1977). In this analysis, we adjust the relationships among segregation, percent in poverty, and school disorder by the total student population of each school to account for the potential effect of school size. School size is averaged over the 1998–1999, 1999–2000, and 2000–2001 school years. 3.5.2. Surveillance Consistent with general theories of crime (Felson, 1996), the number of teachers, administrators, and staff relative to the number of students in a particular school is also likely to influence the level of school disorder. Schools with higher student-to-teacher ratios are likely to have a more difficult time “maintaining adequate surveillance and security” (Welsh et al., 2000, p. 250). To account for the potential effect of increased surveillance, we adjust the relationships in this analysis by the average number of students per classroom. Also,
412
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
since the experience of teachers themselves is likely to influence their effectiveness as agents of social control (e.g., more experienced teachers know better what to look for and where to look than less experienced teachers), we include an indicator of the average number of years of teacher experience in each school. We average both the ratio of students to teachers and teacher experience over three years (the 1998–1999, 1999–2000, and 2000–2001 school years). 3.5.3. Resources Research suggests that the lack of school economic resources is an important barrier to the establishment of successful school programs (see Gottfredson, 2001, p. 240). School budgets, then, are likely to influence the effectiveness of the social control that a school can provide through the use of additional school personnel devoted to at-risk youth, the ability to attract the most qualified teachers and administrators to the school, and teacher training. We include the average per pupil expenditure (in dollars) over three years (1998–1999, 1999–2000, and 2000–2001) as a measure of the level of school economic resources. 3.5.4. Reporting practices There is considerable variation in reporting practices among the 67 school districts in Florida. Some districts report levels of disorder that are consistently high across all schools, while other districts report levels of disorder that are consistently low across all schools. These variations are likely to be partially attributable to reporting practices within a particular district. To adjust for variations in reporting that might not reflect actual differences in levels of disorder, we include a measure of school district disorder in our analysis. We compute district disorder rates by summing the total incidents of disorder over the 1996–1997 and 1997–1998 school years, dividing by total student population during that time period, and multiplying by 1,000.
4. Analysis and results Descriptive statistics and bivariate correlations are presented in Appendix A. As predicted, three variables are positively correlated with rates of school disorder. Those variables are Black/White segregation scores, percent in poverty, and past reporting practices. One variable, teacher experience, is negatively associated with disorder rates, indicating that higher levels of teacher experience are associated with lower rates of school disorder. It is important to point out that we do not find evidence of an association between Hispanic/White segregation scores and school disorder (r = .04; p > .10) in the full data set. We suspect, however, that this may be partially the result of the concentration of Hispanic wealth in Miami-Dade, Florida. For instance, in 2000 there were approximately 833,000 Cubans living in Florida, of whom approximately 75% lived in Miami-Dade. Cubans in Miami-Dade represent nearly 30% of all persons of Hispanic origin in Florida. The remaining Hispanic population throughout Florida consists mainly of non-Cuban Hispanics (e.g., those of Mexican and Puerto
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
413
Rican backgrounds). The most recent report on the Hispanic population in the United States (Therrien & Ramirez, 2001) suggests that Cubans are much more likely than other groups of Hispanic origin to have at least a high school education (73% vs. 57%) and earn at least $35,000 for full-time year-round work (34.4% vs. 23.3%). Cubans are also less likely to live in poverty than other groups of Hispanic origin (17.3% vs. 22.8%). This is especially true in Miami-Dade, where there is more wealth among the Cuban population than in other parts of Florida. If we remove Miami-Dade schools from the analysis (n = 38), the Pearson correlation between Hispanic/White segregation scores and school disorder increases considerably, from .04 (p > .10) to .28 (p < .001). Moreover, the bivariate association between poverty and Hispanic/White segregation increases from .26 to .41 (p < .001) when Miami-Dade schools are excluded from the analysis. The increase in the association between Hispanic/White segregation and poverty and Hispanic/White segregation and school disorder is a substantial one, especially considering the relatively small number of high schools in Miami-Dade. In short, there is good justification for excluding schools in Miami-Dade from the Hispanic portion of this analysis. We use ordinary least squares (OLS) regression to examine the association between racial and ethnic segregation, percent in poverty, and disorder rates. The models are estimated using STATA software, version 7.0. OLS results for our model produce residuals that are skewed, which may distort statistical significance tests; therefore, we transform the dependent variable by taking its natural logarithm. The transformed and untransformed models were not substantially different. Therefore, we present the untransformed models for ease of interpretation (results of transformed models are available from authors upon request). We also discovered problems of heteroscedasticity in both the transformed and untransformed models (Cook-Weisberg; χ2 = 6.46; 1 d.f.; p < .05; full untransformed model). For this reason we report Huber-White robust standard errors based on a heteroscedasticity consistent covariance matrix as recommended by Long and Ervin (2000). Robust standard errors are typically much more conservative estimates than those produced by OLS. It is also important to note that multicollinearity is not likely to be a problem in these data. Initial inspection of our correlation matrix (Appendix A) suggests that only one bivariate correlation is large enough to be considered moderate; this involves the variables studentto-teacher ratio and school size, which have a correlation of .59. Including or removing either of those variables from the model, however, has little impact on the standard errors and coefficients of variables remaining in the model. We also examine variance inflation factor (VIF) scores. None of the VIF scores exceeds 2.1, and the mean VIF score is 1.45. We must conclude that it is highly unlikely that multicollinearity presents a problem for these data. We follow the analytic strategy recommended by Baron and Kenny (1986) to determine whether poverty mediates the relationship between segregation and disorder. First, poverty is regressed on segregation scores and control variables (Appendix B, Model 1); second, school disorder rates are regressed on segregation scores and controls (Tables 1 and 2, Model 2); and third, school disorder rates are regressed on segregation scores, percent in poverty, and controls (Tables 1 and 2, Model 3). If poverty mediates the relationship between segregation scores and disorder rates, four conditions must hold: (1) there must be a relationship between segregation
414
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Table 1 School disorder rates regressed against Black segregation scores, percent in poverty, and controls Variable
Black segregation score Percent in poverty School size (in thousands) Teacher experience (years) Student-to-teacher ratio Per pupil expenditures (in thousands) District reporting patterns Constant 2
R N
Model 2
Model 3
b [Beta]
S.E. (robust S.E.)
b [Beta]
S.E. (robust S.E.)
2355.15*** [0.09]
1029.07 (897.86)
1106.26 [0.04]
1175.26 (1111.00)
– −4.98 [−0.05]
– 5.64 (3.46)
0.69** [0.32] −3.33 [−0.03]
0.32 (0.35) 5.56 (3.65)
−4.67** [−0.13]
1.40 (1.93)
−4.03** [−0.11]
1.42 (1.80)
−0.10 [−0.06]
1.12 (1.25)
0.80 [0.04]
1.26 (1.15)
−5.91 [−0.06]
4.28 (5.27)
−5.96 [−0.06]
4.26 (5.96)
0.89*** [0.66]
0.05 (0.18)
0.86*** [0.65]
0.05 (0.18)
112.89*
40.71 (55.59)
61.36
46.98 (57.34)
0.48 371
0.49 371
∗
p < .10 (two-tailed; based on robust standard errors). p < .05 (two-tailed; based on robust standard errors). ∗∗∗ p < .01 (two-tailed; based on robust standard errors). ∗∗
Table 2 School disorder rates regressed against Hispanic segregation scores, percent in poverty, and controlsa Variable
Model 2 b [Beta]
Hispanic segregation score Percent in poverty School size (in thousands) Teacher experience (years) Student-to-teacher ratio Per pupil expenditures (in thousands) District reporting patterns Constant 2
R N
Model 3 S.E. (robust S.E.)
b [Beta]
S.E. (robust S.E.)
4650.82 [0.10]
1919.93 (2891.27)
2578.78 [0.75]
2146.59 (3249.56)
– −6.31 [−0.05]
– 6.89 (4.48)
0.76* [0.12] −1.40 [−0.01]
0.36 (0.40) 7.23 (4.97)
−4.48** [−0.11]
1.62 (2.23)
−4.45** [−0.12]
1.61 (2.21)
−0.37 [−0.02]
1.27 (1.38)
1.08 [0.05]
1.31 (1.26)
−6.80 [−0.06]
5.80 (8.50)
−6.48 [−0.05]
5.78 (8.62)
0.88*** [0.66]
0.06 (0.19)
0.85*** [0.64]
0.06 (0.18)
111.43
46.73 (72.67)
62.34
51.94 (69.95)
*
0.49 336 a
Dade County schools are excluded. p < .10 (two-tailed; based on robust standard errors). ∗∗ p < .05 (two-tailed; based on robust standard errors). ∗∗∗ p < .01 (two-tailed; based on robust standard errors). ∗
0.49 336
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
415
scores and poverty in the first regression; (2) there must be a relationship between segregation scores and disorder rates in the second regression; (3) there must be a relationship between poverty and school disorder rates in the third regression; and (4) the effect of segregation scores must be less in the third regression than in the second regression (i.e., the estimated coefficient should become less positive). If the effect of segregation becomes statistically insignificant when poverty is controlled, then poverty completely mediates the relationship between segregation and crime. Results of the multiple regression analyses for school disorder rates are presented in Tables 1 and 2, and the logistic regression of the poverty model is presented in Appendix B. Table 1 includes the standardized and unstandardized coefficients as well as the robust and nonrobust standard errors for the Black segregation model. All tests of statistical significance are based on robust standard errors. As Appendix A (bivariate correlations) and Appendix B illustrate, Black segregation scores are related to percent in poverty. This is consistent with Massey and Denton’s assertion that segregation leads to high levels of poverty. It appears that segregation scores are related to rates of school disorder (Table 1, Model 2). For instance, a 1.0-standard-deviation change in Black segregation scores is associated with a 0.09-standarddeviation change in the rates of school disorder. This relationship, while not considerably strong, is one of the more important ones in Model 2 and appears to be about as important in explaining variation in school disorder as teacher experience. Moreover, it is clear that the relationship between segregation and disorder persists despite adjustments for school size, teacher experience, student-to-teacher ratio, per pupil expenditures, and previous district reporting practices. If our first hypothesis is correct, the introduction of percent in poverty (Table 1, Model 3) into the Black segregation-disorder model should (1) reveal that poverty is related to disorder and (2) make the association between Black segregation and rates of school disorder decrease. Model 3 provides considerable evidence in support of our hypothesis that poverty mediates the Black segregation–crime relationship. When introduced into the model, poverty is statistically significant, and a 1.0-standard-deviation change in poverty is associated with a 0.35-standarddeviation change in the rate of school disorder. Moreover, the association between segregation and school disorder rates is no longer statistically significant, and the unstandardized coefficient in Table 1, Model 3, for segregation is much lower than in Table 1, Model 2 (2355.15 vs. 1106.26). Table 2 includes both the standardized and unstandardized coefficients as well as the robust and nonrobust standard errors for the Hispanic segregation model. As Appendix B illustrates (Miami-Dade excluded), segregation scores are related to poverty. Again, this is consistent with Massey and Denton’s assertion, in general. However, it is important to reemphasize that Massey and Denton (1993, p. 77) argue that Hispanics are not hypersegregated to the same extent as Blacks—especially in Miami, where the “Hispanic community is not highly segregated on any dimension [of segregation].” We do not, however, see any substantial evidence suggesting that Black and Hispanic segregation impact poverty or crime differently in this analysis. We also find evidence that Hispanic segregation scores are related to rates of school disorder (Table 2, Model 2). For instance, a 1.0-standard-deviation change in Hispanic segregation scores is associated with a 0.10-standard-deviation change in rates of school disorder. This relationship
416
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
is not considerably strong but is one of the more important ones in the analysis. Moreover, it is clear that the relationship between segregation and disorder persists despite adjustments for school size, teacher experience, student-to-teacher ratio, per pupil expenditures, and previous reporting practices. Just as we found in the analysis of Black segregation and school disorder, the introduction of poverty (Table 2, Model 3) provides considerable evidence in support of our second hypothesis that poverty mediates the relationship between Hispanic school segregation and school disorder. When added to the model, poverty is statistically significant, and a 1.0-standard-deviation change in poverty is associated with a 0.40-standard-deviation change in school disorder rates. The association between Hispanic segregation and school disorder rates is no longer statistically significant, and the unstandardized coefficient for segregation is much lower than in Model 2 (4650.82 vs. 2578.78). Clearly, poverty mediates the relationship between Hispanic segregation and levels of school disorder.
5. Discussion and conclusion Research examining the relationship between segregation and crime is limited. Our study extends this line of research by investigating the relationship between school segregation and rates of school disorder among Florida high schools. Our results highlight previous findings of a relationship between segregation and crime found in the social science literature (Logan & Messner, 1987; Peterson & Krivo, 1993; Shihadeh & Flynn, 1996). Specifically, we find a relationship between segregation and disorder. However, unlike previous studies, we find that the association segregation and disorder is completely mediated by poverty. The finding that poverty mediates the relationship between segregation and crime appears to be unique to the study of schools. It may be that our findings are not consistent with previous research because we study schools rather than large aggregates such as suburban areas (Logan & Messner, 1987), cities (Shihadeh & Flynn, 1996), or central cities (Peterson & Krivo, 1993). While our findings concerning segregation and disorder are not consistent with previous empirical research, they are consistent with the Massey and Denton’s (1993) proposition that Black segregation produces high levels of poverty in minority neighborhoods that, in turn, produces high levels of crime and violence (see also Massey, 2001). These findings are also consistent with Wilson’s (1987) position that high levels of poverty in minority neighborhoods are related to a myriad of social problems—including crime. Levels of school poverty are likely to be related to school disorder through weak amounts of collective socialization, less access to social capital, lower levels of social control, and high levels of social strain (Ainsworth, 2002). It appears, then, that the theoretical arguments of Massey and Denton (1993) and Wilson (1987) can be extended to schools. It is important to emphasize that our analysis should not be interpreted as suggesting school poverty is more important than school segregation in producing school disorder. In fact, our results emphasize the importance of school segregation in shaping rates of school disorder indirectly through levels of poverty. In short, our findings appear to suggest that poverty is mechanism that links segregation and disorder.
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
417
This emphasis on segregation also gives us reason for concern. Recent research indicates that school segregation is currently increasing, which is a counterproductive trend in terms of school disorder. Although overall rates of school disorder appear to be decreasing, the way these rates are distributed by race is what makes the trend counterproductive. At present, there is in a sense a two-faceted school disorder “problem.” As school segregation increases, minorities—as well as the poor—will be more likely to attend schools that have relatively high rates of crime and violence, while Whites and the welloff will be able to attend schools that are relatively free of such problems (Massey, 2001). Thus, school desegregation would have benefits for some—those formerly attending schools with high levels of poverty. However, these benefits come at the expense of those formerly attending White segregated, low-poverty schools. The latter would be placed in a situation in which crime in school would likely be higher than what they currently experience. In this sense, it could be argued that Whites and the well-off presently enjoy low rates of school disorder because minorities, who are more likely to be poor, suffer high rates of crime as a result of segregation. In short, we agree with Massey (2001, p. 334), who argues that most “Americans perceive themselves to actually benefit from the social arrangements that produce segregation.” If poverty rates are higher for Blacks and Hispanics, then by isolating poor Black and Hispanic students, the rest of society insulates children from higher rates of crime and violence in schools.
Appendix A. Descriptive statistics for independent and dependent variables
1
2
3
4
5
6
7
8
9
I. Correlation matrix 1. School disorder rate
1.00
2. Black segregation score
0.19*
1.00
3. Hispanic segregation score
0.04
0.31*
1.00
4. Percent in poverty
0.31*
0.39*
0.26*
*
*
5. School size (in thousands)
−0.05
6. Teacher experience (years)
−0.16* −0.07
0.15
*
0.42
*
0.01
0.17
8. Per pupil expenditures ($)
0.07
0.10* −0.15
9. District reporting practices
0.66
*
0.16
−0.29*
−0.16* −0.27*
7. Student-to-teacher ratio
*
1.00
0.19 0.01
1.00 0.06*
1.00
*
0.09
1.00
0.26* −0.41*
0.03
−0.38*
1.00
0.06
0.18*
−0.37
*
*
0.23
0.59
−0.08
−0.03
Mean
S.D.
95.171
96.78
0.00
0.01
II. Means and standard deviations School disorder rate Black segregation score
1.00
418
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Appendix (continued) Mean Hispanic segregation score
0.00
0.01
28.35
15.54
School size (in thousands)
1.66
0.93
Teacher experience (years)
13.63
2.90
Student-to-teacher ratio
25.58
4.84
Per pupil expenditures ($)
4,632
1,056
District reporting practices
97.01
71.66
Percent in poverty
∗
S.D.
p < .05 (two-tailed).
Appendix B. Percent in poverty regressed against segregation scores and controls
Variable
Model 1 (Black segregation)
Model 1 (Hispanic segregation)
b [Beta] (S.E.)
b [Beta] (S.E.)
Black segregation score
1827.69*** [0.45] (170.03)
–
Hispanic segregation score
–
2733.26*** [0.40] (293.70)
School size (in thousands)
−2.41** [−0.14] (0.93)
−6.47*** [−0.34] (1.05)
Teacher experience (years)
−0.93
−0.04** [−0.01] (0.25)
Student-to-teacher ratio
−1.32*** [−0.26] (0.20)
−0.94*** [−0.37] (0.20)
Per pupil expenditures (in thousands)
−0.08 [−0.12] (0.71)
−0.43 [−0.02] (0.89)
District reporting practices
0.04
***
***
[−0.16] (0.25)
[0.17] (0.01)
***
0.04*** [0.17] (0.01) 64.73*** (7.15)
Constant
75.40
R2
.45
.49
N
371
336
∗∗
(6.73)
p < .05 (two-tailed). ∗∗∗ p < .01 (two-tailed).
References Ainsworth, J. (2002). Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces, 81, 117–152. Anderson, E. (1994). The code of the streets. Atlantic Monthly, 273, 80–91. Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. New York: W. W. Norton and Company. Ballantine, J. H. (2001). The sociology of education: A systematic analysis. Upper Saddle River, NJ: Prentice Hall.
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
419
Bankston, C., & Caldas, S. (1996). Majority African American schools and social injustice: The influence of De Facto segregation on academic achievement. Social Forces, 75, 535–556. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Brooks-Gunn, J., Duncan, G., Klebanov, P., & Sealand, N. (1993). Do neighborhoods influence child and adolescent development? American Journal of Sociology, 99, 353–395. Bureau of Equity, Safety, and School Support. (2002). Statewide report on school safety and discipline data, 2000–2001. Tallahassee, FL: Florida Department of Education. Clotfelter, C. (2001). Are Whites still fleeing? Racial patterns and enrollment shifts in urban public schools, 1987–1996. Journal of Policy Analysis and Management, 20, 199–221. Coleman, J. (1990). Foundations of social theory. Cambridge, MA: The Belknap Press of Harvard University Press. Felson, M. (1996). Crime in everyday life. Thousand Oaks, CA: Pine Forge. Florida Department of Education. (2002). Florida schools indicator report, 1996–2001. Tallahassee, FL: Florida Department of Education. Gottfredson, D. C. (2001). Schools and delinquency. New York: Cambridge University Press. Gottfredson, D. C., & Gottfredson, D. C. (1985). Victimization in schools. New York: Plenum Press. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. Hirschi, T. (1969). Causes of delinquency. Berkeley, CA: University of California Press. Kaufman, J., & Roesnbaum, J. (1992). The education and employment of low-income Black youth in White suburbs. Education Evaluation and Policy Analysis, 14, 229–240. Krivo, L., & Peterson, R. (1996). Extremely disadvantaged neighborhoods and urban crime. Social Forces, 75, 619–648. Logan, J. R., & Messner, S. F. (1987). Racial residential segregation and suburban violent crime. Social Science Quarterly, 68, 528–538. Long, S. J., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the Linear Regression Model. American Statistician, 54, 217–224. Massey, D. S. (1994). America’s apartheid and the urban underclass. Social Service Review, 68, 471–487. Massey, D. S. (2001). Segregation and violent crime in urban America. In D. Massey & E. Anderson (Eds.), Problem of the century: Racial stratification in the United States (pp. 217–346). New York: Russell Sage. Massey, D., & Denton, N. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University. McPartland, J. M., & McDill, E. L. (1977). Violence in schools: Perspectives, programs, and positions. Lexington, MA: Lexington Books. Merton, R. (1938). Social structure and anomie. American Sociological Review, 3, 672–682. Morenoff, J., Sampson, R., & Raudenbush, S. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39, 517–560. National Center for Education Statistics. (2002). Common core data, 2001. Washington, DC: U.S. Department of Education. Peterson, R., & Krivo, L. (1993). Racial segregation and Black urban homicide. Social Force, 71, 1001–1026. Reardon, S. F., Yun, J. T., & Mcnulty Eitle, T. (2000). The changing structure of school segregation: Measurement and evidence of multiracial metropolitan-area school segregation, 1989–1995. Demography, 37, 351–364. Sampson, R., & Groves, B. (1989). Community structure and anomie: Testing social disorganization theory. American Journal of Sociology, 94, 744–802. Shihadeh, E. S., & Flynn, N. (1996). Segregation and crime: The effect of Black social isolation on the rates of Black urban violence. Social Forces, 74, 1325–1352. So, A. (1992). The Black schools. Journal of Black Studies, 22, 523–531. Therrien, M., & Ramirez, R. (2001). The Hispanic population in the United States, March 2000. Current population reports, P20-535. Washington, DC: U.S. Census Bureau. Wells, A. (1995). Reexamining social science research on school desegregation: Long-versus short-term effects. Teachers College Record, 96, 691–707.
420
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Welsh, W. N., Stokes, R., & Greene, J. R. (2000). A macro-level model of school disorder. Journal of Research in Crime and Delinquency, 37, 243–283. Wiatrowski, M. D., Griswold, D. B., & Roberts, M. K. (1981). Social control theory and delinquency. American Sociological Review, 46, 525–541. Williams, K. R., & Flewelling, R. L. (1987). Family, acquaintance, and stranger homicide: Alternative procedures for rate calculations. Criminology, 25, 543–560. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.
Segregation and school disorder Paul B. Stretesky ∗ , Michael J. Hogan Department of Sociology, Colorado State University, Clark Building, Fort Collins, CO 80523, USA
Abstract This investigation extends research on racial and ethnic segregation, poverty, and crime rates to schools. We study 371 high schools in Florida to determine whether poverty mediates the relationship between segregation and rates of school disorder. The data for this work come from the Florida Department of Education and the National Center for Educational Statistics. Consistent with previous studies of racial segregation and crime, we find that school segregation is strongly associated with levels of school disorder. Unlike previous research, however, poverty completely mediates the segregation–disorder relationship. Nevertheless, our findings are highly consistent with contemporary theories of racial segregation and violence. © 2005 Elsevier Inc. All rights reserved.
1. Introduction Massey (2001, p. 334) argues that “segregation is a fundamental factor behind crime.” Studies that investigate the association between residential segregation and crime rates find evidence consistent with Massey’s assertion (Krivo & Peterson, 1996; Logan & Messner, 1987; Shihadeh & Flynn, 1996). However, research that specifically examines the association between school segregation and school crime has yet to be conducted. The lack of focus on schools is surprising for three reasons. First, research has found that community crime rates and school crime rates are not necessarily associated. For instance, Welsh, Stokes, and Greene (2000, p. 269) maintain that “[s]implistic assumptions that communities with high crime rates automatically commit a school to a high rate of disorder are untenable.” Second, research on school segregation has generally found that residential segregation does not necessarily correspond with the segregation of schools (Reardon, Yun, & Eitle, 2000). Third, research
∗ Corresponding author. Tel.: +1 970 491 6825/6044; fax: +1 970 491 2191. E-mail address: [email protected] (P.B. Stretesky).
0362-3319/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.soscij.2005.06.007
406
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
examining the topic of school crime and school segregation has attracted a considerable amount of attention as separate areas of study in the social science literature. The purpose of our research is to study the relationship between school segregation, school poverty, and school disorder. Specifically, we test whether poverty is likely to be the generative mechanism through which segregation influences rates of school disorder. We suspect that if the racial and economic composition of neighborhoods influences crime rates, a high school’s economic, ethnic, and racial status should also be associated with rates of school disorder—especially because high school is such an important part of the social lives of many students (Ballantine, 2001).
2. Background School researchers such as Ballantine (2001, p. 229) believe “that each school has a culture of its own, like a miniature society” which encompasses the beliefs, values, and attitudes of students. In this section, we build upon Ballantine’s (2001) arguments to describe the reasons that school segregation and school poverty may be associated with rates of school disorder. 2.1. School segregation There is good reason to suspect that school segregation is related to school disorder. Massey and Denton (1993), for example, describe how segregation produces an “oppositional culture” that may lead to violence (see also Anderson, 1999; Massey, 2001). Empirical evidence supports the oppositional culture hypothesis among schools since victimization rates for robbery, theft, and violence are much higher in Black schools than in White schools (So, 1992). Research conducted on crime rates and residential segregation suggests that segregation creates a disjunction between cultural values and social structural arrangements and produces high rates of criminal violence because community controls that prevent violence are weakened by a lack of respect for social norms (Logan & Messner, 1987). Research on school segregation is consistent with arguments in the criminological literature. For instance, there is evidence that African Americans attending desegregated schools have much higher occupational aspirations than African Americans attending segregated schools, suggesting a greater commitment in conventional culture (Wells, 1995). Also, Bankston and Caldas (1996) discovered that African Americans attending segregated schools scored much lower on school achievement tests than African Americans attending integrated schools. Finally, Wells (1995) reports that African Americans who attend segregated schools have aspirations that are not compatible with their educational background. This condition may lead to high levels of frustration. Many of the negative outcomes described in the school segregation literature are also related to poverty. Massey (1994, p. 480) argues that racial segregation concentrates poverty and “anything that is correlated with poverty: crime, drug, abuse, welfare dependency, single parenthood, and education difficulties.” It is well documented that African Americans who attend desegregated schools have better reading comprehension, are more likely to
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
407
attend college, will obtain better jobs, and have a greater probability of getting promoted at work than African Americans who attend segregated schools (Ballantine, 2001; Wells, 1995). Our observations concerning educational outcomes lead us to question whether levels of poverty may be the mechanism that links school segregation to rates of school disorder. 2.2. Poverty There is strong theoretical support for the position that levels of school poverty are positively related to rates of school disorder. We expand upon the theoretical arguments presented in the work of Ainsworth (2002; see also Wilson, 1987) to argue that there are four interrelated mechanisms through which levels of school poverty might influence rates of school disorder. We argue that these mechanisms provide the theoretical basis for the association between levels of school poverty and rates of school disorder. Schools with a large percentage of poor students are likely to be faced with (1) weak amounts of collective socialization to prosocial behavior; (2) less access to social capital; (3) lower levels of social control; (4) and high levels of social strain resulting from differential opportunities (see Ainsworth, 2002, p. 118). One way that high levels of school poverty may lead to high rates of school disorganization is through a lack of collective socialization. This is likely because schools with high levels of school poverty have fewer students who come from advantaged neighborhoods where conventional culture is emphasized (Wilson, 1987). Thus, students who attend schools with high levels of poverty are more likely than those students attending affluent schools to come from neighborhoods that exhibit behavior that is in opposition to conventional values. This behavior results from social isolation—a structural condition brought on by the lack of exposure to working adults who serve as conventional role models to neighborhood youth (Wilson, 1987). The result of this social isolation is an oppositional culture that is more accepting of crime and violence. For example, Anderson (1994, p. 82) observes that by the fourth grade the “code of the street”—a set of informal rules “governing interpersonal public behavior, including violence” begins to filter into the school culture and poor schools become a staging area for campaigns for respect. According to Anderson, campaigns for respect often involve the use of violence. Even those students whose home lives reflect mainstream values must be able to handle themselves in a street-oriented environment that is pervasive in low-income schools (Anderson, 1999, p. 33). Thus, the higher the level of school poverty, the more pervasive oppositional values become since there is a greater proportion of students who must adopt the code of the street just to negotiate daily life in school. Unlike students attending high poverty schools, students attending affluent schools are more likely to come from affluent neighborhoods and are therefore more likely to be socialized into conventional norms (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993; Morenoff, Sampson, & Raudenbush, 2001). These students do not need to use violence since they attend schools where there is little or no oppositional culture. A second way that increased levels of poverty may be related to school disorder is through decreased social capital (Sampson & Groves, 1989; Wilson, 1987). Social capital is simply the number and quality of social networks that develop. These relationships and networks are
408
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
typically part of the normative structure and are highly important in schools. Coleman (1990) argues, for instance, that social capital prevents crime though the creation of effective norms that inhibit crime. Social networks provide information that is important in establishing a stake in conventional behavior (Granovetter, 1973). Students attending high poverty schools, for instance, may have less access to college guidance counselors and/or older brothers and sisters who have information about college and are willing to come and talk with students in school. These informational sessions aid in creating a strong attachment to education and help orient students to conventional norms. Moreover, many of the jobs and job contacts high school students receive are from teachers, friends, and friend’s parents who have important connections in the community. Students attending poorer schools are not likely to have the same number and quality of contacts regardless of their own economic status. Thus, students attending poor schools are the least likely to have developed networks that will give them access to more education and meaningful employment (Wilson, 1987). For instance, Kaufman and Roesnbaum (1992) found that low-income students who moved to more affluent suburbs were more likely to finish high school and go on to four-year colleges than students attending school where a majority of the students are poor. Moreover, Kaufman and Roesnbaum (1992) report that students and parents spoke of the advantages of attending high schools where networks and ties to education were available. Informal ties also may extend to student’s peers, who may share information, resources, attitudes toward education, and motivation. Peers are extremely important in schools where high levels of poverty may alter the culture of a school (Coleman, 1990). In short, this suggests that there is a difference between a student who is poor but attends a school with students who are not poor than a student who is poor and attends a school with other poor students. Bankson and Caldas (1996, p. 536) argue that “[p]arents who send their children to good schools provide their children with the advantages of associating with good students, advantages that may outweigh those of superior school facilities and event those of quality of teaching.” This may include intellectual capital, collective motivation, as well as access to the resources of peers such as books, computers, and the Internet (Wells, 1995). Schools with an overwhelming number of poor students may change the school culture in such a way that normative structure discourages or disrupts important social networks. There is considerable empirical evidence in support of this position. For instance, studies find that poor students do much better in school when their peers are affluent as opposed to poor (Wells, 1995). In more affluent schools, there is also a greater amount of parent involvement and parents may serve as an important source of social capital. Students attending schools that lack these informal networks are likely to have less involvement in conventional activities and are therefore—according to many criminological theories—more likely to engage in crime and violence (see Hirschi, 1969; Wiatrowski, Griswold, & Roberts 1981). Social control is a third mechanism that may link levels of school poverty and school disorder rates. Social control is practices that schools use or have available that encourage or promote conformity to conventional behavior. Social control may extend from simple forms of faculty monitoring of students to more important forms of internalization. More affluent schools often have extensive networks which include adults in the community that have the time to engage in school functions and aid in organizing school activities such as sporting events, arts programs,
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
409
social clubs. In fact, schools are often the focal point of youth functions and provide a forum for collective socialization, attainment of social capital, and the promotion of social control because they provide direct constraints on time and behavior. It should be pointed out that in poor schools where there is a lack of effective collective socialization, even a large number of teachers may not be able to effectively provide the level of social control that is needed to prevent high levels of school disorder. The fourth mechanism that is likely to link levels of school poverty to rates of school disorder is differential occupational opportunity. Most students are taught that if they work hard enough in school they will become successful. However, in high poverty schools notions of success are offset by experience. Students in poor schools see that working hard in school is not likely to improve life chances, especially since most students that graduate or leave high poverty highschools do not get well paying conventional jobs (Ballantine, 2001). According to Wilson (1987), many residents in high poverty conditions adopt important elements of mainstream culture, including the desire to achieve the American dream. However, students attending high poverty schools also see limited opportunities for success. This may lead students to reject the idea that schools provide a means to achieve success. This frustration may, in turn, lead to elevated levels of crime and violence, some of which may be directed—out of frustration—at conventional institutions (Merton, 1938).
3. Data and methods In this work, we study high schools in Florida to determine whether poverty levels mediate the relationship between Black and/or Hispanic segregation and school disorder. We propose the following two hypotheses based on our review of existing literature on poverty and school segregation. Hypothesis 1 Poverty levels mediate the relationship between Black segregation and school disorder. Hypothesis 2 Poverty levels mediate the relationship between Hispanic segregation and school disorder. Our data on school disorder were obtained from the Florida Department of Education’s Florida Schools Indicator Report (FSIR) database (2002), which includes information on the total number of incidents of crime and violence reported by schools from the 1996–1997 through the 2000–2001 school years. The FSIR database indicates that during the 1998–1999 school year, there were 397 high schools in existence that were not also classified as special education, adult education, vocational training, or juvenile justice facilities. The latter types of schools tend to have few students and do not report data on crime and violence. We also use data obtained from the National Center for Education Statistics’ “Common Core Data” (2002), which includes information on the racial, ethnic, and economic composition of individual schools. The data obtained from these two sources were merged to construct the variables used in this analysis. We were able to obtain complete information for 371 high schools (approximately 94% of all Florida high schools).
410
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
3.1. Rates of school disorder Florida schools are required by state law to report to the Florida Department of Education incidents of crime and violence that are discovered to have occurred on school grounds, in school transportation, or at school-sponsored events. The principal of each Florida school is required to report incidents of crime and violence through the use of a standardized incident report that is then forwarded to the Florida Department of Education, which verifies the accuracy of the information. Incidents that are reported to the Florida Department of Education include violent acts against persons; possession of alcohol, tobacco, and other drugs; property offenses; fighting and harassment; weapons possession; other nonviolent offenses; and disorderly conduct (Bureau of Equity, Safety, and School Support, 2002). Data on crime and violence are not broken down by category for all years, so we examine total incidents of crime and violence only. Statewide data from 1998 to 2001 indicate that fighting is by far the most frequent incident, comprising approximately 43% of the total reported incidents (Bureau of Equity, Safety, and School Support, 2002). Our dependent variable, then, is a general measure of school disorder and is computed by summing the total incidents of crime and violence for the 1998–1999, 1999–2000, and 2000–2001 school years, dividing by the total student population during that same time period, and then multiplying by 1,000. We average high school disorder rates over three years to minimize potential measurement error produced by misclassification and/or instability resulting from yearly fluctuations in reporting to the Department of Education (see also Williams & Flewelling, 1987). 3.2. Black/White school segregation scores Black/White school segregation scores are computed for each high school in Florida. School segregation scores are calculated by taking the difference between the proportion of Florida’s Black population attending each high school from the 1997–1998 through the 1999–2000 school years and the proportion of the state’s White population attending the high school during the same time frame. School segregation scores may range from −1 (all the White students in the state attend one school that no Black students attend) to +1 (all the Blacks in the state attend one school that no White students attend). School segregation scores are approximately normally distributed. The minimum Black/White school segregation score is −0.008 (average of 23 Black students and 3,084 White students per year for the years 1998–2000). The maximum Black/White school segregation score during the same time period is 0.019 (2,739 Black students and 23 White students). 3.3. Hispanic/White segregation scores While Massey and Denton (1993) argue that Hispanics are not segregated to the extent that Blacks are, other researchers find that Hispanics are becoming increasingly segregated (Clotfelter, 2001; Reardon et al., 2000). Thus, we also include Hispanic/White segregation scores in our analysis. Hispanic/White school segregation scores are computed for each high
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
411
school in Florida by taking the difference between the proportion of Florida’s Hispanic population attending each high school from the 1997–1998 through the 1999–2000 school years and the proportion of the state’s White population attending the high school during the same time frame. Hispanic/White high school segregation scores are also approximately normally distributed. The minimum Hispanic/White school segregation score is −0.007 (average of 91 Hispanic students and 3,084 White students per year for the years 1998–2000). The maximum Hispanic/White school segregation score during the same time period is 0.037 (4,238 Hispanic students and 508 White students). 3.4. Poverty levels Poverty is likely to influence school disorder rates. To assess the impact of poverty’s effects on the relationship between school segregation and school disorder, we use the percentage of a school’s students eligible for free lunch under the National School Lunch Act (NSLA). The NSLA provides cash subsidies for free lunches to students based on family size and income. In our data, 45.5% of all Black high school students in Florida attend a school where at least 30% of the students receive free or reduced-price lunches, while 24.3% of all White high school students in Florida attend such schools. These results are highly consistent with residential studies looking at poverty levels by race (Massey, 2001; Wilson, 1987). 3.5. Control variables 3.5.1. Size School size as measured through total student enrollment and classroom size has been found to be an important variable in some school effect studies (Gottfredson & Gottfredson, 1985). In general, however, the effect of school size on school disorder rates is inconsistent (Gottfredson, 2001; Welsh et al., 2000). School size should matter, since it is often harder to monitor students in large schools. According to Welsh et al. (2000, p. 250), in larger schools “[t]here is more ground to cover, many spaces not easily subject to surveillance, and many interpersonal interactions that increase the opportunities for conflict” (see also Gottfredson & Gottfredson, 1985; McPartland & McDill, 1977). In this analysis, we adjust the relationships among segregation, percent in poverty, and school disorder by the total student population of each school to account for the potential effect of school size. School size is averaged over the 1998–1999, 1999–2000, and 2000–2001 school years. 3.5.2. Surveillance Consistent with general theories of crime (Felson, 1996), the number of teachers, administrators, and staff relative to the number of students in a particular school is also likely to influence the level of school disorder. Schools with higher student-to-teacher ratios are likely to have a more difficult time “maintaining adequate surveillance and security” (Welsh et al., 2000, p. 250). To account for the potential effect of increased surveillance, we adjust the relationships in this analysis by the average number of students per classroom. Also,
412
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
since the experience of teachers themselves is likely to influence their effectiveness as agents of social control (e.g., more experienced teachers know better what to look for and where to look than less experienced teachers), we include an indicator of the average number of years of teacher experience in each school. We average both the ratio of students to teachers and teacher experience over three years (the 1998–1999, 1999–2000, and 2000–2001 school years). 3.5.3. Resources Research suggests that the lack of school economic resources is an important barrier to the establishment of successful school programs (see Gottfredson, 2001, p. 240). School budgets, then, are likely to influence the effectiveness of the social control that a school can provide through the use of additional school personnel devoted to at-risk youth, the ability to attract the most qualified teachers and administrators to the school, and teacher training. We include the average per pupil expenditure (in dollars) over three years (1998–1999, 1999–2000, and 2000–2001) as a measure of the level of school economic resources. 3.5.4. Reporting practices There is considerable variation in reporting practices among the 67 school districts in Florida. Some districts report levels of disorder that are consistently high across all schools, while other districts report levels of disorder that are consistently low across all schools. These variations are likely to be partially attributable to reporting practices within a particular district. To adjust for variations in reporting that might not reflect actual differences in levels of disorder, we include a measure of school district disorder in our analysis. We compute district disorder rates by summing the total incidents of disorder over the 1996–1997 and 1997–1998 school years, dividing by total student population during that time period, and multiplying by 1,000.
4. Analysis and results Descriptive statistics and bivariate correlations are presented in Appendix A. As predicted, three variables are positively correlated with rates of school disorder. Those variables are Black/White segregation scores, percent in poverty, and past reporting practices. One variable, teacher experience, is negatively associated with disorder rates, indicating that higher levels of teacher experience are associated with lower rates of school disorder. It is important to point out that we do not find evidence of an association between Hispanic/White segregation scores and school disorder (r = .04; p > .10) in the full data set. We suspect, however, that this may be partially the result of the concentration of Hispanic wealth in Miami-Dade, Florida. For instance, in 2000 there were approximately 833,000 Cubans living in Florida, of whom approximately 75% lived in Miami-Dade. Cubans in Miami-Dade represent nearly 30% of all persons of Hispanic origin in Florida. The remaining Hispanic population throughout Florida consists mainly of non-Cuban Hispanics (e.g., those of Mexican and Puerto
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
413
Rican backgrounds). The most recent report on the Hispanic population in the United States (Therrien & Ramirez, 2001) suggests that Cubans are much more likely than other groups of Hispanic origin to have at least a high school education (73% vs. 57%) and earn at least $35,000 for full-time year-round work (34.4% vs. 23.3%). Cubans are also less likely to live in poverty than other groups of Hispanic origin (17.3% vs. 22.8%). This is especially true in Miami-Dade, where there is more wealth among the Cuban population than in other parts of Florida. If we remove Miami-Dade schools from the analysis (n = 38), the Pearson correlation between Hispanic/White segregation scores and school disorder increases considerably, from .04 (p > .10) to .28 (p < .001). Moreover, the bivariate association between poverty and Hispanic/White segregation increases from .26 to .41 (p < .001) when Miami-Dade schools are excluded from the analysis. The increase in the association between Hispanic/White segregation and poverty and Hispanic/White segregation and school disorder is a substantial one, especially considering the relatively small number of high schools in Miami-Dade. In short, there is good justification for excluding schools in Miami-Dade from the Hispanic portion of this analysis. We use ordinary least squares (OLS) regression to examine the association between racial and ethnic segregation, percent in poverty, and disorder rates. The models are estimated using STATA software, version 7.0. OLS results for our model produce residuals that are skewed, which may distort statistical significance tests; therefore, we transform the dependent variable by taking its natural logarithm. The transformed and untransformed models were not substantially different. Therefore, we present the untransformed models for ease of interpretation (results of transformed models are available from authors upon request). We also discovered problems of heteroscedasticity in both the transformed and untransformed models (Cook-Weisberg; χ2 = 6.46; 1 d.f.; p < .05; full untransformed model). For this reason we report Huber-White robust standard errors based on a heteroscedasticity consistent covariance matrix as recommended by Long and Ervin (2000). Robust standard errors are typically much more conservative estimates than those produced by OLS. It is also important to note that multicollinearity is not likely to be a problem in these data. Initial inspection of our correlation matrix (Appendix A) suggests that only one bivariate correlation is large enough to be considered moderate; this involves the variables studentto-teacher ratio and school size, which have a correlation of .59. Including or removing either of those variables from the model, however, has little impact on the standard errors and coefficients of variables remaining in the model. We also examine variance inflation factor (VIF) scores. None of the VIF scores exceeds 2.1, and the mean VIF score is 1.45. We must conclude that it is highly unlikely that multicollinearity presents a problem for these data. We follow the analytic strategy recommended by Baron and Kenny (1986) to determine whether poverty mediates the relationship between segregation and disorder. First, poverty is regressed on segregation scores and control variables (Appendix B, Model 1); second, school disorder rates are regressed on segregation scores and controls (Tables 1 and 2, Model 2); and third, school disorder rates are regressed on segregation scores, percent in poverty, and controls (Tables 1 and 2, Model 3). If poverty mediates the relationship between segregation scores and disorder rates, four conditions must hold: (1) there must be a relationship between segregation
414
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Table 1 School disorder rates regressed against Black segregation scores, percent in poverty, and controls Variable
Black segregation score Percent in poverty School size (in thousands) Teacher experience (years) Student-to-teacher ratio Per pupil expenditures (in thousands) District reporting patterns Constant 2
R N
Model 2
Model 3
b [Beta]
S.E. (robust S.E.)
b [Beta]
S.E. (robust S.E.)
2355.15*** [0.09]
1029.07 (897.86)
1106.26 [0.04]
1175.26 (1111.00)
– −4.98 [−0.05]
– 5.64 (3.46)
0.69** [0.32] −3.33 [−0.03]
0.32 (0.35) 5.56 (3.65)
−4.67** [−0.13]
1.40 (1.93)
−4.03** [−0.11]
1.42 (1.80)
−0.10 [−0.06]
1.12 (1.25)
0.80 [0.04]
1.26 (1.15)
−5.91 [−0.06]
4.28 (5.27)
−5.96 [−0.06]
4.26 (5.96)
0.89*** [0.66]
0.05 (0.18)
0.86*** [0.65]
0.05 (0.18)
112.89*
40.71 (55.59)
61.36
46.98 (57.34)
0.48 371
0.49 371
∗
p < .10 (two-tailed; based on robust standard errors). p < .05 (two-tailed; based on robust standard errors). ∗∗∗ p < .01 (two-tailed; based on robust standard errors). ∗∗
Table 2 School disorder rates regressed against Hispanic segregation scores, percent in poverty, and controlsa Variable
Model 2 b [Beta]
Hispanic segregation score Percent in poverty School size (in thousands) Teacher experience (years) Student-to-teacher ratio Per pupil expenditures (in thousands) District reporting patterns Constant 2
R N
Model 3 S.E. (robust S.E.)
b [Beta]
S.E. (robust S.E.)
4650.82 [0.10]
1919.93 (2891.27)
2578.78 [0.75]
2146.59 (3249.56)
– −6.31 [−0.05]
– 6.89 (4.48)
0.76* [0.12] −1.40 [−0.01]
0.36 (0.40) 7.23 (4.97)
−4.48** [−0.11]
1.62 (2.23)
−4.45** [−0.12]
1.61 (2.21)
−0.37 [−0.02]
1.27 (1.38)
1.08 [0.05]
1.31 (1.26)
−6.80 [−0.06]
5.80 (8.50)
−6.48 [−0.05]
5.78 (8.62)
0.88*** [0.66]
0.06 (0.19)
0.85*** [0.64]
0.06 (0.18)
111.43
46.73 (72.67)
62.34
51.94 (69.95)
*
0.49 336 a
Dade County schools are excluded. p < .10 (two-tailed; based on robust standard errors). ∗∗ p < .05 (two-tailed; based on robust standard errors). ∗∗∗ p < .01 (two-tailed; based on robust standard errors). ∗
0.49 336
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
415
scores and poverty in the first regression; (2) there must be a relationship between segregation scores and disorder rates in the second regression; (3) there must be a relationship between poverty and school disorder rates in the third regression; and (4) the effect of segregation scores must be less in the third regression than in the second regression (i.e., the estimated coefficient should become less positive). If the effect of segregation becomes statistically insignificant when poverty is controlled, then poverty completely mediates the relationship between segregation and crime. Results of the multiple regression analyses for school disorder rates are presented in Tables 1 and 2, and the logistic regression of the poverty model is presented in Appendix B. Table 1 includes the standardized and unstandardized coefficients as well as the robust and nonrobust standard errors for the Black segregation model. All tests of statistical significance are based on robust standard errors. As Appendix A (bivariate correlations) and Appendix B illustrate, Black segregation scores are related to percent in poverty. This is consistent with Massey and Denton’s assertion that segregation leads to high levels of poverty. It appears that segregation scores are related to rates of school disorder (Table 1, Model 2). For instance, a 1.0-standard-deviation change in Black segregation scores is associated with a 0.09-standarddeviation change in the rates of school disorder. This relationship, while not considerably strong, is one of the more important ones in Model 2 and appears to be about as important in explaining variation in school disorder as teacher experience. Moreover, it is clear that the relationship between segregation and disorder persists despite adjustments for school size, teacher experience, student-to-teacher ratio, per pupil expenditures, and previous district reporting practices. If our first hypothesis is correct, the introduction of percent in poverty (Table 1, Model 3) into the Black segregation-disorder model should (1) reveal that poverty is related to disorder and (2) make the association between Black segregation and rates of school disorder decrease. Model 3 provides considerable evidence in support of our hypothesis that poverty mediates the Black segregation–crime relationship. When introduced into the model, poverty is statistically significant, and a 1.0-standard-deviation change in poverty is associated with a 0.35-standarddeviation change in the rate of school disorder. Moreover, the association between segregation and school disorder rates is no longer statistically significant, and the unstandardized coefficient in Table 1, Model 3, for segregation is much lower than in Table 1, Model 2 (2355.15 vs. 1106.26). Table 2 includes both the standardized and unstandardized coefficients as well as the robust and nonrobust standard errors for the Hispanic segregation model. As Appendix B illustrates (Miami-Dade excluded), segregation scores are related to poverty. Again, this is consistent with Massey and Denton’s assertion, in general. However, it is important to reemphasize that Massey and Denton (1993, p. 77) argue that Hispanics are not hypersegregated to the same extent as Blacks—especially in Miami, where the “Hispanic community is not highly segregated on any dimension [of segregation].” We do not, however, see any substantial evidence suggesting that Black and Hispanic segregation impact poverty or crime differently in this analysis. We also find evidence that Hispanic segregation scores are related to rates of school disorder (Table 2, Model 2). For instance, a 1.0-standard-deviation change in Hispanic segregation scores is associated with a 0.10-standard-deviation change in rates of school disorder. This relationship
416
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
is not considerably strong but is one of the more important ones in the analysis. Moreover, it is clear that the relationship between segregation and disorder persists despite adjustments for school size, teacher experience, student-to-teacher ratio, per pupil expenditures, and previous reporting practices. Just as we found in the analysis of Black segregation and school disorder, the introduction of poverty (Table 2, Model 3) provides considerable evidence in support of our second hypothesis that poverty mediates the relationship between Hispanic school segregation and school disorder. When added to the model, poverty is statistically significant, and a 1.0-standard-deviation change in poverty is associated with a 0.40-standard-deviation change in school disorder rates. The association between Hispanic segregation and school disorder rates is no longer statistically significant, and the unstandardized coefficient for segregation is much lower than in Model 2 (4650.82 vs. 2578.78). Clearly, poverty mediates the relationship between Hispanic segregation and levels of school disorder.
5. Discussion and conclusion Research examining the relationship between segregation and crime is limited. Our study extends this line of research by investigating the relationship between school segregation and rates of school disorder among Florida high schools. Our results highlight previous findings of a relationship between segregation and crime found in the social science literature (Logan & Messner, 1987; Peterson & Krivo, 1993; Shihadeh & Flynn, 1996). Specifically, we find a relationship between segregation and disorder. However, unlike previous studies, we find that the association segregation and disorder is completely mediated by poverty. The finding that poverty mediates the relationship between segregation and crime appears to be unique to the study of schools. It may be that our findings are not consistent with previous research because we study schools rather than large aggregates such as suburban areas (Logan & Messner, 1987), cities (Shihadeh & Flynn, 1996), or central cities (Peterson & Krivo, 1993). While our findings concerning segregation and disorder are not consistent with previous empirical research, they are consistent with the Massey and Denton’s (1993) proposition that Black segregation produces high levels of poverty in minority neighborhoods that, in turn, produces high levels of crime and violence (see also Massey, 2001). These findings are also consistent with Wilson’s (1987) position that high levels of poverty in minority neighborhoods are related to a myriad of social problems—including crime. Levels of school poverty are likely to be related to school disorder through weak amounts of collective socialization, less access to social capital, lower levels of social control, and high levels of social strain (Ainsworth, 2002). It appears, then, that the theoretical arguments of Massey and Denton (1993) and Wilson (1987) can be extended to schools. It is important to emphasize that our analysis should not be interpreted as suggesting school poverty is more important than school segregation in producing school disorder. In fact, our results emphasize the importance of school segregation in shaping rates of school disorder indirectly through levels of poverty. In short, our findings appear to suggest that poverty is mechanism that links segregation and disorder.
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
417
This emphasis on segregation also gives us reason for concern. Recent research indicates that school segregation is currently increasing, which is a counterproductive trend in terms of school disorder. Although overall rates of school disorder appear to be decreasing, the way these rates are distributed by race is what makes the trend counterproductive. At present, there is in a sense a two-faceted school disorder “problem.” As school segregation increases, minorities—as well as the poor—will be more likely to attend schools that have relatively high rates of crime and violence, while Whites and the welloff will be able to attend schools that are relatively free of such problems (Massey, 2001). Thus, school desegregation would have benefits for some—those formerly attending schools with high levels of poverty. However, these benefits come at the expense of those formerly attending White segregated, low-poverty schools. The latter would be placed in a situation in which crime in school would likely be higher than what they currently experience. In this sense, it could be argued that Whites and the well-off presently enjoy low rates of school disorder because minorities, who are more likely to be poor, suffer high rates of crime as a result of segregation. In short, we agree with Massey (2001, p. 334), who argues that most “Americans perceive themselves to actually benefit from the social arrangements that produce segregation.” If poverty rates are higher for Blacks and Hispanics, then by isolating poor Black and Hispanic students, the rest of society insulates children from higher rates of crime and violence in schools.
Appendix A. Descriptive statistics for independent and dependent variables
1
2
3
4
5
6
7
8
9
I. Correlation matrix 1. School disorder rate
1.00
2. Black segregation score
0.19*
1.00
3. Hispanic segregation score
0.04
0.31*
1.00
4. Percent in poverty
0.31*
0.39*
0.26*
*
*
5. School size (in thousands)
−0.05
6. Teacher experience (years)
−0.16* −0.07
0.15
*
0.42
*
0.01
0.17
8. Per pupil expenditures ($)
0.07
0.10* −0.15
9. District reporting practices
0.66
*
0.16
−0.29*
−0.16* −0.27*
7. Student-to-teacher ratio
*
1.00
0.19 0.01
1.00 0.06*
1.00
*
0.09
1.00
0.26* −0.41*
0.03
−0.38*
1.00
0.06
0.18*
−0.37
*
*
0.23
0.59
−0.08
−0.03
Mean
S.D.
95.171
96.78
0.00
0.01
II. Means and standard deviations School disorder rate Black segregation score
1.00
418
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Appendix (continued) Mean Hispanic segregation score
0.00
0.01
28.35
15.54
School size (in thousands)
1.66
0.93
Teacher experience (years)
13.63
2.90
Student-to-teacher ratio
25.58
4.84
Per pupil expenditures ($)
4,632
1,056
District reporting practices
97.01
71.66
Percent in poverty
∗
S.D.
p < .05 (two-tailed).
Appendix B. Percent in poverty regressed against segregation scores and controls
Variable
Model 1 (Black segregation)
Model 1 (Hispanic segregation)
b [Beta] (S.E.)
b [Beta] (S.E.)
Black segregation score
1827.69*** [0.45] (170.03)
–
Hispanic segregation score
–
2733.26*** [0.40] (293.70)
School size (in thousands)
−2.41** [−0.14] (0.93)
−6.47*** [−0.34] (1.05)
Teacher experience (years)
−0.93
−0.04** [−0.01] (0.25)
Student-to-teacher ratio
−1.32*** [−0.26] (0.20)
−0.94*** [−0.37] (0.20)
Per pupil expenditures (in thousands)
−0.08 [−0.12] (0.71)
−0.43 [−0.02] (0.89)
District reporting practices
0.04
***
***
[−0.16] (0.25)
[0.17] (0.01)
***
0.04*** [0.17] (0.01) 64.73*** (7.15)
Constant
75.40
R2
.45
.49
N
371
336
∗∗
(6.73)
p < .05 (two-tailed). ∗∗∗ p < .01 (two-tailed).
References Ainsworth, J. (2002). Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces, 81, 117–152. Anderson, E. (1994). The code of the streets. Atlantic Monthly, 273, 80–91. Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. New York: W. W. Norton and Company. Ballantine, J. H. (2001). The sociology of education: A systematic analysis. Upper Saddle River, NJ: Prentice Hall.
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
419
Bankston, C., & Caldas, S. (1996). Majority African American schools and social injustice: The influence of De Facto segregation on academic achievement. Social Forces, 75, 535–556. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Brooks-Gunn, J., Duncan, G., Klebanov, P., & Sealand, N. (1993). Do neighborhoods influence child and adolescent development? American Journal of Sociology, 99, 353–395. Bureau of Equity, Safety, and School Support. (2002). Statewide report on school safety and discipline data, 2000–2001. Tallahassee, FL: Florida Department of Education. Clotfelter, C. (2001). Are Whites still fleeing? Racial patterns and enrollment shifts in urban public schools, 1987–1996. Journal of Policy Analysis and Management, 20, 199–221. Coleman, J. (1990). Foundations of social theory. Cambridge, MA: The Belknap Press of Harvard University Press. Felson, M. (1996). Crime in everyday life. Thousand Oaks, CA: Pine Forge. Florida Department of Education. (2002). Florida schools indicator report, 1996–2001. Tallahassee, FL: Florida Department of Education. Gottfredson, D. C. (2001). Schools and delinquency. New York: Cambridge University Press. Gottfredson, D. C., & Gottfredson, D. C. (1985). Victimization in schools. New York: Plenum Press. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. Hirschi, T. (1969). Causes of delinquency. Berkeley, CA: University of California Press. Kaufman, J., & Roesnbaum, J. (1992). The education and employment of low-income Black youth in White suburbs. Education Evaluation and Policy Analysis, 14, 229–240. Krivo, L., & Peterson, R. (1996). Extremely disadvantaged neighborhoods and urban crime. Social Forces, 75, 619–648. Logan, J. R., & Messner, S. F. (1987). Racial residential segregation and suburban violent crime. Social Science Quarterly, 68, 528–538. Long, S. J., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the Linear Regression Model. American Statistician, 54, 217–224. Massey, D. S. (1994). America’s apartheid and the urban underclass. Social Service Review, 68, 471–487. Massey, D. S. (2001). Segregation and violent crime in urban America. In D. Massey & E. Anderson (Eds.), Problem of the century: Racial stratification in the United States (pp. 217–346). New York: Russell Sage. Massey, D., & Denton, N. (1993). American apartheid: Segregation and the making of the underclass. Cambridge, MA: Harvard University. McPartland, J. M., & McDill, E. L. (1977). Violence in schools: Perspectives, programs, and positions. Lexington, MA: Lexington Books. Merton, R. (1938). Social structure and anomie. American Sociological Review, 3, 672–682. Morenoff, J., Sampson, R., & Raudenbush, S. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39, 517–560. National Center for Education Statistics. (2002). Common core data, 2001. Washington, DC: U.S. Department of Education. Peterson, R., & Krivo, L. (1993). Racial segregation and Black urban homicide. Social Force, 71, 1001–1026. Reardon, S. F., Yun, J. T., & Mcnulty Eitle, T. (2000). The changing structure of school segregation: Measurement and evidence of multiracial metropolitan-area school segregation, 1989–1995. Demography, 37, 351–364. Sampson, R., & Groves, B. (1989). Community structure and anomie: Testing social disorganization theory. American Journal of Sociology, 94, 744–802. Shihadeh, E. S., & Flynn, N. (1996). Segregation and crime: The effect of Black social isolation on the rates of Black urban violence. Social Forces, 74, 1325–1352. So, A. (1992). The Black schools. Journal of Black Studies, 22, 523–531. Therrien, M., & Ramirez, R. (2001). The Hispanic population in the United States, March 2000. Current population reports, P20-535. Washington, DC: U.S. Census Bureau. Wells, A. (1995). Reexamining social science research on school desegregation: Long-versus short-term effects. Teachers College Record, 96, 691–707.
420
P.B. Stretesky, M.J. Hogan / The Social Science Journal 42 (2005) 405–420
Welsh, W. N., Stokes, R., & Greene, J. R. (2000). A macro-level model of school disorder. Journal of Research in Crime and Delinquency, 37, 243–283. Wiatrowski, M. D., Griswold, D. B., & Roberts, M. K. (1981). Social control theory and delinquency. American Sociological Review, 46, 525–541. Williams, K. R., & Flewelling, R. L. (1987). Family, acquaintance, and stranger homicide: Alternative procedures for rate calculations. Criminology, 25, 543–560. Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.