Editorial Board:
Editor:
George H. Conklin,
 North Carolina
 Central University

Board:
Bob Davis,
 North Carolina
 Agricultural and
 Technical State
 University

Richard Dixon,
 UNC-Wilmington

Ken Land,
 Duke University

Miles Simpson,
 North Carolina
 Central University

Ron Wimberley,
 N.C. State University

Robert Wortham,
 North Carolina
 Central University

Editorial Assistants

Rob Tolliver,
 Duke University

Shannon O'Connor,
 North Carolina
 Central University

John W.M. Russell,
 Technical
 Consultant

® 

Volume 5, Number 2
Fall 2007
 
 

Disparity in Academic Achievement Between Black and White Students in the Wake County Public School System of North Carolina

by

Patricia Moore Watkins

North Carolina Central University

    After decades of searching for the origins of the "achievement gap" and the discovery of myriad potential causes, it still continues to be unexplained. Therefore efforts to understand the racial disparities in school performance persist. Early research indicates that disparities in academic achievement between Black and White students existed prior to and continues subsequent to desegregation of public schools. 

   Since the desegregation of public schools in the 1950s, studies have been conducted to determine why Blacks lag behind Whites academically. Efforts to understand the racial disparities in school performance continue, and some studies indicate this may be due to neighborhood differences. Although this is not a topic that has been researched in depth, this paper will examine the effects neighborhood of residence and neighborhood of school have on student performance. Earlier research examines the inherent tension between community influence and the effective organization of educational practice (Waller, 1937; Arum, 2000). Demographic characteristics of the neighborhood and social assimilation can have positive or negative effects on student achievements (Gephart, 1997; Arum, 2000). The student's characteristics "may raise or lower the quality of education" because of peer group association and the student's academic performance and attitude toward education--which is linked to the makeup of the family's socioeconomic status (Bankston and Caldas, 1998; Caldas and Bankston, 1997; Caldas and Bankston, 1999). To determine the ecological causes of the achievement gap between Black and White students, this study will explore neighborhood characteristics as well as individual characteristics in order to better understand the disparity in educational performance between the two groups.

    Neighborhoods of working class adults create positive attitudes for children, which promote success in academics and the work place (Wilson, 1996; Ainsworth, 2002).  These research studies examine poverty level as well as high-level neighborhoods of students from one- and two-family households. They show that children in advantaged neighborhoods will more than likely "value education, adhere to school norms, and work hard" because that is what they see modeled for them by neighborhood adults (Wilson, 1996; Ainsworth, 2002).  On the other hand, neighborhoods of non-working adults create an "incoherent" atmosphere for youth because of the lack of working adults as role models (Wilson, 1991, Ainsworth, 2002). For the purpose of this paper, advantaged neighborhoods are those that have on an average at least one person employed per household-in a professional or supervisory position, own at least one vehicle per household, possess home ownership or property, are educated beyond high school, socially interactive, live in non-violent and safe environment to raise children, have health insurance, vast network resources, and low employment rate. Thus, advantaged neighborhoods of working class adults are able to network to provide the necessary tools for their children's success. Furthermore, researchers confirm the widespread belief that Whites, in comparison to other racial and ethnic minority groups, are privileged because they have greater access to resources that promote levels of educational accomplishment as well as to resources that prepare their children for the work world (Betts, 1995; Fischer, Hout, et al., 1996; Cordero-Guzman, 2001). Disadvantaged neighborhoods of non-working adults create inconsistent or disorganized lives for children because they do not provide guidance and positive structure, which working class adults provide (Wilson, 1991; Ainsworth, 2002). Also, adults living in disadvantaged neighborhoods have minimal access to networking resources, and these resources are usually less beneficial than the ones in the advantaged neighborhoods due to the social position of partners, parents, siblings, and friends (Wacquant and Wilson, 1989; Sampson and Groves, 1989; Ainsworth 2002).  In addition, children who live in disadvantaged neighborhoods may attend inferior schools and will not receive the appropriate education in comparison to those who attend schools located in advantaged neighborhoods (Wacquant, 1996).

   The purpose of the paper is to examine the effects that neighborhood of residence and neighborhood of school have on student performance. The datasets consist of demographic information as well as detailed information on the student racial composition of the schools and how the students performed academically. Independent and dependent variables were measured to assess test scores of each ethnic group of students who received, who did not receive, who were denied or who received free and reduced lunch and how it affected their test scores.

    The data were obtained from the Wake County Public School System (WCPSS) and the U. S. Bureau of the Census 2000. Students from the ninth through twelfth grades were included. The datasets consists of both student demographic information as well as End-of-Course test scores on 28,323 high school students of the WCPSS 2002-2003 school year.

    Wake County Public School System has sixteen regular and magnet high schools. Regular (traditional) schools are those that concentrate on the basic curriculum offering seven class periods, advanced studies, and extra-curricular activities. Basic curriculum consists of all required courses with no electives. Regular schools operate under a 180-day calendar schedule. In contrast, in the year-round calendar schedule, the traditional 180-day calendar year is divided into nine-week quarters giving students and teachers intervals of three-week breaks at the end of each quarter (45 days in schools, 15 days off). Magnet high schools offer eight class periods and allow students to take advanced courses in core and elective areas. Courses offered at magnet high schools consist of core and electives with emphasis on creative arts and sciences, gifted and talented, international studies, global communications, International Baccalaureate, community and leadership, university connections, and accelerated studies with year-round and traditional calendar schedules. All sixteen schools were included in this study. (For a detailed description of the high schools, see Appendix A).

Dependent Variables

    In North Carolina all high school students take ten End-of-Course tests: Algebra I, Algebra II, Geometry, English I, U. S. History, Biology, Chemistry, Economic/Legal/Political Systems (ELP), Physical Science, and Physics. These tests are aligned with the Standard Course of Study in the mentioned subject areas, and the test format is multiple-choice. Scale scores are derived from the test scores. The End-of-Course tests count for 25 per cent of a student's final grade in each subject. Students who are either identified as Learning Disabled (LD) with an Individual Education Program (IEP) are enrolled in the Occupational Course of Study are exempt from taking the End-of-Course tests in Algebra I. All graduating students are required to pass English I, ELP and Algebra I courses (WCPSS Evaluation and Research Report No. 02.29, September 2002). See Appendix B for a technical description of the State of North Carolina's scoring system.

    The variables in this study will be scores on three End-of-Course tests: ALGEBRA I, ENGLISH I, and ECONOMIC/LEGAL/POLITICAL SYSTEMS (ELP). The first measure, ALGEBRA I, looks at how well the students can comprehend algebraic language, solving and graphing equations, relations and functions, solving systems of equations and inequalities, polynomials, and non-linear functions. ALGEBRA I students are required to have scale scores of 55-65 (Level III) or 66-87 (Level IV). The second measure, ENGLISH I focuses on students' ability in reading comprehension, study skills, and coherent writing. Scales scores for students in ENGLISH I must be in the range of 52-60 (Level III) or 61-85 (Level IV). The third measure, ELP, is required for graduation and students must demonstrate knowledge in the economic, legal, and political issues; emphasis is placed on local, state, national, and international studies. Students in the ELP course are expected to have scale scores of 52-60 (Level III) or 61-87 (Level IV) in order to meet the state's requirement for passing the End-of-Course test.

Independent Variables

    Seven demographic variables were used in this study that consist of students' sex, (SEX), Male, Female; students' ethnicity, (RACE), Asian, Black, American Indian, Hispanic, White, Multiracial; students' lunch plan, (LUNCH PLAN), received free lunch, received reduced lunch, denied free and reduced lunch, did not receive free or reduced lunch; students' grade (GRADE) 9-12; schools students attended (HIGH SCHOOL), Apex, Athens, Broughton, East Wake, William G. Enloe, Garner Senior, Green Hope, Leesville, Millbrook, Sanderson, Southeast Raleigh, Wake Forest-Rolesville, Middle Creek, Wakefield, Fuquay-Varina, Cary; housing units of students (BLOCK GROUP) blocks of residents; and income level (BLOCK INCOME) salaries of housing units. According to the U. S. Census Bureau, block groups are clusters of blocks that normally contain between 600 and 3,000 people geographically between blocks and census tracts. Census tracts consist of one or more block groups. Major demographic and socioeconomic information is obtained from block groups and census tracts that consist of average household with children and without children, marital status, employment, block income, race, education, and poverty level. According to the American Fact Finder Basic Facts, block income is the total sum of the family's reported sources of income of each housing unit, which includes wages, salary, interest, dividends, public assistance, etc.

Descriptive Statistics

    The mean and standard deviation, N, minimum, and maximum are presented in Table 1. The test scores varied over most of the potential range and the standard deviations are substantially lower than the means.

    Table 2 reports the percentage of students who participated in the End-of-Course tests by categories of SEX, RACE, and FREE LUNCH. The high school students within the Wake County Schools are predominantly White with Blacks representing, only 13.78% receive either free or reduced lunch, and the sex ratio favors males only slightly.

   The mean, standard, deviation, minimum and maximum of the structural variables describing the students' neighborhood, the mean family's income for the neighborhood's Census Block Group and the percent White, the percent Black and the percent Hispanic are located in Table 3. The average of the mean family's income is high, and the racial composition reveals block groups were predominately White and others predominately Black. The highest concentration of Hispanics in a block group was 40 percent. Because the standard deviations between Black and Hispanic students were so widespread, a log transformation was performed on these two variables and the analysis reveals similar results.

Correlation Analysis

    The independent variable RACE (Asian, Black, American Indian, Hispanic, White, and Multiracial), and dependent variables, ELP, ENGLISH I, ALGEBRA I, presented in Table 4 through Table 9 were examined for the degree of interdependence. The dependent variables had roughly the same level of interdependence in racial groups. ELP and English had the strongest correlations in all racial groups.

Multivariate Analysis

    In this analysis, two groups of independent variables were entered into the models as blocks. The first group is individual level variables: RACE, SEX, LUNCH PLAN. The second group includes neighborhood (census block group) level that consists of MEAN INCOME OF BLOCK GROUP, RACIAL COMPOSITION OF BLOCK GROUP. An interaction, BLOCK INCOME BY RACE, will be introduced into Model IV. Grade in school, GRADE, although an individual level variable, is associated with these test scores but is not a focus of the study, so it is included in all models as a control variable. The dependent variables for the End-of-Course tests include ELP, ENGLISH I, and ALGEBRA I in Table 10, Table 11, and Table 12.

   The individual level variables RACE and LUNCH PLAN (an indicator of poverty) predict performance in three knowledge areas of ELP, ENGLISH I, and ALGEBRA I. Sex, on the other hand, reaches significance in all models for all knowledge areas; but as expected, male students did better in Algebra, and females students scored higher in English. Surprisingly, male students also scored higher on ELP.

    The students' neighborhood variables INCOME MEAN OF BLOCK GROUP, RACIAL COMPOSITION OF BLOCK GROUP (percent Hispanic and percent Black) and BLOCK INCOME BY RACE predicted student knowledge as well. In Table 10 for ELP, Table 11 for English I and Table 12 for Algebra I, the income mean of the block group reveals significance in all models. The more wealth in a neighborhood, controlling for individual student characteristics, on average, the better students do on these End of Course tests.

   Racial composition of the students' neighborhood also has an impact on performance based on the higher the neighborhood's percentage of Black and ELP, English I, and Algebra I in Model II, Model III, and Model IV. Thus, for Hispanic in racial composition of block group, there is only a significant relationship for English I and Algebra I in Model II. Table 10 shows for ELP in block income by race, a significant relationship exists for Multiracial in Model IV. This indicates that interaction within the neighborhood affect ELP test scores for Multiracial students. Table 10 reveals Model I explains 18.3% of the variation in ELP. Thus, Model II explains 11.8% of the variation in ELP. In comparison, both Model III and Model IV explain 20.5% of the variation in ELP. Table 11 shows for English I in Model IV, a significant relationship exists for Asians and Hispanics that indicates their performance on the test is affected due to the interaction in the neighborhood. Also, Table 11 in Model I explains 21.1% of the variation in English I. In addition, Model II explains 12.7% of the variation in English I. Model III explains 23.3% of the variation in English I; similarly, Model IV explains 23.4% of the variation in English I. Table 12 for Algebra I shows a significant relationship exists in block income by race for Asian and White students in Model IV. Table 12 in Model I explains 16.5% of the variation in Algebra I. Thus, Model II explains 13.1% of the variation in Algebra I. Model III explains 19.0% of the variation in Algebra I; similarly, Model IV explains 19.1% of the variation in Algebra I. Overall the data shows that student characteristics as opposed to neighborhood characteristics have more of an effect on students' test scores. Concentrations of poor and minorities decrease student performance, even controlling for student background.

Variable	Model 1	Model  2	Model  3	Model  4
GRADE
9	3.82***  (.15)	3.67***  (.16)	3.80***  (.15)	3.80***  (.15)
10	2.81***  (.15)	2.56***  (.16)	2.74***  (.15)	2.73*** (.15)
11	1.43***  (.16)	1.38***  (.16)	1.43***  (.15)	1.43***  (.15)
12	---	---	---	---
RACE
Asian	8.74***  (.28)	---	7.23***  (.29)	2.75**  (.91)
American  Indian	5.09**  (1.17)	---	3.77**  (1.15)	8.85***  (4.91)
Hispanic	1.93***  (.33)	---	1.08**  (.33)	-.71  (1.02)
White	6.77***  (.14)	---	5.13***  (.16)	4.18***  (.49)
Multi-  racial	4.67***  (.51)	---	3.65***  (.50)	4.28**  (1.53)
Black	---	---	---	---
SEX
Male	.22*  (.11)	---	.19  (.10)	.20  (.10)
Female	---	---	---	---
LUNCH PLAN
Not  Receiving  Free or  Reduced  Lunch	2.67***  (.22)	---	2.07***  (.22)	2.07***  (.22)
Denied  Lunch	1.21  (.71)	---	1.10  (.71)	1.19  (.71)
Reduced  Lunch	.76  (.40)	---	.72  (.40)	.74  (.40)
Free  Lunch	---	---	---	---
INCOME  MEAN OF  BLOCK GROUP (1)	---	4.674***  (.324)	4.027***  (.314)	2.167*  (.800)
RACIAL  COMPO- SITION OF  BLOCK GROUP
% Black	---	-10.30***  (.36)	-3.85***  (.39)	-4.22***  (.43)
% Hispanic	---	-3.55*  (1.32)	-1.75  (1.28)	-1.65  (1.60)
BLOCK  INCOME  BY RACE
Asian	---	---	---	6.839***  (1.327)
American  Indian	---	---	---	-7.804  (7.437)
Hispanic	---	---	---	3.208  (1.707)
White	---	---	---	1.731*  (..785)
Multiracial	---	---	---	-.779  (2.346)
Black	---	---	---	---
INTER- CEPT	56.59***  (.24)	63.34***  (.30)	56.50***  (.37)	57.58***  (.57)
R SQUARE	.165	.131	.190	.191
N	21988	21940	21940	21940

Notes for Table 12: 
Numbers in parentheses are standard errors. Student characteristics are included in Model 1, neighborhood characteristics are added in Model 2, student and neighborhood characteristics are added in Model 3, student and neighborhood characteristics and  interaction are added in Model 4. Footnotes 1 and 2: Numbers were converted by multiplying by 10000.  *p. 05; **p.01; ***p.001. 

    The purpose of this study was to explore the achievement gap between Black and White students of the Wake County Public School System by examining the characteristics of the neighborhood of residence and neighborhood of school and their effect on student performance. Several dependent variables and independent variables were integrated to determine the ecological cause of academic disparity in test scores between ethnic groups.

    According to this study, the characteristics of neighborhood and school have profound effects on students' academic success or failure. The students' characteristics have more of an effect on students' test scores as opposed to the neighborhood's characteristics.

   The strongest indicator of effect on test scores was noticed in student characteristics and all of the End-of-Course tests (ELP, ENGLISH I, ALGEBRA I). Because the student characteristics revealed strong effect in all of the test scores, it is apparent that interaction within their families caused negative or positive effects in students obtaining successful educational attainment. One factor of why student characteristics may be more dominant over neighborhood characteristics is parental involvement. Literature reports that students whose parents are involved in school or are present at Parent Teacher Association (PTA) meetings receive better grades than students whose parents are not involved in school activities. In addition studies of family environment find that what happens in the home, such as a press for achievement and parents engaging children in intellectually stimulating conversations about politics and science, predict achievement test scores better than race or social class  (Marjoribanks, 1972). It is imperative that the school system (administration, principals, teachers, counselors, etc.) form a partnership with parents to promote togetherness and this will give parents a sense of welcome at their children's school. Showing parents that they are a part of the school will, in turn, help the school's administration, parents and children by promoting an atmosphere conducive for learning. The school system must address the administration and parent relationship gap due to lack of communication. The neighborhood characteristics also affect students' test scores.

   As more independent variables were integrated with the dependent variables, the weaker the interaction in the neighborhood appeared. However, the characteristics of the neighborhood revealed that interaction within the neighborhood affect students' test scores. Also, the income of the neighborhood of students who received free or reduced lunch affects their test scores and the wealth and racial composition of the neighborhood impacts students' test scores. Furthermore, the correlation output of RACE (Asian, Black, American Indian, Hispanic, White, Multiracial) and End-of-Course tests (ELP, ALGEBRA I, ENGLISH I) reveal disparities between ethnic groups. However, more research is needed.

    The findings indicate that neighborhood characteristics have a strong effect on students' educational attainment. However, future research is needed to investigate neighborhood interrelationships and stressors that affect the quality of living for families who live in disadvantaged neighborhoods as opposed to those who live in advantaged neighborhoods.

    While earlier research by Marjoribanks (1972) revealed parental differences in socialization by using both the environment and intellectual ability of ethnic groups, in-depth research on block groups may give more insight on ecological causes of family environments and their impact on low and high academic achievement for students.

    As previously stated, major demographic and socioeconomic information is obtained from block groups and census tracts that consist of average household with children and without children, marital status, employment, block income, race, education, and poverty level. While using block group as a dummy variable does not explain why there are differences in block group, data reveals that block groups have a tremendous impact on test scores. African-American differences in block group racial composition may account for the impact of differences in test scores. 

   The question remains: How much of an impact, if any, does the racial composition of block groups have on students living in the block groups who are of a different race? More research is needed to determine whether the SES status of African-American families' income disparity has an impact on students' academic performance rather than the racial composition of the block group. Additional research may determine whether students' performance on tests is related to their race or whether it is related to certain races in certain neighborhoods.

    Also, what impact does heterogeneous neighborhoods have on students' educational attainment? Does the racial composition complexity of the neighborhood impact test scores? Other aspects of racial composition and neighborhood characteristics may cause differences between African-American and White students in test scores, employment, housing, etc. Further research is needed.

    Neighborhood plays an important role in students' academic achievement; however, individual characteristics of students have more of an impact on students' educational attainment. Research introducing other variables may give more insight as to why student characteristics as opposed to neighborhood characteristics have more of an impact on students' academic achievement. Again, more research is needed to definitively say one characteristic has a greater impact over another.
 
 

Ainsworth, J. W. 2002. "Why Does It Take a Village? The Mediation of Neighborhood Effects on Educational Achievement." Social Forces, 81(1), 117-152.

Arum, R. (2000). "Schools and Communities: Ecological and Institutional Dimensions." Annual Review of Sociology, 26, 395-418.

Bankston, C. L., III, and Caldas, S. J. 1998. "Race, Poverty, Family Structure, and the Inequality in Schools." Sociological Spectrum, 18, 55-75.

Betts, J. R. 1995. "Does School Quality Matter? Evidence from the National Longitudinal Survey."  Review of Economics and Statistics, May: 231-250.

Caldas, S. J., and  Bankston, C. L., III. 1997. "The Effect of School Population Socioeconomic Status on Individual Student Academic Achievement." The Journal of Educational Research, 90, 269-277.

Caldas, S. J., and Bankston, C. L., III. 1999. "Multilevel Examination of Student, School, and District-level Effects on Academic Achievement." The Journal of Educational of Educational Research, 93(2), 91.

Cordero-Guzman, H. R. 2001.  Cognitive Skills, Test Scores, and Social Stratification: The Role of Family and School-Level Resources on Racial/Ethnic Differences in Scores on Standardized Tests (AFQT)."  The Review of Black Political Economy, 28(4), 31-73.

Fischer, C., Hout, M., Lucas, S., Sanchez-Jankowski, M. Swidler, A., and Voss, K. 1996. Inequality by Design:  Cracks in the Bell Curve Myth.  Princeton, NJ: Princeton University Press.

Gephart, M. A. (1997).  "Neighborhoods and Communities as Contexts for Development." In J. Brooks-Gunn, G. J. Duncan, & J. L. Aber (Eds.), Neighborhood Poverty: Context and Consequences for Children, 1, 1-43. New York: Russell Sage.

Sampson, R. J., and Groves, W. B. 1989. "Community Structure and Crime: Testing Social Disorganization Theory." American Journal of Sociology, 9(4), 774-802.

Wacquant, L. J. D. 1996. "Red Belt, Black Belt: Racial Division, Class Inequality and the State in the French Urban Periphery and the American Ghetto."  In E. Mingione (Ed.), Urban Poverty and the Underclass, (pp.235-308). Blackwell.

Wacquant, L. D. D., and Wilson, W. J. 1989. "The Cost of Racial and Class Exclusion in the Inner City." Annals of the American Academy of Political and Social Sciences, 501, 8-25.

Waller, W. 1937. The Sociology of Teaching. New York: Wiley.

Wilson, W. J. 1991. "Studying Inner-city Social Dislocations: The Challenge of Public Agenda Research. American Sociological Review, 56, 1-14.

Wilson, W. J. 1996. When Work Disappears: The World of the New Urban Poor. Knopf.

Return to Sociation Today, Fall 2007 

Go to the urban sociology reprint file for more articles on urban sociology. 
 

©2007 by the North Carolina Sociological Association