Black-White Test Score Gap Research Articles

Acting White: A Critical Review

The hypothesis of acting White has been heatedly debated and influential over the last 20 years or so in explaining the Black–White test score gap. Recently, economists have joined the debate and started providing new theoretical and empirical analyses of the phenomenon. This paper critically reviews the arguments that have been advanced to support and refute the hypothesis. This review particularly covers the analyses in economics because the economic analyses are relatively new and usually neglected in other disciplines. Also, nationally representative data are emphasized, whenever possible, to improve the generalizability of the arguments. This review concludes that although the analyses in both non-economics and economics are thought-provoking and compelling in some respect, a substantial body of empirical evidence is inconsistent with the assumptions of and results from the analyses.

An Empirical Analysis of the Gender Gap in Mathematics

We document and analyze the emergence of a substantial gender gap in mathematics in the early years of schooling using a large, recent, and nationally representative panel of US children. There are no mean differences between boys and girls upon entry to school, but girls lose more than two-tenths of a standard deviation relative to boys over the first six years of school. The ground lost by girls relative to boys is roughly half as large as the black-white test score gap that appears over these same ages. We document the presence of this gender math gap across every strata of society. We explore a wide range of possible explanations in the data, including less investment by girls in math, low parental expectations, and biased tests, but find little support for these theories. Moving to cross-country comparisons, we find earlier results linking the gender gap in math to measures of gender equality are sensitive to the inclusion of Muslim countries, where, in spite of women's low status, there is little or no gender gap in math. (JEL I23, J26)

Is it Too Late Baby? Pinpointing the Emergence of a Black-White Test Score Gap in Infancy

This research attempts to uncover when the black-white test score gap begins and why it exists by examining a nationally representative sample of infants using the Early Childhood Longitudinal Study–Birth Cohort. The findings demonstrate that there is a small raw gap in cognitive skills between the infants of white and black mothers in the United States. However, through structural equation modeling, results show that when one controls for social, human, and financial capital and for differences in health and type of child care, the infants of African American mothers would score higher than the infants of white mothers because of their precocious motor development. Social capital and low birth weight are found to be key mediators of the small black-white test score gap in infancy.

Black–White Biracial Students in American Schools: A Review of the Literature

With increasing numbers of students who identify as Black and White multi-racial and with the persistence of the Black–White test score gap, the necessity for research regarding these students’ educational experiences cannot be understated. To date, research in this area has been scarce. The purpose of this review is to synthesize the available literature related to the experiences of multiracial—Black–White biracial in particular—students in American schools and to identify areas in need of further research. This review offers a synthesis of the historical, social, and political context of biracial people, as well as a synthesis of issues relevant to biracial students, namely, psychological adjustment, home and parental influence, and school factors. Recommendations and implications for further research related to multiracial students and their schooling are offered.

The black–white test score gap and early home environment

Based on panel data for three age cohorts of children from the Panel Study of Income Dynamics, we examine how early home environment contributes to black–white achievement gaps at different developmental stages and the extent to which early gaps contribute to later racial achievement gaps. We find large black–white test score differences among children of all ages even before children start formal schooling. Except for the oldest cohort, the gaps for all tests widened when children’s cognitive skills were assessed six years later. Racial achievement gaps in applied problem scores by grade three and letter–word scores by grade six, can be accounted for by child’s characteristics, family socioeconomic background, and mother’s cognitive skills. However, these covariates explain an increasingly smaller proportion of the black–white achievement gap as children advance to higher grades. Gaps in early cognitive skills are highly predictive of gaps at later ages, setting off a trajectory of cumulative disadvantage for black children over time. Our results underscore the key role of early home environment and the intergenerational roots of the persistent black–white achievement gap.

On the sources of the black–white test score gap in Europe

Differences between black and white students in Britain are investigated. If black parents would invest in education as much as white parents do, the racial test score gap in mathematics and reading would be reduced by 7 and 9%.

Race of the interviewer and the black–white test score gap

Does the race of interviewers introduce a bias in estimating the test score gap between blacks and whites in the United States? To answer this question, I use an adult sample from the General Social Survey (GSS) in which vocabulary testing involves face-to-face and one-on-one interaction between the respondent and the interviewer. I find that black respondents perform better when tested by a black interviewer as opposed to a white interviewer. For white respondents, however, the race of the interviewer does not have a significant impact on test performance. Because most black respondents are tested by white interviewers in the GSS, the test performance of black respondents is downward biased, and the black–white test score gap is overestimated.

Black–White Achievement Gap and Family Wealth

This article examines the extent to which family wealth affects the Black-White test score gap for young children based on data from the Panel Study of Income Dynamics (aged 3-12). This study found little evidence that wealth mediated the Black-White test scores gaps, which were eliminated when child and family demographic covariates were held constant. However, family wealth had a stronger association with cognitive achievement of school-aged children than that of preschoolers and a stronger association with school-aged children's math than on their reading scores. Liquid assets, particularly holdings in stocks or mutual funds, were positively associated with school-aged children's test scores. Family wealth was associated with a higher quality home environment, better parenting behavior, and children's private school attendance.

Introducing the Issue

Introducing the Issue Ron Haskins (bio) and Isabel Sawhill (bio) Ron Haskins Ron Haskins is senior editors of The Future of Children, co-directors of the Center on Children and Families, and senior fellows in the Economic Studies program at the Brookings Institution, where Sawhill holds the Cabot Family Chair. Haskins is also a senior consultant at the Annie E. Casey Foundation. Isabel Sawhill Isabel Sawhill is senior editors of The Future of Children, co-directors of the Center on Children and Families, and senior fellows in the Economic Studies program at the Brookings Institution, where Sawhill holds the Cabot Family Chair. Notes 1. John Cochran, "New Perspectives on Poverty," Congressional Quarterly Weekly, October 23, 2006, pp. 2802-09; Julie Kosterlitz, "Social Policy: The Katrina Experiment," National Journal, November 5, 2005, pp. 3436-42; Jason DeParle, "Liberal Hopes Ebb in Post-Storm Poverty Debate," New York Times, October 11, 2005, p. A1. 2. Jonathan Gruber and Gary Engelhardt, "Social Security and the Evolution of Elderly Poverty," in Public Policy and the Income Distribution, edited by Alan Auerbach, David Card, and John Quigley (New York: Russell Sage Foundation, 2006), pp. 259-87. 3. For further analyses of economic factors influencing poverty rates, see Hilary Hoynes, Marianne Page, and Ann Stevens, "Poverty in America: Trends and Explanations," Working Paper 11681 (Cambridge, Mass.: National Bureau of Economic Research, October 2005). 4. Adam Thomas and Isabel Sawhill, "For Love and Money? The Impact of Family Structure on Family Income," Future of Children 15, no. 2 (Fall 2005): 57-74; Hoynes, Page, and Stevens, "Poverty in America: Trends and Explanations" (see note 3). 5. Ron Haskins and Isabel Sawhill, "Work and Marriage: The Way to End Poverty and Welfare," Welfare Reform and Beyond Brief 28 (Brookings, September 2003); Isabel Sawhill and Adam Thomas, "A Hand Up for the Bottom Third: Toward a New Agenda for Low-Income Working Families," Working Paper (Brookings, May 2001). 6. Not all government spending counts in the calculation of income when the official poverty rate is calculated. But even if we use alternative Census Bureau definitions of poverty that include more government benefits in the definition of income, poverty has still increased in the face of the huge increase in government spending. 7. Nicholas Eberstadt, "Why Poverty Doesn’t Rate" (Washington: American Enterprise Institute, September 2006). 8. Fred Doolittle and Suzanne Lynn, Working with Low-Income Cases: Lessons for the Child Support Enforcement System from Parents’ Fair Share (New York: MDRC, 1998). 9. Leighton Ku and Matthew Broaddus, "Coverage of Parents Helps Children, Too" (Washington: Center on Budget and Policy Priorities, October 20, 2006). 10. Isabel Sawhill, "Introducing the Issue," Future of Children 16, no. 2 (Fall 2006): 3-17; Isabel Sawhill, "Opportunity in America: Does Education Promote Social Mobility?" Future of Children Policy Brief (Fall 2006). 11. Pamela Morris, Lisa Gennetian, and Greg Duncan, "Effects of Welfare and Employment Policies on Young Children: New Findings on Policy Experiments Conducted in the Early 1990s," Social Policy Report 19, no. 2 (2005); Lisa A. Gennetian and others. How Welfare and Work Policies for Parents Affect Adolescents: A Synthesis of Research (New York: MDRC, 2002); Ron Haskins, Work over Welfare: The Inside Story of the 1996 Welfare Reform Law (Brookings, 2006). 12. Kasia O’Neill Murray and Wendell E. Primus, "Recent Data Trends Show Welfare Reform to Be a Mixed Success: Significant Policy Changes Should Accompany Reauthorization," Review of Policy Research 22 (2005): 301-24. [End Page 15] 13. Sara McLanahan, Elisabeth Donahue, and Ron Haskins, "Introducing the Issue," Future of Children 15, no. 2 (Fall 2005): 3-12. 14. Christopher Jencks and Meredith Phillips, eds., The Black-White Test Score Gap (Brookings, 1998); Cecilia Rouse, Jeanne Brooks-Gunn, and Sara McLanahan, "Introducing the Issue," Future of Children 15, no. 1 (Spring 2005): 5-14. 15. Frances Campbell and others, "Early-Childhood Programs and Success in School: The Abecedarian Study," in Early Care and Education for Children in Poverty, edited by W. Steven Barnett and Sarane Spence Boocock (State University of New York, 1998), pp. 145-66; Frances Campbell and others, "Early Childhood Education: Young Adult Outcomes from the Abecedarian Project," Applied Developmental Science 6, no. 1 (2002): 42-57; and Lawrence...

Racial segregation and the black–white test score gap

Racial segregation is often blamed for some of the achievement gap between blacks and whites. We study the effects of school and neighborhood segregation on the relative SAT scores of black students across different metropolitan areas, using large microdata samples for the 1998–2001 test cohorts. Our models include detailed controls for the family background of individual test-takers, school-level controls for selective participation in the test, and city-level controls for racial composition, income, and region. We find robust evidence that the black–white test score gap is higher in more segregated cities. Holding constant family background and other factors, a shift from a highly segregated city to a nearly integrated city closes about one-quarter of the raw black–white gap in SAT scores. Specifications that distinguish between school and neighborhood segregation suggest that neighborhood segregation has a consistently negative impact while school segregation has no independent effect, though we cannot reject equality of the two effects. Additional tests indicate that much of the effect of neighborhood segregation operates through neighbors' incomes, not through race per se. Data on enrollment in honors courses suggest that within-school segregation increases when schools are more highly integrated, potentially offsetting the benefits of school desegregation and accounting for our findings.

The role of family socioeconomic resources in the black–white test score gap among young children

This paper reviews evidence on the family origins of racial differences in young children’s test scores and considers how much of the gap is due to differences in the economic and demographic conditions in which black and white children grow up. Our review of the literature finds that the estimated size of the gaps varies considerably across studies. However, a surprisingly consistent result is that a collection of measures related to family socioeconomic resources appears to account for a little less than half a standard deviation of the black–white test score gap, regardless of the assessments used or the populations studied. We discuss the policy implications of these findings and suggest avenues for future research.

The Black-White Test Score Gap Through Third Grade

This article describes basic facts regarding the Black-White test score gap over the first four years of school. Black children enter school substantially behind their White counterparts in reading and math, but including a small number of covariates erases the gap. Over the first four years of school, however, Blacks lose substantial ground relative to other races; averaging 0.10 standard deviations per school year. By the end of third grade, there is a large Black-White test score gap that cannot be explained by observable characteristics. Blacks are falling behind in virtually all categories of skills tested, except the most basic. None of the explanations we examine, including systematic differences in school quality across races, convincingly explain the divergent academic trajectory of Black students.

Commentary: The attainability of causal knowledge of genetic effects in complex human traits

Lynch M, Walsh B. Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer, 1998. 4 Jensen AR. How much can we boost IQ and scholastic achievement? Harv Educ Rev 1969;39:1–123. 5 Lewontin RC. Race and intelligence. Bull At Sci 1970;26:2–8. 6 Jensen AR. Race and the genetics of intelligence: A reply to Lewontin. Bull At Sci 1970;26:17–23. 7 Lewontin RC. Further remarks on race and the genetics of intelligence. Bull At Sci 1970;23–25. 8 Lewontin RC. Human Diversity. New York: Freeman Press, 1982. 9 Moffitt TE, Caspi A, Rutter M. Strategy for investigating interactions between measured genes and measured environments. Arch Gen Psychiatry 2005;62:473–81. 10 Paul D. The history of newborn phenylketonuria screening in the US. In: Holtzman NA, Watson MS (eds). Promoting Safe and Effective Genetic Testing in the United States. Baltimore: Johns Hopkins University Press, 1998, pp. 137–60. 11 Pearl J. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press, 2000. 12 Woodward J. Making Things Happen: A Theory of Causal Explanation. New York: Oxford University Press, 2003. 13 Taylor PJ. Unruly Complexity: Ecology, Interpretation, Engagement. Chicago: University of Chicago Press, 2005, pp. 241. 14 Taylor PJ. Heritability and heterogeneity: I. On the limited relevance of heritability in investigating genetic and environmental factors. Biol Theory Integr Dev Evol Cognit 2006;1 (In Press). 15 Byth DE, Eisemann RL, DeLacy IH. Two-way pattern analysis of a large data set to evaluate genotypic adaptation. Heredity 1976;37:215–30. 16 Turkheimer E. Three laws of behavior genetics and what they mean. Curr Dir Psychol Sci 2000;9:160–64. 17 Taylor PJ. Heritability and heterogeneity: II. On the irrelevance of heritability in explaining differences between means for different human groups or generations. Biol Theory Integr Dev Evol Cognit 2006;1 (In Press). 18 Flynn JR. IQ gains over time. In: Sternberg RJ (ed.). Encyclopedia of Human Intelligence. New York: Macmillan, 1994, pp. 617–23. 19 Parens E. Genetic differences and human identities: On why talking about behavioral genetics is important and difficult. Hastings Cent Rep 2004;34:S1–S36. 20 Jencks C, Phillips M (eds). The Black-White Test Score Gap. Washington, DC: Brookings Institution Press, 1998. 21 Kendler KS, Gardner CO, Prescott CA. Towards a comprehensive developmental model for major depression in women. Am J Psychiatry 2002;159:1133–45. 22 Ou S-R. Pathways of long-term effects of an early intervention program on educational attainment: Findings from the Chicago longitudonal study. J Appl Dev Psychol 2005;26:478–611. 23 Taylor PJ. Gene-environment complexities: What is interesting to measure and to model? In: Singh R, Jain S, Uyenoyama M (eds). The Evolution of Population Biology: Modern Synthesis. Cambridge: Cambridge University Press, 2003, pp. 233–53.

Stereotype Threat Research and the Assumptions Underlying Analysis of Covariance.

Comments on an article by Paul Sackett, Chaitra Hardison and Michael Cullen entitled On Interpreting Stereotype Threat as Accounting for African American-White Differences on Cognitive Tests (see record 2004-10043-001). Sackett, Hardison, and Cullen discussed the role of covariates in Steele and Aronson's (see record 2000-16592-021) seminal research on the effects of stereotype threat on scores of African American test takers. Besides highlighting some common misinterpretations that stem from the use of covariance-adjusted means in reporting Steele and Aronson's (Study 2) experimental results, Sackett et al. argued that these results indicate that Black-White testscore difference within the no-stereotype threat (i.e., nondiagnostic) condition actually reflects the test score difference on the SAT (i.e., the covariate). This implies that stereotype threat effects add to the often found Black-White test score gap instead of partly accounting for it (Sackett et al., 2004). Here the author comments on the use of analysis of covariance (ANCOVA) in stereotype threat (ST) experiments, because ST theory implies violations of the assumptions underlying ANCOVA. Such violations could result in incorrect Type I error rates and distortions in the adjustment of means. Because of this, ANCOVA appears inappropriate for analyzing (quasi-) experimental results of ST research. In addition, the interpretation proposed by Sackett et al. of Steele and Aronson's results may be due to distortions of mean adjustments caused by violations of model assumptions. While avoiding technical detail, the author provides the assumptions underlying ANCOVA and discuss why these assumptions do not sit well with several aspects of ST theory.

Introducing the Issue

Introducing the Issue Cecilia Rouse (bio), Jeanne Brooks-Gunn (bio), and Sara McLanahan (bio) Cecilia Rouse Cecilia Rouse is Director of the Education Research Section and Professor of Economics and Public Affairs at Princeton University. Jeanne Brooks-Gunn Jeanne Brooks-Gunn is Virginia and Leonard Marx Professor of Child Development and Education at Teachers College and the College of Physicians and Surgeons, Columbia University. Sara McLanahan Sara McLanahan is Director of the Center for Research on Child Wellbeing and Professor of Sociology and Public Affairs at Princeton University. Endnotes 1. Wendy S. Grigg and others, The Nation’s Report Card: Reading 2002, NCES 2003-521 (U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, 2003). 2. Christopher Jencks and Meredith Phillips, "The Black-White Test Score Gap: An Introduction," in The Black-White Test Score Gap, edited by Jencks and Phillips (Brookings, 1998), pp. 3–4. 3. Meredith Phillips, James Crouse, and John Ralph, "Does the Black-White Test Score Gap Widen after Children Enter School?" in The Black-White Test Score Gap, edited by Jencks and Phillips (Brookings, 1998). 4. James J. Heckman and Alan B. Krueger, Inequality in America (MIT Press, 2004). 5. Nazli Baydar, Jeanne Brooks-Gunn, and Frank F. Furstenberg, "Early Warning Signs of Functional Illiteracy: Predictors in Childhood and Adolescence," Child Development 64 (1993): 815–29; Jeanne Brooks-Gunn, "Do You Believe in Magic? What Can We Expect from Early Childhood Intervention Programs?" Social Policy Report of the Society for Research in Child Development 17, no. 1 (2003) (http://www.srcd.org/spr17-1.pdf). 6. National Education Goals Panel, Special Early Childhood Report (Government Printing Office, 1997). 7. Sheila Heaviside and Elizabeth Farris, Public School Kindergarten Teachers’ Views of Children’s Readiness for School, NCES 1993-410 (Department of Education, National Center for Education Statistics, 1993). 8. C. Cybele Raver, "Emotions Matter: Making the Case for the Role of Young Children’s Emotional Development for Early School Readiness," Social Policy Report of the Society for Research in Child Development 16, no. 3 (2002) (http://www.srcd.org/spr16-3.pdf); Lisa McCabe and others, "Games Children Play: Observing Young Children’s Self-Regulation across Laboratory, Home and School Settings," in Handbook of Infant and Toddler Mental Health Assessment, edited by Rebecca DelCarmen-Wiggins and Alice Carter (Oxford University Press, 2004), pp. 491–521. 9. B. R. Hamre and Robert C. Pianta, "Early Teacher-Child Relationships and the Trajectory of Children’s School Outcomes through Eighth Grade," Child Development 72 (2001): 625–88. 10. Sara E. Rimm-Kaufman, Robert C. Pianta, and Martha J. Cox, "Teachers’ Judgments of Problems in the Transition to Kindergarten," Early Childhood Research Quarterly 15 (2000): 147–66. 11. Roland G. Fryer and Steven D. Levitt, "Understanding the Black-White Test Score Gap in the First Two Years of School," Review of Economics and Statistics 86, no. 2 (May 2004): 447–64. [End Page 14] Copyright © 2005 The Woodrow Wilson School of Public and International Affairs at Princeton University and The Brookings Institution

Using Scientific Knowledge to Inform Preschool Assessment: Making the Case for “Empirical Validity”

The No Child Left Behind Act of 2001 crystallized the concern for accountability in education. National testing was mandated as a way to improve the “broken” educational system. Publicly funded early education programs were not spared from such testing. While the positive effects of high-quality early education on children’s later school achievement is well demonstrated, too many early care and educational settings in the United States are of minimal or poor quality. Accountability is clearly important for increasing the quality of our early childhood programs, however, it is not yet evident how best to formulate a standard of accountability that reflects the body of knowledge we have gained concerning how young children learn. In this report, we propose two major thrusts designed to bring about a more scientifically informed accountability system: reconceptualizing the ways in which we think about the validity of our test instruments, and reconceptualizing markers of development from products of learning (performance standards) to processes of learning. We introduce the term “empirical validity” to draw attention to the fact that assessments should be built on current empirical work in the various developmental domains. This report focuses on the domains of language and literacy, two areas of major concern for the Federal Head Start program and for which there is an abundance of current research. This body of knowledge provides many examples illustrating how an emphasis on process rather than product can be vital for improving the quality of education. For example, although vocabulary is centrally important and psychometrically adequate tests of early vocabulary exist, these tests do not measure essential aspects of word learning that have been identified as predictive of later language and reading success in early language learning literature. Our case study of language and literacy illustrates how today’s developmental science offers a new knowledge that can be strategically incorporated in assessments for empirically valid testing of children’s competencies. The same argument for “empirically valid” and evidence-based assessments applies to other domains of cognitive growth and to socio-emotional development. The future of preschool assessment would be well served by attention to primary research that focuses on the processes of learning. In this report, we also suggest that one possible avenue for progress in assessment would center on integrative and dynamic assessment techniques that would comprehensively capture the nature of children’s learning, minimize validity concerns related to context and culture, and evaluate how competencies in different developmental domains interact for optimal learning. To bridge the gap between science and policy, developmental scientists and test developers are urged to work together to create innovative ways to chart the developmental processes that support learning and progress toward social maturity in ways designed to ensure that research findings are continuously reflected in current assessments.

Has Curriculum Closed the Test Score Gap in Math?

Abstract This study examines the extent to which convergence in mathematics course-taking behavior is responsible for narrowing the Hispanic-white and the black-white test score gaps during the 1980s. Mathematics curriculum is measured in detail using high school transcript data from both High School and Beyond and the National Education Longitudinal Study of 1988. After controlling for demographic, family, and school characteristics, changes in curriculum account for about 60 percent of the narrowing Hispanic-white test score gap between 1982 and 1992. However, the black-white test score gap did not drop significantly.

The Production of Cognitive Achievement in Children: Home, School and Racial Test Score Gaps

This paper studies the determinants of children's scores on tests of cognitive achievement in math and reading. Using rich longitudinal data on test scores, home environments, and schools, we implement alternative specifications for the production function for achievement and test their assumptions. We do not find support for commonly used restrictive models that assume test scores depend only on contemporaneous inputs or that assume conditioning in a lagged score captures the effects of all past inputs. Instead, the results show that both contemporaneous and lagged inputs matter in the production of current achievement and that it is important to allow for unobserved child-specific endowment effects and endogeneity of inputs. Using a specification that incorporates these features, we analyze sources of test score gaps between black, white and Hispanic children. The estimated model captures key patterns in the data, such as the widening of minority-white test score gaps with age, which is most pronounced for black children. The parameter estimates indicate that home inputs are significant determinants of achievement, while the effects of school inputs (as measured by pupil-teacher ratios and teacher salaries) are imprecisely measured in specifications that allow for unobserved child endowments. We find that equalizing home inputs at the average levels of white children would close the black-white test score gap by about 25% and close the Hispanic-white gap by about 30%.

The Black-White Test Score Gap

The Supreme Court's 2003 decision to uphold affirmative action in college admissions suggests that special treatment may be unnecessary in 25 years. But achieving equality without affirmative action will require overcoming a black-white test score gap that appears as early as preschool and is rooted in child-rearing practices.

The Link between Instructional Practice and the Racial Gap in Middle Schools

The No Child Left Behind Act of 2001 calls for improving test scores of minority students in third through eighth grades, making middle level schools a key venue for closing the racial gap in achievement. This study, by analyzing data on the 15,000 eighth graders who took the 2000 National Assessment of Educational Progress (NAEP) in mathematics, provides evidence that middle level schools can make a difference in Black-White and Latino-White test score gaps. Using multi-level Hierarchical Linear Modeling techniques, this study finds that depending upon the instructional practices that middle school teachers utilize, the achievement gap can be reduced substantially for African American and Latino students.