The end of legal segregation followed by efforts to equalize spending since has made a substantial difference for student achievement. On the Scholastic Aptitude Test SAT , the scores of African-American students climbed 54 points between and , while those of white students remained stable. Edited by Paul E. Peterson and Martin R. Sawhill Even so, educational experiences for minority students have continued to be substantially separate and unequal.
Two-thirds of minority students still attend schools that are predominantly minority, most of them located in central cities and funded well below those in neighboring suburban districts. Recent analyses of data prepared for school finance cases in Alabama, New Jersey, New York, Louisiana, and Texas have found that on every tangible measure—from qualified teachers to curriculum offerings—schools serving greater numbers of students of color had significantly fewer resources than schools serving mostly white students.
As William L. Taylor and Dianne Piche noted in a report to Congress: Inequitable systems of school finance inflict disproportionate harm on minority and economically disadvantaged students.
On an inter-state basis, such students are concentrated in states, primarily in the South, that have the lowest capacities to finance public education. On an intra-state basis, many of the states with the widest disparities in educational expenditures are large industrial states.
In these states, many minorities and economically disadvantaged students are located in property-poor urban districts which fare the worst in educational expenditures or in rural districts which suffer from fiscal inequity. Jonathan Kozol s Savage Inequalities described the striking differences between public schools serving students of color in urban settings and their suburban counterparts, which typically spend twice as much per student for populations with many fewer special needs.
Contrast MacKenzie High School in Detroit, where word processing courses are taught without word processors because the school cannot afford them, or East St. Louis Senior High School, whose biology lab has no laboratory tables or usable dissecting kits, with nearby suburban schools where children enjoy a computer hookup to Dow Jones to study stock transactions and science laboratories that rival those in some industries.
Or contrast Paterson, New Jersey, which could not afford the qualified teachers needed to offer foreign language courses to most high school students, with Princeton, where foreign languages begin in elementary school. L Linda Darling-Hammond. Even within urban school districts, schools with high concentrations of low-income and minority students receive fewer instructional resources than others.
And tracking systems exacerbate these inequalities by segregating many low-income and minority students within schools. In combination, these policies leave minority students with fewer and lower-quality books, curriculum materials, laboratories, and computers; significantly larger class sizes; less qualified and experienced teachers; and less access to high-quality curriculum.
Many schools serving low-income and minority students do not even offer the math and science courses needed for college, and they provide lower-quality teaching in the classes they do offer. It all adds up. Since the Coleman report, Equality of Educational Opportunity, another debate has waged as to whether money makes a difference to educational outcomes. It is certainly possible to spend money ineffectively; however, studies that have developed more sophisticated measures of schooling show how money, properly spent, makes a difference.
Over the past 30 years, a large body of research has shown that four factors consistently influence student achievement: all else equal, students perform better if they are educated in smaller schools where they are well known to students is optimal , have smaller class sizes especially at the elementary level , receive a challenging curriculum, and have more highly qualified teachers.
Minority students are much less likely than white children to have any of these resources. In predominantly minority schools, which most students of color attend, schools are large on average, more than twice as large as predominantly white schools and reaching 3, students or more in most cities ; on average, class sizes are 15 percent larger overall 80 percent larger for non-special education classes ; curriculum offerings and materials are lower in quality; and teachers are much less qualified in terms of levels of education, certification, and training in the fields they teach.
After controlling for socioeconomic status, the large disparities in achievement between black and white students were almost entirely due to differences in the qualifications of their teachers. Follow University World News on Facebook. Receive UWN's free weekly e-newsletters. Jordan looks to international rankings to improve universities. Biggest drop in college enrolment in half a century. Cambridge is first university to return Benin Bronzes. How can universities in the US tackle anti-Asian racism?
What Anthropocene means for social sciences, humanities. Racial profiling of Chinese scientists is spreading fear. Taliban reversal on appointment of Kabul University head. Is there a significant relationship between achievement and economic and social outcomes? The answer is both obvious and complex.
With regard to the obvious, higher test scores are associated with a greater probability of completing high school, and attending and completing a four-year college. Higher levels of school attainment, in turn, are strongly related to improved life outcomes, including but not limited to higher earnings Belfield and Levin ; Alexander, Entwisle, and Oslon For example, interventions such as increasing the academic activities of low-income youth in summer could have a large enough effect on their test scores to significantly increase the probability that they will attend a four-year college Alexander, Entwisle, and Oslon Murnane, Willett, and Levy , and Bowles, Gintis, and Osbourne estimate that, controlling for other factors—including social class—a very large increase of one standard deviation of a test score about 34 percentage points on a point scale is associated with only a 9 to 10 percent increase in wages.
With this in mind, we consider that achievement differences between groups do have economic and social meaning; they give us insights into how well our school systems and society are adapting to demographic, social, and political changes.
In other words, we have a system where higher scores produce better outcomes but we have a labor market that seems to reward higher test scores much less than the political rhetoric would have us believe. Since Hispanics and particularly Asians are more concentrated in certain states that may perform lower, on average, than others, controlling for state fixed effects may influence estimates of ethnic achievement differences. We repeat these procedures for male and female students separately to test whether gender differences are important in regard to how race and social class influence student performance.
Finally, we estimate tercile regressions to assess whether changes in race and class achievement gaps vary across student achievement levels. The results yield important insights into the changing nature of inequality in the U. We confirm earlier studies showing that although the black-white test score gap remains large, it is gradually declining Hedges and Nowell ; Magnuson, Rosenbaum, and Waldfogel The gap in test scores between Hispanics and whites also continues to decline for non-English language learners ELL students, as does the negative female-male gap in math and the positive female-male gap in reading.
Because we use individual student data for our analysis, we can identify whether students have been assigned to the English language learner track or not.
We find large and somewhat increasing gaps in mathematics and reading achievement between Hispanic and Asian ELL students and other groups, including whites and non-ELL Hispanics and Asians. This is in stark contrast to the achievement gap between white students and non-ELL Hispanic students, which decreased substantially from to , and the achievement gap between whites and Asian non-ELLs, which increased substantially during this period.
It is important to note that English language designation is not an innate characteristic, but one that can change as the student becomes proficient in English. Therefore, it does not have the same meaning as race, ethnicity, gender, and some elements of social class. We are not able to confirm that the achievement gap is unequivocally increasing between students from high- and low-social class families; changes in the gap vary by subject and grade.
It is only at the very top of the income distribution top 10 percent where analysts have found student achievement rising compared to everyone else Reardon Further, we show that as the overall proportion of poor students in schools increased from to , the percentage of both black and Hispanic students in high-poverty schools rose substantially. We find inconsistent evidence that the achievement gaps for blacks and for Hispanics in high-poverty schools are increasing. However, we do find that among black and Hispanic students, the achievement gap are increasing between those blacks and Hispanics who attend schools with a high concentration of black plus Hispanic students versus those who do not.
One is the seeming increase in opposition to Hispanic immigration among less-educated whites, which some speculate is rooted in shifts from an industrial to a service economy. But these anti-immigration sentiments could conceivably be fueled by the rising school performance and labor competitiveness of non-ELL second- and third-generation Hispanics, as well as their growing numbers. This pressure to increase test scores may help explain why the gaps in test scores among different social classes were expanding until recently Reardon ; Reardon, Waldfogel, and Bassok The paper is divided as follows: In the next section, we present our estimation strategy.
The section after that presents the results, and the final section discusses the results and draws conclusions. NAEP assessments are administered uniformly using the same set of test booklets across the nation, and the assessment stays essentially the same from year to year and includes only carefully documented changes.
This permits the NAEP to provide a clear picture of student academic progress over time. The NAEP is applied to students in specific grades fourth, eighth, and twelfth to obtain a stratified random sample of schools in each state. We know that average test scores can differ among states for reasons that are unrelated to individual characteristics of students or the demographic composition of schools in the state.
If minority or lower-SES groups are not randomly distributed across states, this could influence relative test scores over time in ways that are not related to either ethnicity or social class. The complete model for each year, subject, and two grades of the NAEP test can be represented as follows:. In equation 1b, A ijs is achievement on NAEP mathematics or reading tests of student i in the fourth or eighth grade in school j in state s. State s are state dummies. As mentioned above, equations 1a and 1b are estimated following a step-wise procedure.
To test for heterogeneity of our parameter estimates by gender and student performance level, we also estimate equation 1a, without the state fixed effects for male and female students separately and for terciles of student achievement. We caution that these are not causal estimates. Two major trends characterized the student composition of U. First, the proportion of non-Hispanic white and black students declined.
This trend contrasted with the steady increase in the proportion of Hispanic and Asian students. Further, even with the decline in the proportion of black students, minority students of black or Hispanic origin increased greatly, from In , students eligible for FRPL represented The proportion of whites in the NAEP samples also tends to be higher: In addition to sampling students in public schools, the NAEP samples students in private schools, where students are much more likely to be white and less likely to be classified eligible for FRPL.
Thus, in the NAEP eighth-grade math sample, white students in were about Because of the rapid increase of Hispanics in the younger population, the proportion of Hispanic students is higher in the fourth than in the eighth grade.
Notes: ELL stands for English language learner; IEP stands for individualized education plan special education students have such plans ; FRPL stands for free or reduced-price lunch federally funded meal programs for students of families meeting certain income guidelines.
Notes: ELL stands for English language learner; IEP stands for Individualized education plan special education students have such plans ; FRPL stands for free or reduced-price lunch federally funded meal programs for students of families meeting certain income guidelines. We found that largely because of the increases in the percent of students eligible for free or reduced-price lunch, the proportion of fourth-graders in schools with more than 50 percent of students eligible for FRPL also increased—from The proportion of fourth-graders attending schools that were more than 25 percent minority also increased, from The first characteristic is that a much higher fraction of black and Hispanic students attend high-poverty schools than white or Asian students.
The second characteristic is that black and Hispanic students are much more likely to attend high-poverty schools even when they are not poor; i. Poor students, and black and Hispanic students, were much more likely to attend a school with a high percentage of students eligible for FRPL, and to attend a school with a high percentage of black and Hispanic students see Tables 3a, 3b, and 3c, and Table 4, year panels.
For example, in Table 3a , only 6. Only A much larger proportion of students who were not eligible for FRPL At the other extreme, Thus, a high proportion of poor students attend schools with other poor students, and a high proportion of students who are not poor attend schools with relatively few poor students.
In contrast, only An even lower 6. Further, a very low 3. In contrast, among advantaged black students, That is, poor black students were three times as likely to attend a high-poverty school as poor white students, and non-poor or advantaged blacks were more than six times as likely to attend a high-poverty school as non-poor whites.
Advantaged Hispanic non-ELL students were less likely than advantaged black students to attend a high-poverty school, but much more likely than advantaged white students to attend high-poverty schools Also, a slightly lower proportion of poor Hispanic non-ELLs However, relative to white students of similar poverty levels, a much higher proportion of both advantaged and poor Hispanic ELL students attended a high-poverty school: Among non-poor students, Hispanic ELL students were twice as likely as Hispanic non-ELLs, and 10 times as likely as whites, to attend high-poverty schools.
On the other hand, advantaged Asian non-ELL students were more likely than advantaged whites to attend very low-poverty schools schools where 10 percent or less of students were eligible for free or reduced-price lunch. The share of non-poor Asian non-ELL students attending very low-poverty schools was Not surprisingly, U. This is particularly important because, as we show below, the proportion of blacks and Hispanics in the schools these students attend is negatively correlated with their individual achievement.
In see Table 4 , a white eighth-grader in the math sample was Yet, a black student or a Hispanic non-ELL student were, respectively, In addition, black and Hispanic non-English language learners were about 43 percent likely to attend a school with 75 percent or more black or Hispanic students The figures for Hispanic ELL students were 9.
Asian ELL students had only a Our results show that in the first decade of the 21 st century, there was a large increase in the percentage of students eligible for free or reduced-price lunch our measure of student poverty.
Largely because of this, the total percentage of students in schools with more than 75 percent poor students increased from to We also find that, following what had started in previous decades, there was a large increase in the proportion of Hispanic students, which raised the total percentage of schools with large shares of minority students.
This to a certain degree expanded the concentration of blacks and Hispanics in schools with high concentrations of FRPL-eligible students, since these black and Hispanic students were also more likely to be poor than the average student. The percentage of students eligible for free or reduced-price lunch increased from to , from The total percentage of eighth-graders in schools with more than 75 percent FRPL students, for example, increased between and , from The main increases in the percentage of those attending schools with high percentages of FRPL students occurred for those less-poor students eligible for reduced-price lunch Table 3a.
Notes: In this analysis, we use the free or reduced-price lunch FRPL status classification for individual poverty, and the proportion of students who are FRPL eligible in the school for school poverty.
Students who are not eligible for free or reduced-price lunch are not poor; students who are eligible for reduced-price lunch RPL are poor, and students who are eligible for free lunch FL are the most poor. At the same time, the proportion of students attending a high-poverty school increased more overall for blacks and Hispanics than for whites and Asians Table 3b.
It was not the proportion of the poorest blacks and Hispanics those eligible for free lunch attending a high-poverty school that increased; rather, the increase was highest among less-poor blacks and Hispanics those eligible for a reduced-price lunch. Although less-poor Hispanics constitute a much smaller group than those eligible for free lunch, it is possible that for this less-poor group of blacks and Hispanics, the negative effect of being in a high-poverty school might be greater.
We test this proposition in the analysis below. Source: National Assessment of Educational Progress microdata, , , and Students who are not eligible for free or reduced-price lunch are not poor; students who are eligible for reduced-price lunch RPL are poor, and students who are eligible for free lunch FL are the most poor; ELL stands for English language learner.
We also find that as the percentage of black and Hispanic students increased from to , the likelihood that students of all ethnic groups would attend schools with a high fraction of black and Hispanic students also increased. In percentage-point terms the increase was modest for white eighth-grade students from 5. The proportion of Asians attending schools with more than 50 percent Hispanics or blacks also increased from Thus, Table 4 shows some evidence of a greater concentration of blacks and Hispanics in schools with high concentrations of black and Hispanic students, particularly between and Even Asian students attended schools that were likely to have a higher fraction of black and Hispanic students than those attended by whites.
Our main findings on changes in student achievement during this period are that the black-white and the non-ELL Hispanic-white achievement gaps fell in the late s and the first decade of the s, while the non-ELL Asian-white gap in favor of Asians increased substantially. This was not the case for Hispanic English language learners and Asian English language learners, as the large negative gap between white students and both groups increased during this period.
We also find that the social-class achievement gap between students from poor and non-poor families decreased in the s, but then increased somewhat in the s.
These trends were generally the same for both fourth- and eighth-graders. The scores of Hispanic English language learners and blacks were much lower than those of whites. In , the white-black test score gap was The white-Asian ELL gap was Yet, even in , the black-white gap remained high, at The largest gap remained the white-Hispanic ELL gap, at about Hispanic non-ELL students did better than black students 0. Asian non-ELL students performed at the same level as white students in reading but higher in mathematics 0.
The one-fourth of Hispanic students classified as ELLs in eighth grade math sample in scored much lower, about one standard deviation below whites in both math and reading. Asian English language learners scored higher than blacks in math 0. Asian non-ELL students did not score significantly higher than whites in reading but scored 0. Noteworthy is the major role of language and the interaction between schooling and language ELL designation in school achievement.
Whether Hispanic or Asian, English language learners scored lower in both math and reading, and the results are similar for fourth grade, where English language learner designation is more common. For students eligible for reduced-price lunch, the gap was about one-half that in both subjects. The gaps were larger for students eligible for free or reduced-price lunch in fourth-grade. In , black students eligible for free lunch in poverty scored about 1. The gap was even larger for poor Hispanic students designated ELLs.
Notes: The table shows regression estimates measured in standard deviations—how students in different minority groups scored relative to white peers top panel and how students who were eligible for reduced-price lunch or free lunch scored relative to students who were not eligible bottom panel. Notes on data controls: a. How did these gaps change over the decade of the s, a period marked by an increasing proportion of fourth- and eighth-graders who are poor and Hispanic, and a period in which all ethnic groups—particularly Hispanics—trend toward attending schools with higher concentrations of low-income and minority Hispanic plus black students?
The patterns over time of black-white, Hispanic-white, and Asian-white achievement gaps for eighth-grade mathematics and reading scores are shown in Tables 6a and 6b. Model II estimates the achievement gaps for the Model I variables, plus the percentage of students eligible for FRPL and the percentage of black and Hispanic students in the school each individual student attends.
In all models for math performance, we offer estimates with and without state fixed effects. The results show that in all three estimated models, the adjusted black-white achievement gap and the achievement gap between whites and Hispanic non-ELLs in eighth grade decreased from to , and so did the black-white reading gap, though the decline was much smaller proportionately.
For blacks, the math gap closes steadily over the 10 years, but for Hispanic non-English language learners, almost all the change in the math gap occurred from to However, if the requirements had changed, the gap between whites and Hispanics designated ELLs would have also decreased, as a result of improved test-taking capacity of the English language learner group due to fewer higher-scoring Hispanics being reassigned to the non-English language learner group.
It is also possible that ELL assignment has become less stringent. A smaller proportion of Hispanics and Asians were in ELL courses in fourth and eighth grade in than in , which could reflect a smaller percentage of new immigrants in each group or less stringent assignment to ELL.
We only observe this pattern in Model III where interactions between race-ethnicity and school SES and race-ethnicity composition are included in the estimate probably because the percentage of Hispanic ELL students in high concentration, low-SES, and Hispanic schools is increasing over time, and this explains an increasing fraction of the individual Hispanic ELL achievement gap over time. Notes: The table shows regression estimates measured in standard deviations—how female students scored relative to male peers and how students in different minority groups scored relative to white peers.
Tables 6a and 6b show that the achievement gap between whites and Asian non-English language learners greatly increased. The increase was especially large in mathematics. By , as the data indicate, Asian non-ELL students scored about one-half a standard deviation higher than white students—up about 0. Although the increase in the reading gap is smaller, it is nonetheless about 0.
This supports the notion that, on average, school districts are not imposing stricter rules in designating students English language learners. When we added state fixed effects to the three models for mathematics Table 6a, bottom panel , the estimated coefficients for black and non-ELL Hispanic students rose somewhat, especially for Hispanics.
Nevertheless, the largest jumps in coefficients, controlling for state fixed effects, were in the math scores of Asian non-English language learners in About 40 percent of fourth- and eighth-grade Asian non-ELLs in the sample lived in those two states. To the contrary, the negative coefficients for Hispanic and Asian ELLs were less negative when we included state fixed effects. Such changes in coefficients, which resulted from controlling for state fixed effects, suggest that the Asian-white gap would be even greater in favor of Asian non-ELL students especially in were they living in average-scoring states rather than heavily concentrated in lower-scoring California and Hawaii.
The argument by Joo, Reeves, and Rodrigue that Asian-American students tend to attend higher-scoring schools would be even truer were Asian-Americans, on average, living in states where test scores were above rather than below the U.
Tables 6a and 6b also show that females are catching up in math: while eighth-grade females still score lower than males in math, the gap has shrunk in the past 10 years. And males are also catching up in reading, but to a lesser degree. This suggests that the female advantage in reading—at least in middle school—may be more durable than the female disadvantage in math. Unlike the marked decrease in the achievement gaps between white-black and white-Hispanic non-ELLs in to , and the increase in the white-Asian non-ELL achievement gap, we only observe a small increase in the achievement gap between those somewhat poorer students eligible for reduced-price lunch and those students who were not eligible for FRPL under Model III, from In addition, no change is observed in the achievement gap between the poorest students—those eligible for free lunch—and students not eligible for FRPL under Model III, about Nor do we observe more than a small decline in the achievement gap between students whose parents have less than a college education and those whose parents had some college or completed college Table 7.
Thus, because these measures of SES differences cover fairly broad SES categories, we cannot pick up the increase in the achievement gap between students from very high-income families and everyone else found by Reardon in Rather, our evidence shows no significant change. It is worth noting that Reardon, Waldfogel, and Bassok also find no increase—in fact they find a modest decrease in the social class gap in this first decade of the 21 st century, at least for children entering kindergarten.
More research on this pattern and its causes is underway. This suggests that poor students are somewhat more likely than others to live in poor states—not much of a surprise.
Further, when we control for average FRPL eligibility and racial concentration in schools Model III , the coefficients change little by including state fixed effects. Table 7 How eighth-graders perform on math tests relative to their peers, by socioeconomic status SES , , , and Black-white, Hispanic-white, Asian-white, and SES achievement gaps in the fourth grade were similar to those in the eighth grade, with some exceptions.
Hence, they are not strictly comparable with the eighth-grade estimates. Two differences are notable between the fourth- and eighth-grade patterns. First, in eighth grade, the decline in the black-white math gap is greater than the decline in the reading gap, and in fourth grade, the opposite is true. Second, the math achievement gap among fourth-graders in favor of boys has declined more than the gap in eighth grade, and the fourth-grade reading gap in favor of girls is lower and has declined much more than it has in eighth grade Table 8.
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