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Black Americans Have a Clear Vision for Reducing Racism but Little Hope It Will Happen

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Black Americans Have a Clear Vision for Reducing Racism but Little Hope It Will Happen

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"Roughly eight-in-ten Black adults with lower (78%), middle (77%) and upper incomes (79%) say the descendants of people enslaved in the U.S. should receive reparations."

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Black Progress: How far we've come, and how far we have ...

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Black Progress: How far we've come, and how far we have ...

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orty-two percent own their own homes, a figure that rises to 75 percent if we look just at black married couples. Black two-parent families earn only 13 percent less than those who are white. Almost a third of the black population lives in suburbia.

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Black Progress: How far we've come, and how far we have to go | Brookings

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Brookings Review article by Abigail Thernstrom and Stephan Thernstrom (Spring 1998)

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"Education inequalities at the school starting gate"

Source- Economic Policy Institute, September 27,2017

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What this study finds: Extensive research has conclusively demonstrated that children's social class is one of the most significant predictors-if not the single most significant predictor-of their educational success. Moreover, it is increasingly apparent that performance gaps by social class take root in the earliest years of children's lives and fail to narrow in the years that follow. That is, children who start behind stay behind-they are rarely able to make up the lost ground.

Using data from two academic cohorts, the kindergarten classes of 1998 and 2010, this study examines the relationship between children's socioeconomic status (SES) and their cognitive and noncognitive skills when starting school. We find that large performance gaps exist between children in the lowest and highest socioeconomic-status (SES) quintiles and that these gaps have persisted from the 1998 cohort to the 2010 cohort. The positive news is that the gaps have not grown, even as economic inequalities between these two groups of students have grown. The negative news is that the gaps have not narrowed, despite the fact that low-SES parents have substantially increased their engagement in their children's early education.

Why it matters: These performance gaps reflect extensive unmet needs and thus untapped talents among low-SES children. The development of strong cognitive and noncognitive skills is essential for success in school and beyond. Low educational achievement leads to lowered economic prospects later in life, perpetuating a lack of social mobility across generations. It is also a loss to society when children's talents are allowed to go fallow for lack of sufficient supports. The undeniable relationship between economic inequalities and education inequalities represents a societal failure that betrays the ideal of the "American dream."

What can be done about it: Greater investments in pre-K programs can narrow the gaps between students at the start of school. And to ensure that these early gains are maintained, districts can provide continued comprehensive academic, health, nutrition, and emotional support for children through their academic years, including meaningful engagement of parents and communities. Such strategies have been successfully implemented in districts around the country, as described in this report, and can serve to mitigate the impact of economic inequalities on children's educational achievement and improve their future life and work prospects.

Executive summary

High and rising inequality is one of the United States' most pressing economic and societal issues. Since the early 1980s, the total share of income claimed by the bottom 90 percent of Americans has steadily decreased, with the majority of income gains going to the top 1 percent. These trends would not be such a major concern if our education system compensated for these inequities by helping level the playing field and enabling children to rise above their birth circumstances.

But that is hardly the case. Rather, the fraction of children who earn more than their parents (absolute mobility) has fallen from approximately 90 percent for children born in 1940 to 50 percent for children born in the 1980s. And the tight links between economic inequalities and achievement gaps cast doubt on asserted equality of opportunity that promotes social mobility and puts the "American Dream" within viable reach.

Extensive research has conclusively demonstrated that children's social class is one of the most significant predictors-if not the single most significant predictor-of their educational success. Moreover, it is increasingly apparent that performance gaps by social class take root in the earliest years of children's lives and fail to narrow in the years that follow.

Much is known about the determinants and mechanisms that drive early skills gaps among children of different backgrounds, but our failure to narrow social-class-based skills gaps from one generation of students to the next calls for further analysis to determine the degree of influence these factors have and how interventions employed in recent years to address these factors have or have not worked and why. Moreover, shifting economic and demographic landscapes emphasize the need for more robust policy strategies to address the gaps. This three-part study thus combines a statistical analysis of early skills gaps among a recent cohort of children and changes in them over time with a qualitative study of multifaceted, school-district-level strategies to narrow them.

What we do: Questions, data and methodology

In this paper, we:

Use data from the National Center for Education Statistics (NCES): the Early Childhood Longitudinal Study of the Kindergarten Classes of 1998-1999 and 2010-2011 to measure gaps in skills by social class. To measure gaps by social class, we use the socioeconomic status (SES) metric (primarily), a composite of information on parents' educational attainment and job status as well as household income. We compare the average performance of children in the top fifth of the socioeconomic status distribution (high-SES) with the average performance of children in the bottom fifth (low-SES). Skills measured include reading and mathematics, as well as self-control and approaches to learning as reported by both teachers and parents.

Examine SES-based gaps at kindergarten entry among the most recently surveyed cohort (the kindergarten class of 2010-2011). We study how gaps manifest in both cognitive and so-called noncognitive skills, as both skill types are important components of children's development.

Compare these SES gaps with those of an earlier cohort (1998-1999), with a focus on changes in the skills gaps between children in the high- and low-SES quintiles. We also analyze how sensitive gaps are to the inclusion of key determinants of student performance, such as family composition, children's own characteristics, pre-K participation, and parental and educational practices at home.

Review a set of 12 case studies of communities that have employed comprehensive educational strategies and wraparound supports to provide more children (especially low-income children) with strong early academic foundations, and to sustain and build on early gains throughout their K-12 school years.

Based on examples from these diverse communities, we discuss implications: strategies that districts can employ and district and state policy changes to make those strategies easier to adopt and more sustainable. The report ends with conclusions and recommendations for further research, practice, and policy.

What we find
Our quantitative research produces a broad set of findings:

Very large SES-based gaps in academic performance exist and have persisted across the two most recent cohorts of students when they start kindergarten. The estimated gaps between children in the highest and lowest fifths of the SES distribution are over a standard deviation (sd) in both reading and math in 2010 (unadjusted performance gaps are 1.2 and 1.3 sd respectively). Gaps in noncognitive skills such as self-control and approaches to learning are roughly between one-third and one-half as large (unadjusted performance gaps are about 0.4 sd in self-control, and slightly over 0.5 sd in approaches to learning in 2010).

SES-based gaps across both types of skills among the 2010 kindergartners are virtually unchanged compared with the prior academic generation of students (the class of 1998). The only unadjusted cognitive skills gap between children in the high-SES and low-SES fifths that changed significantly over this period was the gap in reading skills, which increased by about a tenth of a standard deviation. Gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank between 1998 and 2010 by roughly the same amount (0.1 sd). Gaps in mathematics, in approaches to learning as reported by parents, and in self-control as reported by teachers did not change significantly.

Taking into account children's individual and family characteristics, we find that parental activities, parental expectations for their children's attainment, and pre-K participation reduce the gaps between high-SES and low-SES children somewhat but do not come close to eliminating them.

This means that though part of the SES gap is attributable to differences in these characteristics and in family investments between children in the high and low parts of the SES distribution, a substantial share of SES-related factors is not captured by these controls, but is important to explaining how and why gaps develop, and thus how to narrow them.

Moreover, the capacity for these other factors to narrow gaps has decreased over time-as a whole, they accounted for a smaller share of the gaps in 2010 than they had in 1998. This suggests that, while such activities as parental time spent with children and center-based pre-K programs cushion the negative consequences of growing up in a low-SES household, they can do only so much, and that the consequences of poverty are increasingly hard to compensate for. This resistance of gaps to these controls is thus a matter of serious concern for researchers and policymakers alike.

The characteristics of children in the lowest-SES quintile and highest-SES quintile changed between 1998 and 2010.

Among children in the low-SES quintile, in 2010 a larger share lived in poverty (84.6 percent, up from 71.3 percent in 1998), did not live with two parents (54.9 percent vs. 45.6 percent), and lived in homes where the main language is not English (40.3 percent vs. 31.2 percent). Just over half of these children (50.4 percent) were Hispanic (in 1998, 39.8 percent were).

These children's likelihood of attending center-based pre-K did not change significantly across generations (about 44 percent for both cohorts: 44.3 percent in 2010 vs. 43.7 percent in 1998). However, in 2010 their parents reported having a somewhat larger number of books at home for the children, and there was also an increase in both indices of activities (literacy/reading activities and other educational and engagement activities).

In addition to doing more for their children, low-SES parents have greater expectations for their children's educational attainment-a much smaller share saw them going no further than high school graduation, while a much greater share anticipated their children attaining bachelor's and even advanced degrees in 2010.

Among children in the high-SES quintile, the group in 2010 includes a lower share of white children (falling from 78.8 percent in 1998 to 71.3 percent) and a larger share of Asian children (increasing from 4.7 percent in 1998 to 8.7 percent).

They were slightly more likely to live with two parents (the share not living with two parents decreased from 11.1 percent in 1998 to 9.6 percent) and to have attended center-based pre-K (the share in center-based pre-K increased from 65.8 in 1998 to 69.9 percent in 2010).

The share of high-SES homes reporting having more than 200 children's books slightly increased in 2010, as did parents' expectations for their children's educational attainment.

Although research uses various indicators to measure individuals' social class, from composite measures such as the socioeconomic status index we use to single indicators such as mother's education or income, some sensitivity of the results to the indicator used is found. In our analyses, we find that all are equally reliable social-class proxies for the estimation of early achievement gaps, though absolute gaps and trends in them vary slightly depending on the indicator used.

Our qualitative review of community interventions also provides valuable information:

A growing number of school districts across the country have embraced systems of comprehensive enrichment and supports for many or even all their students, based on the understanding that nurturing healthy child development requires leveraging the entire community. These districts took different approaches to enacting those comprehensive strategies, based on each community's particular mix of needs and assets, ideological leaning, available sources of funding, and other factors. But all begin very early in children's lives and align enriching school strategies with a targeted range of supports for children and their families.

Moreover, school districts embracing what we refer to as "whole-child" approaches to education are seeing better outcomes for students, from improved readiness for kindergarten to higher test scores and graduation rates and narrower achievement gaps. They thus can provide guidance to other districts and to policymakers regarding how to implement such approaches, what to expect in terms of benefits, and which policies at the local and state levels can advance those approaches.

Conclusions

While the persistence of large skills gaps at kindergarten entry is troubling, the fact that, by and large, they did not grow in a generation-despite steadily increasing income inequality compounded by the worst economic crisis in many decades-is a good thing. But we must still be very concerned about these gaps. We would have liked to see evidence that parents' increased dedication to and investments in their children's early development, and increased investments in pre-K programs and other early education and economic supports, closed these gaps. However, the data suggest that these efforts simply contained them, and that these positive trends were insufficient to narrow the skills gaps at kindergarten entry. This failure to narrow gaps points to a lack of appropriate policy response at all levels of government, the neglect of decades of research across multiple disciplines on child development, and the resulting waste of critical opportunities to nurture an entire generation of children.

The policy recommendations of this report strengthen the idea that we need much greater investments in pre-K programs and continued comprehensive support for children through their academic years, including meaningful engagement of parents and communities, if we are to substantially improve the odds for disadvantaged children, in light of their extensive unmet needs and untapped talents.

Introduction: Facts about income inequality and its growth over time

One of today's most pressing economic issues is the worrisome level of income inequality. Since 1979, the total share of income claimed by the bottom 90 percent of Americans has steadily decreased (Bivens 2016). In 1979, that 90 percent received about 67 percent of cash, market-based income (i.e., pretax income). By 2015, their share had decreased to about 52 percent of pretax income. The majority of income gains during this period went to the top 1 percent (EPI 2013; Mishel and Schieder 2016; Saez 2016). Polls reflect widespread concern about income and wage inequalities and associated trends and the desire for policies to address these inequalities ( New York Times 2015).

Rising inequality might not be such a major concern if our education, economic, and social protection systems acted as compensatory mechanisms, helping individuals, and especially children, rise above their birth circumstances and improve their mobility. But that is hardly the case. Rather, the fraction of children who earn more than their parents (a measure of what social scientists refer to as absolute mobility) has fallen from approximately 90 percent for children born in 1940 to 50 percent for children born in the 1980s (Chetty et al. 2016). Children of certain ethnic and racial minorities who are disproportionately likely to live in concentrated poverty are also more likely to do so over prolonged periods of time (Sharkey 2013). And the close connections between education inequalities and economic inequalities cast doubt on assertions that America provides "equality of opportunities" that promotes social mobility (Mishel 2015).

The influence of income inequality affects multiple aspects of society's functioning, from health outcomes and even life expectancy to democratic ideals (Putnam 2015; Schanzenbach et al. 2016; Stringhini et al. 2017). In the education arena, children's socioeconomic status (SES), of which income is a key component, is considered one of the most significant predictors-if not the most significant predictor-of educational success. A number of studies show the strong relationship between social class (of which socioeconomic status is a frequent measure) and test scores, educational attainment, and college attendance and completion (see Duncan, Morris, and Rodrigues 2011; García 2015; García and Weiss 2015; Lee and Burkam 2002; Mishel et al. 2012; Putnam 2015; among others).

As a result of these trends and associations, achievement gaps by social class have grown substantially since the 1960s, especially between children at the highest end of the income distribution and all of the others (Reardon 2011). Some researchers have identified a large increase in parental investment in education among high-SES parents as one driver of the divergence in education outcomes (Duncan and Murnane 2011), among other contributing factors, such as time parents spend with their children and time parents devote to education-enhancing activities (Morsy and Rothstein 2015; Van Voorhis et al. 2013): Spending on education-enhancing activities by parents in the top income fifth nearly tripled between the 1970s and the 2000s (from $3,500 in 1972 to $8,900 in 2006), while such spending by parents in the bottom income fifth remained low and changed much less (from $800 in 1972 to $1,300 in 2006) (Duncan and Murnane 2011). 1 More time can mean more frequent interactions during playtime, more time spent reading to children, and other parenting practices that contribute to children's learning and development (Barbarin et al. 2010). In general, more leisure and educational time with children can promote their development and school readiness (Brooks-Gunn and Markman 2005; Hart and Risley 1995; Phillips 2011; Rothstein 2004; Van Voorhis et al. 2013; Waldfogel 2006). Given the evidence that parental engagement and spending directly and continuously translate into improvements in children's achievement and preparation, the presence of the various achievement gaps are not surprising.

Education researchers and policymakers have long been attentive to issues related to equity-by race/ethnicity, SES, gender, and other characteristics. At least since the 1966 publication of the "Coleman Report" by sociologist James S. Coleman and coauthors, researchers and policymakers have understood the critical impacts of race, poverty, and segregation on educational attainment (Coleman et al. 1966). And educational inequities remain a major problem today. Rigorous research demonstrates that inequalities in both opportunity and outcomes along the lines of race and social class begin early and often persist throughout students' K-12 years and beyond, and that they are much larger in the United States than in comparable countries (Bradbury et al. 2015; Putnam 2015). Some of the research carefully describes the specific contexts and challenges that minority and lower-social-class students face and how these challenges create early education gaps. Other studies illustrate the consequences of these gaps for children's later learning and development (Duncan et al. 2007; Duncan and Magnuson 2011). 2 And though this body of research is smaller, a few studies have looked at trends in inequalities across cohorts (Carnoy and García 2017; Magnuson and Duncan 2016; Reardon 2011; Reardon and Portilla 2016), with mixed or inconclusive findings regarding the changes in the gaps. 3 In addition, these latter studies, however, do not address causes that could drive changes in the gaps over time. As such, there is a need both for a better understanding of these causes and for strategies to counter them.

In this paper, we describe recent skills gaps and trends in them by social class, as measured by socioeconomic status; analyze some of the major factors driving the gaps; and explore a set of diverse school district-level initiatives that are helping to narrow gaps. The paper is structured in three sections.

First, we examine social-class-based gaps at kindergarten entry among the most recently surveyed kindergarten cohort (the kindergarten class of 2010-2011). We study how gaps manifest in both cognitive and so-called noncognitive skills, as both skill types are important components of children's development.

Next we compare these gaps with those of an earlier kindergarten cohort. We look at changes from 1998 to 2010 in the skills gaps between children in the top and bottom social-class quintiles (primarily using SES as the proxy for social class). We also analyze how sensitive gaps are to the inclusion of several key determinants of student performance, such as children's own characteristics, family composition, and parental and education practices at home.

Then we review a set of case studies of school districts that have employed comprehensive educational strategies to provide more children (especially low-income children) with strong early academic and life foundations, and to sustain and build on early gains throughout the K-12 school years.

Finally, we look at the implications of our findings, and, based on the case study examples from diverse communities, we discuss strategies that districts can employ along with district and state policy changes that will make those strategies easier to adopt and more sustainable.

For the first two analyses, we use two nationally representative studies from the National Center for Education Statistics (NCES): the Early Childhood Longitudinal Study of the Kindergarten Classes of 1998-1999 and 2010-2011. These data provide information about children's skills and about the children themselves, such as their race/ethnicity, socioeconomic status, language spoken at home, etc. The data also provide information on the children's experiences in their early years, such as how actively their parents engaged them in enriching activities, whether they attended prekindergarten care, and the number of books the child has (see Appendix A). This information allows us to test the associations between children's characteristics and their educational outcomes at school entry. For the second analysis, we draw on 12 case studies of community and school districts employing comprehensive educational strategies (Weiss 2016a-h). We explore the qualitative information provided on investments these districts have made in early childhood education, on both within-school and broader K-12 supports for children, and on evidence that these investments are delivering both improved academic achievement and broader gains for children. Based on this evidence, the report ends with conclusions and recommendations for further research, practice, and policy. Appendices A and B provide detailed discussions of the data and methodology used in this paper.

How large are recent performance gaps at kindergarten entry?

This section documents inequalities among the most recently tracked cohort of students as they entered kindergarten in 2010. It provides us with the most recently available view of the various aspects of gaps at the school starting gate, all of which are critically important for understanding the implications of those gaps. The findings below draw on the Early Childhood Longitudinal Study of the Kindergarten Class of 2010-2011, and we use data from the fall measurement in the kindergarten year. (This section partly builds on our previous work; see García 2015 and García and Weiss 2015. See Appendices A and B for details on the variables and methodology used.)

Our decision to examine performance in both cognitive and noncognitive skills reflects growing acceptance that children's development is a complex process in which both skill types build on and interact with each other, and on evidence of the roles that both types of skills play in the education process and adulthood outcomes (see García 2015; García and Weiss 2016; Levin 2012a, 2012b). Traits and skills such as critical thinking, creativity, problem-solving, persistence, and self-control are vitally important to children's full development, and are nurtured through life and school experiences. These skills, sometimes referred to as noncognitive or social and emotional skills, tend to develop-or lag-in tandem with cognitive skills. Noncognitive or social and emotional skills are thus linked to academic achievement, and also to outcomes in adult life, such as productivity and collegiality at work, good health, and civic participation.

For these analyses, we use a measure of socioeconomic status that has three components: the educational attainment of parents or guardians, parents' occupational prestige (determined by a score), and household income (see more details about the SES construct in Tourangeau et al. 2013, 7-56 to 7-60). We divide children of the 2010-2011 kindergarten class into five groups based on SES quintile. To measure the gaps in performance by socioeconomic status, we compare the average performance of children in the top fifth of the SES distribution with the average performance of children in the bottom fifth. This provides an estimate of the relative advantage of a child in the top fifth of the SES distribution (referred to in this report as "high-SES") with respect to a child in the bottom fifth ("low-SES").

Children are not equally prepared for school when they enter kindergarten, and our analyses show that students' social class strongly determines their relative position in the performance distribution. Most socioeconomically disadvantaged children lag substantially in both reading and math skills, and these skills levels rise along with socioeconomic status (sometimes referred to as socioeconomic gradients). Children in the highest socioeconomic group score significantly higher in reading and math than children in the lowest socioeconomic group. As Table 1 shows, the relative unadjusted gaps in reading and math, i.e., the advantages of high-SES children relative to low-SES children in 2010 are 1.17 and 1.25 sd, respectively (Table 1 also shows that, after controlling for clustered data, the gaps are 0.94 and 0.91 sd, respectively). 4 Reading and math skills advantages of children in the middle of the SES distribution relative to the lowest SES group are roughly half as large as the advantages of high-SES children to the lowest SES group. 5

Children in the lowest socioeconomic quintile also lag substantially in noncognitive skills, based on assessments by both parents and teachers, although these gaps are smaller than those in reading and math. Socioeconomic-based gaps in self-control and approaches to learning are approximately one-third to one-half as large as gaps in reading and math. 6 In 2010, children in the high-SES quintile scored 0.38 sd and 0.51 sd higher in self-control and approaches to learning as reported by teachers (0.36 sd and 0.56 sd after clustering; see Table 1) than children at the low-SES quintile (see Figure A). Using parents' assessments of the same skills, the gaps are 0.39 sd and 0.56 sd, respectively (0.33 sd and 0.46 sd after clustering; see Table 1).

Our analyses also document stark socioeconomic disparities in inputs, child and family characteristics, and other factors that can affect school readiness ( Table 2). Here too we find a correlation between socioeconomic status and other factors that impede educational development. Low-SES students are more likely than their high-SES peers to be immigrants and less likely to speak English at home, to live with two parents, to have participated in center-based pre-K care activities in the previous year, and to have engaged in early literacy practices at home. Among children in the low-SES group, half (50.4 percent) are Hispanic, 23.1 percent are white, 19.6 percent are black, and 2.5 percent are Asian. 7

Though these gaps in both cognitive and noncognitive skills are troubling and call for policy recommendations, better policy solutions can be designed if we understand how these gaps have changed over time and what factors have played a role in those changes. Education outcomes are the product of a combination of multiple factors, which can reinforce or mitigate relative advantages or disadvantages in a dynamic fashion. We examine these issues in the rest of the paper.

How do the performance gaps in the 2010-2011 kindergarten class compare with the gaps in the prior generation?

The analyses presented in this section compare the inequities in inputs and the performance gaps between high-SES and low-SES students who began kindergarten in 2010 with the gaps among high-SES and low-SES schoolchildren in the prior academic generation, the 1998 cohort. We also analyze factors that have had major influences on the changes in performance of kindergartners, and briefly discuss the research and policy implications of our findings.

How have the characteristics of the children in the lowest and highest SES groups changed in a generation?

We first analyze children's characteristics by SES quintiles in the two cohorts. This enables us to identify differences in the characteristics of low-SES kindergartners in 2010 versus in 1998. These changes may help explain why the performance gaps we are studying grow or shrink (for example, if children in the low-SES quintile in 2010 were more likely than their 1998 peers to have access to public programs such as pre-K, they might be more prepared for kindergarten, and thus the relative advantage of high-SES children might shrink). 8

Table 2 shows the student and family characteristics of the kindergarten classes of 1998-1999 and of 2010-2011, by SES quintile. The table also includes pre-K care arrangements and two indices of developmental activities parents undertake with their children-indices of "literacy/reading activities" and "other activities"). 9 The table also summarizes parents' expectations regarding their children's educational attainment. To some extent, expectations are based on hope, but they can also respond to behavioral patterns children are exhibiting that hint at their future success. Expectations can also influence outcomes by representing how motivated parents are for their children's education. The ECLS-K survey does not ask parents how their expectations (and changes in their expectations) affect their provision of educational activities or support, but their answers to the expectations question can be used as a reasonable proxy of the degree to which parents are aware of their children's education and willing to support it. 10

The most significant changes in children's characteristics by SES quintile are for children in the bottom of the distribution. In 2010, a greater share of children in this group are Hispanic (50.4 percent, an increase of 10.6 percentage points relative to the 1998 share of 39.8 percent), live in homes where the main language is not English (40.3 percent, an increase of 9.1 percentage points from 31.2 percent in 1998), and are immigrants (49.8 percent, an increase of 19.5 percentage points from 30.3 percent in 1998). In 2010, a greater share of children do not live with two parents (54.9 percent, an increase of 9.3 percentage points from 45.6 percent in 1998), and live in poverty (84.6 percent, an increase of 13.3 percentage points from 71.3 percent in 1998). These substantially greater disadvantages for children at the bottom of the SES scale could all be reflections of both the much weaker national economic context in 2010 versus 1998 and the growing inequality described above.

These children's likelihood of attending center-based pre-K did not change significantly across generations (about 44 percent for both cohorts), but they were more likely to be looked after by parents or relatives (with the share increasing from 46.4 percent in 1998 to 50.9 percent in 2010). These children's parents also reported having a somewhat larger number of books at home for the children, and there were increases in their indices of educational and engagement activities (two composite measures, with the literacy/reading index measuring how frequently parents read books to their child, tell stories, sing songs, and talk about nature and how frequently the child reads picture books and reads outside of school, and the "other" index measuring how frequently parents and children play games or do puzzles, play a sport or exercise together, and build something or play with construction toys; and how often parents help children do arts and crafts and involve children in household chores). These parents' expectations about their children's educational attainment also changed significantly: the share who expected their children to attain no more than a high school diploma decreased by more than half (from 24.1 percent in 1998 to 11.4 percent in 2010), and the share of parents who expected their children to attain at least a bachelor's degree increased, markedly for those expecting their children to obtain an advanced degree (a master's degree, Ph.D., or M.D.).

Among children in the high-SES quintile, the group in 2010 includes a lower share of white children (falling from 78.8 percent in 1998 to 71.3 percent) and a larger share of Asian children (increasing from 4.7 percent in 1998 to 8.7 percent). Children in the high-SES group became slightly more likely to live with their two parents (the share of children who lived with one parent decreased from 11.1 percent in 1998 to 9.6 percent), and to have attended center-based pre-K (65.8 percent in 1998 and 69.9 percent in 2010). We only see a small increase in the reported number of books at home. 11 The share of homes reporting having more than 200 books-the maximum-increased slightly in 2010, across all SES quintiles except for the middle quintile). As was true of low-SES parents, those in the highest quintile raised their expectations for their children's educational attainment from 1998 to 2010. Compared with the 1998 cohort, a larger proportion of high-SES children in the 2010 cohort were expected by their parents to attain an advanced degree (master's degree or higher), while a lower share expected their children to attain a bachelor's degree only.

How did the performance gaps between the children in the lowest and highest SES groups change in a generation?

Changes over time in the input factors by socioeconomic status (child and family characteristics, early-education practices, and parents' expectations) explored above have been found by researchers to have major impacts on the outcomes (test scores on reading and math, and measures of noncognitive skills) explored in this section. 12 In other words, we would expect that changes in the unadjusted skills gaps (gap measures that do not include controls for child and family characteristics, early-education practices, and parents' expectations) would partially reflect the compositional differences between the class of 2010-2011 and the class of 1998-1999. For example, we would anticipate that if the more recent generation's low-SES parents read to their children more frequently, helped them do more arts and crafts, or had higher expectations for them, these factors would correlate with narrowing skills gaps. Also, we would expect that the adjusted skills gaps (gap measures that are net of the influence of child and family characteristics, early-education practices, and parents' expectations, and thus reflect the SES gaps) would be different for the two cohorts if the correlations between inputs and outcomes had changed over time or if the share of children's outcomes the adjustments account for had changed over time.

To understand these factors' potential influence on gaps, we examine both unadjusted and adjusted gaps in the tables in this section. We also examine gaps by some of the components of the SES index, such as household income or mother's educational attainment, and by other variables that are sometimes used as proxies of the child's socioeconomic background, such as number of books in the home. If the gaps by SES components and proxies somewhat differ, this tells us that researchers' choices about how to divide children into groups and compare them matter-both for their findings and for their policy recommendations.

Table 3 shows the unadjusted and adjusted gaps between the standardized scores in reading and math of kindergarten children in the top SES quintile relative to the bottom SES quintile in 1998 and the change in that gap by 2010. 13 Table 4 performs the same analysis for gaps in measured noncognitive skills. The tables show two somewhat perplexing patterns. On the one hand, the cognitive and noncognitive skills gaps between high-SES and low-SES children are large and statistically significant in both cohorts. But while significant social-class-based performance gaps persist from one kindergarten generation to the next, there is not the same consistency in how the high-SES to low-SES gaps change. For some cognitive and noncognitive skills, the performance gaps grow, while for others the gaps shrink, or remain the same from one generation to the next (which may complicate the process of understanding why performance gaps have changed over time).

Beginning with our unadjusted model (data column one), the only substantial increase in the gap between high- and low-SES children from 1998 to 2010 was in reading skills, which increased by one-tenth of a standard deviation. There were no significant changes in gaps in math skills, which, as the literature indicates, are less sensitive than reading skills to parents' activities at home (see Rothstein 2004, 2010). Similarly, gaps in approaches to learning as reported by parents and in self-control as reported by teachers did not change significantly, and gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank by roughly the same amount as the reading gap (about a tenth of a standard deviation-0.12 and 0.08 sd, respectively). Figure A provides a graphic illustration of the unadjusted gaps in cognitive and noncognitive skills of high- and low-SES children across the two cohorts.

The additional models estimated for each outcome and shown in Tables 3 and 4 offer other key findings. In Model 1, we used the full samples for the two cohorts but did not include any controls that capture characteristics of children or their parents or the early education practices in which families engage. Model 2 partitions the data into schools and classes, or clusters, so that the subjects in the clusters are more similar to one another than to those in other groups. Under this adjustment, the gaps shrink substantially, by between 15 and 25 percent across the skills, and the regression fit improves significantly (see increased adjusted R-squared, i.e., this model explains more of the total variation in the outcomes than the first model). This clustering takes into account school segregation, that is, that children are not randomly distributed but tend to concentrate in schools or classrooms with children of the same race, social class, etc. Clustered estimates provide a comparison of the skills gaps of peer students-those in the same schools and classrooms-rather than a comparison across schools. García (2015) and Magnuson and Duncan (2016) offer these estimates too.

How do child and family characteristics, activities, and expectations affect SES-based performance and performance gaps?

We next examine the contribution of the certain variables of interest to SES-based performance gaps. We approach this in two ways. First, we examine the changes in the gaps (Tables 3 and 4, Models 3 and 4) and the overall reduction in the gaps that results from controlling for children and their family characteristics, early literacy practices, and parental expectations of educational achievement ( Table 5). Second, we assess the influence of select early educational practices on performance and how that influence has changed over time by looking at the associations between these inputs and performance ( Table 6).

Models 3 and 4 in Tables 3 and 4 use the samples that result from removing observations without full information for the controls of interest. 14 Adding controls is important because performance gaps based on socioeconomic status may be explained by differences in variables other than the child's socioeconomic status. In other words, we aim to determine which part of the gap is attributable to children's SES, net of other factors that matter for performance. Thus, in the third data column (Model 3), we add controls for individual and family characteristics (gender, race/ethnicity, whether English is the primary language spoken at home, disability, age, whether children live with two parents) and early educational and play activities (center-based pre-K care, indices for literacy/reading activities and other activities, and total number of books the child has). Model 3 also includes the interactions between the early education variables with time. 15 In the fourth data column (Model 4), we control for the same factors as in Model 3 but add controls for parental expectations of children's educational attainment (whether they expect their children's highest level of education attained will be high school diploma or less, some college or vocational studies, bachelor's degree, or advanced degree) and their interaction with time. 16 We describe these results in the next section.

Including covariates changes the estimates of SES-based skills gaps in various ways. First, the gaps between the top- and bottom-SES quintiles shrink, showing that SES-based gaps are partially explained by the variation in the controls (which is not visible in the tables). 17 Second, controls do not significantly change the SES-based gaps over time, in general; i.e., the coefficients associated with changes in the gaps between high- and low-SES children remain almost the same, or change very minimally, depending on the skill measured. The statistical significance of the SES-based skills gaps in 1998 is not affected by the inclusion of the controls (see rows "Gap in 1998-1999" in tables), but the statistical significance of the changes in the gaps between 1998 and 2010 (see rows "Change in gap by 2010-2011" in tables) is somewhat affected by the inclusion of the controls (note that the sizes of the coefficients measuring gaps in 1998 change after the inclusion of the controls, but that the sizes of the coefficients measuring changes in them between 1998 and 2010 do not change significantly). In reading, the change in the gap between 1998 and 2010 diminishes and becomes statistically insignificant in the last model (the relative gap increases by 0.08 sd but this change is not statistically significant), meaning that adding parental expectations of education accounts for some of the increase in the gap detected in Models 1 to 3. The only SES-based skills gap that shows a statistically significant increase from 1998 to 2010 once parental expectations are controlled for is the gap associated with parents' assessment of approaches to learning, which increases by 0.11 sd. Gaps between high- and low-SES children in cognitive and noncognitive skills after adjustments are made are shown in Figure B.

As mentioned above, the fact that the skills gaps decrease after controls are taken into consideration affirms that SES-based gaps are due in part to variation in the controls among high- versus low-SES children. This trend can be seen in Table 5, which, as noted above, shows the overall reduction in gaps that results from controlling for child and family characteristics, early literacy practices, and parental expectations of educational achievement. With respect to cognitive skills, the 1998 gaps shrink by 46 percent and 53 percent, respectively, after the inclusion of the covariates. About half of the gaps are thus due to other factors that are associated both with SES status and with the outcomes themselves. The reduction in the 1998 gaps for noncognitive skills varies from 28 percent (approaches to learning as reported by teachers) to 74 percent (approaches to learning as reported by parents). (For self-control as reported by teachers, the reduction is 51 percent versus 35 percent when reported by parents.)

While the gaps hold after the inclusion of controls across outcomes, gaps in 2010 are less sensitive to the inclusion of the covariates than they were in 1998. This trend can also be seen in Table 5. 18 Declining values from 1998 to 2010 indicate that factors such as early literacy activities and other controls are not, as a group, explaining SES-based gaps as much as they had a decade prior. This change could be due to the failure of the index to fully capture parents' efforts to nurture their children's development and/or the index becoming somewhat out-of-date. In any event, the resistance of gaps to these controls should worry researchers and policymakers. The waning influence of these controls makes it harder to understand what drives SES gaps. It also suggests that the gaps may be growing more intractable or, at least are less easily narrowed via the enactment of known policy interventions.

Finally, we examine the association of performance outcomes (not performance gaps) with selected early educational practices, including having attended center-based pre-K, literacy/reading activities and other activities, and total number of children's books in the home (Table 6). 19 We are mainly interested in two potential patterns: whether these factors are associated with outcomes (and, if so, how intense the associations are), and whether the relationships have changed over time.

In keeping with established research, having attended center-based pre-K is positively associated with children's early reading and math skills. For 1998, the estimated coefficients are 0.11 sd for reading skills and 0.10 sd for math skills, substantial associations that do not change significantly over time. In other words, attending pre-K in 1998 improved kindergartners' reading skills by 0.11 sd and improved kindergartners' math skills by 0.10 sd relative to not attending pre-K. However, while center-based pre-K continues to reduce self-control as reported by teachers in 2010, the effect is less negative in 2010 (the 0.06 improvement from 1998 to 2010 shown in the bottom panel of the table shows us that the effect in 2010 was -0.07 [-0.13 plus 0.06], compared with -0.13 sd in 1998). We find no independent effect of center-based prekindergarten schooling (i.e., no effect in addition to SES, in addition to other individual and family characteristics, or in addition to other SES-mediated factors), on approaches to learning or on self-control as reported by parents. 20

The number of books children have at home likewise supports their skills at the beginning of kindergarten. Indeed, this factor is positively associated with all outcomes but self-control reported by parents. The coefficients are very small, of about 0.01 to 0.02 sd (associated with changes in outcomes for each 10 additional/fewer books the child has, as expressed by the continuous scale with which number of books in the home is measured, which is divided by 10 for the analyses (as mentioned in Appendix A), and these relationships do not change over the time period.

The two types of parenting activities that are summarized by the indices "reading/literacy activities" and "other activities" show interesting correlations with performance and patterns over time. On the one hand, the "reading/literacy activities" index (a composite of how frequently parents read books to their child, tell stories, sing songs, and talk about nature, and how frequently the child reads picture books and reads outside of school) is strongly and positively associated with all outcomes other than children's self-control as reported by the teacher. The associations with cognitive skills, especially with reading, are strong and statistically significant-0.17 sd for reading performance and 0.07 sd for math-and these associations did not change significantly between 1998 and 2010. For noncognitive skills, the relationships are strong for those assessed by parents, though they shrink by about half over time: self-control is 0.14 sd in 1998 and decreases by 0.08 sd by 2010; approaches to learning is 0.32 sd in 1998 and decreases by 0.17 sd by 2010). The relationship is much weaker, though still statistically significant, for teachers' assessed approaches to learning (it is 0.03 sd in 1998 and does not change significantly by 2010).

On the other hand, the index that measures other enrichment activities that parents do with their children (a composite of how frequently parents and children play games, do sports, build things, work on puzzles, do arts and crafts, and do chores) shows significant correlations with all of the skills, but they may be either positively correlated or negatively correlated, depending on the skill. For cognitive skills, the associations are statistically significant and negative, though stronger and somewhat more meaningful or more intense with reading achievement (-0.12 sd in 1998) than with math achievement (-0.04 sd). 21 These associations did not intensify nor weaken over time. For noncognitive skills the associations are highly positive and statistically significant, and very strong for parents' assessment of approaches to learning (0.29 sd in 1998). As explained by García (2015), these correlations between "other activities" and noncognitive skills as assessed by parents could be bidirectional: engaging children in enrichment activities might enhance their noncognitive skills, but, at the same time, parents who are more inclined to participate in their children's early play and educational time are probably more likely to perceive or judge that their engagement has an impact on their children's skills. But the fact that both the frequency with which parents engage in most of these activities and the importance of this index for parent-assessed skills increased noticeably from 1998 to 2010 (by 0.22 sd for self-control and 0.27 sd for approaches to learning) suggests that parents are growing more informed and involved in their children's early education over time. It also indicates that parents are increasingly acting on this knowledge and that this involvement will continue to grow, albeit potentially with decreasing marginal returns to time and resources invested. The association between "other activities" and teachers' assessments of children's noncognitive skills is also positive but weaker than that of parents' assessments (about 0.03 sd for approaches to learning and 0.05 sd for self-control), and remained unchanged during the time period studied.

Finally, we find a strong association between parental expectations for their children's educational attainment and all measured skills. In other words, net of socioeconomic status, the higher the expectations, the higher cognitive skills children have, and the higher the assessments by parents and teachers of children's noncognitive skills. The parental expectations portion of the table measures children's performance relative to children whose parents' expectations are the lowest (high school diploma or less). While the expectation that a child will pursue some vocational education or complete college has a statistically positive influence on all skills measures except for reading, the expectation that their children will complete a bachelor's degree or more education has a stronger influence, including on reading skills: between 0.11 to 0.16 sd higher in reading and between 0.17 to 0.22 sd higher in math in 1998. High expectations for children's educational attainment also have a statistically positive effect on noncognitive skills. When the expectation is for an advanced degree (master's or higher), coefficients vary from 0.12 sd in self-control by teachers to 0.38 sd in approaches to learning by parents in 1998. In addition, most of these associations-particularly the cognitive gradients-grow in 2010. Relative to children whose parents have low expectations, children whose parents have the highest expectations for their children's attainment (graduate studies) perform much better in reading and math than in 1998 (relative gaps grow by 0.19 and 0.12 sd respectively). A similarly stronger association is noted for noncognitive skills assessed by teachers (though not for parents' assessments of their children's skills).

Sensitivity analyses: Do performance gaps vary based on which proxy for social class (socioeconomic status) is used?

Part of the challenge to making conclusive statements about trends in education gaps by social class is the existence of multiple valid proxies for measuring children's social class or socioeconomic status. 22 Although researchers treat these proxies as equivalent, and even interchangeable, the lack of a comparison of results obtained using various indicators limits our capacity to extract major conclusions on social-class trends and their drivers, and hence hinders the plausibility and effectiveness of the policy recommendations that build on any specific indicator's findings (net of other methodological and instrumental differences that may exist across studies).

We thus conduct analyses using several of the main proxies employed to measure socioeconomic status. The purpose of these analyses is twofold. The first purpose is to test the sensitivity of the estimated relative gaps, and of trends in them, to changes in the measurement of this key predictor of education performance. (In other words, if all the indicators are reliable proxies of SES, gaps and trends obtained using the various metrics should be similar.) The second purpose is to increase the comparability of the results of studies addressing trends in education inequalities that use various metrics of social class. This is an important issue; in addition to helping reconcile diverse results found in the literature, these analyses may reveal why patterns differ, and have significant policy implications.

As such, instead of the SES composite measure we use to estimate SES-based gaps in this report, we use three alternative indicators to run our analyses: mother's educational attainment, household income, and number of books the child has in the home. Unlike the SES composite measure, two of these measures offer the advantage of being directly comparable over time. Both mother's educational attainment and number of books the child has are objective categories. As a limitation, and mainly associated with the information that is available in the raw data, none of these categories can be transformed into a percentile-variable without major transformations. (The adjustments to ensure comparability over time are explained in Appendix A. See Reardon and Portilla 2016 for an analysis with a transformation of the income variable that offers a proper percentile comparison, based on the methodology developed by Reardon 2011.) Still, they are variables associated with social class and can be ordered in groups or categories that identify high- and low-social-class statuses. Thus, with the necessary caution when interpreting and using the findings, we offer this comparison of results as a sensitivity analysis.

We create five categories with these indicators, maintaining the structure of comparing "high-SES" (top quintile) with "low-SES" (bottom quintile) as in Tables 1-5 (note that we are using "SES" interchangeably with "social class" here). For simplicity, Tables 7-9 show only the results from two models: one without covariates (Model 1, baseline estimates) and one with all covariates (Model 4, fully adjusted estimates). We focus on the findings for the baseline relative gaps in 1998 and 2010 first ( Figures C-E). The overall patterns found in the results suggest that all social-class gaps are statistically significant and sizable. However, the exact sizes of the gaps vary depending on the social-class indicator used and the outcome being assessed. Also, the changes in the gaps over time vary depending on the indicator used to capture children's social class.

In addition to these general findings, we note some more detailed ones. For 1998, gaps by mother's educational attainment (Figure C; Table 7) are the largest across all indicators (except for the gap in self-control as assessed by teachers, which is slightly smaller than the gaps as measured using household income and number of books the child has), while gaps by number of books (Figure E; Table 9) are the smallest across all indicators (except for the gap in approaches to learning as assessed by parents, which is slightly larger than the gap for household income). Again, according to the 1998 data, the coefficients of gaps by mother's educational attainment are generally larger-and in three cases much larger-than those obtained using number of books in the home as the indicator of social class. For example, the relative gap is 1.29 sd in reading and 1.46 sd in math when mother's education is the SES proxy, compared with gaps of 0.74 sd and 0.97 sd when number of books in the home is the SES proxy.

It is also important to note that gaps by mother's educational attainment (Figure C; Table 7) and income (Figure D; Table 8)-two of the five components of the SES construct-are very close to the ones obtained by our SES composite measure (as shown in Figure A). All in all, results seem internally consistent as well as generally consistent with prior results on this topic (Reardon and Portilla 2016).

In terms of changes in the performance gaps over time (unadjusted), the findings vary depending on which indicators of social class are used, with mother's education and household income being the indicators associated with the largest changes in the gaps. Changes in the performance gaps in cognitive skills between 1998 and 2010 by our composite SES measure and books are similar: an increase in the reading gap between children in the top and bottom quintiles of about a tenth of a standard deviation (0.10 sd with the composite SES measure [Figure A] and 0.08 sd if SES is proxied with books), and no significant change in mathematics (there are some differences in the noncognitive outcomes).

However, by mother's educational attainment, there are no changes in relative reading and approaches to learning gaps reported by parents over time, and a significant reduction in the gaps in the remaining outcomes. Meanwhile, income-based gaps for the two cognitive skills-reading and math-decreased by -0.13 and -0.23 sd respectively, and for approaches to learning as reported by teachers by -0.13 sd. No significant changes occurred for the remaining noncognitive skills.

In sum, this sensitivity analysis demonstrates that all of the indicators are reliable proxies of SES for the estimation of early achievement gaps, though absolute gaps may vary slightly depending on the indicator used. However, the proxies are not equally reliable when we assess trends in the gaps by SES or their drivers. As such, aside from differences in the definitions and procedures used to construct each SES proxy, the proxies should not be treated as fully equivalent. The decomposition conducted here helps clarify the different weights that various components of SES may have in driving changes in gaps by social class. For example, variation in income across groups over time is associated with decreased performance gaps in the cognitive skills between 1998 and 2010, and variation in educational attainment quintiles or categories over time is associated with decreased performance gaps across cohorts in most noncognitive skills. But variation in books in the home over time and among groups is associated with increased gaps in reading and in parents' assessed approaches to learning. Such findings also point to very different policy solutions: if mothers' education is the main driver, enhancing that will improve children's prospects. On the other hand, findings that indicate that income inequality is the larger culprit would point to the need for policies that reduce such inequalities. Future research should consider and look more closely into these questions.

What can we learn from these analyses?

The multiple factors and relationships examined in this section can now be examined from a policy perspective. If the aim is to increase equity, to improve children's development across the board, and to improve our understanding of children's development, there are two major policy recommendations:

1. Directly support less-resourced families so that they have greater access to educational and economic resources (for the latter, see García and Weiss 2017). All the early educational and play activities measured, which include center-based pre-K care and literacy/reading and other activities, as well as the number of books a child has, are positively associated with children's readiness, and in part account for social-class gaps, but are much less accessible to children of lower socioeconomic status. Virtually all of the associations between these factors and outcomes were strong and positive (with a handful of exceptions), and some even grew over time. A related research recommendation of particular interest would be to examine whether the intensity of these activities or practices has any threshold level of effectiveness (after which point they no longer affect children's development). Also, it would be helpful to understand why parents' expectations of their children's educational attainment increased so much and how this has affected children's development. For example, do parents have a better understanding of the relationship between educational attainment and prospects for success in life and the workforce? Are children performing better because their parents expect more, or because parents who expect more are also delivering more in the form of enriching activities?

2. Design and implement strategies that compensate at the community level for children's lack of access to key foundational resources (economic and educational). These strategies can be considered indirect supports for less-resourced families that reduce inequities and complement the direct supports described above. Examples of communities that have enacted such comprehensive support initiatives provide a good starting point to explore how and why they emerge; the types of supports they provide (from preschool programs and home visits with parents to enriching summer programs, school-based health clinics, and more); the challenges of scaling them up and sustaining them; the benefits they deliver for students, and particularly for disadvantaged students; and their implications for policy at the local, state, and even federal levels. The next section of this report thus presents an analysis based on qualitative data from promising initiatives in a dozen school districts across the country (Weiss 2016a-h).

What are pioneering school districts doing to combat these inequities and resulting gaps?

This section of the report draws on a set of case studies published by the Broader, Bolder Approach to Education (BBA), a national campaign that advances evidence-based strategies to mitigate the impacts of poverty-related disadvantages on teaching and learning. 24 The case studies feature school districts that have employed comprehensive educational strategies to ensure that more children, especially low-income children, have strong early academic and life foundations, and that resulting early gains are sustained and built on through children's K-12 years. (These strategies are often referred to as "whole-child" approaches to education, in reflection of their holistic nature.) We explore the premise that school districts that take a whole-child approach to education and a whole-community approach to delivering it are likely to enjoy larger gains in academic achievement and to narrow their race- and income-based achievement gaps. In doing so, we are building on evidence suggesting that consistent, strong supports for children and their families-both in and out of school-can avoid the "fade-out" seen among graduates of many pre-K programs and even enhance those programs' early benefits.

This section is thus divided into four parts: (1) an introduction to the case study districts, followed by discussions of (2) how these districts invest in early childhood care and education, (3) how the districts' investments in K-12 strategies sustain and boost the early childhood investments, and (4) how academic gains and narrowing achievement gaps indicate that the investments are paying off. Table 10 provides basic information on the 12 school districts/communities studied; Appendix E at the end of this report provides more information on key characteristics of these districts. 25

Introduction to the case studies: Why these districts enacted whole-child strategies

Large and growing disparities in the economic well-being of children in America and extensive evidence linking those disparities to widely diverging educational outcomes have prompted action among a growing number of communities and school districts. Heeding the evidence that out-of-school factors play even larger roles than school-based factors in school performance, these districts are seeking ways to mitigate the poverty-related impediments to effective teaching and learning.

These districts have benefited from a substantial body of research on strategies with promise to address core challenges that students and schools face-strategies that have been shown to shrink achievement gaps by narrowing major disparities in opportunity (Carter and Welner 2013). The first, and perhaps best-documented, of these strategies is high-quality early child care and education, especially when it engages parents early and in meaningful ways. High-quality early childhood education programs not only narrow achievement gaps at kindergarten entry but also deliver long-term benefits to children, their families, and society as a whole (Chaudry et al. 2017; Rolnick and Grunewald 2003).

Programs that support students' physical and mental health and improve their nutrition are also known to reduce chronic absence and keep students focused and learning, and thus improve their academic performance (CDC 2016). Well-designed after-school and summer-enrichment programs likewise boost achievement, both directly and indirectly by enhancing students' engagement in and attachment to school (Peterson 2013).

Whole-child approaches integrate these and other strategies into a comprehensive set of aligned interventions, leveraging the whole community's resources to meet the broad range of student needs. While the impact of such comprehensive approaches has not been studied as extensively as the individual components, considerable theoretical and emerging empirical research point to the strong potential of such strategies to boost achievement and narrow gaps (Child Trends 2014; Oakes, Maier, and Daniel 2017; Weiss 2016i).

This section of the report seeks to add to that knowledge base by sharing qualitative information on how such comprehensive approaches have emerged and grown, what they look like when they are successfully implemented, and what types of outcomes and benefits result and how outcomes vary across diverse communities.

How are whole-child initiatives launched?

Each of the districts studied has distinct circumstances, and thus distinct reasons for coming to the conclusion, as a community, that it needed to take a comprehensive approach to education. At the same time, demographic trends that are affecting virtually every state-and many, if not most, school districts across the country-have played major roles in that decision in every case. 26 Indeed, community and school leaders in all of these districts cited students' poverty (and, in some districts, demographic shifts) as posing challenges that required looking beyond the school walls to address.

How these factors triggered the initiative's launch varied, but poverty was at the core in each community's decision. For example, in 2008, community leaders identified East Durham as one of Durham, North Carolina's, most distressed areas, based on a community risk assessment conducted by Duke University's Children's Environmental Health Initiative. The 120-block area's 11,000 residents had a 40 percent poverty rate and a homeownership rate of just 19 percent, along with high rates of crime and unemployment, putting its 3,000 children and youth at high risk of academic failure (Weiss 2016e).

Across the country, in Vancouver, Washington, the share of children eligible for subsidized school meals rose from 39 percent to over 50 percent in less than a decade, such that, by 2015, in some central-city schools, more than four in five students qualified for subsidized school meals in 2015 (Weiss 2016b). In another distressed community, in north Minneapolis, median family income was just $18,000 in 2011, and fully one-fourth of the 5,500 Northside students were homeless or "highly mobile" (in such unstable housing that they were at risk of homelessness) (Weiss 2016d). In Pea Ridge, Arkansas, schools "had difficulty finding resources that met the needs of kids," says superintendent Rick Neal. "We knew that we were not identifying all the needs that were there. I think that's the way a lot of districts are" (Weiss 2016f). And in the early 1990s, the Tangelo Park neighborhood in Orlando, Florida-an isolated enclave of 3,000 residents, almost all low-income and African American-caught the attention of hotelier and philanthropist Harris Rosen, who was looking for a neighborhood in which to invest (Alvarez 2015).

Each of these districts took different approaches to enacting those comprehensive strategies, based on the community's specific mix of needs and assets, ideological leaning, available sources of funding, and other factors. One of the most politically progressive of the districts studied, Montgomery County Public Schools (MCPS) in Maryland, paved the way for a whole-child approach in the early 1970s when it enacted housing policy that uses mixed-income residential developments to create communities with families of different income levels. In the 1990s, the county developed Linkages to Learning, a "community schools"-type approach targeted to engaging and partnering with low-income and immigrant parents and families and connecting them with a broad range of community resources (MCPS 2016). (Community schools are known for building partnerships with community agencies and private service providers to meet student and family needs.) Austin Independent School District (AISD), also in a politically progressive jurisdiction, began its whole-child efforts through parent- and community-organizing in schools. It has since invested in social and emotional learning and in a community schools strategy (CASEL 2017).

At the other end of the spectrum are whole-child approaches in Joplin, Missouri, and Pea Ridge, Arkansas, districts located in more politically conservative southern states. These districts operate under the umbrella of Bright Futures USA (a spinoff national nonprofit that began with Joplin's Bright Futures initiative). The Bright Futures districts take a more individualistic angle, asserting that every member of the community has "time, talent, or treasure" to offer that can help children overcome disadvantage and ensure more equal opportunity (Weiss 2016a).

Two other districts have modeled their efforts on the Harlem Children's Zone (HCZ). The Northside Achievement Zone in Minneapolis is funded through a grant from the federal Promise Neighborhoods initiative, enacted by the Obama Administration to help more communities dramatically improve the academic success for low-income children by adopting HCZ-like strategies. The East Durham Children's Initiative in North Carolina is entirely privately funded so far (Weiss 2016e).

In both Kalamazoo, Michigan, and Orlando, Florida, pledges of "Promise" college scholarships have evolved into broader whole-child efforts (Alvarez 2015; Miller-Adams 2015).

Districts also take different approaches based on density. New York City-home to dozens of full-service community schools supported by the Children's Aid Society and rapidly expanding to more-and Boston-home to the City Connects initiative-leverage a broad range of their respective cities' arts and cultural offerings, along with health and nutrition and other social services (Weiss 2016g, 2016h). Cultural offerings to supplement other well-rounded services are also part of the full-service community schools district initiative in Vancouver, Washington. In contrast, Partners for Education, which serves the isolated region surrounding Berea College in Kentucky, was the first rural organization to receive a Promise Neighborhood grant and, thus, is a pioneer in exploring how well the model works outside the urban context (Berea College 2013).

What do whole-child initiatives do?

The sections below describe commonalities across these different approaches in terms of investments in children's earliest years (before school starts), building on these investments throughout children's K-12 years (both in and out of school), and the gains students and schools enjoy as a result of those investments. 27

How the case study districts invest in early childhood care and education

In keeping with their whole-child approaches to education policy and practice, every one of the 12 districts highlighted as a BBA case study has made investments in early childhood care and education, many of them substantial. These districts' efforts begin long before children enter school and go beyond pre-K offerings to equip parents in the effort to ensure their children's readiness for school.

One-on-one engagement with new parents

Investing in babies by engaging parents can include providing new parents with key information about child development and how to keep children healthy and safe. In Joplin, Missouri, Bright Futures Joplin partners with two of the area's hospitals to deliver new baby "kits" with child development and early literacy information and is trying to raise funds to sustain the project long term and to expand it to reach every new parent (Weiss 2016a). In Vancouver, Washington, 6,000 "literacy packets" are delivered annually to families with children up to age five, providing child-development activities and lessons that families can complete at home (Weiss 2016b).

The districts leverage partnerships to connect parents with a range of school and community resources that support children from birth through kindergarten entry. In Eastern Kentucky, the whole-child program called Partners for Education works with Community Early Childhood Councils to host events such as Week of the Young Child, the Dolly Parton Imagination Library, and Kindergarten Transition Programs (Weiss 2016c). In Montgomery County, Maryland, "Judy Centers"-early child care and family education centers-leverage partnerships with social service agencies and local community nonprofits to increase parents' access to mental health, nutrition, and other key services (Maryland State Department of Education 2017).

Educating and engaging parents early helps prepare children for school both academically and more broadly for healthy development. Those are the twin goals of the Minneapolis Northside Achievement Zone (NAZ), where currently only one in four preschoolers in the zone is ready for kindergarten based on standardized tests. To improve those odds, the zone has a team of "NAZ Navigators" who work with families to set and track progress toward goals in early childhood and to link this area of family support to goals in academics, housing, career and finance, and behavioral health (Weiss 2016d).

Parenting classes

Parents are children's first and most important teachers. Like the one-on-one strategies described above, classes for parents provide information on child development, early literacy, health, and constructive disciplinary practices, and offer more specific guidance tailored to specific parents' needs. Almost every district studied provides new-parent classes. The 1-2-3 Grow and Learn program is a weekly 90-minute literacy-rich program for young children and their parents offered at 12 elementary schools in high-poverty Vancouver neighborhoods. It lays the foundations for school readiness through social and education experiences. In addition, the district's Family and Community Resource Centers offer parent workshops, groups, and courses to help parents support their children's learning, while empowerment and skill-enhancement programs-such as job preparation, housing assistance, and parent leadership advisory groups-strengthen parents' basic skills. Family Academy classes in the North Minneapolis Northside Achievement Zone include "College Bound Babies" (for parents of children up to three years old), which teaches early literacy, numeracy, and positive discipline skills, and "Foundations," which empowers parents to feel confident talking with their children's teachers and advocating for their children and their children's schools.

In many cases, districts employ a combination of one-on-one and group supports, along the lines of Early Head Start. 28 The East Durham Children's Initiative, a private program modeled loosely after the Harlem Children's Zone, includes Durham Connects, a home visiting program that supports zone families with children up to age 3 and is followed by weekly or biweekly in-home parent education and support provided by two nonprofit social service providers, Healthy Families Durham and Jumpstart (Weiss 2016e). In Montgomery County, Maryland, family social workers collaborate with classroom teachers to help them develop Family Partnership Agreements, which are based on the strengths, needs, and personal goals of each family. A social worker-led team follows up by phone and with visits. In two of the district's highest-poverty schools, these supports are complemented by early child care and family education centers (Judy Centers), which provide comprehensive early childhood education and support to children from birth to age five and their families (Marietta 2010).

Big investments in prekindergarten programs

Almost every state in the country now invests at least minimally in pre-K programs for disadvantaged children, and a growing share of states make these programs widely available. 29 Most of the districts we studied, however, have gone far beyond state programs through one or more strategies and funding mechanisms.

A few of these districts benefit from high-quality state pre-K programs that serve a large share of children, freeing the districts to invest in other aspects of early childhood enrichment. The Partners for Education initiative based in Berea, Kentucky, leverages the state pre-K program, which serves all three- and four-year olds who are either low-income or have other risk factors. This enables Partners for Education to use Promise Neighborhood grant funds to place early childhood specialists in pre-K classrooms throughout the four-county region (the region is a Promise Neighborhood region, which means that federal funds are available for a variety of education- and health-related investments). The specialists also provide coaching, professional development, and support for Head Start classrooms, as well as in-home tutoring over the summer.

In East Durham, North Carolina, strong state early education programs are supplemented by partner-led low-cost half-day preschool and a summer kindergarten readiness program, and home visits by parent advocates provide a range of supports, such as connections to state pre-K. In Kalamazoo, Michigan, the Pre-Kindergarten Early Education Program (PEEP) offers half- or full-day pre-K classes in elementary schools for four-year-olds at or below 250 percent of the federal poverty level, per state law, but it adds transportation and meals for those children. PEEP also works with other programs such as Head Start to provide families who are ineligible for PEEP with other options for low- or no-cost quality early education (KPS 2017).

Other districts with less comprehensive state support use federal resources to expand local options. For example, Vancouver draws on both state and federally funded early learning programs to provide pre-K in seven schools, along with district-supported programs for children in Title I schools. As of fall 2015, Vancouver's new early learning center serves up to 100 additional children or more, with hot meals and playground space from an adjacent elementary school. Montgomery County also enhances state and federal programs with district-level investments: it provides the same literacy-rich curriculum in its Head Start classrooms as in district pre-K classrooms. And Montgomery County uses a blend of federal Title I and Head Start dollars to offer full-day Head Start in 18 of the poorest schools, serving 460 children (Marietta 2010). The Northside Achievement Zone in north Minneapolis uses federal Race to the Top Early Learning Fund money for scholarships for three- and four-year-olds to attend high-quality pre-K, serving 127 children in 2012-2013 and 156 in 2013-2014.

Local programs can also fill in where state programs are weak. Austin, Texas, uses local funds to provide enriching, hands-on full-day programs for the four-year-olds who would otherwise participate in lower-quality half-day state programs. Austin also provides a half-day program for three-year-olds who aren't served by the state. Families who qualify for both state pre-K and Head Start also receive nutrition, health, and other services (AISD 2017).

Pea Ridge is another community using local resources to supplant state resources. A lack of available seats for children who are eligible for the state's high-quality Arkansas Better Chance (ABC) pre-K program prompted Pea Ridge to seek a grant to open its own program, which serves 40 children: 20 at-risk children, who receive tuition scholarships, and 20 others whose parents can pay tuition (Weiss 2016f). Missouri's pre-K program also has too few slots, so Bright Futures Joplin is building a new early childhood learning center that will be funded jointly by the district and the state.

Strengthening the transition to kindergarten

Featured districts also build on pre-K gains and help narrow school-readiness gaps with such programs as full-day kindergarten. Montgomery County Public Schools first started full-day kindergarten in "red zone schools," those deemed to be most affected by high rates of student poverty, in 2000. Full-day kindergarten has since expanded to every school in the district (Marietta 2010). And Vancouver offers Kindergarten Jump Start, a school readiness program, at all 21 elementary schools, and full-day kindergarten; both programs seek to enhance the transition from pre-K into formal schooling.

Other investments in young children and their families

In addition to the above range of supports for infants, toddlers, and preschoolers and their parents, several of the districts studied by BBA have made additional investments in young children and their families. The Community Storywalk in Clay County, Kentucky, and the Born Learning Trail in Joplin, Missouri, provide opportunities for parents and paid caregivers to learn with their children in a hands-on way through outdoor and physical activities. In Eastern Kentucky, Partners for Education's Promise Neighborhood grant supports work by national nonprofit Save the Children to improve the health and education outcomes of the region's children through a literacy program that provides kids ages 5-12 with books and tools to develop strong reading skills. The Promise Neighborhood grant also allows Partners for Education to offer the Children's Healthy Choices program, which provides healthy snacks and 30 minutes of daily physical activity for children in districts across Eastern Kentucky.

Joplin's Little Blue Bookshelf program gives age-appropriate books to those children whose families cannot afford them, making the goal of 1,000 hours of reading by kindergarten a viable reality for every child. And the city's Lend & Learn Libraries provide stimulating toys and socialization time for young children and their parents.

How the school districts invest in K-12 strategies to sustain and boost their early childhood investments

The whole-child approaches these communities embrace for children from birth to five years old continue as those children transition to kindergarten and through elementary, middle, and high school. This represents a sharp difference from most other districts, which focus heavily on narrow academic factors and assessments and thus neglect characteristics emphasized in pre-K, such as building strong teacher-student relationships and attending to the full range of children's assets and needs. As these examples illustrate, students continue to benefit from a more comprehensive approach to education and there is an array of strategies school districts can use to deliver that comprehensive approach.

Enriching K-12 curricula and activities to sustain pre-K's whole-child emphasis

A broad set of investments and activities can help sustain pre-K's whole-child approach, including enhancing classroom experiences, aligning classroom lessons with out-of-school activities that expand children's worldviews, and using targeted strategies to improve students' readiness for college, careers, and civic engagement.

Schools that ensure hands-on learning both in and out of the classroom make the most of this opportunity. Joplin and Pea Ridge students and their teachers enjoy service learning projects that are a core component of the Bright Futures strategy. These range from kindergartners organizing coat drives and canned food drives for their neighbors to high school students designing and implementing water research projects and reporting on the health and safety of Joplin's water supply to the city's water management agency. In East Durham, partnerships with community agencies and nonprofits enable clubs, field trips to museums, and other enrichment activities.

After-school and summer programs help students build on what they learned during the school year, broaden students' worldviews and skills, and reduce summer learning loss. In most of the districts studied, schools partner with organizations such as the YMCA, Boys and Girls Clubs, Boy Scouts, and Girl Scouts to provide out-of-school enrichment programs that range from organized sports and help with homework to math and book clubs, theater, and robotics. In addition to boosting student engagement, some focus in particular on academic and college preparatory help, and many also provide snacks or even full meals. Summer camps in Boston and East Durham and book deliveries and clubs in Pea Ridge and Eastern Kentucky-where online options help bridge long distances in rural areas-keep students reading, engaged, and on track for fall classes.

In several districts, the focus on nurturing not only students' academic skills but also their social and emotional skills strengthens the transition to kindergarten and development throughout the K-12 years. Vancouver's schools teach and model social and emotional learning in classrooms as part of the district's work to improve school climate and track student data on engagement and mental health. Under City Connects-the whole-child collaboration among Boston College, Boston Public Schools, and community agencies-school coordinators meet at the start of the year with teachers to discuss the particular strengths and needs of each student and develop plans to support teachers with academic and enrichment activities and meet student needs with small-group sessions on healthy eating and dealing with bullies, referrals to mental health providers, and a range of other supports (Weiss 2016g).

Two districts have made social and emotional learning a particularly high priority. Austin is one of eight districts working with the Collaborative for Academic, Social, and Emotional Learning (CASEL) to comprehensively embed social and emotional learning in teacher training, teacher standards, curricula, and metrics for assessing student and school progress (CASEL 2017). In Montgomery County, former superintendent Joshua Starr drew on the Common Core's emphasis on problem-solving and critical thinking to lead the design of a new curriculum and classroom practices that nurture social and emotional skills. These are complemented by enhanced support for teachers to nurture social and emotional learning in daily classroom practice, by standards-based report cards that track key social and emotional skills, and by constructive disciplinary policies that reengage students and build their soft skills instead of punishing them for infractions. 30

Several of the districts focus in particular on helping students-many of whom will be the first in their families to go to college-prepare for and make that leap. Strategies include middle-to-high-school transition programs in Joplin and Vancouver and clubs and specialized courses that advance students' social and organizational skills in Vancouver and Montgomery County. In East Durham, three initiatives (Communities in Schools Durham, Student U, and Citizens in Schools) support youth who are preparing for graduation. They offer site-based mentoring from current undergraduates. Middle and high school students in the North Minneapolis Northside Achievement Zone receive similar assistance. And Vancouver's GRADS Teen Parent program helps teen parents stay in school, graduate, and be more effective parents. De-tracking, an intentional decision to not separate students who are achieving at different levels into different classrooms or types of courses, which is the norm in Austin and in some Montgomery County high schools, helps ensure that college preparatory classes serve students of all income levels rather than just wealthier, nonminority students. 31

College readiness is also a high priority for many Bright Futures districts. In Joplin, programs such as Operation College Bound enhance students' understanding of and access to postsecondary education, complementing initiatives that help students navigate transitions to higher education and other sensitive periods of their academic lives. And in Pea Ridge, specialized high schools such as the Manufacturing and Business Academy and Pea Ridge Academy provide targeted support for students who want to go straight to jobs and careers or need special academic supports.

Mentoring and tutoring to get and keep students engaged

In the case study districts, the whole-child approach includes understanding the critical importance of one-on-one relationships with caring adults who support children's academic and broader needs. Strategies can be as simple as the car and bus "buddies" who greet children in Pea Ridge each morning as they arrive at school, or as intensive as the volunteer "lunch buddies" who meet regularly with Joplin and Pea Ridge students to eat with them, talk about their days, and offer guidance. Northside Achievement Zone in North Minneapolis partners with Big Brothers Big Sisters to connect students with mentors, and over 500 volunteer mentors in Vancouver, Washington, support students in Family and Community Resource Centers.

These relationships are key to efforts in large urban districts and remote rural ones. The Children's Aid Society has partnered with the New York City Department of Education to integrate a strong school curriculum with out-of-school enrichment programming, as well as provide child and family support services designed to remove barriers to students' learning (Weiss 2016h). Children's Aid community schools offer both tutoring and mentoring among their after-school options, as do Boston's City Connects schools. In Eastern Kentucky, to bridge the long distances between one school and community and another, mentors use Skype to connect with eighth- and ninth-graders in Promise Neighborhood area schools.

Supports for student health and family wellness as a tool for sustaining early gains

Several of the districts studied have established health clinics in some or all of their schools, including Montgomery County, Vancouver, and New York City. In some other districts, such as Austin, school coordinators can arrange for mobile clinics to come to schools. These clinics provide basic preventive care through immunizations and check-ups, along with prescriptions and other care for sick children, physical and mental health screenings, follow-up counseling, mental health care, and even crisis intervention when needed.

Nutrition is another critical factor that affects physical and mental health and thus learning. In East Durham, Back Pack Buddies and summer lunch programs prevent hunger and keep kids nourished. Food and clothing pantries plus social media outreach in Pea Ridge and Joplin enable counselors and teachers to meet targeted immediate needs so students can focus and learn. Montgomery County has expanded its breakfast-in-the-classroom program to serve all students in a growing share of schools (MCPS 2017).

Many of these districts look beyond meeting students' basic health and nutrition needs to advancing their and their families' wellness and strengthening their ties to the community. Vancouver's GoReady! back-to-school festivals provide backpacks, school supplies, shoes and socks, immunizations and dental screenings, and even haircuts, plus resources from community partners. In Eastern Kentucky, physical and mental health supports provided through state-supported Family Resource and Youth Service Centers are complemented by school-community collaborative activities through a run/walk club, a summer fitness program, a Jump Start program, and gardening and food preservation activities. And the East Durham Children's Initiative runs a Healthy Living Initiative that refers families to nutrition counseling programs, Zumba classes, cooking demonstrations, and walking groups; it also distributes children's bicycles and partners with local farmers markets to provide families with fresh produce.

Though research has long affirmed the importance of parental engagement, many schools struggle to meaningfully engage parents. The case study districts show how it can be done. In the rural regions around Berea, Kentucky, where physical distance makes engagement difficult, Partners for Education's Families and Schools Together project convenes parents, school staff, and local agency professionals to help parents build social networks. In the North Minneapolis Northside Achievement Zone (NAZ), a high-poverty heavily minority area, regular one-on-one meetings between parents and "connectors"-specialized social workers who grew up in the area, are familiar with its challenges, and are a core component of the NAZ strategy-provide opportunities to conduct family needs assessments and provide referrals to relevant services. These regular meetings lead to deeper parental engagement in their children's schools.

And full-service community schools such as those in Vancouver and New York City specialize in parent outreach and engagement. Community schools in these districts draw on parental input to shape school policies and practices and provide parents with an opportunity to meet one another. For example, a "parents' coffee room" in a New York City school with a large Dominican population evolved from simply providing a space for parents to hang out after student drop-off to a center for parent-led workshops, parent-student collaborative plays, and more.

Other targeted supports provide added help for the most vulnerable students and their families. In Vancouver, for example, student advocates conduct home visits to parents of kindergartners and first-graders who are at risk of chronic absenteeism. In these visits, the advocates emphasize the importance of attendance and brainstorm with parents ways to reduce specific barriers to attendance. Complementary in-school efforts reward strong attendance. High-risk Montgomery County Public Schools students benefit from an unusual, but very effective, system of targeted support. Specifically, the districts' funding system redistributes money from wealthier schools to higher-poverty schools, enabling the latter to provide smaller classrooms, more individualized attention, and more specialists in English language learning, special education, and other areas (Elmore, Thomas, and Clayton 2006).

How academic gains, including smaller achievement gaps, indicate that the investments are paying off

Providing children from birth through 12th grade and their families with targeted supports both within and outside of school has enabled these communities to make progress toward a range of goals. First, compared with students in peer districts, these districts' students tend to have better outcomes on traditional measures of academic achievement such as test scores and graduation rates. Second and just as, if not more, important, these districts have improved students' kindergarten readiness, engagement, and health and well-being, and helped the students be better prepared for college, careers, and civic engagement. This is true in large part due to these districts' intentional bucking of a growing trend of diverging practices in which students in high-poverty schools are subject to narrow academic drilling while students in wealthy schools benefit from a broader set of activities and learning experiences beyond a narrow focus on preparing for standardized tests. These districts ensure enrichment for all students, regardless of socioeconomic status. Finally, in contrast with the national trend in recent decades of rapidly growing achievement gaps between wealthy and poor students, these districts are also narrowing race- and income-based achievement gaps: while all students are gaining ground, those who started off behind tend to see the largest gains.

Most of the data presented in this section do not come from experimental studies; with a few exceptions (which are noted in the case studies), they rely on nonexperimental comparisons with a similar nontreatment group, such as other low-income children in the district or other high-poverty districts in the state. However, they are gathered from official district, state, or federal resources in all cases, except for the minority of cases in which such data are not publicly available. Perhaps most importantly, in contrast with many other programs that have reported substantially improved outcomes for very vulnerable groups of students, these programs do not cherry-pick students to get these results. Rather, these initiatives serve all students in the enrollment area for a school, a cluster of schools, or, in many cases, an entire district; as described above, they are serving some of the nation's most vulnerable students and their families. 32 Moreover, many of these efforts are, for lack of a better term, "turnarounds." That is, students in an existing system that is considered to be failing are offered a new approach in the same school building, making the large gains reported particularly striking given the notable lack of similar progress from much-larger-scale, more publicized attempts at employing other turnaround strategies. 33

Establishing more expansive goals and implementing ways to track progress toward those goals also offers timely guidance, given that the Every Student Succeeds Act (ESSA) asks states, districts, and schools to do just that. These districts have not only set broader goals, they are demonstrating real progress toward achieving these goals. Because of their success, many now serve as role models for other districts or entire regions, and a few are beginning to influence state policy as well.

Higher rates of kindergarten readiness predict school success

Some of the kindergarten readiness efforts described above have translated into improved readiness to learn and, thus, greater odds of success in kindergarten and throughout the K-12 years. In Eastern Kentucky, East Durham, and Minneapolis, children who participated in early learning programs significantly increased their rates of kindergarten readiness across a range of metrics and developmental domains. A study of Montgomery County Public Schools found much larger gains in reading for children in the full-day Head Start program than for children in the half-day program, with full-day students more than doubling their reading scores over the year and especially pronounced gains for the most vulnerable students: Hispanics and English language learners (Marietta 2010).

Rising test scores and narrowing gaps in core academic subjects are an important sign of sustained early gains

While only one of many indicators, rising test scores and narrowing gaps in core academic subjects are an important sign that schools in case study districts have sustained and enhanced early gains. Despite serving a higher percentage of low-income, black, Hispanic, and English language learner students than the district average, Austin's Alliance Schools-schools in which community organizers have worked to empower parents in conjunction with teacher advocacy efforts-saw substantial gains in scores on the Texas Assessment of Academic Skills, the state's main standardized test, in the three years after parent-organizing efforts began. Increases varied from four points to 15-19 points, with the latter increases occurring in schools with the highest levels of parental engagement (Henderson 2010). Subsequent rollout of social and emotional learning in district schools (some of which were also Alliance schools) produced gains in the share of students deemed proficient on the State of Texas Assessment of Academic Readiness (STAAR, the next-generation state assessments) in the years following that rollout, with students in the first set of schools with social and emotional learning programs scoring higher on state math and reading exams than those in later school cohorts. The small group of Minneapolis Northside Achievement Zone students who were tested increased their proficiency on the Minnesota Comprehensive Assessments (MCA) exam, with the share scoring as proficient rising from 14 percent in the 2012-2013 academic year to 22 percent in 2013-2014. 34 Students who had enrolled in the Northside Achievement Zone in 2013 had larger gains than those who enrolled in 2014, and, overall the largest proficiency gains were among first- and second-graders, with the smallest gains in middle schools.

Despite serving a much poorer and socially and economically isolated student body than in state schools overall, the Eastern Kentucky schools served by Partners for Education have seen substantially higher increases in test scores: from 2012 to 2015, math test scores in the Promise Neighborhood region rose 7.0 percentage points compared with 4.4 percentage points across the state, and reading scores rose 7.3 percentage points, compared with 5.8 percentage points statewide.

An independent study of middle school students who participated in the after-school programs run by Children's Aid Society community schools in New York City had bigger gains in math and reading test scores than peers who did not participate. They also had higher relative increases in school attendance and in teacher-reported "motivation to learn." And while the Children's Aid Society did not make early childhood education investments a core component of its strategy, its Zero-to-Five program, which connects the federal Early Head Start and Head Start programs, produced relative test score gains among participants. Specifically, a study found that participants outperformed their peers 97 percent of the time on third-, fourth-, and fifth-grade standardized tests in math and reading, demonstrating a significant long-term positive effect (Caspe and Lorenzo Kennedy 2014).

Increases (or lack of decreases) in reading scores over the summer months (between the end of the school year and the start of the following year) can be an especially important indicator of sustainable academic achievement, since low-income students tend to lose substantial ground when they are out of school for the summer. Students who attended the North Minneapolis Northside Achievement Zone's extended learning summer programs increased their reading test scores between the end of one school year and the beginning of the next, a period when scores normally decrease. And an evaluation of students who attended the East Durham Children's Initiative's summer camp in the summer of 2014 found that they lost no ground in literacy over those months.

Case study districts with more mature initiatives and those offering higher or more intensive doses of whole-child interventions are producing particularly large academic gains. Students enrolled in City Connects elementary schools in Boston score significantly higher on tests of both academic and noncognitive skills in elementary and secondary school, with the highest-risk students, such as English language learners, showing especially large gains. Scores of City Connects elementary school students on the Stanford Achievement Test version 9 increased between one-fourth and one-half a standard deviation greater than scores of their non-City Connects peers. And graduates of City Connects secondary schools are more likely to attend one of Boston's three most selective public high schools.

Better student attendance and engagement are also predictors of academic gains

Chronic absenteeism depresses achievement, particularly among low-income students. A 2009 study found that New York City Children's Aid Society's community schools had "far higher" attendance than peer schools, and that schools with health centers tended to have higher attendance than those without health centers (Clark et al. 2009). Students attending City Connects high schools in Boston have significantly lower rates of chronic absenteeism than their peers (Boston College Center for Optimized Student Support 2012). In Joplin, Missouri, attendance rates among high school students increased 3.7 percentage points, rising from 91.3 percent in 2008 to 95.0 percent in 2012; black and Hispanic students closed gaps with their white peers over that period. At the same time, reportable disciplinary incidents-which keep students out of school and are found to drive at-risk students to disengage-dropped by over 1,000, from 3,648 in 2008 to 2,376 in 2012. 35

Every infant and toddler in East Durham whose family participated in the Healthy Families Durham home visiting program is up to date on immunizations; this helps at-risk children avoid missing school due to illness. In Pea Ridge, collaboration with one of the city's doctors enabled the district to provide physical exams for high school students who would otherwise go without them. This not only improved their health but enabled them to participate in the kinds of extracurricular sports activities that boost student engagement. And City Connects' practice of helping families draw on Medicaid coverage and of referring eligible students to insurance-eligible providers increases students' access to both physical and mental health care. Given extensive evidence linking reduced absenteeism and improved physical and mental health to academic gains, these initiatives' records of boosting both attendance and health represent another pathway to student success. 36

Increases in advanced coursework and completion of associated exams suggest improved college and career readiness

Because most of the initiatives studied have been in place for less than 10 years, and a few for five or fewer, there is less evidence of their impact on high school graduation and college enrollment. Nonetheless, the degree to which low-income and minority students in these districts perform better and have seen greater gains on these key indicators than their peers in comparable districts or across the state highlights the promise of comprehensive education approaches and, in some instances, their capacity to sustain and even boost children's early gains.

Parent-organizing in Austin helped establish a program to get more low-income and minority middle school students into rigorous science and math programs, enabling them to successfully compete for slots in the prestigious LBJ High School Science Academy. From the 2007-2008 to the 2014-2015 academic year, the number of Kalamazoo Public School students taking Advanced Placement (AP) courses more than doubled, with low-income and African American students experiencing the largest absolute gains in participation and Hispanic students experiencing the largest percentage gains. Black and low-income students roughly quadrupled their participation in such courses; 263 black students and 193 low-income students took AP classes during the 2014-2015 academic year, up from 63 and 53 respectively in 2007-2008 (Miller-Adams 2015). Over the same period, the number of Hispanic students taking AP courses increased by a magnitude of 10-from just 8 to 78. And in Vancouver, which also made socioeconomic diversity of students in advanced courses a priority, enrollment in AP courses rose by 67 percent overall from 2007-2008 to 2013 -2014, and nearly three times as fast, by almost 200 percent, among low-income students.

Higher graduation rates and increasing college attendance of disadvantaged students are another measure of success of comprehensive strategies

In the early 2000s, the graduation rate at Austin's Reagan High School fell below 50 percent and enrollment dropped to just 600 students. By 2015, with the benefit of a community schools strategy, the school was serving more than 1,200 students and had a graduation rate of 85 percent.

In the first six years of Bright Futures, Joplin's graduation rate rose from 73 to 87 percent; from 2012 to 2015 it rose 13 percentage points, versus just 5 percentage points across the state as a whole. At the same time, the cohort dropout rate fell from 6.4 percent to 2.8 percent, with the dropout rate for black students falling slightly more. And in Kalamazoo, incentives to finish high school have proven to be powerful tools for disadvantaged students when combined with mentoring, tutoring, and after-school options. The district's graduation rate rose from 64 percent in 2009 to 69 percent in 2014, with "five-year cohort graduation rates consistently higher than four-year rates, suggesting that some students may be opting to stay in school an extra year (or even just for the summer) to complete the credits necessary to get a high school diploma" (Miller-Adams 2015, 67). Moreover, African American girls in Kalamazoo graduate at higher rates than their peers across the state, and 85 percent of those graduates go to college.

Initiatives that have had time to mature have made particularly large gains. Montgomery County's Linkages to Learning initiative began in 1993 and it substantially expanded its pre-K program around a decade later; a county policy responsible for improved racial integration has been in place even longer, since the early 1970s. Hispanic, low-income, and African American students in Montgomery County Public Schools are much more likely than their counterparts across the state to graduate from high school-80.0 vs. 77.5 percent, 81.0 vs. 77.8 percent, and 86.4 versus 80.5 percent, respectively. And from 2011 to 2014, a period when the share of students in poverty and the share of minority students rose in the district, overall graduation rates rose 2.9 percentage points, from 86.8 to 89.7 percent. There were much larger gains for Hispanic and black students, whose graduation rates rose (respectively) by 4.7 percentage points (from 75.3 to 80.0 percent) and 5.1 percentage points (from 81.3 to 86.4 percent), thus narrowing their gaps with their white peers by 3.4 and 3.8 percentage points, respectively (MCPS 2015). Participation in Boston's City Connects program, which began in 2001, cuts a student's odds of dropping out of high school nearly in half: 8.0 percent versus 15.2 percent for comparison students (Boston College Center for Optimized Student Support 2014). In Vancouver, the four-year graduation rate rose from 64 percent in 2010 to almost 80 percent in 2013, and the five-year rate rose from 69 percent in 2010 to over 80 percent in 2013. Vancouver's Hispanic students had five-year graduation rate gains of over 15 percentage points.

Strong parent and community engagement is another sign of progress

The comprehensive, whole-child, whole-community approaches in the featured school districts have built strong school-community partnerships. Two indicators of the strength of the partnerships are the levels of parent and community engagement. In Joplin, 194 more adults are now serving as mentors and tutors than five years ago. And the American Association of School Administrators, National School Public Relations Association, and Blackboard Connected selected Vancouver Public Schools Superintendent Steve Webb and Chief of Staff Tom Hagley for their 2011 Leadership through Communication Award for their successful efforts to increase family engagement in high-poverty VPS schools.

Parental engagement boosts student achievement both directly and through other improvements to families' situations. As they work actively with their "connectors," Northside Achievement Zone parents in North Minneapolis become more likely to make academics a priority, to engage with their children's schools, and to be focused on sending their children to college. The support also helps more families connect with stable housing, substantially reducing the number of times that some vulnerable families move. In 2014-2015, up to 300 Austin families benefited from help with legal, employment, health, and housing issues at the family resource center, which also provides classes for parents, including English language learning classes. And Montgomery County Public Schools social workers who specialize in early childhood education make an average of 200 home visits, 1,000 phone contacts, and 300 direct contacts with parents at school or conferences each month. These lead to roughly 1,000 monthly referrals to community services-many of them emergency interventions dealing with food, clothing, and housing-that help families meet their children's basic needs and, thus, support their children's education (Marietta 2010).

In some cases, engagement enhances school leadership. Through access to supports such as social services and adult education, parents of students in New York's Children's Aid Society community schools got more involved in their children's schools, took more responsibility for their children's schoolwork, reported feeling more welcome within the schools, and were observed to be a greater presence in the community schools than in comparison schools. And over 2,000 Kentucky parents have undergone training at the Berea Commonwealth Institute for Parent Leadership since its creation in 1997. Many of these parents have gone on to join school boards, serve on school councils, and engage in day-to-day educational advocacy.

Expansion of these initiatives shows that other districts, and even state policymakers, consider them successful

After City Connects succeeded in improving student achievement in over a dozen of Boston's highest-poverty schools, the initiative caught the attention of state policymakers, who recruited City Connects to help turn around schools in Springfield, home to another large high-poverty urban district in Massachusetts. Aided by federal School Improvement Grant funds, City Connects has operated in Springfield since 2010, expanding from six to 13 schools in its first four years there. In New York City, the Children's Aid Society played a central role in Mayor Bill de Blasio's 2016 decision to employ a community schools strategy to turn around 100 of the city's most struggling schools. And in both Vancouver and Austin, district leaders have led advocacy efforts to bring community schools to other communities in the region and to support the introduction of state-level legislation to enhance the work.

Bright Futures began in Joplin, Missouri, in 2009 but is now a national organization. Bright Futures USA has 50 affiliates in eight states, many of which-such as Pea Ridge-are just two or three years old. The newest affiliate, in Fairbanks, Alaska, has just been made official. In Virginia, Dave Sovine, superintendent of a second-year affiliate, Frederick County Public Schools, is reaching out to several of his counterparts across the region to create the first regional Bright Futures initiative (Gizriel 2016). If established, this would allow for the kind of cross-district collaboration identified by Bright Futures founder C.J. Huff as critical to breaking down the silos created by arbitrary boundaries that reflect political preferences rather than children's daily realities. 37

Conclusions

As this report demonstrates, very large social-class-based gaps in academic performance exist and have persisted across the two most recently studied cohorts of students starting kindergarten. The estimated gap between children in the top fifth and the bottom fifth of the SES distribution is over a standard deviation in both reading and math in 2010 (unadjusted performance gaps are 1.17 and 1.25 sd respectively). Gaps in noncognitive skills such as self-control and approaches to learning-which are critical not only as foundations for academic achievement but also more broadly for children's healthy development-are about half as large (about 0.4 sd in self-control, and slightly over 0.5 sd in approaches to learning in 2010).

Another important finding from our study is that gaps were not, on average, sensitive to the set of changes that may have occurred between 1998 and 2010: gaps across both types of skills are virtually unchanged compared with the prior generation of students-those who entered school in 1998. The only cognitive gap that changed substantially was in reading skills, which increased by about a tenth of a standard deviation. The gaps by SES in mathematics, in approaches to learning as reported by parents, and in self-control as reported by teachers did not change significantly. And relative gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank between 1998 and 2010, by about a tenth of a standard deviation. 38

We also find that, while taking into account children's personal and family characteristics, parental activities, and other factors reduces the gaps somewhat, it does not come close to eliminating them. This means that there is a substantial set of SES-related factors that are not captured by the traditional covariates used in this study but that are important to understanding how and why gaps develop. Moreover, the capacity for these other factors-child and family characteristics, early education investments, and expectations-to narrow gaps has decreased over time. This suggests that, while such activities as parental time spent with children and center-based pre-K programs cushion the negative consequences of growing up in a low-social-class context, they can do only so much, and that the overall toxicity of lacking resources and supports is increasingly hard to compensate for. The resistance of gaps to these controls should thus be a matter of real concern for researchers and policymakers.

These troubling trends point to critical implications for policy and for our society: clearly, we are failing to provide the foundational experiences and opportunities that all children need to succeed in school and thrive in life. The failure to narrow gaps between 1998 and 2010 suggests, too, that investments in pre-K programs and other early education and economic supports were insufficient to counter rising rates of poverty and its increasing concentration in neighborhoods where black and Hispanic children tend to live and learn.

But there is also good news. The case study review in the previous section of this report explores district-level strategies to address these gaps, strategies that are being implemented in diverse communities across the country. The most effective ones begin very early in children's lives and are sustained throughout their K-12 years and beyond. The communities studied all employ comprehensive educational approaches that align enriching school strategies with a range of supports for children and their families. Their implementation is often guided by holistic data and, to the extent possible, this report provides a summary, as well, of student outcomes, using both traditional academic measures and a broad range of other measures.

These findings also point to further research questions that need to be addressed, including why gaps changed or did not change, for whom they changed (or did not change), and what is the absolute change in children's skills over time. 39

Parents are doing what they need to do, and a growing number of communities are, too, but as a society, we are still falling far short

Over the period studied, parents across all social class groups became more involved in their young children's early education and development, with increases in involvement being especially pronounced among low-SES parents. Parents were more likely in 2010 than in 1998 to read regularly to their children; to sing to them; to play games with them; and to enroll them in center-based pre-K programs. Parents in 2010 also had significantly higher expectations for their children's educational attainment, and mothers themselves were more highly educated-both factors that are associated with higher achievement for those children. In other words, parents' actions show that they are doing more of what the brain science indicates they need to do, which either suggests that information about children's needs during those years is more widely disseminated than it was for the prior cohort we studied, or that parenting styles have changed in a way that benefits the development in the early years.

And, as the case studies indicate, the number of communities that have embraced systems of comprehensive enrichment and supports ("Broader, Bolder Approaches to Education") is growing. As these communities have shown, such comprehensive education policies are feasible; embedded in these policies is an understanding that children's development involves nurturing a variety of competencies throughout the stages of development, that there are many individuals participating in these processes, and that coordinated efforts by various stakeholders are needed to put these processes to work. Key principles that span across the case studies include very early interventions and supports, parental engagement and education, pre-K, kindergarten transitions, whole-child approaches to curricula, and wraparound supports that are sustained through the K-12 years. Given the significant need for more such strategies, it is important to understand the factors that drove their enactment in a diverse set of communities, and to continue to monitor both the challenges these communities (and others like them) encounter and the outcomes/benefits of the initiatives.

However, despite the abundance of child development information available to researchers and parents-about the serious impacts of child poverty, about what works to counter those effects, about the importance of the first years of life for children, and about the value of education-our data indicate insufficient policy response at all levels of government. Pre-K programs have expanded incrementally and unevenly, with both access and quality still wildly disparate across states and overall availability severely insufficient. There is a dearth of home visiting programs and of quality child care (Bivens et al. 2016). Child poverty has increased (see Proctor, Semega, and Kollar 2016 for recent trends in child poverty rates). And the schools these children enter face increasing economic and racial segregation but with even fewer resources than they had in 1998 to deal with them (Adamson and Darling-Hammond 2012; Baker and Corcoran 2012; Carnoy and García 2017). And while a growing number of districts have embraced Broader, Bolder approaches, that number is failing to keep up with high and growing need.

In sum, it is actually positive, and somewhat impressive, that gaps by and large did not grow in the face of steadily increasing income inequality, compounded by the worst economic crisis in many decades (EPI 2012, 2013; Saez 2016). But it is disappointing and troubling that new policy investments made in the previous decade were insufficient to make even a dent in these stubborn gaps. We cannot ensure real opportunities for all our children unless we tackle the severe inequities underlying our findings. And while momentum to enact comprehensive and sustained strategies to close gaps is growing, such strategies are not being implemented nearly as quickly as children need them to be.

Next policy steps

These data on large, stubborn gaps across both traditional cognitive and noncognitive skills should guide the design of education policies at the federal, state, and local levels; the combined resources and support of government at all three levels are needed if we are to tackle these inequalities effectively. 40

Policymakers can begin by learning from the small-scale, district-level strategies presented in the review of case studies above (see the section "What are pioneering school districts doing to combat these inequities and resulting gaps?" above). Looking at these case studies, policymakers can ask: What are the key strategies these communities employed, what main components characterize these strategies, and how did these communities effectively implement the strategies? What challenges did these communities face, what was needed to overcome the challenges, and how can we shape policies that better support other communities' abilities to respond to such challenges and, to the extent possible, avert them? The latter set of questions is particularly pertinent to issues of scalability, financing, and sustainability, all of which have posed significant challenges for the districts studied and others like them. Policymakers can further ask: What other sources or examples might we learn from? Obvious ones include other districts that employ "community schools" strategies (as Vancouver, New York City, and Austin do) and Promise Neighborhood initiatives beyond Berea/Eastern Kentucky and the Northside Achievement Zone. Bright Futures affiliates now exist in 50 districts across eight states-and the program continues to grow-offering another set of communities to look to.

Also, new opportunities under the Every Student Succeeds Act (ESSA)-from funding to expand and align early childhood education programs to broader and more supports-based educator- and school-accountability systems-provide another avenue for exploration and educational improvement. This is already the focus of states and districts across the country-as well as of education policy nonprofits and associations-and is a focus that has the potential to inspire viable larger-scale models (Cook-Harvey et al. 2016).

We must take action, in particular, in those areas of policy related to early education in which we have seen little or no progress over the past decade. These include child care: comprehensive supports that engage parents as partners in their children's education must start early and be of high quality to prevent the emergence of gaps and provide time to close any gaps that emerge (Bivens et al. 2016, among others). Quality preschool, among the most-agreed-upon strategies to avert and narrow early gaps, continues to be much talked about but far too little invested in and far too infrequently and shoddily implemented. The advantages of preschool have been known for decades, and significant progress has been made in preschool enrollment over that time; however, preschool enrollment stagnated soon after 2000 (Barnett et al. 2017; U.S. ED 2015) and there continue to be significant inequities in access (see Table 2; García 2015) and, just as important, in quality (NIEER 2016). And the gains made through these early, whole-child-oriented supports must be sustained through children's K-12 years, with attention to issues of funding levels and equity, racial and socioeconomic integration, and enriching opportunities in the hours after school and in the summer months.

Altogether, this report adds to the strong evidentiary base that identifies strategies to reduce the education consequences of economic inequality. It also sheds light on the need to conduct further research on the channels that drive or cushion changes in readiness. A close follow-up of these trends in the near future and of the measures adopted to really tackle inequities will not only determine what type of society we will be, but will also say a lot about what type of society we actually are. This study, affirming a growing number of other studies on these issues, points to an "American Dream" that is alive in public pronouncements but dormant and pale in reality.

About the authors

Emma García is an education economist at the Economic Policy Institute, where she specializes in the economics of education and education policy. Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Prior to joining EPI, García conducted research for the Center for Benefit-Cost Studies of Education and other research centers at Teachers College, Columbia University, and did consulting work for the National Institute for Early Education Research, MDRC, and the Inter-American Development Bank. García has a Ph.D. in economics and education from Teachers College, Columbia University.

Elaine Weiss served as the national coordinator for the Broader, Bolder Approach to Education (BBA) from 2011 to 2017, in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive. Weiss came to BBA from the Pew Charitable Trusts, where she served as project manager for Pew's Partnership for America's Economic Success campaign. Weiss was previously a member of the Centers for Disease Control and Prevention's task force on child abuse and served as volunteer counsel for clients at the Washington Legal Clinic for the Homeless. She holds a Ph.D. in public policy from the George Washington University and a J.D. from Harvard Law School.

Acknowledgments

An earlier version of this paper was prepared for "Strong Foundations: The Economic Futures of Kids and Communities," the Federal Reserve System Community Development Research Conference, Washington, D.C., March 23-24, 2017. We appreciate the feedback we received from our discussant Richard Todd and from the audience. The authors gratefully acknowledge Rob Grunewald and Milagros Nores for their insightful comments and advice on earlier drafts of the paper. Special gratitude is expressed to Sean Reardon, for his advice and thorough guidance on the sensitivity analyses affecting the measurement of the cognitive skills and their implications for our study, and for sharing useful materials to help test our results. We thank Ben Zipperer and Yilin Pan for their advice on issues associated with multiple imputation of missing data. We are also grateful to Lora Engdahl and Krista Faries for editing this report, and to Margaret Poydock for her work preparing the tables and figures and formatting the report. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford, Kayla Blado, and Elizabeth Rose.
Address correspondence to: Economic Policy Institute, 1225 Eye St. NW, Suite 600, Washington, D.C., 20005. Email: egarcia@epi.org; eweiss@boldapproach.org.
Figures and tables

Endnotes

1. Values are in 2008 dollars.

2. Early investments in education strongly predict adolescent and adult development (Cunha and Heckman 2007; Heckman 2008; Heckman and Kautz 2012). For instance, students with higher levels of behavioral skills learn more in school than peers whose attitudinal skills are less developed (Jennings and DiPrete 2010). In general, as Heckman asserted, "skills beget skills," meaning that creating basic, foundational knowledge makes it easier to acquire skills in the future (Heckman 2008). Conversely, children who fail to acquire this early foundational knowledge may experience some permanent loss of opportunities to achieve to their full potential. Indeed, scholars have documented a correlation between lack of kindergarten readiness and not reading well at third grade, which is a key point at which failing to read well greatly reduces a child's odds of completing high school (Fiester 2010; Hernandez 2011).

3. Research by Reardon (2011) had found systematic increases in income gaps among generations. Recent studies by Bassok and Latham (2016) and Reardon and Portilla (2016), however, show narrower achievement gaps at kindergarten entry between a recent cohort and the previous one, and thus a possible discontinuation or interruption of that trend. (Bassok et al. [2016] use an SES construct to compare relative teacher assessments of cognitive and behavioral skills among low-SES children versus all children, adjusted by various other characteristics; Reardon and Portilla [2016] look at relative performance of children in the 90th and 10th income percentiles, and use age-adjusted, standardized, outcome scores.) Research by Carnoy and García (2017) shows persistent social-class gaps, but no solid evidence regarding trends: their findings for students in the fourth and eighth grades, in math and reading, show that achievement gaps neither shrink nor grow consistently (they are a function of the social-class indicator, the grade level, or the subject).

4. Clustering takes into account the fact that children are not randomly distributed, but tend to be concentrated in schools or classrooms with children of the same race, social class, etc. These estimates offer an estimate of gaps within schools. See Appendix B for more details.

5. Results available upon request. See García 2015 for results for all SES-quintiles (the baseline or unadjusted gaps in that report correspond with Model 2 in this paper).

6. The Early Childhood Longitudinal Study asks both parents and teachers to rate children's abilities across a range of these skills. The specific skills measured may vary between the home and classroom setting. Teachers likely evaluate their students' skills levels relative to those of other children they teach. Parents, on the other hand, may be basing their expectations on family, community, culture, or other factors.

7. See García 2015 for a discussion of which factors in children's early lives and their individual and family characteristics (in addition to social class) drive the gaps among children of the 2010 kindergarten class.

8. Note that the SES quintiles are constructed using each year's distribution, and that changes in the overall and relative distribution may affect the characteristics of children in the different quintiles each year (i.e., there may be some groups who are relatively overrepresented in one or another quintile if changes in the SES components changed over time).

9. The detailed frequency with which parents develop or practice some activities with their children at home and others is available upon request.

10. Literature on expectations and on parental behaviors in the home find that they positively correlate with children's cognitive development and outcomes (Simpkins, Davis-Kean, and Eccles 2005; Wentzel, Russell, and Baker 2016). This literature acknowledges the multiple pathways through which expectations and behaviors influence educational outcomes, as well as the importance of race, social class, and other factors as moderators of such associations (Davis-Kean 2005; Redd et al. 2004; Wentzel, Russell, and Baker 2016; Yamamoto and Holloway 2010).

11. This may be affected by the fact that the highest number of reported books in 1998 was "more than 200," while in 2010 parents could choose from more categories, up to "more than 1,000." We had to use 200 as our cap in order to compare data for the two kindergarten classes.

12. Evidence also points to many other factors that affect children's school readiness, and these, too, likely changed over this time period. For example, access to prenatal care, health screenings, and nutritional programs could all have affected children's development differently across these two cohorts, but we do not have access to these data and thus cannot control for them in our study. For links between school readiness, children's health, and poverty, see AAP COCP 2016; Currie 2009; U.S. HHS and U.S. ED 2016.

13. Models include all quintiles in their specification. Tables that offer a comparison for all quintiles relative to the first quintile are available upon request. We focus the discussion on the gap between the top and bottom.

14. As a result, sample sizes become smaller (see Appendix Table C1). Assuming "missingness" (observations without full information) is completely at random, the findings are representative of the original sample and of the populations they represent. Analytic samples once missingness is accounted for are called the complete case samples. We tested to see whether the unadjusted gaps estimated above with the full sample remained the same when using the complete case samples. For Model 1, we found an average difference of 0.01 sd in the estimates of 1998 SES gaps, and an average difference of 0.02 sd in the estimates of the change in the gaps. For Model 2, the differences were 0.01 sd for the gaps' estimates and 0.04 for changes in the gaps' estimates. In terms of statistical significance, there are no significant changes in the estimates associated with the 1998 gaps, but there are two changes in the statistical significance of the estimates associated with the changes in the gaps by 2010 -2011, and one change in the magnitude of the coefficient. The first change in the statistical significance of the estimates associated with the changes in the gaps by 2010 - 2011 is the change in the gap in approaches to learning as reported by parents, which is statistically significant when using the restricted sample (0.07 sd, at the 10 percent significance level, Model 1); and the second is the change in the gap in math which also becomes statistically significant when using the restricted sample (0.09, at the 10 percent significance level, Model 2). Finally, the one change in the magnitude of the coefficient, in this model, is the estimate of the change in the gap in reading, which increases when using the restricted sample (from 0.12 sd to 0.18 sd). Results are available upon request.

15. These interactions between inputs and time test for whether the influence of inputs in 2010 is smaller than, the same as, or larger than the influence of inputs in 1998. Also, although only the fully specified results are shown, as noted in Appendix B, these sets of controls are entered parsimoniously in order to determine how sensitive gaps and changes in gaps over time are to the inclusion of family characteristics only, to the added inclusion of family investments, and, finally, to the inclusion of parental expectations (for the inclusion of parental expectations, we incorporated interactions of the covariates with time parsimoniously as well). For all outcomes, and focusing on the models without interactions between covariates and time, we find that all gaps in 1998 continuously shrink as we add more controls. For example, in reading, adding family characteristics reduces the gap in 1998 by 11 percent, adding investments further reduces it by 15 percent, and adding expectations further reduces it by 9 percent. In math, these changes equal to 16 percent, 13 percent, and 10 percent. For changes in the gap by 2010-2011, for both reading and math, adding family characteristics and investments shrink the changes in the gaps, but adding expectations slightly increases the estimated coefficients (which are statistically significant for reading, but not for math in these models. For self-control (as reported by teachers) and approaches to learning (by parents), which are the only two noncognitive skills for which the change in the gap is statistically significant, adding family characteristics reduces the change in the "gap [by 2010-2011" coefficient], but adding investments increases it, and adding expectations further increases the changes in the gaps by 2010-2011. These results are not shown in the appendices, but are available upon request.

16. The interactions between parental expectations of children's educational attainment and the time variable test for whether the influence of expectations in 2010 is smaller, the same, or larger, than the influence of expectations in 1998.

17. The change in the skills gaps by SES in 2010 due to the inclusion of the controls is not directly visible in the tables in this report. To see this, see the comparison of estimates of models MS1-MS3 in García 2015. The change in the skills gaps by SES in 1998 is directly observable in Tables 3 and 4 and is discussed below.

18. The numbers in the "Reduction" column in Table 5 (showing the shares of the SES-based skills gaps that are accounted for by controls) are always higher for 1998 than for 2010.

19. Please note that until this point in the report we have been concerned with SES gaps and not with performance directly (though SES gaps are the result of the influence of SES on performance, which leads to differential performance of children by SES and hence to a performance gap). The paragraphs above emphasize how controls mediate or explain some of the skills gaps by SES, so, in a way, controls inform our analysis of gaps because they reveal how changes in gaps may have been affected by changes in various factors' capacity to influence performance. Now the focus is on exploring the independent effect of the covariates of interest on performance. In this report, because we address whether the education and selected practices affect outcomes, the main effect is measured for the 1998 cohort, and we measure how it changed between 1998 and 2010. The detailed discussion for the correlation between covariates and outcomes in 2010 is provided in Table 3 in García 2015.

20. This variable indicates whether the child was cared for in a center-based setting during the year prior to the kindergarten year, compared with other options (as explained in García 2015, these alternatives include no nonparental care arrangements; being looked after by a relative, a nonrelative, at home or outside; or a combination of options. Any finding associated with this variable may be interpreted as the association between attending prekindergarten programs, compared with other options, but must be interpreted with caution. In other words, the child may have attended a high-quality prekindergarten program, which could have been either private or public, or a low-quality one, which would have different impacts. He or she might have been placed in (noneducational) child care, either private or public, of high or low quality, for few or many hours per day, with very different implications for his or her development (Barnett 2008; Barnett 2011; Magnuson et al. 2004; Magnuson, Ruhm, and Waldfogel 2007; Nores and Barnett 2010). For the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010, and for a meta-analysis of results, see Duncan and Magnuson 2013. Thus, more detailed information on the characteristics of the nonparental care arrangements (type, quality, and quantity) would help researchers further disentangle the importance of this variable. This additional information would provide a much clearer picture of the effects of early childhood education on the different educational outcomes.

21. Because these associations seemed counterintuitive, we tested whether they were sensitive to the composition of the index. We removed one component of the index at a time and created five alternative measures of other enrichment activities that parents do with their children. The results indicate that the negative association between the index and reading is not sensitive to the components of the index (the coefficients for the main effect, i.e., for the effect in 1998 range between -0.14 and -0.09, are all statistically significant). For math, the associations lose some precision, but retain the negative sign (negative association) in four out of the five cases (minimum coefficient is -0.06). As a caveat, these components do not reflect whether the activities are undertaken by the child or guided by the adult, the time devoted to them, or how much they involve the use of vocabulary or math concepts. The associations could indicate that time spent on nonacademic activities detracts from parents' time to spend on activities that are intended to boost their reading and math skills, among other possible explanations. These results are available upon request.

22. Note that in this section, "social class" and "socioeconomic status" (SES) are treated as equivalent terms; in the rest of the report, we refer to SES as a construct that is one measure of social class. See Appendices C and D for discussions of two other sensitivity analyses, one based on imputation of missing values for the main analysis in this paper, and the other on the utilization of various metrics of the cognitive variables. Overall, our findings were not sensitive to various multiple imputation tests. In terms of the utilization of different metrics for the cognitive variables, some sensitivity of the point estimates was detected.

23. With certain activities that are already so provided to high-SES children, there may be little room for doing more for them. For example, there are only 24 hours per day to read to your child, so there is a cap on reading from a cap on time. But perhaps there is still room to improve the influence of reading, if, for example, the way reading is done changes.

24. Eight of the 12 districts explored in this paper are the subjects of published case studies. Case studies for the other four are in progress and will be published later this year. When citing information from the published case studies, we cite the specific published study. For the four that are not yet published, we refer to the original sources being used to develop the case studies.

25. Missing or incomplete cells in the table indicate that data were not available on that aspect of student demographics or other characteristics. As per the source note, most data came either from the districts' websites or from NCES.

26. In the country as a whole, poverty rates, which had been rising prior to 2007, sped up rapidly during the recession and in its aftermath (through 2011-2012), and minority students (mainly Hispanic and Asian) grew as a share of the U.S. public school student body. Between 2000 and 2013, even with a decline in the proportion of black students, the share of the student body that is minority (of black or Hispanic origin) increased from 30.0 percent to 40.5 percent, and the proportion of low-income students (those eligible for free or reduced-price lunch) also increased, up from 38.3 percent of all public school students in 2000 to 52.0 percent in 2013 (Carnoy and García 2017). The Southern Education Foundation revealed a troubling tipping point in 2013: for the first time since such data have been collected, over half of all public school students (51 percent) qualified for free or reduced-priced meals (i.e., over half of students were living in households at or below 185 percent of the federal poverty line). Across the South, shares were much higher, with the highest percentage, 71 percent-or nearly three in four students-in Mississippi (Southern Education Foundation 2015).

27. A full cross-cutting analysis of why and how these districts have employed whole-child/comprehensive educational approaches will be published as part of a book that draws on these case studies.

28. The federal Early Head Start (EHS) program includes both a home visiting and a center-based component, with many of the low-income infants and toddlers served benefiting from a combination of the two. Studies of EHS find improved cognitive, behavioral, and emotional skills for children as well as enhanced parenting behaviors.

29. According to one important source for data on access to and quality of state pre-K programs, the State of Preschool yearbook produced annually by the National Institute for Early Education Research (NIEER) at Rutgers University, as of 2015, 42 states and the District of Columbia were funding 57 programs. Moreover, programs continued to recover from cuts made during the Great Recession; enrollment, quality, and per-pupil spending were all up, on average, compared with the year before, albeit with the important caveat that two major states-Texas and Florida-lost ground, and that "[f]or the nation as a whole,...access to a high-quality preschool program remained highly unequal, and this situation is unlikely to change in the foreseeable future unless many more states follow the leaders" (NIEER 2016).

30. Elaine Weiss interview with Joshua Starr, June 2017.

31. Murnane and Levy 1996; Elaine Weiss interview with Joshua Starr, June 2017.

32. In recent years, a growing number of reports have emerged that some charter schools-which are technically public schools and often tout their successes in serving disadvantaged students-keep out students unlikely to succeed through complex application processes, fees, parent participation contracts, and other mechanisms, and then further winnow the student body of such students by pushing them out when they struggle academically or behaviorally. For more on this topic, see Burris 2017, PBS NewsHour 2015, and Simon 2013.

33. See AIR 2011 and Sparks 2017. The federal school improvement models, in order of severity (from lightest to most stringent) are termed "transformation," "turnaround," "restart," and "closure" (AIR 2011, 3).

34. While the cut score on any given assessment/test needed for a student to be considered "proficient" is an arbitrary one, and, in Minnesota and many other states, changes from year to year and from one assessment to another, these gains are a helpful indicator of program effectiveness, as they are comparable over the time period described.

35. Joplin statistics are from internal data produced for the superintendent at that time that are no longer available.

36. Attendance Works, a national campaign to reduce chronic absence, points to a range of studies that document and explain the connections between chronic absenteeism, student physical and mental health, and student achievement. Areas of research include elementary school absenteeism, middle and high school absenteeism, health issues, and state and local data on how these problems play out, among others.

37. Elaine Weiss interview with C.J. Huff, June 2016.

38. See Appendix D for a discussion of results using other metrics for reading and math achievement. Results are not meaningfully different across metrics, though the point estimates differ slightly.

39. This last feature will be explored in a companion paper to this one, as soon as the necessary information is released by NCES. (As Tourangeau et al. [2013] note, the assessment scores for the 2010-2011 cohort are not directly comparable with those for the 1998-1999 cohort. We are waiting on the availability of this data to conduct a companion study that allows us to learn whether starting levels of knowledge rose over these years, and what the relative gains were for different demographic groups.)

40. We acknowledge that there are multiple noneducation public policy and economic policy areas to be called upon to address the problems studied in this report, namely, all the ones that ensure other factors that correlate with low-SES are attended, and, obviously, the ones that lead to fewer low-SES children. These other policies could help ensure that more children grow up in contexts with sufficient resources and healthy surroundings, or would leave fewer children without built-in supports at home that need to be compensated for afterwards. We made these points in two early studies, and in the policy brief companion to this study (García 2015; García and Weiss 2015; García and Weiss 2017). A similar comprehensive approach in terms of policy recommendations was used by Putnam (2015).

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Appendices

Appendix A. Data

Introduction

Our research benefits from the existence of two companion studies conducted by the National Center for Education Statistics (NCES), the Early Childhood Longitudinal Study of the Kindergarten Class of 1998-1999 and the Early Childhood Longitudinal Study of the Kindergarten Class of 2010-2011 (hereafter, ECLS-K 1998-1999 and ECLS-K 2010-2011). The data from these studies come with multiple advantages and a few disadvantages.

The studies follow two nationally representative samples of children starting in their kindergarten year and continuing through their elementary school years (eighth grade for 1998-1999 cohort and fifth grade for the 2010-2011 cohort). The tracking of students over time is one of the most valuable features of the data. The studies include assessments of the children's cognitive performance and knowledge as well as skills that belong in the category of noncognitive, or social and emotional, skills. The studies also include information on teachers and schools (provided by teachers and administrators) and interviews with parents.

Another valuable feature of the data is the availability of two ECLS-K studies (ECLS-K 1998-1999 and ECLS-K 2010-2011), which allows for cross-comparisons "of two nationally representative kindergarten classes experiencing different policy, educational, and demographic environments" (Tourangeau et al. 2013). The two studies are 12 years apart, or a full school cycle apart: when the 2010-2011 kindergarten class was starting school, the 1998-1999 class was starting the grade leading to their graduation. A comparison of the studies thus offers insightful information about the consequences of changes in the system that may have occurred during an entire cohort's school life. For the 2010 study, the sample included 18,174 children in 968 schools. i The 1998 study sample included 21,409 children in 903 schools. ii

This existence of data from two cohorts is also a limitation to the current study, as explained by Tourangeau et al. (2013), who note that the assessment scores for the 2010-2011 class are not directly comparable with those developed for the class of 1998-1999. Although the IRT (Item Response Theory) procedures used in the analysis of data were similar across the two studies, each study incorporated different items, which means that the resulting scales are different. Tourangeau et al. (2013) state that "a subsequent release of the ECLS-K: 2010-2011 data will include IRT scores that are comparable with the ECLS-K 1998 cohort." Up to the point of publication of the current study, this information had not yet been released, and we use standardized scores, instead of raw scores, for the outcomes examined. We can assess changes in the relative position in a distribution (i.e., how far apart high- and low-SES children are in 1998 and how far apart high- and low-SES children are in 2010), but not overall changes in their performance (i.e., it is not possible to ascertain whether performance has improved overall, or if gaps are smaller or larger due to an improvement in performance of children at the low end (specifically the lowest fifth) of the distribution or due to a decrease in the performance of children at the high end (highest fifth) of the distribution, etc.). A full comparison remains to be produced, upon data availability.

We use data for the first wave of each study, corresponding with fall kindergarten (or school entry).

Outcomes

For the analyses, we use the by-year standardized scores corresponding to the fall semester. (The 1998 IRT scale scores for reading and mathematics achievement and assessments of noncognitive skills are standardized using the 1998 distribution and its mean and sd; for 2010, we use the mean and sd of the 2010 distribution.)

Cognitive skills

Cognitive skills are assessed with instruments that measure each child's:

Reading skills: print familiarity, letter recognition, beginning and ending sounds, rhyming words, word recognition, vocabulary knowledge, and reading comprehension

Math skills: conceptual knowledge, procedural knowledge, and problem-solving; number sense, properties, and operations; measurement; geometry and spatial sense; data analysis, statistics, and probability; and patterns, algebra, and functions

Principal noncognitive skills

We use the term "principal" to identify a set of noncognitive skills that are measured by both the ECLS-K 1998-1999 and 2010-2011 surveys, and that have been relatively extensively used in research.

Teachers are asked to assess each child's:

Self-control: ability to control behavior by respecting the property rights of others, controlling temper, accepting peer ideas for group activities, and responding appropriately to pressure from peers

Approaches to learning: organizational skills (keeps belongings organized); curiosity (is eager to learn new things); independence (works independently); adaptability (easily adapts to changes in routine); persistence in completing tasks; focus (ability to pay attention); and ability to follow classroom rules

Parents are asked to assess their child's:

Self-control: ability to control behavior by refraining from fighting, arguing, throwing tantrums, and getting angry

Approaches to learning: persistence (keeps working at something until finished); curiosity (shows interest in a variety of things); focus (concentrates on a task and ignores distractions); helpfulness (helps with chores); intellectual curiosity (is eager to learn new things); and creativity (in work and play)

Covariates

For the analyses, we use the following set of covariates. The definitions, and the coding used for the covariates, by year, are shown in Appendix Table A1.

Appendix B. Methodology

Gaps by socioeconomic status

The expressions below show the specifications used to estimate the socioeconomic status-based (SES-based) performance gaps. For any achievement outcome A, we estimate four models:

Model 1 shows the unadjusted (descriptive) differences for children belonging to different racial/ethnic groups or SES quintiles (the reference group is children in the lowest SES quintile, "low SES").

Model 2 adjusts for school clustering of students in different schools (i.e., gaps of students in the same schools). The purpose of this clustering is to account for school segregation (i.e., concentration of children of the same race, socioeconomic status, etc., in schools, which causes the raw average performance of students to differ from the adjusted-by-clustering average). It offers a comparison of the gaps shown by peer students in the same schools and classrooms (García 2015; Magnuson and Duncan 2016 offer these estimates as well).

These estimates build on all the available observations (i.e., only those children who have missing values in the outcome variables are eliminated from the analysis).

Because of lack of response in some of the covariates used as predictors of performance, we construct a common sample with observations with no missing information in any of the variables of interest (see information about missing data for each variable in Appendix Table C1). We estimate two more models: iii

Model 3 shows gaps adjusted for child and family characteristics, prekindergarten care arrangements, number of books the child has, and early literacy practices at home

Finally, Model 4 shows the fully adjusted differences (adjusted for child and family characteristics, prekindergarten care arrangements, early literacy practices at home, number of books the child has, and parental expectations)

The equation below shows the equation we estimate for Models 1 through 4.

The main parameters of interest are and : These show the performance of low-SES children in 1998, the gap between high- and low-SES children in 1998, the change in the scores of low-SES children from 1998 to 2010 and the change in the gap between high- and low-SES children from 1998 to 2010.

The high-SES versus low-SES gap in 1998 equals (the coefficient of SES5). The high-SES versus low-SES gap in 2010 equals (the coefficients of SES5 and Year2010xSES5). If is positive and statistically significant, it means that the gap between high- and low-SES children increased during those years. Conversely, if is negative and statistically significant, it shows a reduction in the SES-gap.

Appendix C. Sensitivity analysis (I): Multiple imputation

Following standard approaches in this field, we use multiple imputation to impute missing values in both the independent and dependent variables, for the analysis of skills gaps and changes in them from 1998 to 2010 by socioeconomic status (main analysis). See share of missing data by variable in Appendix Table C1. We use the mi commands in Stata 14, using chained equations, which jointly model all functional terms. The number of iterations was set up equal to 20. Imputation is performed by year.

Our functional form of the imputation model is specified using SES, gender, race, disability, age, type of family, number of books, educational activities, and parental expectations, as well as the original cognitive and noncognitive variables, as variables to be imputed. We use various specifications, combining different sets of auxiliary variables, mi impute methods, and other parameters, to capture any sensitivity of the results to the characteristics of the model. For example, income, family size, and ELL status are set as auxiliary variables and used in several of the imputation models. Another imputation option that was altered across models is the use of weights, as we ran out of imputation models using weights and not using them.

In the imputation model, in order to impute categorical variables' missingness, we use the option augment, to prevent the large number of categorical variables to be imputed from causing problems of perfect prediction (StataCorp. 2015). The rest of the variables are first imputed as continuous variables. In a second exercise, we also impute SES and educational expectations as ordinal variables (also using the option augment).

In order to calculate the standardized dependent variables, we use the variables derived from the imputation variables (also known as passive imputation). This "fills in only the underlying imputation variables and computes the respective functional terms from the imputed variables" (StataCorp. 2015). In one case, we imputed the dependent variables directly as continuous variables (though we anticipated that the distribution of the scores imputed this way would not necessarily have a mean of 0 and a standard deviation of 1).

Using the imputed data, we estimate Models 1 through 4 following the specifications explained above (from no regressors to fully specified models).

The main findings of our analysis are not sensitive to missing data imputation. The estimates of the gaps in 1998 and the changes in the gaps from 1998 to 2010 are consistent across models in terms of statistical significance. There are some minor changes in the sizes of the estimated coefficients, especially those associated with the changes in the gaps (though all are statistically not different from 0, as discussed in the report using the results from the analysis with the complete cases). There are also some minor changes in the standard errors, though they are small enough to widen the coefficients' statistical bandwidth to not include the 0.

Appendix D. Sensitivity analysis (II): The different scores available in ECLS-K and the sensitivity of the results to changing them

Children's reading and mathematics skills are measured using several different metrics in ECLS-K. Among these, the best-known or more commonly used metrics in research are the IRT-based theta scores and the IRT-based scale scores (IRT stands for Item Response Theory). NCES provides data users with definitions of these metrics and recommendations on how to appropriately choose among the different metrics. NCES explains that both theta and IRT-based scale scores are valid indicators of ability. This makes them suitable for research purposes, even though each is expressed in its own unit of measurement. NCES recommends that analysts "consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience" when choosing the appropriate score for analysis (see Tourangeau et al. 2013).

Although nothing would indicate that this could be the case, our work noted that results of analyses such as the one developed in this study are in some ways sensitive to the metrics used as dependent variables. v Thus, the purpose of this appendix is to illustrate the differences in the results associated with different analytic decisions in terms of the metrics used. As we will see, in essence, point estimates depend on the metric used, but the results do not change in a meaningful way and conclusions and implications remain unchanged. That is, although caution is required when interpreting the results obtained using different combinations of metrics, procedures (including standardization), and data waves, it is important to state that the main conclusions of this study-that social-class gaps in cognitive and noncognitive skills are large and have persisted over time- hold. So do the policy recommendations derived from those findings: sufficient, integrated, and sustained over-time efforts to tackle early gaps in a more effective manner.

The scores: Which one to use and definitions

NCES makes the following recommendations for researchers who are choosing among scales (see Tourangeau et al. 2013): vi

When choosing scores to use in analysis, researchers should consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience. [...]

The IRT-based scale scores [...] are overall measures of achievement. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or in different rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics.

[...] Results expressed in terms of scale score points, scale score gains, or an average scale score may be more easily interpretable by a wider audience than results based on the theta scores.

The IRT-based theta scores are overall measures of ability. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or across rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics.

[...] The theta scores may be more desirable than the scale scores for use in a multivariate analysis because generally their distribution tends to be more normal than the distribution of the scale scores. However, for a broader audience of readers unfamiliar with IRT modeling techniques, the metric of the theta scores (from -6 to 6) may be less readily interpretable. [...]

The two scores are defined as follows (see Tourangeau et al. 2013, section "3.1 Direct Cognitive Assessment: Reading, Mathematics, Science"):

The IRT-based scale score is an estimate of the number of items a child would have answered correctly in each data collection round if he or she had been administered all of the questions for that domain that were included in the kindergarten and first-grade assessments.

To calculate the IRT-based overall scale score for each domain, a child's theta is used to predict a probability for each assessment item that the child would have gotten that item correct. Then, the probabilities for all the items fielded as part of the domain in every round are summed to create the overall scale score. Because the computed scale scores are sums of probabilities, the scores are not integers.

The IRT-based theta score is an estimate of a child's ability in a particular domain (e.g., reading, mathematics, science, or SERS) based on his or her performance on the items he or she was actually administered. [...]

The theta scores are reported on a metric ranging from -6 to 6, with lower scores indicating lower ability and higher scores indicating higher ability. Theta scores tend to be normally distributed because they represent a child's latent ability and are not dependent on the difficulty of the items included within a specific test.

Reardon (2007) describes the calculation of the theta scores in the following manner: vii

For each test [math and reading], a three-parameter IRT model was used to estimate each student's latent ability...at each wave.... The IRT model assumes that each student's probability of answering a given test item correctly is a function of the student's ability and the characteristics [discrimination, difficulty, and guessability] of the item.... Given the pattern of students' responses to the items on the test that they are given, the IRT model provides estimates of both the person-specific latent abilities at each wave... and the item parameters. (Reardon 2007, 10) viii

He also notes that "[b]ecause the ECLS-K tests contain many more 'difficult' items than 'easy' items, the relationship between theta and scale scores is not linear (a unit difference in theta corresponds to a larger difference in scale scores at theta=1 than at theta=-1, for example). The scale scores are difficult to interpret as an interval-scale metric (or are an interval-scaled metric only with respect to the specific set of items on the ECLS-K tests)," while he shows that the "theta scores are interval-scale metrics, in a behaviorally-meaningful sense" (Reardon 2007, 11, 13). ix

The analyses

For the analyses, both the scale and the theta scores need to be standardized by year (the original variables are not directly comparable because they rely on different instruments, as explained by NCES, and the resulting standardized variables have mean 0 and standard deviation 1). This is a common practice in the education field, as it allows researchers to use data that come from different studies and would not have a common scale otherwise. We need to take into consideration that the underlying units of measurement for each variable are different, but after standardization, the metrics are common, expressed in standard deviations and represent the population's distribution of abilities.

The distributions of the scale and theta scores are shown in Appendix Figures D1 and D2. In each figure, the plots reflect a more normally distributed pattern for the theta scores (right panel) than for the scale scores (left panel). The companion table, Appendix Table D1, shows the range of variation for the four outcomes (mean and standard deviations are 0 and 1 as per construction).

We next offer a comparison of the results obtained when using the scale scores versus using the theta scores ( Appendix Table D2). We highlight the following main similarities and differences between the results obtained using the scale scores and the results using the theta scores.

Gaps are all equally statistically significant and persistent.

Estimated gaps are larger if you use the theta scores than if you use the scale scores.

For example, looking at the unadjusted estimates in reading, the gap in 1998 between high- and low-SES children is 1.071 sd if using the scale scores and 1.233 sd if using the theta scores. In math, the gap between high- and low-SES children in 1998 is 1.258 sd if using the scale scores and 1.330 sd if using the theta scores.

Looking at the adjusted estimates in reading, the 1998 gap between high- and low-SES children is 0.596 sd if using the scale scores and 0.684 sd if using the theta scores. In math, the gap between high- and low-SES children is 0.610 sd if using the scale scores and 0.632 sd if using the theta scores.

The trends in gaps (i.e., whether the gaps increased or decreased between 1998 and 2010) do differ depending on whether you use one dependent variable or the other. This sometimes affects the point estimate's size and even sign, although the sizes of these coefficients are very small (i.e., indicating that gaps haven't really changed over time).
For example, looking at the unadjusted estimates in reading, the change in the gap between 1998 and 2010 for high- and low-SES children is 0.098 sd if using the scale scores and -0.052 sd (not statistically significant) if using the theta scores. In math, the change in the gap between high- and low-SES children is -0.008 sd (not statistically significant) if using the scale scores and -0.078 sd if using the theta scores.

In Appendix Table D3, we compare the results obtained using the different scales and the different proxies of socioeconomic status (our composite SES index, mother's education, number of books, and household income).

Gaps are larger, as mentioned above, when we use the theta scores than when we use the scale scores.

Among the four social-class proxies, the largest gaps are associated with mother's education, and the smallest gaps are associated with number of books. All are statistically significant.

Looking at the unadjusted gaps, we note that trends are the same (and similar in size) if income is used as the proxy. For mother's education, the change in the gap between 1998 and 2010 is -0.020 sd in reading (not statistically significant) and -0.154 sd in math if using the scale scores and -0.135 sd in reading and -0.218 sd in math if using the theta scores.

With respect to the adjusted gaps, changes in the gaps are larger when using the theta scores both for household income and mother's education as indicators of social class. Using the theta scores, the gaps in reading and math shrank over time, while using the scale scores, the only significant reduction was in math when mother's education was the social class proxy.

Other considerations

There are two other significant pieces of information affecting the cognitive scores in more recent documentation released by NCES. In 2015, NCES announced in its ECLS-K User's Manual that a

change in methodology required a re-calibration and re-reporting of the kindergarten reading scores since the release of the base-year file. Therefore, the kindergarten reading theta scores included in the K-1 data file are calculated differently than the previously released kindergarten theta scores and replace the kindergarten reading theta scores included in the base-year data file. The modeling approach stayed the same for mathematics and science, so the recalculation of kindergarten mathematics and science theta scores was not needed. (Tourangeau et al. 2015)

Following up on this, the most recent (2017) data user's manual explains that

The method used to compute the theta scores allows for the calculation of theta for a given round that will not change based on later administrations of the assessments (which is not true for the scale scores, as described in the next section). Therefore, for any given child, the kindergarten, first-grade, and second-grade theta scores provided in subsequent data files will be the same as theta scores released in earlier data files, with one exception: the reading thetas provided in the base-year data file. After the kindergarten-year data collection, the methodology used to calibrate and compute reading scores changed; therefore, the reading thetas reported in the base-year file are not the same as the kindergarten reading thetas provided in the files with later-round data [emphasis added]. Any analysis involving kindergarten reading theta scores and reading theta scores from later rounds, for example an analysis looking at growth in reading knowledge and skills between the spring of kindergarten and the spring of first grade, should use the kindergarten reading theta scores from a data file released after the base year. The reading theta scores released in the kindergarten-year data file are appropriate for analyses involving only the kindergarten round data; analyses conducted with only data released in the base-year file are not incorrect, since those analyses do not compare kindergarten scores to scores in later rounds that were computed differently. However, now that the recomputed kindergarten theta scores are available in the kindergarten through first-grade and kindergarten through second-grade data files, it is recommended that researchers conduct any new analyses with the recomputed kindergarten reading theta scores. For more information on the methods used to calculate theta scores, see the ECLS-K: 2011 First-Grade and Second-Grade Psychometric Report (Najarian et al. forthcoming). (Tourangeau et al. 2017)

Therefore, because of these changes in NCES methodology and reporting, and in light of the comparisons in this appendix, one could expect additional slight changes in the estimates using the IRT-theta scores for reading for kindergarten if using rounds of data posterior to the first round (and probably if using the IRT-scale scores as well, as these values are derived from the theta scores), relative to the first data file of ECLS-K: 2010-2011 released by NCES in 2013. We would not necessarily expect, though, any changes when using the standardized transformation of those scores, because NCES's documentation does not mention changes to the distribution of the scores, only to their values. We will explore these issues further upon the release of the scores that are comparable across the two ECLS-K studies without any transformation.

Appendix E. Descriptions of 12 community-level whole-child education initiatives

Initiatives that serve part of a school district

Austin, Texas

The needs of children in Austin Independent School District (AISD) schools with the highest concentrations of poor, immigrant, and non-English-speaking families are supported through a combination of parent-organizing (schools with parent-organizing programs, led by the nonprofit Austin Interfaith, form a network of "Alliance Schools"), intensive embedding of social and emotional learning (SEL) in all aspects of school policy and practice, and the transformation of schools into "community schools" (i.e., schools that are hubs for the provision of academic, health, and social services).

Boston, Massachusetts

The City Connects program provides targeted academic, social, emotional, and health supports to every child in 20 of the city's schools with the highest shares of low-income, black, Hispanic, and immigrant students.

Durham, North Carolina

The East Durham Children's Initiative (EDCI) concentrates services and supports for the children and their families living in a 120-block, heavily distressed area of concentrated poverty and high crime within the city.

Minneapolis, Minnesota

The Northside Achievement Zone (NAZ) is a Promise Neighborhood, a designation awarded by the U.S. Department of Education Promise Neighborhoods program to some of the most distressed neighborhoods in the nation. Through the program, children and families who live in the 13-by-18 block NAZ receive individualized supports.

New York, New York

Through a collaboration between The Children's Aid Society and the New York City Department of Education, 16 community schools in some of the most disadvantaged neighborhoods in three of the city's five boroughs provide wraparound health, nutrition, mental health, and other services to students along with enriching in-and-out-of-school experiences, amplified by extensive parental and community engagement.

Orange County, Florida

The Tangelo Park Project (TPP) provides cradle-to-college support for all children residing in Orlando's high-poverty, heavily African American Tangelo Park neighborhood.

Initiatives that serve all of a school district

Joplin, Missouri

Joplin's Bright Futures initiative (which has spawned dozens of other Bright Futures affiliate districts under a Bright Futures USA umbrella since it launched in 2010) has a rapid response component that addresses children's basic needs (within 24 hours of a need being reported), while strong school-community partnerships help meet students' longer-term needs. Bright Futures also provides meaningful service learning opportunities in every school.

Kalamazoo, Michigan

The "Kalamazoo Promise," a guarantee by a group of anonymous local philanthropists to provide full college scholarships in perpetuity for graduates of the district's public high schools brought Kalamazoo Public Schools (KPS), the city, and the community together to develop a set of comprehensive supports that enable more students to use the scholarships.

Montgomery County, Maryland

All students in Montgomery County Public Schools (MCPS) benefit from zoning laws that advance integration and strong union-district collaboration on an enriching, equity-oriented curriculum. These efforts are bolstered by extra funding and wraparound supports for high-needs schools and communities.

Pea Ridge, Arkansas

The Pea Ridge School District, a small suburban-rural district outside Fayetteville, Arkansas, is among the newer affiliates of Bright Futures USA, a national umbrella group that grew out of Bright Futures Joplin. As a Bright Futures affiliate, Pea Ridge is making good progress toward identifying and meeting students' basic needs, engaging the community to meet longer-term needs, and making service learning a core component of school policy and practice.

Vancouver, Washington

Family and Community Resource Centers (FCRCs) currently serve 16 of the highest-needs Vancouver Public Schools (VPS) district schools, with mobile and lighter-touch support in other schools and plans to expand districtwide by 2020.

Initiative that serves multiple school districts

Eastern (Appalachian) Kentucky

A federal Promise Neighborhood grant helps Berea College's Partners for Education provide intensive supports for students and their families in four counties in the Eastern (Appalachian) region of Kentucky and provide lighter-touch supports in an additional 23 surrounding counties. (Berea College, which was established in 1855 by abolitionist education advocates, is unique among U.S. higher-education institutions. It admits only economically disadvantaged, academically promising students, most of whom are the first in their families to obtain postsecondary education, and it charges no tuition, so every student admitted can afford to enroll and graduates debt-free.)

Organizing partners: Berea College launched Partners for Education (PfE), which is now a fully staffed nonprofit that runs the initiative.

Schools and students reached: PfE serves 35,000 students in 22 schools in Clay, Jackson, Knox, and Owsley counties; tens of thousands more are served less intensively in an additional 23 counties in the region.

General makeup of the student body: The Appalachian region is rural, very poor, and heavily white. The regional poverty rate is around 27 percent (in 2015), and reaches as high as 40 percent in some counties. About 80 percent of students are FRPL-eligible and 97 percent are white.

Key features: Family engagement specialists meet directly with families and help coordinate services provided by a range of community partners. Other specialists provide basic academic, college preparatory, and health and other wraparound services to students.

Core funding: Federal Promise Neighborhood, Full Service Community Schools, and Investing in Innovation grants are the most prominent sources of funding, but the initiative receives a range of other cash and in-kind supports.

Appendix tables and figures

Endnotes to the appendices

i. The sample design used to select the individuals in the study was a three-stage process that involved using primary sampling units and schools with probabilities proportional to the number of children and the selection of a fixed number of children per school. In the last stage, children enrolled in kindergarten or ungraded schools were selected within each sampled school. A clustered design was used to limit the number of geographic areas and to minimize the number of schools and the costs of the study (Tourangeau et al. 2013, 4-1).

ii. The dataset in the first year followed a stratified design structure (Ready 2010, 274), in which the primary sampling units were geographic areas consisting of counties or groups of counties. About 1,000 schools - 903 for 1998 and 968 for 2010-were selected, and about 24 children per school were surveyed. Assessment of the children was performed by trained evaluators, while parents were surveyed over the telephone. Teachers and school administrators completed the questionnaires in their schools.

iii. As a sensitivity check, we estimate Models 1 and 2 using Models 1's and Model 2's specifications but using the restricted sample (these results are not shown here, but are available upon request).

iv. As a sensitivity check, we estimate Model 3 parsimoniously, by including family characteristics only, and then adding family investments (prekindergarten care arrangements, early literacy practices at home, and number of books the child has), and then adding parental expectations (with and without interactions with time); results of the sensitivity check are not shown, but are available upon request).

v. We refer to the fact that we are using the same data and that the scale and theta scores are based on the same instruments and are not independent from each other. Advice on this possibility is found in Reardon (2007), who cites work by Murnane et al. (2006) and Selzer, Frank, and Bryk (1994) that also warn about this option.

vi. From NCES: "IRT uses the pattern of right and wrong responses to the items actually administered in an assessment and the difficulty, discriminating ability, and guess-ability of each item to estimate each child's ability on the same continuous scale. IRT has several advantages over raw number-right scoring. By using the overall pattern of right and wrong responses and the characteristics of each item to estimate ability, IRT can adjust for the possibility of a low-ability child guessing several difficult items correctly. If answers on several easy items are wrong, the probability of a correct answer on a difficult item would be quite low. Omitted items are also less likely to cause distortion of scores, as long as enough items have been answered to establish a consistent pattern of right and wrong answers. Unlike raw number-right scoring, which treats omitted items as if they had been answered incorrectly, IRT procedures use the pattern of responses to estimate the probability of a child providing a correct response for each assessment question" (Tourangeau et al. 2017, 3-2).

vii. The quoted text is abridged to remove variables and formulas specific to Reardon's study and not central here.

viii. Also, "the estimated scale score is the estimated number of questions the student would have gotten correct if he or she had been asked all of the items on the test. The estimated scale score is obtained by summing the predicted probabilities of a correct response over all items, given the student's estimated theta score and the estimated item parameters" (Reardon 2007, 11).

ix. They are equally spaced units along the scale without a predefined zero point.

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Education inequalities at the school starting gate: Gaps, trends, and strategies to address them

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As economic inequities in the United States endure and, in some instances, grow, and the large achievement gaps they help drive persist, calls for policy strategies to address these gaps increase as well. It is increasingly apparent that performance gaps take root in the earliest years of children's lives and do not vanish.

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"American Black women face disproportionately high rates of maternal mortality" - PBS, June 2023

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"American Black women face disproportionately high rates of maternal mortality" - PBS, June 2023

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The CDC estimates that, in 2021, the maternal mortality rate among Black women was nearly 70 deaths for every 100,000 live births. That is 2.6 times the rate for white women, regardless of income or education.

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American Black women face disproportionately high rates of maternal mortality

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The sudden death of Olympic sprinter Tori Bowie at age 32 sent shockwaves around the world. The idea that a young, elite athlete could go into labor and die stunned many. But this problem is more common than many realize, and it’s particularly bad for Black women. William Brangham discussed more wit…

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published - september 1 2023

“three times the rate of white women and more than 12 times the rate of white men.”

During both periods, Black women and their health bore the brunt of the consequences of white men’s control.

source- The conversation

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U.S. Rep. Cori Bush, a Democrat from Missouri, after participating in an abortion rights sit-in on July 19, 2022, in Washington. Anna Moneymaker/Getty Images

More than a year after the Supreme Court ended federal protection for abortion rights in the United States, disagreements over abortion bans continue to reverberate around the country. Candidates sparred over the idea of a federal abortion ban during the Aug. 23, 2023, Republican presidential debate. And abortion is likely to figure prominently in the November 2023 contest for a seat on the Pennsylvania Supreme Court.

When the U.S. Supreme Court overturned Roe v. Wade in June 2022, removing women's federal constitutional right to get abortions and giving states the power to pass laws about the legality of the procedure, the 6-3 vote was by a four white men, one Black man and a white woman majority.

Since that decision - Dobbs v. Jackson Women's Health Organization - more than 1,500 state legislators, who are overwhelmingly white men, have voted for full or partial abortion bans.

This is not the first period in U.S. history when white men have exercised control over women's right to bear - or not bear - children, including during slavery. Then, it was a matter of numbers. The more people they enslaved, the more money white male enslavers could earn either from selling the enslaved or from the forced labor of the enslaved. White men controlled people's reproductive rights during the 20th century, too, with the American eugenics movement.

From the late 1800s until the 2000s, white proponents of eugenics - the selective breeding of people - tried to determine who was fit or unfit to have children. While the American eugenics movement affected people of other races and ethnic backgrounds, as well as men, it was particularly harmful to Black women who, data from 1950 to 1966 shows, were sterilized at " three times the rate of white women and more than 12 times the rate of white men."

During both periods, Black women and their health bore the brunt of the consequences of white men's control.

As a researcher who specializes in the history of race and racism in the U.S., I study historical issues related to race, gender and social justice.

Enslaved women forced to reproduce

African midwives, imported and enslaved as early as the 1600s, attended to the birthing needs of the enslaved and enslavers until the beginning of the 19th century.

But, after 1808, enslavers in the United States could no longer legally import enslaved people. With this shift, enslavers stepped up the forced breeding of enslaved women. White men raped the Black women and girls they enslaved, and then enslaved the children born from those rapes. White men also forced the Black women and Black men they enslaved to have sex with one another to generate more babies, who would be born into slavery.

This was a systemic way of ensuring enslaved women bore more children, which would increase profits for their enslavers.

Because the Black midwives and enslaved women often were blamed for or suspected of using birth control and abortions to resist forced pregnancy and the enslavement of their offspring, enslavers turned increasingly away from midwives and to white male doctors to figure out why nearly half of enslaved infants were stillborn or died within their first year of life and why so many enslaved women were infertile. These doctors also helped with difficult births.

In the two decades after 1810, the population growth rate of the enslaved averaged about 30%, despite the ban on slave importation. This was just under the 1800 to 1809 average of 31.6% which was a century high.

In the 1800s, as the slave population increased, profits in cotton did too. And after the legal importation of slaves ended, the value of Black women of childbearing age increased significantly. The forced breeding of these enslaved women was linked to the profitability of southern economies.

Eugenics and control over women's bodies

Eugenicists believed that increased breeding by white people, whom they assumed had high IQs, would benefit American society. But people who did not embody their idea of racial perfection, such as Black people, Native Americans, certain immigrants, poor white people and people with disabilities, should be sterilized - typically via tubal ligation and vasectomy.

In this debunked pseudo-science, eugenicists often used intelligence tests to determine who was fit or unfit to reproduce and to predict who would commit crimes, end up in poverty or have children who were mentally ill or intellectually disabled. And they worked to incorporate their ideas into state laws.

Thirty-two states, between 1907 and 1937, enacted forced sterilization mandates to prevent births by people eugenicists considered socially inadequate.

State-mandated procedures resulted in the coerced sterilization of women, particularly African American, Native American and Hispanic American women, and those from Southern and Eastern Europe.

Beginning in 1948 with President Harry Truman's executive order to integrate the military, which extended to other areas, including education, employment and commerce, sterilization rates for Black women increased. For example, in North Carolina, which had the country's third-highest sterilization rate, far more women than men were forcibly sterilized. And in the 1960s, Black women in the state made up 65% of the women sterilized, while only making up 25% of the population.

Between 1930 and 1970, close to 33% of the women in Puerto Rico, a U.S. territory, were forcibly sterilized. In California, between 1997 and 2003, 1,400 female inmates, mostly Black, were forcibly sterilized.

The post-Dobbs era

White nationalists and some right-wing politicians in the U.S. see the nation's demographic changes as dangerous. The Census Bureau projects that in the 2040s, non-Hispanic white people will no longer make up a majority of the U.S. population. The nation's racial and ethnic makeup will then be what some call " majority-minority." Those projections scare racists, who believe in a conspiracy about white people being destroyed, which they label the great replacement theory because they fear losing social, political and economic power.

There is no way to know if this theory factored into the majority's votes in the Dobbs decision, but the argument that not enough white people are being born has been a common historical thread in the American anti-abortion movement.

But, while believers in the great replacement conspiracy want white women to have more babies, actual anti-abortion decisions like the Dobbs ruling harm Black women more than any other group. Black women represent 39% of the country's abortion patients, but many live in communities that have limited access to family planning clinics. And they have disproportionately higher rates of complications during pregnancy.

As a result, Black women - who experience higher maternal complications and mortality rates - will be forced to give birth to more babies.

This is another period in the country in which the reproductive health decisions made by mostly white men will harm Black women.

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White men have controlled women's reproductive rights throughout American history - the post-Dobbs era is no different

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In the US, white men have long had the power to make decisions about women's reproductive health care. Those decisions have often been especially harmful to Black women.

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"The role of race and ethnicity in Americans’ personal lives"

Source - Pew Research Center

April 9, 2019

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Pew Research Center's Social & Demographic Trends Project

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In addition to their different assessments of the current state of race relations and racial inequality in the United States, Americans across racial and ethnic groups also see race and ethnicity playing out differently in their personal lives. On balance, blacks are more likely to say their race has hurt, rather than helped, their ability to get ahead. Among whites, Hispanics and Asians, more say their race or ethnicity has been an advantage than an impediment.

Blacks are also far more likely than other groups to say their race is very or extremely important to how they think about themselves, but half or more Hispanics and Asians also say their racial or ethnic background is central to their overall identity; only 15% of whites say the same.

This chapter also looks at personal experiences with discrimination and the extent to which people of different backgrounds say their family talked to them about challenges or advantages they might face because of their race and ethnicity when they were growing up.

Blacks more likely than other groups to say their race has hurt their ability to succeed; whites most likely to say their race has helped

About half of black Americans say being black has hurt their ability to get ahead, including 18% who say it has hurt a lot; 17% say being black has helped them at least a little, while 29% say it has neither hurt nor helped their ability to get ahead. In contrast, roughly four-in-ten or more whites, Hispanics and Asians say their race or ethnicity hasn't had much impact on their ability to get ahead - and to the extent that it has, more say it has helped than say it has hurt.

Whites are especially likely to say their race has given them some advantages: 45% say being white has helped them get ahead at least a little, while 50% say it has neither helped nor hurt and just 5% say being white has hurt their ability to get ahead. Three-in-ten Hispanics say being Hispanic has helped them, while 37% of Asians say the same about their racial background. About a quarter of each say being Hispanic or Asian, respectively, has hurt their ability to get ahead at least a little.

Among whites, education and partisanship are linked to views of white advantage in their own life. Six-in-ten white college graduates say being white has helped their ability to get ahead, compared with 39% of whites with some college and 35% of those with less education. And while 66% of white Democrats and Democratic leaners say their race has helped at least a little, only 29% of white Republicans say the same. Most white Republicans say being white has neither helped nor hurt.

Education is also a factor in how blacks assess the impact their race has had on their ability to succeed. About six-in-ten blacks with at least some college experience (57%) say being black has hurt, compared with 47% of blacks with a high school diploma or less education.

The survey also asked whether factors such as gender, family finances and hard work helped or hurt people's ability to get ahead. Overall, Americans are far more likely to point to their own hard work than to any other attribute as having helped their ability to get ahead.

Across racial and ethnic groups, about half of men say their gender has helped them at least a little. White and black women are more likely to say their gender has been an impediment than an advantage, while Hispanic women are more divided. White women (44%) are more likely than black (38%) or Hispanic (32%) women to say their gender has hurt at least a little.

Majorities of blacks, Asians and Hispanics say they have faced discrimination

About three-quarters of blacks and Asians (76% of each) say they have experienced discrimination or have been treated unfairly because of their race or ethnicity at least from time to time; 58% of Hispanics say the same. Most whites (67%) say they have never experienced this.

Blacks with at least some college experience are more likely than those with less education to say they have experienced racial discrimination, but majorities in both groups say this has happened to them (81% and 69%, respectively). Among Hispanics, 63% of those with some college or more - vs. 54% of those with less education - say they have faced discrimination because of their race or ethnicity.

Most blacks say people have acted as if they were suspicious of them or as if they thought they weren't smart

Asked about specific situations they may have faced because of their race or ethnicity, 65% of blacks say someone has acted as if they were suspicious of them, and 60% say someone has acted as if they thought they weren't smart. About half say they have been subject to slurs or jokes (52%) or that they have been treated unfairly by an employer in hiring, pay or promotions (49%), while about four-in-ten say they have been unfairly stopped by police (44%) or feared for their personal safety (43%) because of their race or ethnicity.

Blacks are more likely than whites, Hispanics and Asians to say they have faced most of these situations. Asians are more likely than other groups to say they've been subject to slurs or jokes because of their race or ethnicity (61% of Asians say this has happened to them), while whites are the most likely to say someone assumed they were racist or prejudiced (45%).

Within racial and ethnic groups, experiences differ significantly by gender. Among blacks and Hispanics, larger shares of men than women say they have been unfairly stopped by police, been subject to slurs or jokes, or that people have acted as if they were suspicious of them because of their race or ethnicity. Hispanic men are also more likely than Hispanic women to say they have been treated unfairly in employment situations.

Blacks with at least some college experience are more likely than those with less education to say they have faced certain situations because of their race. For example, 67% of blacks with some college or more education say people have acted as if they thought they weren't smart because of their race or ethnicity; 52% of blacks with a high school diploma or less education say the same. And while about six-in-ten blacks in the more educated group (58%) say they have been subject to slurs or jokes, 45% of blacks who didn't attend college say this has happened to them.

Many of these experiences are also more common among Hispanics who were born in the U.S. than among those who were born in another country.

Most blacks say their family talked to them about challenges they might face because of their race

More than six-in-ten black adults (64%) say that, when they were growing up, their family talked to them about challenges they might face because of their race or ethnicity at least sometimes (32% say this happened often). In contrast, about nine-in-ten whites (91%), as well as 64% of Hispanics and 56% of Asians, say their family rarely or never had these types of conversations when they were growing up.

Black men and women, as well as blacks across age groups, are about equally likely to say their family talked to them about challenges they might face because of their race or ethnicity. Seven-in-ten blacks with at least some college experience say their family had these types of conversations at least sometimes, compared with 57% of those with a high school diploma or less education.

Across racial and ethnic groups, majorities say their family rarely or never had conversations about advantages they might have because of their race or ethnicity, but blacks (32%), Hispanics (26%) and Asians (26%) are more likely than whites (11%) to say these conversations took place at least sometimes when they were growing up. About two-in-ten white adults younger than 30 (22%) say their family talked to them about advantages they might have, compared with about one-in-ten whites ages 30 and older.

Most blacks see their race as central to their overall identity

About three-quarters of black adults (74%) say being black is very important to how they think about themselves, including 52% who say it is extremely important. About six-in-ten Hispanics (59%) say being Hispanic is extremely or very important to their identity, and 56% of Asians say the same about being Asian. In contrast, only 15% of whites say being white is as important to their identity; 19% of whites say it is moderately important, while 18% say it's only a little important and about half (47%) say their race is not at all important to how they think about themselves.

Among blacks and whites, those younger than 30 see their race as less central to their identity than their older counterparts. Still, majorities of blacks - and relatively small shares of whites across age groups - say their race is extremely or very important to how they think about themselves.

Hispanics born in another country (65%) are more likely than those born in the U.S. (52%) to say being Hispanic is at least very important to their overall identity.

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3. The role of race and ethnicity in Americans' personal lives

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In addition to their different assessments of the current state of race relations and racial inequality in the United States, Americans across racial and

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"Supreme Court allows West Point to continue using race as a factor in admissions, for now"

Source- Washington Post

Date- Feb 2, 2024

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WASHINGTON - The Supreme Court is allowing West Point to continue taking race into account in admissions, while a lawsuit over its policies continues.

The justices on Friday rejected an emergency appeal seeking to force a change in the admissions process at West Point. The order, issued without any noted dissents, comes as the military academy is making decisions on whom to admit for its next entering class, the Class of 2028.
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The military academy had been explicitly left out of the court's decision in June that ended affirmative action almost everywhere in college admissions.

The court's conservative majority said race-conscious admissions plans violate the U.S. Constitution, in cases from Harvard University and the University of North Carolina, the nation's oldest private and public colleges, respectively. But the high court made clear that its decision did not cover West Point and the nation's other service academies, raising the possibility that national security interests could affect the legal analysis.

In their brief unsigned order Friday, the justices cautioned against reading too much into it, noting "this order should not be construed as expressing any view on the merits of the constitutional question."

Students for Fair Admissions, the group behind the Harvard and North Carolina cases, sued the U.S. Military Academy at West Point in September. It filed a similar suit against the U.S. Naval Academy in October.

Lower courts had declined to block the admissions policies at both schools while the lawsuits are ongoing. Only the West Point ruling has been appealed to the Supreme Court.

"Every day that passes between now and then is one where West Point, employing an illegal race-based admissions process, can end another applicant's dream of joining the Long Gray Line," lawyers for Students for Fair Admissions wrote in a court filing.

West Point graduates account make up about 20% of all Army officers and nearly half the Army's current four-star generals, the Justice Department wrote in its brief asking the court to leave the school's current policies in place.

In recent years, West Point, located on the west bank of the Hudson River about 40 miles (about 65 kilometers) north of New York City, has taken steps to diversify its ranks by increasing outreach to metropolitan areas including New York, Atlanta and Detroit.

"For more than forty years, our Nation's military leaders have determined that a diverse Army officer corps is a national-security imperative and that achieving that diversity requires limited consideration of race in selecting those who join the Army as cadets at the United States Military Academy at West Point," wrote Solicitor General Elizabeth Prelogar, the Biden administration's top Supreme Court lawyer.

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Supreme Court allows West Point to continue using race as a factor in admissions, for now

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The Supreme Court is allowing West Point to continue taking race into account in admissions, while a lawsuit over its policies continues

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"How Socioeconomic status impacts thought, feeling, and behavior."

Source - National Library of Medicine

Date - Feb 2018

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The British Journal of Social Psychology

Abstract

Drawing on recent research on the psychology of social class, I argue that the material conditions in which people grow up and live have a lasting impact on their personal and social identities and that this influences both the way they think and feel about their social environment and key aspects of their social behaviour. Relative to middle‐class counterparts, lower/working‐class individuals are less likely to define themselves in terms of their socioeconomic status and are more likely to have interdependent self‐concepts; they are also more inclined to explain social events in situational terms, as a result of having a lower sense of personal control. Working‐class people score higher on measures of empathy and are more likely to help others in distress. The widely held view that working‐class individuals are more prejudiced towards immigrants and ethnic minorities is shown to be a function of economic threat, in that highly educated people also express prejudice towards these groups when the latter are described as highly educated and therefore pose an economic threat. The fact that middle‐class norms of independence prevail in universities and prestigious workplaces makes working‐class people less likely to apply for positions in such institutions, less likely to be selected and less likely to stay if selected. In other words, social class differences in identity, cognition, feelings, and behaviour make it less likely that working‐class individuals can benefit from educational and occupational opportunities to improve their material circumstances. This means that redistributive policies are needed to break the cycle of deprivation that limits opportunities and threatens social cohesion.

Keywords: social class, socioeconomic status, identity, self‐construal, personal control, empathy, prejudice, economic inequality

Background

We are all middle class now.

John Prescott, former Labour Deputy Prime Minister, 1997

Class is a Communist concept. It groups people as bundles and sets them against one another.

Margaret Thatcher, former Conservative Prime Minister, 1992

One of the ironies of modern Western societies, with their emphasis on meritocratic values that promote the notion that people can achieve what they want if they have enough talent and are prepared to work hard, is that the divisions between social classes are becoming wider, not narrower. In the United Kingdom, for example, figures from the Equality Trust ( 2017) show that the top one‐fifth of households have 40% of national income, whereas the bottom one‐fifth have just 8%. These figures are based on 2012 data. Between 1938 and 1979, income inequality in the United Kingdom did reduce to some extent, but in subsequent decades, this process has reversed. Between 1979 and 2009/2010, the top 10% of the population increased its share of national income from 21% to 31%, whereas the share received by the bottom 10% fell from 4% to 1%. Wealth inequality is even starker than income inequality. Figures from the UK's Office for National Statistics (ONS, 2014) show that in the period 2012-2014, the wealthiest 10% of households in Great Britain owned 45% of household wealth, whereas the least wealthy 50% of households owned <9%. How can these very large divisions in material income and wealth be reconciled with the view that the class structure that used to prevail in the United Kingdom until at least the mid‐20th century is no longer relevant, because the traditional working class has 'disappeared', as asserted by John Prescott in one of the opening quotes, and reflected in the thesis of embourgeoisement analysed by Goldthorpe and Lockwood ( 1963)? More pertinently for the present article, what implications do these changing patterns of wealth and income distribution have for class identity, social cognition, and social behaviour?

The first point to address concerns the supposed disappearance of the class system. As recent sociological research has conclusively shown, the class system in the United Kingdom is very much still in existence, albeit in a way that differs from the more traditional forms that were based primarily on occupation. In one of the more comprehensive recent studies, Savage et al. ( 2013) analysed the results of a large survey of social class in the United Kingdom, the BBC's 2011 Great British Class Survey, which involved 161,400 web respondents, along with the results of a nationally representative sample survey. Using latent class analysis, the authors identified seven classes, ranging from an 'elite', with an average annual household income of £89,000, to a 'precariat' with an average annual household income of £8,000. Among the many interesting results is the fact that the 'traditional working‐class' category formed only 14% of the population. This undoubtedly reflects the impact of de‐industrialization and is almost certainly the basis of the widely held view that the 'old' class system in the United Kingdom no longer applies. As Savage et al.'s research clearly shows, the old class system has been reconfigured as a result of economic and political developments, but it is patently true that the members of the different classes identified by these researchers inhabit worlds that rarely intersect, let alone overlap. The research by Savage et al. revealed that the differences between the social classes they identified extended beyond differences in financial circumstances. There were also marked differences in social and cultural capital, as indexed by size of social network and extent of engagement with different cultural activities, respectively. From a social psychological perspective, it seems likely that growing up and living under such different social and economic contexts would have a considerable impact on people's thoughts, feelings and behaviours. The central aim of this article was to examine the nature of this impact.

One interesting reflection of the complicated ways in which objective and subjective indicators of social class intersect can be found in an analysis of data from the British Social Attitudes survey (Evans & Mellon, 2016). Despite the fact that there has been a dramatic decline in traditional working‐class occupations, large numbers of UK citizens still describe themselves as being 'working class'. Overall, around 60% of respondents define themselves as working class, and the proportion of people who do so has hardly changed during the past 33 years. One might reasonably ask whether and how much it matters that many people whose occupational status suggests that they are middle class describe themselves as working class. Evans and Mellon ( 2016) show quite persuasively that this self‐identification does matter. In all occupational classes other than managerial and professional, whether respondents identified themselves as working class or middle class made a substantial difference to their political attitudes, with those identifying as working class being less likely to be classed as right‐wing. No wonder Margaret Thatcher was keen to dispense with the concept of class, as evidenced by the quotation at the start of this paper. Moreover, self‐identification as working class was significantly associated with social attitudes in all occupational classes. For example, these respondents were more likely to have authoritarian attitudes and less likely to be in favour of immigration, a point I will return to later. It is clear from this research that subjective class identity is linked to quite marked differences in socio‐political attitudes.

A note on terminology

In what follows, I will refer to a set of concepts that are related but by no means interchangeable. As we have already seen, there is a distinction to be drawn between objective and subjective indicators of social class. In Marxist terms, class is defined objectively in terms of one's relationship to the means of production. You either have ownership of the means of production, in which case you belong to the bourgeoisie, or you sell your labour, in which case you belong to the proletariat, and there is a clear qualitative difference between the two classes. This worked well when most people could be classified either as owners or as workers. As we have seen, such an approach has become harder to sustain in an era when traditional occupations have been shrinking or have already disappeared, a sizeable middle‐class of managers and professionals has emerged, and class divisions are based on wealth and social and cultural capital.

An alternative approach is one that focuses on quantitative differences in socioeconomic status (SES), which is generally defined in terms of an individual's economic position and educational attainment, relative to others, as well as his or her occupation. As will be shown below, when people are asked about their identities, they think more readily in terms of SES than in terms of social class. This is probably because they have a reasonable sense of where they stand, relative to others, in terms of economic factors and educational attainment, and perhaps recognize that traditional boundaries between social classes have become less distinct. For these reasons, much of the social psychological literature on social class has focused on SES as indexed by income and educational attainment, and/or on subjective social class, rather than social class defined in terms of relationship to the means of production. For present purposes, the terms 'working class', which tends to be used more by European researchers, and 'lower class', which tends to be used by US researchers, are used interchangeably. Similarly, the terms 'middle class' and 'upper class' will be used interchangeably, despite the different connotations of the latter term in the United States and in Europe, where it tends to be reserved for members of the land‐owning aristocracy. A final point about terminology concerns 'ideology', which will here be used to refer to a set of beliefs, norms and values, examples being the meritocratic ideology that pervades most education systems and the (related) ideology of social mobility that is prominent in the United States.

Socioeconomic status and identity

Social psychological analyses of identity have traditionally not paid much attention to social class or SES as a component of identity. Instead, the focus has been on categories such as race, gender, sexual orientation, nationality and age. Easterbrook, Kuppens, and Manstead ( 2018) analysed data from two large, representative samples of British adults and showed that respondents placed high subjective importance on their identities that are indicative of SES. Indeed, they attached at least as much importance to their SES identities as they did to identities (such as ethnicity or gender) more commonly studied by self and identity researchers. Easterbrook and colleagues also showed that objective indicators of a person's SES were robust and powerful predictors of the importance they placed on different types of identities within their self‐concepts: Those with higher SES attached more importance to identities that are indicative of their SES position, but less importance on identities that are rooted in basic demographics or related to their sociocultural orientation (and vice versa).

To arrive at these conclusions, Easterbook and colleagues analysed data from two large British surveys: The Citizenship Survey (CS; Department for Communities and Local Government, 2012); and Understanding Society: The UK Household Longitudinal Study (USS; Buck & McFall, 2012). The CS is a (now discontinued) biannual survey of a regionally representative sample of around 10,000 adults in England and Wales, with an ethnic minority boost sample of around 5,000. The researchers analysed the most recent data, collected via interviews in 2010-2011. The USS is an annual longitudinal household panel survey that began in 2009. Easterbrook and colleagues analysed Wave 5 (2013-2014), the more recent of the two waves in which the majority of respondents answered questions relevant to class and other social identities.

Both the CS and the USS included a question about the extent to which respondents incorporated different identities into their sense of self. Respondents were asked how important these identities were 'to your sense of who you are'. The CS included a broad range of identities, including profession, ethnic background, family, gender, age/life stage, income and education. The USS included a shorter list of identities, including profession, education, ethnic background, family, gender and age/life stage. When the responses to these questions were factor analysed, Easterbrook and colleagues found three factors that were common to the two datasets: SES‐based identities (e.g., income), basic‐demographic identities (e.g., age), and identities based on sociocultural orientation (e.g., ethnic background). In both datasets, the importance of each of these three identities was systematically related to objective indicators of the respondents' SES: As the respondent's SES increased, the subjective importance of SES‐related identities increased, whereas the importance of basic‐demographic and (to a lesser extent) sociocultural identities decreased. Interestingly, these findings echo those of a qualitative, interview‐based study conducted with American college students: Aries and Seider ( 2007) found that affluent respondents were more likely than their less affluent counterparts to acknowledge the importance of social class in shaping their identities. As the researchers put it, 'The affluent students were well aware of the educational benefits that had accrued from their economically privileged status and of the opportunities that they had to travel and pursue their interests. The lower‐income students were more likely to downplay class in their conception of their own identities than were the affluent students' (p. 151).

Thus, despite SES receiving relatively scant attention from self and identity researchers, there is converging quantitative and qualitative evidence that SES plays an important role in structuring the self‐concept.

Contexts that shape self‐construal: Home, school, and work

Stephens, Markus, and Phillips ( 2014) have analysed the ways in which social class shapes the self‐concept through the 'gateway contexts' of home, school, and work. With a focus on the United States, but with broader implications, they argue that social class gives rise to culture‐specific selves and patterns of thinking, feeling, and acting. One type of self they label 'hard interdependence.' This, they argue, is characteristic of those who grow up in low‐income, working‐class environments. As the authors put it, 'With higher levels of material constraints and fewer opportunities for influence, choice, and control, working‐class contexts tend to afford an understanding of the self and behavior as interdependent with others and the social context' (p. 615). The 'hard' aspect of this self derives from the resilience that is needed to cope with adversity. The other type of self the authors identify is 'expressive independence', which is argued to be typical of those who grow up in affluent, middle‐class contexts. By comparison with working‐class people, those who grow up in middle‐class households 'need to worry far less about making ends meet or overcoming persistent threats ... Instead, middle‐class contexts enable people to act in ways that reflect and further reinforce the independent cultural ideal - expressing their personal preferences, influencing their social contexts, standing out from others, and developing and exploring their own interests' (p. 615). Stephens and colleagues review a wide range of work on socialization that supports their argument that the contexts of home, school and workplace foster these different self‐conceptions. They also argue that middle‐class schools and workplaces use expressive independence as a standard for measuring success, and thereby create institutional barriers to upward social mobility.

The idea that schools are contexts in which social class inequalities are reinforced may initially seem puzzling, given that schools are supposed to be meritocratic environments in which achievement is shaped by ability and effort, rather than by any advantage conferred by class background. However, as Bourdieu and Passeron ( 1990) have argued, the school system reproduces social inequalities by promoting norms and values that are more familiar to children from middle‐class backgrounds. To the extent that this helps middle‐class children to outperform their working‐class peers, the 'meritocratic' belief that such performance differences are due to differences in ability and/or effort will serve to 'explain' and legitimate unequal performance. Consistent with this argument, Darnon, Wiederkehr, Dompnier, and Martinot ( 2018) primed the concept of merit in French fifth‐grade schoolchildren and found that this led to lower scores on language and mathematics tests - but that this only applied to low‐SES children. Moreover, the effect of the merit prime on test performance was mediated by the extent to which the children endorsed meritocratic beliefs. Here, then, is evidence that the ideology of meritocracy helps to reproduce social class differences in school settings.

Subjective social class

Stephens et al.'s ( 2014) conceptualization of culture‐specific selves that vary as a function of social class is compatible with the 'subjective social rank' argument advanced by Kraus, Piff, and Keltner ( 2011). The latter authors argue that the differences in material resources available to working‐ and middle‐class people create cultural identities that are based on subjective perceptions of social rank in relation to others. These perceptions are based on distinctive patterns of observable behaviour arising from differences in wealth, education, and occupation. 'To the extent that these patterns of behavior are both observable and reliably associated with individual wealth, occupational prestige, and education, they become potential signals to others of a person's social class' (Kraus et al., 2011, p. 246). Among the signals of social class is non‐verbal behaviour. Kraus and Keltner ( 2009) studied non‐verbal behaviour in pairs of people from different social class backgrounds and found that whereas upper‐class individuals were more disengaged non‐verbally, lower‐class individuals exhibited more socially engaged eye contact, head nods, and laughter. Furthermore, when naïve observers were shown 60‐s excerpts of these interactions, they used these disengaged versus engaged non‐verbal behavioural styles to make judgements of the educational and income backgrounds of the people they had seen with above‐chance accuracy. In other words, social class differences are reflected in social signals, and these signals can be used by individuals to assess their subjective social rank. By comparing their wealth, education, occupation, aesthetic tastes, and behaviour with those of others, individuals can determine where they stand in the social hierarchy, and this subjective social rank then shapes other aspects of their social behaviour. More recent research has confirmed these findings. Becker, Kraus, and Rheinschmidt‐Same ( 2017) found that people's social class could be judged with above‐chance accuracy from uploaded Facebook photographs, while Kraus, Park, and Tan ( 2017) found that when Americans were asked to judge a speaker's social class from just seven spoken words, the accuracy of their judgments was again above chance.

The fact that there are behavioural signals of social class also opens up the potential for others to hold prejudiced attitudes and to engage in discriminatory behaviour towards those from a lower social class, although Kraus et al. ( 2011) focus is on how the social comparison process affects the self‐perception of social rank, and how this in turn affects other aspects of social behaviour. These authors argue that subjective social rank 'exerts broad influences on thought, emotion, and social behavior independently of the substance of objective social class' (p. 248). The relation between objective and subjective social class is an interesting issue in its own right. Objective social class is generally operationalized in terms of wealth and income, educational attainment, and occupation. These are the three 'gateway contexts' identified by Stephens et al. ( 2014). As argued by them, these contexts have a powerful influence on individual cognition and behaviour who operate within them, but they do not fully determine how individuals developing and living in these contexts think, feel, and act. Likewise, there will be circumstances in which individuals who objectively are, say, middle‐class construe themselves as having low subjective social rank as a result of the context in which they live.

There is evidence from health psychology that measures of objective and subjective social class have independent effects on health outcomes, with subjective social class explaining variation in health outcomes over and above what can be accounted for in terms of objective social class (Adler, Epel, Castellazzo, & Ickovics, 2000; Cohen et al., 2008). For example, in the prospective study by Cohen et al. ( 2008), 193 volunteers were exposed to a cold or influenza virus and monitored in quarantine for objective and subjective signs of illness. Higher subjective class was associated with less risk of becoming ill as a result of virus exposure, and this relation was independent of objective social class. Additional analyses suggested that the impact of subjective social class on likelihood of becoming ill was due in part to differences in sleep quantity and quality. The most plausible explanation for such findings is that low subjective social class is associated with greater stress. It may be that seeing oneself as being low in subjective class is itself a source of stress, or that it increases vulnerability to the effects of stress.

Below I organize the social psychological literature on social class in terms of the impact of class on three types of outcome: thought, encompassing social cognition and attitudes; emotion, with a focus on moral emotions and prosocial behaviour; and behaviour in high‐prestige educational and workplace settings. I will show that these impacts of social class are consistent with the view that the different construals of the self that are fostered by growing up in low versus high social class contexts have lasting psychological consequences.

Social cognition and attitudes

The ways in which these differences in self‐construal shape social cognition have been synthesized into a theoretical model by Kraus, Piff, Mendoza‐Denton, Rheinschmidt, and Keltner ( 2012). This model is shown in Figure 1 . They characterize the way lower‐class individuals think about the social environment as 'contextualism', meaning a psychological orientation that is motivated by the need to deal with external constraints and threats; and the way that upper‐class people think about the social environment as 'solipsism', meaning an orientation that is motivated by internal states such as emotion and by personal goals. One way in which these different orientations manifest themselves is in differences in responses to threat. The premise here is that lower‐class contexts are objectively characterized by greater levels of threat, as reflected in less security in employment, housing, personal safety, and health. These chronic threats foster the development of a 'threat detection system', with the result that people who grow up in such environments have a heightened vigilance to threat.

Another important difference between the contextualist lower‐class orientation and the solipsistic upper‐class one, according to Kraus et al. ( 2012), is in perceived control. Perceived control is closely related to other key psychological constructs, such as attributions. The evidence shows very clearly that those with lower subjective social class are also lower in their sense of personal control, and it also suggests that this reduced sense of control is related to a preference for situational (rather than dispositional) attributions for a range of social phenomena, including social inequality. The logic connecting social class to perceptions of control is straightforward: Those who grow up in middle‐ or upper‐class environments are likely to have more material and psychological resources available to them, and as a result have stronger beliefs about the extent to which they can shape their own social outcomes; by contrast, those who grow up in lower‐class environments are likely to have fewer resources available to them, and as a result have weaker beliefs about their ability to control their outcomes. There is good empirical support for these linkages. In a series of four studies, Kraus, Piff, and Keltner ( 2009) found that, by comparison with their higher subjective social class counterparts, lower subjective social class individuals (1) reported lower perceived control and (2) were more likely to explain various phenomena, ranging from income inequality to broader social outcomes like getting into medical school, contracting HIV, or being obese, as caused by external factors, ones that are beyond the control of the individual. Moreover, consistent with the authors' reasoning, there was a significant indirect effect of subjective social class on the tendency to see phenomena as caused by external factors, via perceived control.

Another important social cognition measure in relation to social class is prejudice. There are two aspects of prejudice in this context. One is prejudice against people of a different class than one's own and especially attitudes towards those who are poor or unemployed; the other is the degree to which people's prejudiced attitudes about other social groups are associated with their own social class. Regarding attitudes to people who belong to a different social class, the UK evidence clearly shows that attitudes to poverty have changed over the last three decades, in that there is a rising trend for people to believe that those who live in need do so because of a lack of willpower, or because of laziness, accompanied by a corresponding decline in the belief that people live in need because of societal injustice (Clery, Lee, & Kunz, 2013). Interestingly, in their analysis of British Social Attitudes data over a period of 28 years, Clery et al. conclude that 'there are no clear patterns of change in the views of different social classes, suggesting changing economic circumstances exert an impact on attitudes to poverty across society, not just among those most likely to be affected by them' (p. 18). Given the changing attitudes to poverty, it is unsurprising to find that public attitudes to welfare spending and to redistributive taxation have also changed in a way that reflects less sympathy for those living in poverty. For example, attitudes to benefits for the unemployed have changed sharply in the United Kingdom since 1997, when a majority of respondents still believed that benefits were too low. By 2008, an overwhelming majority of respondents believed that these benefits were too high (Taylor‐Gooby, 2013). The way in which economic austerity has affected attitudes to these issues was the subject of qualitative research conducted by Valentine ( 2014). Interviews with 90 people in northern England, drawn from a range of social and ethnic backgrounds, showed that many respondents believed that unemployment is due to personal, rather than structural, failings, and that it is a 'lifestyle choice', leading interviewees to blame the unemployed for their lack of work and to have negative attitudes to welfare provision. Valentine ( 2014, p. 2) observed that 'a moralised sense of poverty as the result of individual choice, rather than structural disadvantage and inequality, was in evidence across the majority of respondents', and that 'Negative attitudes to welfare provision were identified across a variety of social positions and were not exclusively reserved to individuals from either working class or middle class backgrounds'.

Turning to the attitudes to broader social issues held by members of different social classes, there is a long tradition in social science of arguing that working‐class people are more prejudiced on a number of issues, especially with respect to ethnic minorities and immigrants (e.g., Lipset, 1959). Indeed, there is no shortage of evidence showing that working‐class white people do express more negative attitudes towards these groups. One explanation for this association is that working‐class people tend to be more authoritarian - a view that can be traced back to the early research on the authoritarian personality (Adorno, Frenkel‐Brunswik, Levinson, & Sanford, 1950). Recent research providing evidence in favour of this view is reported by Carvacho et al. ( 2013). Using a combination of cross‐sectional surveys and longitudinal studies conducted in Europe and Chile, these authors focused on the role of ideological attitudes, in the shape of right‐wing authoritarianism (RWA; Altemeyer, 1998) and social dominance orientation (SDO; Sidanius & Pratto, 1999), as mediators of the relation between social class and prejudice. To test their predictions, the researchers analysed four public opinion datasets: one based on eight representative samples in Germany; a second based on representative samples from four European countries (France, Germany, Great Britain, and the Netherlands); a third based on longitudinal research in Germany; and a fourth based on longitudinal research in Chile. Consistent with previous research, the researchers found that income and education, the two indices of social class that they used, predicted higher scores on a range of measures of prejudice, such that lower income and education were associated with greater prejudice - although education proved to be a more consistently significant predictor of prejudice than income did. RWA and SDO were negatively associated with income and education, such that higher scores on income and education predicted lower scores on RWA and SDO. Finally, there was also evidence consistent with the mediation hypothesis: The associations between income and education, on the one hand, and measures of prejudice, on the other, were often (but not always) mediated by SDO and (more consistently) RWA. Carvacho and colleagues concluded that 'the working class seems to develop and reproduce an ideological configuration that is generally well suited for legitimating the social system' (p. 283).

Indeed, a theme that emerges from research on social class and attitudes is that ideological factors have a powerful influence on attitudes. The neoliberal ideology that has dominated political discourse in most Western, industrialized societies in the past three decades has influenced attitudes to such an extent that even supporters of left‐of‐centre political parties, such as the Labour Party in the United Kingdom, regard poverty as arising from individual factors and tend to hold negative beliefs about the level of welfare benefits for the unemployed. Such attitudes are shared to a perhaps surprising extent by working‐class people (Clery et al., 2013) and, as we have seen, the research by Carvacho et al. ( 2013) suggests that working‐class people endorse ideologies that endorse and preserve a social system that materially disadvantages them.

The notion that people who are disadvantaged by a social system are especially likely to support it is known as the 'system justification hypothesis', which holds that 'people who suffer the most from a given state of affairs are paradoxically the least likely to question, challenge, reject, or change it' (Jost, Pelham, Sheldon, & Sullivan, 2003, p. 13). The rationale for this prediction derives in part from cognitive dissonance theory (Festinger, 1957), the idea being that it is psychologically inconsistent to experience oppression but not to protest against the system that causes it. One way to reduce the resulting dissonance is to support the system even more strongly, in the same way that those who have to go through an unpleasant initiation rite in order to join a group or organization become more strongly committed to it.

Two large‐scale studies of survey data (Brandt, 2013; Caricati, 2017) have cast considerable doubt on the validity of this hypothesis, showing that any tendency for people who are at the bottom of a social system to be more likely to support the system than are their advantaged counterparts is, at best, far from robust. Moreover, it has been argued that there is in any case a basic theoretical inconsistency between system justification theory and cognitive dissonance theory (Owuamalam, Rubin, & Spears, 2016). However, the fact that working‐class people may not be more supportive of the capitalist system than their middle‐ and upper‐class counterparts does not mean that they do not support the system. Thus, the importance of Carvacho et al.'s ( 2013) findings is not necessarily undermined by the results reported by Brandt ( 2013) and Caricati ( 2017). Being willing to legitimate the system is not the same thing as having a stronger tendency to do this than people who derive greater advantages from the system.

The finding that there is an association between social class and prejudice has also been explained in terms of economic threat. The idea here is that members of ethnic minorities and immigrants also tend to be low in social status and are therefore more likely to be competing with working‐class people than with middle‐class people for jobs, housing, and other services. A strong way to test the economic threat explanation would be to assess whether higher‐class people are prejudiced when confronted with immigrants who are highly educated and likely to be competing with them for access to employment and housing. Such a test was conducted by Kuppens, Spears, Manstead, and Tausch ( 2018). These researchers examined whether more highly educated participants would express negative attitudes towards highly educated immigrants, especially when threat to the respondents' own jobs was made salient, either by drawing attention to the negative economic outlook or by subtly implying that the respondents' own qualifications might be insufficient in the current job market. Consistent with the economic threat hypothesis, a series of experimental studies with student participants in different European countries showed that attitudes to immigrants were most negative when the immigrants also had a university education.

The same researchers also combined US census data with American National Election Study survey data to examine whether symbolic racism was higher in areas where there was a higher number of Blacks with a similar education to that of the White participants. In areas where Blacks were on average less educated, a higher number Blacks was associated with more symbolic racism among Whites who had less education, but in areas where Blacks were on average highly educated, a higher number of Blacks was associated with more symbolic racism on the part of highly educated White people. Again, these findings are consistent with the view that prejudice arises from economic threat.

Research reported by Jetten, Mols, Healy, and Spears ( 2017) is also relevant to this issue. These authors examined how economic instability affects low‐SES and high‐SES people. Unsurprisingly, they found that collective angst was higher among low‐SES participants. However, they also found that high‐SES participants expressed anxiety when they were presented with information suggesting that there was high economic instability, that is, that the 'economic bubble' might be about to burst. Moreover, they were more likely to oppose immigration when economic instability was said to be high, rather than low. These results reflect the fact that high‐SES people have a lot to lose in times of economic crisis, and that this 'fear of falling' is associated with opposition to immigration.

Together, these results provide good support for an explanation of the association between social class and prejudice in terms of differential threat to the group (see also Brandt & Henry, 2012; Brandt & Van Tongeren, 2017). Ethnic minorities and immigrants typically pose most threat to the economic well‐being of working‐class people who have low educational qualifications, and this provides the basis for the observation that working‐class people are more likely to be prejudiced. The fact that higher‐educated and high‐SES people express negative views towards ethnic minorities and immigrants when their economic well‐being is threatened shows that it is perceived threat to one's group's interests that underpins this prejudice. It is also worth noting that the perception of threat to a group's economic interests is likely to be greater during times of economic recession.

Emotion and prosocial behaviour

A strong theme emerging from research investigating the relation between social class and emotion is that lower‐class individuals score more highly on measures of empathy. The rationale for expecting such a link is that because lower‐class individuals are more inclined to explain events in terms of external factors, they should be more sensitive to the ways in which external events shape the emotions of others, and therefore better at judging other people's emotions. A complementary rationale is that the tendency for lower social class individuals to be more socially engaged and to have more interdependent social relationships should result in greater awareness of the emotions experienced by others. This reasoning was tested in three studies reported by Kraus, Côté, and Keltner ( 2010).

In the first of these studies, the authors examined the relation between educational attainment (a proxy for social class) and scores on the emotion recognition subscale of the Mayer‐Salovey‐Caruso Emotional Intelligence Test (Mayer, Salovey, & Caruso, 2002). High‐school‐educated participants attained a higher score than did their college‐educated counterparts. In a second study, pairs of participants took part in a hypothetical job interview in which an experimenter asked each of them a set of standard questions. This interaction provided the basis for the measure of empathic accuracy, in that each participant was asked to rate both their own emotions and their partner's emotions during the interview. Subjective social class was again related to empathic accuracy, with lower‐class participants achieving a higher score. Moreover, lower‐class participants were more inclined to explain decisions they made in terms of situational rather than dispositional factors, and the relation between subjective social class and empathy was found to be mediated by this tendency to explain decisions in terms of situational factors. The researchers conducted a third study in which they manipulated subjective social class. This time they assessed empathic accuracy using the Reading the Mind in the Eyes Test (Baron‐Cohen, Wheelwright, Hill, Raste, & Plumb, 2001). Participants who were temporarily induced to experience lower social class were better at recognizing emotions from the subtle cues available from the eye region of the face.

These findings are compatible with the view that lower social class individuals are more sensitive to contextual variation and more inclined to explain events in situational terms. However, some aspects of the results are quite surprising. For example, there seems to be no compelling reason to predict that greater sensitivity to contextual variation would be helpful in judging static facial expressions, which were the stimuli in Studies 1 and 3 of Kraus et al.'s ( 2010) research. Thus, the relation between social class and emotion recognition in these studies would seem to depend on the notion that the greater interdependence that is characteristic of lower‐class social environments fosters greater experience with, and therefore knowledge of, the relation between facial movement and subjective emotion, although it still seems surprising that a temporary induction of lower subjective social class, as used in Study 3, should elicit the same effect as extensive real‐life experience of inhabiting lower‐class environments.

If lower‐class individuals are more empathic than their higher‐class counterparts, and are therefore better at recognizing the distress or need of others, this is likely to influence their behaviour in settings where people are distressed and/or in need. This, indeed, is what the evidence suggests. In a series of four studies, Piff, Kraus, Côté, Cheng, and Keltner ( 2010) found a consistent tendency for higher‐class individuals to be less inclined to help others than were their lower‐class counterparts. In Study 1, participants low in subjective social class made larger allocations in a dictator game (a game where you are free to allocate as much or as little of a resource to another person as you want) played with an anonymous other than did participants high in subjective social class. In Study 2, subjective social class was manipulated by asking participants to compare themselves to people either at the very top or very bottom of the status hierarchy ladder, the idea being that subjective social class should be lower for those making upward comparisons and higher for those making downward comparisons. Prosocial behaviour was measured by asking participants to indicate the percentage of income that people should spend on a variety of goods and services, one of which was charitable donations. Participants who were induced to experience lower subjective social class indicated that a greater percentage of people's annual salary should be spent on charitable donations compared to participants who were induced to experience higher subjective social class. In Study 3, the researchers used a combination of educational attainment and household income to assess social class and used social value orientation (Van Lange, De Bruin, Otten, & Joireman, 1997) as a measure of egalitarian values. These two variables were used to predict behaviour in a trust game. Consistent with predictions, lower‐class participants showed greater trust in their anonymous partner than did their higher‐class counterparts, and this relation was mediated by egalitarian values. In their final study, the researchers manipulated compassion by asking participants in the compassion condition to view a 46‐s video about child poverty. Higher‐ and lower‐class participants were then given the chance to help someone in need. The researchers predicted that helping would only be moderated by compassion among higher‐class participants, on the grounds that lower‐class participants would already be disposed to help, and the results were consistent with this prediction. Overall, these four studies are consistent in showing that, relative to higher‐class people, lower‐class people are more generous, support charity to a greater extent, are more trusting towards a stranger, and more likely to help a person in distress.

The reliability of this finding has been called into question by Korndörfer, Egloff, and Schmukle ( 2015), who found contrary evidence in a series of studies. One way to resolve these apparently discrepant findings is to argue, as Kraus and Callaghan ( 2016) did, that the relation between social class and prosocial behaviour is moderated by a number of factors, including whether the context is a public or private one. To test this idea, Kraus and Callaghan ( 2016) conducted a series of studies in which they manipulated whether donations made to an anonymous other in a dictator game were made in a private or public context. In the private context, the donor remained anonymous. In the public context, the donor's name and city of residence were announced, along with the donation. Lower‐class participants were more generous in private than in public, whereas the reverse was true for higher‐class participants. Interestingly, higher‐class participants were more likely to expect to feel proud about acting prosocially, and this difference in anticipated pride mediated the effect of social class on the difference between public and private donations.

The fact that lower‐class people have been found to hold more egalitarian values and to be more likely to help regardless of compassion level suggests that it is the greater resources of higher‐class participants that makes them more selfish and therefore less likely to help others. This 'selfishness' account of the social class effect on prosocial behaviour is supported by another series of studies reported by Piff, Stancato, Côté, Mendoza‐Denton, and Keltner ( 2012), who found that, relative to lower‐class individuals, higher‐class people were more likely to show unethical decision‐making tendencies, to take valued goods from others, to lie in a negotiation, to cheat to increase their chances of winning a prize and to endorse unethical behaviour at work. There was also evidence that these unethical tendencies were partly accounted for by more favourable attitudes towards greed among higher‐class people. Later research shows that the relation between social class and unethical behaviour is moderated by whether the behaviour benefits the self or others. Dubois, Rucker, and Galinsky ( 2015) varied who benefited from unethical behaviour and showed that the previously reported tendency for higher‐class people to make more unethical decisions was only observed when the outcome was beneficial to the self. These findings are consistent with the view that the greater resources enjoyed by higher‐class individuals result in a stronger focus on the self and a reduced concern for the welfare of others.

Interestingly, this stronger self‐focus and lesser concern for others' welfare on the part of higher‐class people are more evident in contexts characterized by high economic inequality. This was shown by Côté, House, and Willer ( 2015), who analysed results from a nationally representative US survey and showed that higher‐income respondents were only less generous in the offers they made to an anonymous other in a dictator game than their lower‐income counterparts in areas that were high in economic inequality, as reflected in the Gini coefficient. Indeed, in low inequality areas, there was evidence that higher‐income respondents were more generous than their lower‐income counterparts. To test the causality of this differential association between income and generosity in high and low inequality areas, the authors conducted an experiment in which participants were led to believe that their home state was characterized by high or low degree of economic inequality and then played a dictator game with an anonymous other. High‐income participants were less generous than their low‐income counterparts in the high inequality condition but not in the low inequality condition.

A possible issue with Côté et al. ( 2015) research in the current context is that it focuses on income rather than class. Although these variables are clearly connected, class is generally thought to be indexed by more than income. The research nevertheless suggests that economic inequality plays a key role in shaping the attitudes and behaviours of higher‐class individuals. There are at least three (not mutually exclusive) explanations for this influence of inequality. One is that inequality increases the sense of entitlement in higher‐class people, because they engage more often in downward social comparisons. Another is that higher‐class people may be more concerned about losing their privileged position in society if they perceive a large gap between the rich and the poor. A final explanation is that higher‐class people may be more highly motivated to justify their privileged position in society when the gap between rich and poor is a large one. Whichever of these explanations is correct - and they may all be to some extent - the fact that prosocial behaviour on the part of higher‐class individuals decreases under conditions of high economic inequality is important, given that the United States is one of the most economically unequal societies in the industrialized world. In unequal societies, then, it seems safe to conclude that on average, higher‐class individuals are less likely than their lower‐class counterparts to behave prosocially, especially where the prosocial behaviour is not public in nature.

Universities and workplaces

The selective nature of higher education (HE), involving economic and/or qualification requirements to gain entry, makes a university a high‐status context. Working‐class people seeking to attain university‐level qualifications are therefore faced with working in an environment in which they may feel out of place. Highly selective universities such as Oxford and Cambridge in the United Kingdom, or Harvard, Stanford, and Yale in the United States, are especially likely to appear to be high in status and therefore out of reach. Indeed, the proportion of working‐class students at Oxford and Cambridge is strikingly low. According to the UK's Higher Education Statistics Agency, the percentage of students at Oxford and Cambridge who were from routine/manual occupational backgrounds was 11.5 and 12.6, respectively, in the academic year 2008/9. This compares with an ONS figure of 37% of all people aged between 16 and 63 in the United Kingdom being classified with such backgrounds. The figures for Oxford and Cambridge are extreme, but they illustrate a more general phenomenon, both in the United Kingdom and internationally: students at elite, research‐led universities are more likely to come from middle‐ and upper‐class backgrounds than from working‐class backgrounds (Jerrim, 2013).

The reasons for the very low representation of working‐class students at these elite institutions are complex (Chowdry, Crawford, Dearden, Goodman, & Vignoles, 2013), but at least one factor is that many working‐class students do not consider applying because they do not see themselves as feeling at home there. They see a mismatch between the identity conferred by their social backgrounds and the identity they associate with being a student at an elite university. This is evident from ethnographic research. For example, Reay, Crozier, and Clayton ( 2010) interviewed students from working‐class backgrounds who were attending one of four HE institutions, including an elite university (named Southern in the report). A student at Southern said this about her mother's reaction to her attending this elite university: 'I don't think my mother really approves of me going to Southern. It's not what her daughter should be doing so I don't really mention it when I go home. It's kind of uncomfortable to talk about it' (p. 116). In a separate paper, Reay, Crozier, and Clayton ( 2009) focus on the nine students attending Southern, examining whether these students felt like 'fish out of water'. Indeed, there was evidence of difficulty in adjusting to the new environment, both socially and academically. One student said, 'I wasn't keen on Southern as a place and all my preconceptions were "Oh, it's full of posh boarding school types". And it was all true ... it was a bit of a culture shock' (p. 1111), while another said, 'If you were the best at your secondary school ... you're certainly not going to be the best here' (p. 1112). A similar picture emerges from research in Canada by Lehmann ( 2009, 2013), who interviewed working‐class students attending a research‐intensive university, and found that the students experienced uncomfortable conflicts between their new identities as university students and the ties they had with family members and non‐student friends.

Such is the reputation of elite, research‐intensive universities that working‐class high‐school students are unlikely to imagine themselves attending such institutions, even if they are academically able. Perceptions of these universities as elitist are likely to deter such students from applying. Evidence of this deterrence comes from research conducted by Nieuwenhuis, Easterbrook, and Manstead ( 2018). They report two studies in which 16‐ to 18‐year‐old secondary school students in the United Kingdom were asked about the universities they intended to apply to. The studies were designed to test the theoretical model shown in Figure 2 , which was influenced by prior work on the role of identity compatibility conducted by Jetten, Iyer, Tsivrikos, and Young ( 2008). According to the model in Figure 2 , SES influences university choice partly through its impact on perceived identity compatibility and anticipated acceptance at low‐ and high‐status universities.

In the first study conducted by Nieuwenhuis and colleagues, students who were 6 months away from making their university applications responded to questions about their perceptions of two universities, one a research‐intensive, selective university (SU), the other a less selective university (LSU). Both universities were located in the same geographical region, not far from the schools where the participants were recruited. In the second study, students who were 6 weeks away from making their university applications responded to similar questions, but this time about three universities in the region, two of which were the same as those in Study 1, while the third was a highly selective institution (HSU). The questions put to respondents measured their perceptions of identity compatibility (e.g., consistency between family background and decision to go to university) and anticipated acceptance (e.g., anticipated identification with students at the university in question). Measures of parental education and academic achievement in previous examinations were taken, as well as the three universities to which they would most like to apply, which were scored in accordance with a published national league table.

In both studies, it was found that relatively disadvantaged students (whose parents had low levels of educational attainment) scored lower on identity compatibility and that low scores on identity compatibility were associated with lower anticipated acceptance at the SU (Study 1) or at the HSU (Study 2). These anticipated acceptance scores, in turn, predicted the type of university to which participants wanted to apply, with those who anticipated feeling accepted at more selective universities being more likely to apply to higher status universities. All of these relations were significant while controlling for academic achievement. Together, the results of these studies show that perceptions of acceptance at different types of university are associated with HE choices independently of students' academic ability. This helps to explain why highly able students from socially disadvantaged backgrounds are more likely to settle for less prestigious universities.

Alternatively, working‐class students may opt out of HE altogether. Hutchings and Archer ( 2001) interviewed young working‐class people who were not participating in HE and found that a key reason for their non‐participation was a perception that the kinds of HE institutions that were realistically available to them were second‐rate: '[O]ur respondents constructed two very different pictures of HE. One was of Oxbridge and campus universities, pleasant environments in which middle‐class students ... can look forward to achieving prestigious degrees and careers. The second construction was of rather unattractive buildings in which "skint" working‐class students ... have to work hard under considerable pressure, combining study with a job and having little time for social life. This second picture was the sort of HE that our respondents generally talked about as available to them, and they saw it as inferior to 'real' HE' (p. 87).

Despite the deterrent effect of perceived identity incompatibility and lack of psychological fit, some working‐class students do gain entry to high‐status universities. Once there, they are confronted with the same issues of fit. Stephens, Fryberg, Markus, Johnson, and Covarrubias ( 2012) describe this as 'cultural mismatch', arguing that the interdependent norms that characterize the working‐class backgrounds of most first‐generation college students in the United States do not match the middle‐class independent norms that prevail in universities offering 4‐year degrees and that this mismatch leads to greater discomfort and poorer academic performance. Their cultural mismatch model is summarized in Figure 3 .

To test this model, Stephens, Fryberg, Markus, et al. ( 2012) surveyed university administrators at the top 50 national universities and the top 25 liberal arts colleges. The majority of the 261 respondents were deans. They were asked to respond to items expressing interdependent (e.g., learn to work together with others) or independent (e.g., learn to express oneself) norms, selecting those that characterized their institution's culture or choosing statements reflecting what was more often emphasized by the institution. More than 70% of the respondents chose items reflecting a greater emphasis on independence than on interdependence. Similar results were found in a follow‐up study involving 50 administrators at second‐tier universities and liberal arts colleges, showing that this stronger focus on independence was not only true of elite institutions. Moreover, a longitudinal study of first‐generation students found that this focus on independence did not match the students' interdependent motives for going to college, in that first‐generation students selected fewer independent motives (e.g., become an independent thinker) and twice as many interdependent motives (e.g., give back to the community), compared to their continuing‐generation counterparts, and that this greater focus on interdependent motives was associated with lower grades in the first 2 years of study, even after controlling for race and SAT scores.

As Stephens and her colleagues have shown elsewhere (e.g., Stephens, Brannon, Markus, & Nelson, 2015), there are steps that can be taken to reduce working‐class students' perception that they do not fit with their university environment. These authors argue that 'a key goal of interventions should be to fortify and to elaborate school‐relevant selves - the understanding that getting a college degree is central to "who I am", "who I hope to become", and "the future I envision for myself"' (p. 3). Among the interventions that they advocate as ways of creating a more inclusive culture at university are: providing working‐class role models; diversifying the way in which university experience is represented, so that university culture also provides ways of achieving interdependent goals that may be more compatible with working‐class students' values; and ensuring that working‐class students have a voice, for example, by providing forums in which they can express shared interests and concerns.

Although there is a less well‐developed line of work on the ways in which high‐status places of work affect the aspirations and behaviours of working‐class employees, there is good reason to assume that the effects and processes identified in research on universities as places to study generalize to prestigious employment organizations as places to work (Côté, 2011). To the extent that many workplaces are dominated by middle‐class values and practices, working‐class employees are likely to feel out of place (Ridgway & Fisk, 2012). This applies both to gaining entry to the workplace, by negotiating the application and selection process (Rivera, 2012), and (if successful) to the daily interactions between employees in the workplace. In the view of Stephens, Fryberg, and Markus ( 2012), many workplaces are characterized by cultures of expressive independence, where working‐class employees are less likely to feel at home. As Stephens et al. ( 2014, p. 626) argue, 'This mismatch between working‐class employees and their middle‐class colleagues and institutions could also reduce employees' job security and satisfaction, continuing the cycle of disadvantage for working‐class employees.'

Towards an integrative model

The work reviewed here provides the basis for an integrative model of how social class affects thoughts, feelings, and behaviour. The model is shown in Figure 4 and builds on the work of others, especially that of Nicole Stephens and colleagues and that of Michael Kraus and colleagues. At the base of the model are differences in the material circumstances of working‐class and middle‐class people. These differences in income and wealth are associated with differences in social capital, in the form of friendship networks, and cultural capital, in the form of tacit knowledge about how systems work, that have a profound effect on the ways in which individuals who grow up in these different contexts construe themselves and their social environments. For example, if you have family members or friends who have university degrees and/or professional qualifications, you are more likely to entertain these as possible futures than if you do not have these networks; and if through these networks you have been exposed to libraries, museums, interviews, and so on, you are more likely to know how these cultural institutions work, less likely to be intimidated by them, and more likely to make use of them. In sum, a middle‐class upbringing is more likely to promote the perception that the environment is one full of challenges that can be met rather than threats that need to be avoided. These differences in self‐construal and models of interpersonal relations translate into differences in social emotions and behaviours that are noticeable to self and others, creating the opportunity for people to rank themselves and others, and for differences in norms and values to emerge. To the extent that high‐status institutions in society, such as elite universities and prestigious employers, are characterized by norms and values that are different from those that are familiar to working‐class people, the latter will feel uncomfortable in such institutions and will perform below their true potential.

Also depicted in Figure 4 is the way in which ideology moderates the relations between social class, on the one hand, and social cognition and social behaviour, on the other, and the ways in which economic inequality and threat moderate the relations between psychological dispositions and social behaviour. Although there is good evidence for many of the proposed relations depicted in the model, there is relatively little hard evidence concerning the moderating roles of ideology and economic inequality and threat. There is evidence that economic threat is associated with prejudice (e.g., Billiet, Meuleman, & De Witte, 2014), and that this also applies to higher‐educated people (e.g., Kuppens et al., 2018). There is also evidence that high economic inequality increases the tendency for high‐income people to be less generous to others (Côté et al., 2015), but these are influences that need further examination. Likewise, there is evidence of the moderating impact of ideology on the translation from social class to social cognition and behaviour (e.g., Wiederkehr, Bonnot, Krauth‐Gruber, & Darnon, 2015), but this, too, is an influence that merits additional investigation. A further point worth making is that much of the work on which this integrative model is based was conducted in the United States, which raises the question of the extent to which it is applicable to other contexts. There are some differences between the United States and other Western, industrialized countries that are relevant to the model. For example, the United States is more economically unequal than virtually every other industrialized country (Piketty & Saez, 2014). At the same time, the perceived degree of social mobility is greater in the United States than in other countries (Isaacs, 2008) - although the reality is that social mobility is lower in the United States (and indeed in the United Kingdom; see Social Mobility Commission, 2017) than in many other industrialized counties (Isaacs, 2008). These differences in economic inequality and ideology mean that the moderating roles played by these factors may vary from one country to another. For example, there is evidence that those in Europe who are poor or on the left of the political spectrum are more concerned with and unhappy about inequality than are their American counterparts, which may be related to different beliefs about social mobility (Alesina, Di Tella, & MacCulloch, 2004). Although there seems to be no good reason to question the generalizability of the other relations posited in the model, there is an obvious need to expand the research base on which the model is founded.

Prospects for social change

The cycle of disadvantage that starts with poor material conditions and ends with lower chances of entering and succeeding in the very contexts (universities and high‐status workplaces) that could increase social mobility is not going to be changed in the absence of substantial pressure for social change. It is therefore interesting that when people are asked about social inequality, they generally say that they are in favour of greater equality.

Norton and Ariely ( 2011) asked a nationally representative sample of more than 5,500 Americans to estimate the (then) current wealth distribution in the United States and also to express their preferences for how wealth should be distributed. The key findings from this research were (1) that respondents greatly underestimated the degree of wealth inequality in the United States, believing that the wealthiest 20% of the population owned 59% of the wealth, where the actual figure is 84% and (2) that their preferred distribution of wealth among citizens was closer to equality than even their own incorrect estimations of the distribution (e.g., they expressed a preference that the top 20% should own 32% of the nation's wealth). This also held for wealthy respondents and Republican voters - albeit to a lesser extent than their poorer and Democrat counterparts. Similar results for Australian respondents were reported by Norton, Neal, Govan, Ariely, and Holland ( 2014).

These studies have been criticized on the grounds that the 'quintile' methodology they use provides respondents with an anchor (20%) from which they adjust upwards or downwards. However, when Eriksson and Simpson ( 2012) used a different methodology, they found that although American respondents' preferences for wealth distribution were more unequal than those found using the quintile methodology, they were still much more egalitarian than the actual distribution. Similar conclusions were reached in a study of American adolescents conducted by Flanagan and Kornbluh ( 2017), where participants expressed a strong preference for a much more egalitarian society than the degree of stratification they perceived to exist in the United States. It is also worth noting that similar findings have been reported in a study of preferences for income inequality (Kiatpongsan & Norton, 2014), where it was found that American respondents underestimated the actual difference in income between CEOs and unskilled workers (354:1), and that their preferences regarding this difference (7:1) were more egalitarian than were their estimates (30:1).

Given the evidence that citizens consistently express a preference for less wealth and income inequality than what currently prevails in many societies, it is worth considering why there is not greater support for redistributive policies. It is known that one factor that weakens support for such policies is a belief in social mobility. American participants have been found to overestimate the degree of social mobility in the United States (Davidai & Gilovich, 2015; Kraus & Tan, 2015), and Shariff, Wiwad, and Aknin ( 2016) have shown, using a combination of survey and experimental methods, that higher perceived mobility leads to greater acceptance of income inequality. These authors also showed that the effect of their manipulation of perceived income mobility on tolerance for inequality was mediated by two factors: the expectation that respondents' children would be upwardly mobile; and perceptions of the degree to which someone's economic standing was the result of effort, rather than luck. This suggests that people's attitudes to income inequality - and therefore their support for steps to reduce it - are shaped by their perceptions that (1) higher incomes are possible to achieve, at least for their children, and (2) when these higher incomes are achieved, they are deserved. It follows that any intervention that reduces the tendency to overestimate income mobility should increase support for redistributive policies.

Another factor that helps to account for lack of support for redistribution is people's perceptions of their own social standing or rank. Brown‐Iannuzzi, Lundberg, Kay, and Payne ( 2015) have shown that subjective status is correlated with support for redistributive policies, and that experimentally altering subjective status leads to changes in such support. In both cases, lower subjective status was associated with stronger support for redistribution, even when actual resources and self‐interest were held constant. So one's perception of one's own relative social rank influences support for redistribution. This points to the importance of social comparisons and suggests that those who compare themselves with others who have a lower social standing are less likely to be supportive of redistribution.

Evidence that people's attitudes to inequality and to policies that would reduce it can be influenced by quite straightforward interventions comes from research reported by McCall, Burk, Laperrière, and Richeson ( 2017). In three studies, these researchers show that exposing American participants to information about the rising economic inequality, compared to control information, led to stronger perceptions that economic success is due to structural factors rather than individual effort. In the largest of the three studies, involving a representative sample of American adults, it was also found that information about rising inequality led to greater endorsement of policies that could be implemented by government and by business to reduce inequality. This research shows that, under the right conditions, even those living in a society that is traditionally opposed to government intervention would support government policies to reduce inequality.

Also relevant to the likelihood of people taking social action on this issue is how descriptions of inequality are framed. Bruckmüller, Reese, and Martiny ( 2017) have shown that relatively subtle variations in such framing, such as whether an advantaged group is described as having more or a disadvantaged group is described as having less, influence perceptions of the legitimacy of these differences; larger differences between groups were evaluated as less legitimate when the disadvantaged group was described as having less. Perceptions of the illegitimacy of inequality in group outcomes are likely to evoke group‐based anger, which in turn is known to be one of the predictors of collective action (Van Zomeren, Spears, Fischer, & Leach, 2004).

Conclusion

There is solid evidence that the material circumstances in which people develop and live their lives have a profound influence on the ways in which they construe themselves and their social environments. The resulting differences in the ways that working‐class and middle‐ and upper‐class people think and act serve to reinforce these influences of social class background, making it harder for working‐class individuals to benefit from the kinds of educational and employment opportunities that would increase social mobility and thereby improve their material circumstances. At a time when economic inequality is increasing in many countries, this lack of mobility puts a strain on social cohesion. Most people believe that economic inequality is undesirable and, when presented with the evidence of growing inequality, say that they would support government policies designed to reduce it. Given that the social class differences reviewed here have their origins in economic inequality, it follows that redistributive (or 'predistributive'; Taylor‐Gooby, 2013) policies are urgently needed to create greater equality.

Acknowledgements

Thanks are due to Colin Foad, Matt Easterbrook, Russell Spears and John Drury for their helpful comments on a previous version of this paper.

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The psychology of social class: How socioeconomic status impacts thought, feelings, and behaviour

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Drawing on recent research on the psychology of social class, I argue that the material conditions in which people grow up and live have a lasting impact on their personal and social identities and that this influences both the way they think and feel ...

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"However, it is unclear whether other forms of racial stereotypicality, such as a person’s way of speaking, also activate stereotypes about Black Americans. We investigated the links between speech stereotypicality and racial stereotypes (Experiment 1) and racial phenotype bias (Experiment 2). In Experiment 1, participants listened to audio recordings of Black speakers and rated how stereotypical they found the speaker, the likely race and nationality of the speaker, and indicated which adjectives the average person would likely associate with this speaker. In Experiment 2, participants listened to recordings of weakly or strongly stereotypical Black American speakers and indicated which of two faces (either weakly or strongly phenotypical) was more likely to be the speaker’s."

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Front Psychol. 2021; 12: 785283.

Abstract

Black Americans who are perceived as more racially phenotypical-that is, who possess more physical traits that are closely associated with their race-are more often associated with racial stereotypes. These stereotypes, including assumptions about criminality, can influence how Black Americans are treated by the legal system. However, it is unclear whether other forms of racial stereotypicality, such as a person's way of speaking, also activate stereotypes about Black Americans. We investigated the links between speech stereotypicality and racial stereotypes (Experiment 1) and racial phenotype bias (Experiment 2). In Experiment 1, participants listened to audio recordings of Black speakers and rated how stereotypical they found the speaker, the likely race and nationality of the speaker, and indicated which adjectives the average person would likely associate with this speaker. In Experiment 2, participants listened to recordings of weakly or strongly stereotypical Black American speakers and indicated which of two faces (either weakly or strongly phenotypical) was more likely to be the speaker's. We found that speakers whose voices were rated as more highly stereotypical for Black Americans were more likely to be associated with stereotypes about Black Americans (Experiment 1) and with more stereotypically Black faces (Experiment 2). These findings indicate that speech stereotypicality activates racial stereotypes as well as expectations about the stereotypicality of an individual's appearance. As a result, the activation of stereotypes based on speech may lead to bias in suspect descriptions or eyewitness identifications.

Keywords: stereotypes, social categorization, race, Black Americans, phenotype, dialect, speech perception

Introduction

Every day, we interact with those we do not know in order to perform our jobs, run our errands, or engage in other, more leisurely activities. Making use of the available social information, we quickly form impressions about these unfamiliar people and use those impressions to guide our interactions. For instance, we may use the available cues to make assumptions about another person's membership in certain social categories, such as their likely gender, ethnic group, occupation, or social status, and the stereotypes or beliefs we associate with those social categories influence the traits we expect this person to possess.

How strongly we link stereotypes for a social category to a specific individual often depends on the extent to which that individual is seen as a typical member or exemplar of that group. Individuals who possess more of the features related to their social group are often more closely associated with stereotypes about that group ( Blair et al., 2004b; Walker and Wänke, 2017). Unfortunately for Black Americans, these stereotypes include expectations about criminality ( Eberhardt et al., 2004) and may influence how more stereotypical Black Americans are perceived and treated by the legal system. More stereotypically Black individuals are more likely to be associated with crime or a criminal label both by members of the general public and police officers ( Eberhardt et al., 2004; Kleider et al., 2012) and are more likely to be misidentified as a suspect by eyewitnesses ( Knuycky et al., 2014; Kleider-Offutt et al., 2017). The influence of stereotypicality extends to sentencing, such that when the victim is White, Black defendants with more stereotypical features are more likely to be given the death penalty than less stereotypical looking Black defendants ( Eberhardt et al., 2006).

Studies investigating stereotypicality often focus on physical characteristics; for example, those with fuller lips or a wider nose are more likely to be viewed as phenotypically Black ( Blair et al., 2002, 2004b; Hagiwara et al., 2012). These Afrocentric facial features can be used separately or in combination with skin tone to influence judgments about race typicality ( Stepanova and Strube, 2009, 2012b; Dunham et al., 2015). However, judgments about individuals are influenced by more than their static visual appearance; indeed, some of our interactions do not include visual information, e.g., telephone calls or online voice chat. It is unclear how other aspects of an individual-specifically how they speak-contribute to judgments of whether an individual is "stereotypically Black."

Language as a Marker of Social Category

Language is an important tool in social categorization ( Rakiæ et al., 2011b; Dragojevic et al., 2018). How a person speaks can act as an index or sign of one's background ( Bucholtz and Hall, 2005), and this indexical information about a person's social category information is quickly and automatically accessed. For instance, information about a person's likely gender category can be accessed around 150 ms after voice presentation ( Munson and Solum, 2010). In some cases, the way a person talks can be an even more important indicator of the social category to which an individual belongs than facial features ( Rakiæ et al., 2011a).

Social category information obtained from a person's speech can activate stereotypes or other assumptions not only about the speaker's linguistic background, but also the social groups to which they likely belong ( Giles and Rakiæ, 2014), providing a gateway for individuals to make judgments about the speaker ( Giles, 1970; Mulac and Rudd, 1977). Importantly, the stereotypes and attitudes activated in relation to a given speaker depend heavily on the listener. The stereotypes a listener associates with a specific social group are dictated by that listener's social environment and cultural context, to include both implicitly and explicitly held beliefs, as well as the listener's ability to notice and classify certain linguistic features, the listener's expectations about the conversation, and their own communication goals ( Cargile and Bradac, 2001; Preston, 2018).

One cultural factor that shapes how speakers are perceived is the level of standardization of the dialect they employ. Dialects, or the way of speaking associated with a certain regional, cultural, or ethnic group, are often described as either "standard" or "non-standard," with standard variants of a language being ones that are supported by the state and/or other influential institutions ( Milroy, 2001; Milroy and Milroy, 2012). Accordingly, the identities and dialects of those in power influence which dialects are considered standard ( Lippi-Green, 1997). Standard dialects, such as General American English in the United States (U.S.) or Received Pronunciation in the United Kingdom (UK), are often viewed more favorably and seen as more prestigious than non-standard dialects ( Dent, 2004; Morales et al., 2012), even by speakers of non-standard dialects ( Anisfeld et al., 1962; Carter and Callesano, 2018). How those who use non-standard dialects are viewed often depends on how the people most closely associated with that dialect are perceived. For instance, people from the Southern U.S. are stereotyped as being uneducated, poor, and lazy ( Slade and Narro, 2012). Unsurprisingly, speakers using a Southern U.S. dialect, a non-standard dialect of American English associated with this region, are seen as less wealthy, less intelligent, less healthy, and less attractive than speakers using a more standard American dialect ( Dent, 2004; Phillips, 2010; Shamina, 2016). Further, linguistic features associated with Southern U.S. English are implicitly associated with blue collar jobs and lower intelligence ( Campbell-Kibler, 2012; Loudermilk, 2015). In this way, judgments about speakers of a given dialect can reflect the stereotypes about members of that social group.

Sounding Black

Given the interconnectivity between language, social categorization, and stereotypes, it is likely that individuals who "sound Black" are more likely to be identified as Black Americans and therefore more likely to be associated with stereotypes about the group. One way individuals may be thought to "sound Black" is through their use of African American Vernacular English (AAVE). AAVE is a non-standard dialect of American English closely associated with and spoken predominantly (but not only) by Black Americans ( Cutler, 2003; Rickford, 1999). Often denigrated as slang or improper English, AAVE is in fact a valid language system, with regular phonological and grammatical features such as -ing dropping (e.g., "goin"' vs. "going"), r-lessness (e.g., "fo"' vs. "four"), negative concord (e.g., "He ain't seen nothin"'), and the use of habitual be (e.g., "She be workin"' indicates "She's often working") ( Pullum, 1999; Thomas, 2007; see Jones, 2015 for more on regional variations in AAVE). Like speakers of other non-standard dialects, speakers of AAVE are seen less favorably than speakers of the more standard General American English in most contexts ( Payne et al., 2000; Koch et al., 2001; Dent, 2004; Rodriguez et al., 2004; Billings, 2005). Speakers of AAVE are seen as less competent, less sociable, less professional, less educated, and of poorer character than speakers of more standard American English ( Payne et al., 2000; Koch et al., 2001; Dent, 2004; Billings, 2005). As with the Southern U.S. dialect, many of the traits associated with AAVE are also associated with its dominant speakers: Black Americans ( Devine and Elliot, 1995; Maddox and Gray, 2002). For instance, individuals show a greater implicit association between weapons and AAVE speakers than more standard speakers ( Rosen, 2017), suggesting that stereotypes about criminality and violence, often associated with Black Americans, are also linked to AAVE speakers. Further, AAVE's close association with Black Americans has also led to linguistic profiling, or discrimination against those who speak a certain way due to their assumed membership in a social group. Discriminating against someone for their way of speaking can allow for anti-Black bias to circumvent legal protections, leading to worse outcomes and fewer opportunities in areas such as housing for those who use AAVE and are assumed to be Black ( Purnell et al., 1999; Massey and Lundy, 2001).

However, not all Black Americans speak AAVE, and those who do speak AAVE do not use it all the time or in all contexts ( Rickford, 1999; McCluney et al., 2021). Yet listeners can reliably identify the race of Black speakers regardless of dialect with over 85% accuracy in longer (10 second) clips ( Kushins, 2014) and approximately 60-70% accuracy after listening to a one-second clip or a single word ( Walton and Orlikoff, 1994; Purnell et al., 1999). This ability to quickly identify the racial identity of a speaker is likely due to the presence of certain phonological features, e.g., final consonant dropping or vowel quality ( Walton and Orlikoff, 1994; Thomas and Reaser, 2004; Perrachione et al., 2010), but there is no consensus on which specific linguistic cues trigger the perception of a speaker as Black. Despite this lack of scientific consensus, it is likely that listeners have learned, through their social or cultural environment, to associate certain linguistic features with Black Americans and use those cues to identify speaker race ( Perrachione et al., 2010). As a result, Black speakers who do not employ the expected linguistic features can be miscategorized as members of other races or ethnic groups ( Thomas and Reaser, 2004; Perrachione et al., 2010).

Regardless of whether listeners are picking up on AAVE or other linguistic features associated with Black Americans, the strength with which speakers employ these features likely predicts whether listeners will categorize speakers as Black and the stereotypes assigned to them. Rodriguez et al. (2004) found that speakers who had a stronger AAVE dialect (i.e., used more AAVE features) were rated less favorably than those with a more moderate AAVE dialect. Thus, one would expect that speakers who have stronger dialects and sound "more Black" to listeners will be not only more likely to be identified as Black, but also will be more associated with stereotypes about Black Americans-including expectations about criminality and violence. However, this has yet to be directly investigated.

If sounding "more Black" does lead to an increase in Black stereotypes, it could also lead to the assumption that the speaker has a more stereotypically Black appearance as well. Previous work has found that stereotypes can influence expectations about appearance. Hughes and Miller (2016) found that, in line with the "what sounds beautiful is good" stereotype ( Zuckerman and Driver, 1989), individuals with more attractive voices are expected to have more attractive faces. Separately, Osborne and Davies (2013) found that participants who watched a video of a crime stereotypically associated with Black people remembered the perpetrator as appearing more phenotypically Black than those who watched the perpetrator commit a crime stereotypically associated with White people, even when the crimes were matched on severity and violence. Being perceived as sounding more stereotypically Black could similarly activate listeners' stereotypes about Blackness and influence the expectations a listener has for their appearance-a supposition with critical implications for the legal system, e.g., in ensuring reliable suspect identifications.

Given that linguistic profiling and discrimination based on how a person speaks are not explicitly prohibited under U.S. law ( Wiehl, 2002; MacNeal et al., 2019), understanding how speech stereotypicality influences assumptions about a speaker is needed before any countermeasures to minimize linguistic bias can be developed. To address this issue, we conducted two experiments to investigate the relationship between sounding more stereotypically Black and the assignment of stereotypical traits associated with Black Americans (Experiment 1) and decisions about likely appearance (Experiment 2). In both experiments, participants listened to audio recordings from Black speakers before making their judgments. We expected that speakers whose speech is perceived as more stereotypically Black would be associated with more stereotypical traits about Black Americans and with a more phenotypical Black appearance.

Experiment 1

Those who are perceived as looking more phenotypically Black are also more likely to be associated with stereotypes about Black Americans ( Blair et al., 2004b). In Experiment 1 we explored whether this pattern extended to those who are perceived as sounding more Black. Participants listened to and evaluated audio recordings taken from the internet of American and British Black male speakers. We had two hypotheses for this experiment. First, we expected that participants would assign more stereotypical traits to speakers they rated as sounding more stereotypically Black. Second, since listeners rely on learned linguistic cues to identify Black speakers, we anticipated that our U.S.-based listeners' concept of "stereotypically Black" would be informed by their cultural context. Thus, they would assign more stereotypes to more stereotypical-sounding Black speakers who were also perceived as American.

Materials and Methods

Participants

We recruited 75 participants from Amazon's Mechanical Turk (MTurk) using the TurkPrime interface ( Litman et al., 2017). Only U.S.-based workers who had completed at least 100 Human Intelligence Tasks (HIT) and who had HIT approval rates of 98% or greater were allowed to participate in this study. Workers were paid $0.50 for approximately 10 min of work. The sample was predominantly female (62.7% women; 34.7% men; 1.3% gender neutral; 1.3% prefer not to answer) and White (69.3% White; 5.3% Black; 8.0% Asian; 5.3% Hispanic or Latino/a; and 12.0% multiracial), and the mean age of the sample was 39.77 years ( SD = 12.72; Range = 20-71). Participants were overall well-educated; 45.3% of the sample reported they had a bachelor's degree, and 26.7% reported they had at least some college credit. Only 2.7% reported they did not possess at least a high school diploma or its equivalent.

Participants used a variety of terms when freely describing their own dialects, with the most common labels being some derivative of "American English" (13.3%), "Midwestern" (13.3%), or "Standard American" (10.7%). Other expected regional (e.g., "American East Coast," "Bostonian," "Texan," or "Southern English") and racial/ethnic terms (e.g., "African American," "Chinese English," or "Italian American") also appeared. Interestingly, some participants labeled their dialects as "White" or "Caucasian" (6.7%).

In order to minimize low-effort or bot responses, we removed four participants' data for providing nonsensical or off-topic responses (e.g., "NICE") to our free response dialect question. Other responses that were related to a way of speaking but did not describe a dialect per se (e.g., "slang" or "soft spoken") were retained in analyses, leaving us with data from 71 participants (675 observations). According to G*Power ( Faul et al., 2009), a priori power analysis for a two-tailed multiple regression with our seven predictors would require a sample size of at least 55 to detect an interaction of medium effect size ( f ² = 0.15), with α = 0.05 and 1-β = 0.80. Using these same parameters for α and 1-β, our sample of 71 would allow us to detect at least an effect size of f ² = 0.11.

Materials

Audio Recordings

We created 20 audio recordings featuring 14 Black North American male and 6 Black British male speakers. To have more ecologically valid recordings, the audio was taken from YouTube videos by searching terms such as ''Black British,'' ''Black English,'' and ''Black American.'' The audio was then shortened to a sample of the speaker's speech. These clips ranged between 18 and 35 s due to variations in the length of speakers' utterances. In the clips, speakers discussed a variety of topics, i.e., travel experiences ( n = 3), restaurants/food/diet ( n = 7), domestic/foreign culture ( n = 5), working abroad ( n = 2), creating a better life for one's family ( n = 1), comedians ( n = 1), and sport ( n = 1)1. The audio clips were screened for any explicit mentions of the speaker's race or national origin. After data collection, one of the British speakers (who discussed sports) was revealed to be an American actor; as a result, this speaker was removed from analyses, leaving us with data on 19 speakers. Although we could reasonably assume the rest of our British speakers were from the UK due to the content of either the full video or their profiles, we could not find information about the nationality of two of the American speakers. Due to their use of American English dialects, we assume these speakers are from the U.S.; however, we recognize that some of these speakers could be Canadian given the overlap between Canadian and American English (see Labov et al., 2008).

Stereotypical Traits

To rate the speakers from the voice clips, participants were shown a list of 30 adjectives taken from Devine and Elliot (1995) and Maddox and Gray (2002). The adjectives included those associated with Black American stereotypes (athletic, criminal, lazy, poor, rhythmic, uneducated, unintelligent, hostile, loud, dirty, inferior, ostentatious, sexually aggressive, and aggressive), as well as counter-stereotypic (intelligent, kind, educated, motivated, and wealthy), and neutral adjectives (attractive, bad attitude, self-assured, unattractive, superstitious, naïve, unreliable, talkative, materialistic, arrogant, and ambitious). To create our dependent variable, we calculated the proportion of stereotypical adjectives out of all the adjectives a participant assigned to a given voice.

Procedure

After indicating their informed consent to the study procedures, all participants were assigned a subject number to safeguard their identity. At the beginning of the study, participants supplied demographic information and completed a short audio test to ensure that they were able to hear the recordings. Participants then listened to 10 of the 20 audio clips; the selection of audio clips and order of presentation was randomly determined. After listening to a clip, participants selected the adjectives that they believed the average person would use to describe the speaker. The instructions emphasized that the participant did not have to personally agree with the description. Next, participants chose the speaker's likely race, nationality, and age and rated how attractive the speaker's voice was. Finally, participants indicated how stereotypical the speaker's voice was for their perceived race (as chosen by the participant) before choosing a likely dialect for the speaker and rating how familiar they were with that dialect. The audio clip remained on the screen while participants provided their ratings so participants could refer back to it. After rating all of their assigned voice clips, participants were asked to provide a term or label to describe their own dialect or way of speaking. Upon completing all study procedures, participants were thanked for their work and debriefed on the purpose of the study.

Results

Data Analysis

To explore how perceived speech stereotypicality influences the traits people assign to a speaker, we ran a mixed effects model predicting the proportion of stereotypical traits assigned. Due to the aforementioned overlap between U.S. and Canadian speakers, we coded responses of the U.S. and Canada to the speaker's perceived country question under the umbrella of North American (N. Am.). Therefore, the model included fixed effects for perceived race (Non-Black, Black), perceived country of origin (Non-N. Am., N. Am.), speech stereotypicality (mean-centered by participant), and their interactions, and random intercepts for participants and the individual speakers.

We also ran an additional model with perceived speaker age and voice attractiveness (mean-centered by participant) included as covariates. Speaker age was controlled for as age may moderate how stereotype content may vary not only by race, but also by the age of the individual (e.g., Andreoletti et al., 2015), which may affect both how stereotypical listeners rated speakers' voices as well as what traits they associated with the speaker. Stereotypes about Blackness in particular may be more salient for younger rather than older Black men, as an analysis of Pennsylvania sentencing data from the late 1980s to early 1990s revealed that, controlling for crime severity and other court-related factors, young Black men received harsher sentences than older Black men and White men and White and Black women of any age ( Steffensmeier et al., 1998). Voice attractiveness was included as a covariate as more attractive voices are often associated with more positive traits ( Zuckerman and Driver, 1989), which may influence the adjectives listeners associate with speakers. How stereotypical a speaker sounds as a member of their perceived race and how attractive their voice is rated are likely related, given that conceptions of attractiveness broadly favor Eurocentric traits ( Maddox, 2004). However, previous work investigating facial features and attractiveness found racial typicality and attractiveness had small to moderate correlations ( Stepanova and Strube, 2018).

In both models, predictors were sum coded, with Non-Black and Non-N. Am. serving as the reference groups. Significance tests were run by conducting likelihood ratio tests comparing the full model to the model without the predictor of interest for all predictors. Follow-up pairwise comparisons were Bonferroni-adjusted for four tests. The data were imported into R Studio ( RStudio Team, 2019) using the haven package ( Wickham and Miller, 2019). Data were analyzed using the psych ( Revelle, 2017), gmodels ( Warnes et al., 2018), afex ( Singmann et al., 2019), lme4 ( Bates et al., 2015), and emmeans ( Lenth, 2019) packages.

Speaker Perceived Demographics

Speaker Perceived Race

Although all speakers were Black, participants identified speakers as Black only around two-thirds of the time (61.3%). Out of all possible racial options, participants identified our UK speakers nearly equally often as White (49.2%) or Black (44.7%), whereas our N. Am. speakers were identified primarily as Black (67.3%), with White as the second most frequent option (26.4%). A logistic mixed effects model on perceived race (Non-Black, Black) with fixed effects for speaker's actual country (UK, N. Am.) and their perceived country (Non-N. Am., N. Am.) and random effects for participants and speakers found that neither actual nor perceived country of origin predicted perceptions of the speaker's race ( p s ≥ 0.240).

Speaker Dialect Familiarity

Using the 5-point scale, participants indicated they were fairly familiar with General American English ( M = 3.78, SD = 1.04, n = 289) and AAVE ( M = 3.61, SD = 0.91, n = 179). As expected, participants were less familiar with Standard British English ( M = 2.74, SD = 0.98, n = 125) and Black British English ( M = 2.36, SD = 1.06, n = 66).

Proportion of Stereotypical Traits

On average, participants assigned each speaker 4.21 traits ( SEM = 0.09). All participants assigned speakers at least one trait (Range = 1-17). Around a third of all assigned adjectives were stereotypical ( M = 0.31, SEM = 0.01). The percent of each stereotypical trait assigned to speakers based on perceived country and perceived race are presented in Table 1 .

TABLE 1

Adjective

Non-North American

North American

Athletic

16.5

3.9

10.6

11.4

Ostentatious

7.1

12.6

4.0

2.5

Poor

2.4

4.9

19.5

2.5

Rhythmic

15.3

9.7

15.5

6.3

Aggressive

10.6

12.6

12.2

11.4

Uneducated

9.4

11.7

24.9

10.1

Unintelligent

5.9

10.7

20.1

14.6

Loud

22.4

25.2

15.8

20.3

We observed a significant two-way interaction of perceived race and speech stereotypicality on proportion of stereotypical traits assigned, B = -0.03, SE = 0.01, p = 0.005. A test of simple slopes indicated that the slope of stereotypicality on assigned stereotypical traits was greater for perceived Black speakers than non-Black speakers, t = 2.83, p = 0.005. For perceived Black speakers, those rated 1 SD above the mean on speech stereotypicality were assigned more traits than those rated 1 SD below the mean, t = 2.94, p = 0.014. The proportion of assigned traits did not differ by rating for non-Black speakers, t = -1.14, p > 0.999, nor did it differ by race for those rated 1 SD above or below the mean ( p s > 0.185).

The two-way interaction was qualified by a significant three-way interaction of perceived race, perceived country, and speech stereotypicality, B = 0.02, SE = 0.01, p = 0.027. To follow-up the three-way interaction, we conducted tests of simple slopes for the perceived race by speech stereotypicality interaction separately for perceived N. Am. and Non-N. Am. speakers. For perceived N. Am. speakers, the slope of stereotypicality on assigned traits was greater for perceived Black speakers than perceived non-Black speakers, t = 4.84, p < 0.001. Pairwise comparisons indicated that among perceived Black speakers, those rated 1 SD above the mean on speech stereotypicality were assigned more stereotypical traits than those rated 1 SD below the mean, t = 4.65, p < 0.001. For perceived non-Black speakers, this pattern was reversed; speakers rated 1 SD above the mean on speech stereotypicality were assigned fewer stereotypical traits than those rated 1 SD below the mean, t = -2.61, p = 0.037. Additionally, at 1 SD above the mean on stereotypicality ratings, perceived Black speakers were assigned more stereotypical traits than their non-Black counterparts, t = 3.53, p = 0.002, but at 1 SD below the mean perceived Black speakers were assigned fewer traits than their counterparts, t = -2.88, p = 0.017. In other words, as perceived Black American speakers were rated as more stereotypical-sounding, they were also assigned more stereotypical traits, but as perceived non-Black American speakers were rated more stereotypical-sounding, they were assigned fewer stereotypical traits. There was no such difference in slopes of stereotypicality for those perceived as Non-N. Am. speakers, t = -0.38, p = 0.704. The mean proportion of stereotypical traits assigned to speakers by perceived country, race, and stereotypicality are presented in Table 2 .

TABLE 2

Perceived country

Stereotypicality rating

Black

Non-Black

Non-North American

-1 SD

0.26 (0.05)

0.26 (0.05)

+1 SD

0.33 (0.08)

0.29 (0.06)

North American

-1 SD

0.22 (0.04)

0.35 (0.04)

+1 SD

0.39 (0.04)

0.23 (0.04)

There were no significant main effects of perceived race, B = -0.009, SE = 0.02, p = 0.547, perceived country, B = -0.08, SE = 0.02, p = 0.663, nor speech stereotypicality, B = 0.01, SE = 0.01, p = 0.202. No other interactions were significant ( p s > 0.691).

Perceived age was significantly correlated to sterotypicality (uncentered), although the relationship was small, r = 0.13, p = 0.001. Voice attractiveness (uncentered), on the other hand, had a small negative correlation with streotypicality, r = -0.15, p < 0.001. When adding perceived age and voice attractiveness as covariates to our model, the pattern of results remained the same. The two- and three-way interactions remained significant, B = -0.02, SE = 0.01, p = 0.045; and B = 0.02, SE = 0.01, p = 0.007, respectively. Only voice attractiveness ratings predicted proportion of stereotypical traits, B = -0.07, SE = 0.01, p < 0.001. As perceptions of voice attractiveness increased, the proportion of stereotypical traits decreased. Age did not have a significant effect, B = 0.0005, SE = 0.002, p = 0.793.

Discussion

The findings from Experiment 1 provide support to our two hypotheses. First, speakers who were perceived as sounding more stereotypically Black were assigned more stereotypes associated with Black Americans compared to those who were perceived as less stereotypically Black sounding. Second, the relationship between sounding stereotypically Black and the assignment of Black stereotypes was true only for those seen as Black Americans. For those perceived as North Americans, more stereotypically sounding Black speakers were assigned a larger proportion of stereotypical traits than less stereotypical-sounding Black speakers. Interestingly, this pattern was the opposite for those perceived as non-Black speakers: more stereotypically sounding non-Black speakers were assigned fewer traits than less stereotypical-sounding non-Black speakers. We did not observe any differences in how speech stereotypicality affected the assignment of traits for those not perceived as North Americans. Notably, the observed pattern results remained when perceived age and voice attractiveness were added to the model. Although voice attractiveness did significantly predict the proportion of stereotypic traits assigned, it was not strongly correlated with perceived stereotypicality, echoing previous findings investigating race-related facial features ( Stepanova and Strube, 2018).

It is unclear from this study why non-Black American speakers would be assigned more Black stereotypes as they sounded less stereotypically non-Black. This may represent a sort of black sheep effect ( Marques et al., 1988), where our mostly White listeners are more biased against perceived non-Black (predominantly identified as White) speakers who do not conform to their expected speech. However, future research is needed to determine whether this pattern replicates when potential confounds, such as the option to identify speakers as belonging to races other than White or Black, are minimized. Regardless, our findings regarding Black American speakers are consistent with previous work on racial phenotypes. Like those seen as phenotypically Black, those who are rated as more stereotypically Black (American) sounding are associated with more stereotypes about Black Americans. Additionally, as these stereotypical traits are specific to Black Americans, listeners use vocal cues about nationality to distinguish between those from the U.S. and Canada and those from other nations.

Experiment 2

Because we found that speech stereotypicality elicited stereotypes in a similar manner as racial phenotypes, we next investigated in Experiment 2 whether speakers who sounded more stereotypical were also expected to appear more phenotypical. After piloting audio from male Black American speakers and images of Black male faces to find high and low stereotypicality exemplars, we had participants listen to a speaker and choose which of two faces was most likely to be the speaker. We expected that participants would be more likely to choose a face that matched the stereotypicality level of the speaker's voice; in other words, that speakers who sounded less stereotypically Black would be associated with less phenotypically Black faces and those who sounded more stereotypically Black would be associated with more phenotypically Black faces. Further, we expected that participants' social attitudes, such as their feelings about Black Americans or their willingness to respond in a desirable manner, would be significant covariates for participants' choice of face.

Materials and Methods

Participants

Participants ( N = 155) were recruited from Amazon's MTurk and paid $1.00 for successful completion of the 15-min study. Only U.S.-based workers who had completed at least 100 HIT and who had HIT approval rates of 90% or greater were allowed to participate in this study, and those who participated in the pilot were not allowed to participate in this study. Five participants were removed from analyses due to failure to follow instructions, resulting in a final sample size of 150 ( M Age = 34.40, SD = 11.17; Range = 20-70). Participants were relatively equally divided by gender (56.7% men; 43.3% women). Participants were predominantly White (64.0%), with Black (16.7%), Hispanic and Latino/a (8.0%), Native American (5.3%), and Asian (4.7%) individuals also represented. Most participants reported having a Bachelor's degree (46.0%), followed by those with some college (19.3%), a high school degree or equivalent (14.0%), and Master's degree (12.7%). No participants reported having an education level lower than that of a high school degree.

To calculate an a priori power analysis for a two-tailed logistic regression, we used the accuracy index of 72% found by Purnell et al. (1999) as the probability that participants would choose a face that matched the stereotypicality level of the speaker's voice. Thus, the probability of choosing a face that did not match the stereotypicality level of the speaker's voice ( p ₁) was 0.28. According to G*Power ( Faul et al., 2009), a sample size of at least 40 would be needed to detect an odds ratio of 6.61, with α = 0.05 and 1-β = 0.80. Using these same parameters for α, 1-β, and p ₁, a sample size of 150 would allow us to detect an odds ratio of at least 2.59.

Materials

Stimuli

Twenty-four Black male faces and 12 Black male voices were selected based on data from a pilot study rating Black male faces and voices (see Supplementary Material). The faces were from the Chicago Face Database ( Ma et al., 2015), and the voices were from the ( International Dialects of English Archive [IDEA], 2011). In the audio recordings from the International Dialects of English Archive (2011), the speakers all read from one of two standard passages: one about a veterinary nurse and one about rainbows. The faces were organized into pairs of strongly and weakly phenotypical faces with similar age and attractiveness based on the normed ratings from the Chicago Face Database and confirmed by the results from our pilot. We matched the 12 face pairs with the 12 voices based on perceived age to ensure that the pairs would be believable. The face pairs were randomly assigned to be viewed while participants listened to only a predetermined portion (22-40 s) of a strongly or weakly stereotypical voice, such that participants saw six of the face pairs with a strongly stereotypical voice and the remaining six with a weakly stereotypical voice. The order of the voices and the side on which the high phenotypicality face appeared (left or right) were counterbalanced to account for any order effects.

Social Attitude Scales

Participants completed a series of measures designed to assess their beliefs about the criminal justice system, racial bias against Black Americans, their own racial identity, and likelihood of engaging in desirable responding.

Procedure

After indicating their informed consent, participants filled out demographic information and completed an audio check. Next, participants were told a cover story that the research team was interested in understanding how well people can identify a speaker's face from an audio recording. Participants were told that they could see either White or Black faces in the study, although only Black faces were used. Participants listened to either a strongly or weakly stereotypical voice before choosing which of a pair of faces taken from the pilot was more likely to be the speaker and indicating their confidence in their decision. Participants made 12 two-alternative forced choice decisions and 12 confidence ratings in total. Finally, all participants answered the manipulation checks and completed the social attitude scales before being debriefed and thanked for their work.

Results

Data Analysis

To explore how perceived speech stereotypicality influences face selections, we first ran a mixed effects logistic regression on participants' chosen faces (Low or High Phenotypicality). The initial model included voices (Low or High Stereotypicality) as a fixed effect and participants and the individual face pairs entered as random intercepts. We also ran a mixed effects regression on choice confidence with the same fixed and random effects to see if speech stereotypicality had any undue influence on participants' confidence in their face selections.

We followed up both of these analyses by adding our social attitude scales to control for social attitudes about race as well as desirable responding. The racial bias subscale of the PJAQ and the SRS were included, as well as their interaction terms with voices, to investigate whether racism against minorities related to crime or racism against Black Americans affected which face was selected or moderated the effect of voice stereotypicality on face choices. Work by Stepanova and Strube (2012a) found that implicit bias against Blacks moderated the effect of skin color on ratings of racial typicality, indicating that an individual's racial bias may affect how individuals are categorized. The CSE-R and two BIDR subscales were added as covariates as previous work has found that higher collective self-esteem can affect how individuals categorize faces ( Elliott et al., 2017), and higher socially desirable responding, whether intentional or unintentional, may have influenced participants' face choices, e.g., in an effort to appear less biased. In all models, the voices variable was sum coded, with Low Stereotypicality serving as the reference group. Each social attitude scale was mean-centered before being added to the models. Significance tests were run by conducting likelihood ratio tests comparing the full model to the model without the predictor of interest for all predictors. Follow-up pairwise comparisons were Bonferroni-adjusted for two tests. The data were imported into R Studio using the haven package ( Wickham and Miller, 2019). Data were analyzed using the afex ( Singmann et al., 2019), lme4 ( Bates et al., 2015), and emmeans ( Lenth, 2019) packages.

Manipulation Checks

The majority of participants correctly answered our manipulation check questions. For the race of the faces, 88.67% of participants noted the faces were Black/African American. For our question on the task, 85.33% of participants correctly noted they were asked to identify a speaker's face from an audio recording. Despite 34 participants failing at least one of our manipulation check questions, our pattern of results did not differ when these participants were included in the dataset (see Supplementary Material). Thus, our reported results include the full sample of 150 ( N = 1,800 choices).

Face Selection

We began by examining the results from our model on face choice. As expected, speech stereotypicality influenced participants' decisions about which face was more likely to belong to the speaker, B = −0.60, SE = 0.15, p < 0.001. Participants who heard the low stereotypicality voice were less likely to choose the high phenotypicality face. After hearing the low stereotypicality voice, participants chose the low phenotypicality face 64.44% of the time and chose the high phenotypicality face 35.56% of the time ( Figure 1 ).

We then looked at the model on choice confidence. Participants' confidence ratings spanned the range of possible scores ( M = 66.68, SEM = 0.47, Range = 0--100); however, their level of confidence in their decision did not significantly differ by which voice they heard, B = --0.47, SE = 0.82, p = 0.5762.

Finally, we looked at the models with the scores from the racial bias subscale from the PJAQ race bias subscale, the SRS, CSE-R, and the two BIDR subscales added (means and SEMs are reported in Table 3 ). For choice, speech stereotypicality remained a significant predictor, B = -0.61, SE = 0.16, p < 0.001. The interactions between the racial bias subscale and voice and the SRS and voice were also significant, B = 0.11, SE = 0.02, p < 0.001, and B = -0.06, SE = 0.01, p < 0.001, respectively. The slope for racial bias on face choice was greater for low stereotypicality voices than high stereotypicality voices, z = 5.71, p < 0.001. At 1 SD above the mean on the racial bias subscale, participants were not significantly more likely to choose the high phenotypicality face after hearing the high stereotypicality voice, z = 1.22, p = 0.442. However, at 1 SD below the mean, participants were over seven times more likely to choose the high phenotypicality face after hearing the high rather than low stereotypicality voice, z = 5.85, p < 0.001, OR = 7.58.

Separately, the slope for racism against Black Americans on face choice was greater for high stereotypicality voices than low stereotypicality voices, z = 4.05, p < 0.001. At 1 SD above the mean on the SRS, participants were around six times more likely to choose the high phenotypicality face after hearing the high vs. low stereotypicality voice, z = 5.19, p < 0.001, OR = 6.01. At 1 SD below the mean, participants were not significantly more likely to choose the high phenotypicality face after hearing the high stereotypicality voice, z = 1.90, p = 0.114. No other predictors were significant, p s ≥ 0.197.

For confidence, speech stereotypicality remained non-significant, B = -0.47, SE = 0.81, p = 0.575. However, the racial bias subscale of the PJAQ, B = 1.87, SE = 0.41, p < 0.001, and the BIDR self-deceptive enhancement subscale were significant predictors of confidence, B = 0.32, SE = 0.14, p = 0.026. As participants showed greater racial bias and as their own tendency to unintentionally engage in socially desirable responding increased, so too did people's confidence in their choices. The other predictors were not significant, p s ≥ 0.053.

Discussion

Speech stereotypicality influenced people's judgments about physical appearance in Experiment 2. Participants were more likely to choose faces that matched the level of stereotypicality (low or high) of the voice they heard. Critically, even when controlling for participants' social attitudes, the link between speech and face stereotypicality remained. However, participants' explicit and subtle racism moderated the effect of speech stereotypicality on their face choices. Specifically, as participants expressed more racial bias on the PJAQ, they were less likely to choose the face that matched the level of stereotypicality with the voice they heard. Separately, as participants endorsed more subtle racist beliefs against Black Americans, they were more likely to choose the face that had the same level of stereotypicality as the voice. In other words, participants who endorsed more racist beliefs about minorities and crime appeared less sensitive to the linguistic features when making their face selections, and those who showed more subtle racism against Black Americans appeared more sensitive to those features. The latter finding is consistent with prior work that found those with higher implicit racism against Blacks showed a stronger relationship between skin tone and judgments of race typicality ( Stepanova and Strube, 2012a) and suggests that implicit or less direct racial bias influences how individuals categorize others. Why those with more racist beliefs related to crime were less likely rather than more likely to discriminate between the faces remains to be determined, but the low reliability of this scale may have been a factor.

The stereotypicality of the voice did not appear to affect their confidence in their judgments, and this pattern remained when the covariates were added. However, the racial bias subscale of the PJAQ and the self-deceptive enhancement subscale of the BIDR-16 did influence confidence ratings. Those who indicated more racial bias were more confident in their decisions. As the other racism measure was not a significant predictor, it is again unclear what aspect of this racial bias subscale is related to confidence. Those who indicated a greater tendency to engage in unintentional socially desirable responding were also more confident in their decisions, likely reflecting their desire to respond in an overly positive manner ( Hart et al., 2015).

From these findings, it appears that how a speaker sounds activates certain expectations about what that speaker should look like, and that these expectations persist regardless of a listeners' self-reported attitudes about Black Americans, their own racial or ethnic identity, or their propensity to respond in a socially desirable manner.

General Discussion

Individuals are not evaluated in a vacuum. Rather, people use multiple cues to classify individuals into social categories, which can then activate assumptions about that person's abilities, traits, and social status. Individuals seen as more typical members of their given social group are more strongly associated with stereotypes about that group ( Blair et al., 2002; Walker and Wänke, 2017). Most studies exploring the relationship between how stereotypical an individual is perceived to be and the activation of stereotypes focus on stereotypicality in terms of physical features. We demonstrated that language is another important source of social category information that informs stereotypicality judgments. In two experiments, we investigated whether individuals who sound more stereotypically Black are also more likely to be seen as more stereotypically Black in terms of character traits (Experiment 1) and in terms of their appearance (Experiment 2). Individuals perceived by listeners as sounding more stereotypically Black were associated with more stereotypical traits about Black Americans and with more phenotypically Black faces. Importantly, these findings suggest that speech stereotypicality may operate similarly to phenotypicality, as the linguistic features associated with Black Americans appear to activate stereotypes about members of that group without the listener's knowledge of the actual race of the speaker.

In Experiment 1, we extended work on perceived stereotypicality and the activation of stereotypical traits from the domain of phenotypicality to speech stereotypicality. Speakers who were rated as sounding more stereotypically Black were assigned a greater proportion of stereotypical traits associated with Black Americans than those who were rated as sounding less stereotypically Black. These results are in line with previous work that greater use of AAVE features, which may be comparable to sounding more stereotypically Black, led to lower ratings of attractiveness and social status ( Rodriguez et al., 2004). Our finding also suggests that poorer ratings of speakers who use more AAVE features may be related to a greater activation of stereotypes about Blackness, many of which are negative (e.g., criminal, aggressive, uneducated, lazy).

We also found evidence that this relationship between race, speech stereotypicality, and assigned stereotypic traits only applied to those who were perceived as both Black and North American. This finding, along with the fact that our U.S.-based listeners were less likely to identify UK speakers as Black and did not categorize our Black speakers with 100% accuracy, is consistent with the expectations of the Dialectal-Race Hypothesis; that is, to categorize speakers, listeners rely on their cultural knowledge of how a group of speakers talk ( Perrachione et al., 2010). If our listeners were relying only on acoustic features caused by anatomical differences that may exist between groups, we would expect that listeners would have no problems correctly identifying the race of our Black speakers and that perceptions of "sounding stereotypically Black" alone, regardless of country of origin, would be sufficient to activate stereotypes about Black Americans. Instead, our results suggest that listeners' knowledge of dialects influenced how they attributed traits to stereotypical-sounding Black speakers from different regions.

In Experiment 2, speech stereotypicality activated expectations about phenotypicality, such that strongly stereotypical Black voices were associated with more phenotypically Black faces, and weakly stereotypical Black voices were associated with less phenotypically Black faces. While some physical characteristics do influence how speakers sound, such as height and weight ( Krauss et al., 2002) or age-related changes to the structure of the vocal tract ( Caruso et al., 1995), many of the associations between voice and appearance are informed by the surrounding cultural context. Given that there is no inherent reason why more stereotypical-sounding Black speakers should also look more stereotypically Black, the results from Experiment 2 suggest that the linguistic cues that listeners have associated with being more stereotypically Black activate expectations about the speaker's appearance.

Notably, while anti-Black bias can influence face identifications (e.g., when choosing between faces with lighter or darker skin; Alter et al., 2016), the relationship between voice and face stereotypicality persisted regardless of participants' social attitudes. As is true with physical Afrocentric features, listeners appear unaware of the influence of linguistic features on their judgments. Blair and colleagues ( Blair et al., 2002, 2004b) found that individuals relied on Afrocentric features independently from race to assign stereotypic traits, and that although they could suppress the influence from race, they could not suppress the influence of Afrocentric features, even when they were instructed to. Further, their inability to suppress the influence of Afrocentric features was not due to ignorance, as individuals were able to identify Afrocentric features when asked. Linguistic cues associated with "sounding Black" could be another example of feature-based stereotyping. Participants in our studies may have been similarly unaware that they were relying on these cues to assign stereotypic traits or to make face judgments because discrimination against others for their way of speaking is not particularly taboo in American society. It remains to be determined if participants could suppress the influence of linguistic features when made aware of them. More conclusive research on this topic is needed to determine whether linguistic features are used for feature-based stereotyping, particularly to establish whether these cues operate independently from race, as all of the speakers in our study were from the same racial group.

These results have critical implications about how speakers who sound more stereotypically Black may be perceived and treated by others. Previous work has found that those who use AAVE are less likely to have access to housing compared to those using General American English ( Purnell et al., 1999; Massey and Lundy, 2001), and those who can be identified as Black from speech earn 12% less than their White counterparts, even when controlling for skill, family background, and schooling ( Grogger, 2011). How speech patterns influence such biases, particularly wage discrimination, is unknown, but stereotypicality is likely a contributing factor. Concerningly, given the association between Blackness and criminality, speakers who sound more stereotypically Black may also face similar biases within the legal system. Mock courtroom studies have previously found that in some circumstances defendants who are Black and use a non-standard dialect are judged more harshly than those who use a more standard dialect ( Dixon et al., 2002; Cantone et al., 2019; cf. Kurinec and Weaver, 2019). However, whether the perceived stereotypicality of the speaker moderates this effect and whether it influences other judgments, such as those made by law enforcement, judges, and eyewitnesses, remains an area for investigation.

That speech stereotypicality could influence decisions in the legal system despite people's awareness of anti-Black discrimination in the field is not entirely unfounded, as stereotypical features have previously been shown to affect these types of judgments. More phenotypically Black individuals are more likely to be misidentified by eyewitnesses ( Knuycky et al., 2014; Kleider-Offutt et al., 2017), particularly when other Black stereotypes (e.g., drug dealer) are activated ( Kleider et al., 2012; Osborne and Davies, 2013). Additionally, regardless of race, those with more Afrocentric features are more likely to receive longer criminal sentences ( Blair et al., 2004a). Speech stereotypicality may operate in a similar manner. For instance, individuals who sound more stereotypically Black may be expected to appear more phenotypically Black, biasing eyewitness's memory for the individual and leading to misidentifications. Separately, those who sound more Black may directly activate negative stereotypes about Black Americans, impacting identifications and legal judgments. Future research is needed to determine how speech stereotypicality may influence eyewitness identifications and other forms of legal decision-making.

Although this work provides initial evidence that speech stereotypicality activates racial stereotypes, several topics warrant further investigation. First, both experiments in this study used predominantly non-Black samples, with the majority of participants identifying as White. While it is possible that using such a sample may have exacerbated out-group biases, strengthening the relationships between speech stereotypicality and stereotypical traits or more phenotypical faces, previous work has found that Black Americans often report similar perceptions of AAVE as White Americans, rating the use of AAVE more negatively than standard American English ( Doss and Gross, 1992; Payne et al., 2000; McCluney et al., 2021). Further, previous work looking at the stereotypes associated with light- and dark-skinned Blacks found that both White and Black participants associated more stereotypical Black traits with darker-skinned Blacks ( Maddox and Gray, 2002). Given that darker skin is seen as more phenotypically Black, it is likely that a sample of only Black Americans would similarly associate more Black stereotypes-to include stereotypes about appearance-with those perceived as sounding more stereotypically Black. However, it is important to note that there are instances when Black Americans express more favorable opinions of AAVE, such as in less formal community settings or when the individual has greater commitment to their Black identity ( White et al., 1998; Koch et al., 2001; Rahman, 2008). Thus, whether these effects would replicate in an all-Black sample may depend on how formal the participants perceive the setting and the strength of their identification as members of the Black community.

Second, the experiments in this study intentionally included multiple speakers using different dialects at differing degrees, both to capture more natural speech utterances and, in Experiment 1, to provide listeners with a range of speakers to evaluate. Had we utilized a handful of speakers switching between a more or less stereotypical-sounding style of speech, we could have risked speakers sounding unauthentic or forced, impacting our ratings ( Garrett, 2010; Guy and Cutler, 2011). However, if at all possible, future work should try to find speakers who are sufficiently proficient in shifting their degree of AAVE or similar dialect use. This would allow researchers to implement the matched-guise technique, which utilizes the same speaker for two or more dialects or languages ( Lambert, 1967), minimizing the influence of any linguistic variables that are not of interest (e.g., intonation, pace) while maintaining the desired range in dialect strength in an authentic way.

Third, we relied on listeners' ratings to determine how stereotypical speakers sounded. Listeners have been shown to have similar ideas of the standardness of a given dialect as that of researchers ( Schüppert et al., 2015; Nejjari et al., 2019), but it is unclear whether this reliability in ratings extends to perceived stereotypicality. However, what constitutes stereotypically Black speech is likely subjective, depending on an individual listener's own familiarity with Black speech and their social or cultural knowledge of what does or doesn't sound stereotypical. It would be beneficial to have a more objective approach to stereotypicality research. For instance, the audio recordings used in stereotypicality research could be analyzed for the linguistic features present to determine whether an increase in the quantity of linguistic features associated with Black Americans leads to an increase in perceived stereotypicality, or if there are specific features that trigger such judgments.

Finally, the speakers in this study varied in the content of their utterances. The speakers in Experiment 1 talked about a variety of topics and spoke in either more casual (e.g., video blog) or more formal (e.g., panel talk) settings. Alternatively, the speakers in Experiment 2 read one of two standard texts. On one hand, the audio used in Experiment 1 likely provided more natural utterances for listeners to evaluate. On the other hand, the audio in Experiment 2 more carefully controlled for the content of the utterances, and previous research suggests that judgments about speakers who sound Black may be moderated by their message content ( Johnson and Buttny, 1982). Thus, it would be worthwhile for future research to find a balance between natural sounding utterances and ensuring that the content of said utterances is suitably limited to avoid influencing judgments of stereotypicality.

In sum, this work provides an initial look into the relationship between how stereotypically Black an individual speaker sounds and the activation of stereotypes about character traits and phenotypicality. These findings extend previous work on stereotypicality and suggest that other aspects of an individual beyond physical appearance may serve as cues that inform how stereotypical an individual is believed to be. Since discrimination based on how a person speaks is not explicitly prohibited under U.S. law, understanding how speech stereotypicality contributes to judgments about others is needed to guide the development of regulations and other protections to ensure more equitable treatment for such speakers in housing, employment, and the legal system. Those currently in those fields should be mindful of how speech stereotypicality may be influencing decision-making.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://osf.io/ungrs/.

Ethics Statement

The studies involving human participants were reviewed and approved by the Baylor University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

CK and CW contributed to the conception and design of the study. CK oversaw the data collection and analysis and wrote the initial draft of the manuscript. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to acknowledge Shelby Livingstone, Alaina Baird, Ebose Ogbebor, Veronica Prince, Ryan Rahman, and Bella Sanchez for their assistance on this project.

References

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"Sounding Black": Speech Stereotypicality Activates Racial Stereotypes and Expectations About Appearance

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Black Americans who are perceived as more racially phenotypical-that is, who possess more physical traits that are closely associated with their race-are more often associated with racial stereotypes. These stereotypes, including assumptions ...

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"Americas Social ills are not simply due to inequality and despair"

Source- washington post

Date- 1/25/24

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An unhoused person's tent on a bridge overlooking the 101 Freeway in Los Angeles on Feb. 2, 2023. (Jae C. Hong/AP)
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There is a grim, fairly popular story of the American social contract that goes roughly like this: Motivated by entrenched racial hostility, the greed of the rich (or maybe something else), the richest country on the planet refuses to develop a true welfare state that might secure the well-being of its citizens.

Taxation and redistribution have been successfully resisted, branded as illegitimate scams to feather the beds of welfare queens. Globalization and technological disruption have been embraced even as the institutions designed to protect the most vulnerable workers - unions, minimum wages - have lost their power to provide for a dignified living.

In this American story, the less fortunate - Black, Brown and White - are left to scratch by as best they can, often falling into a deep well of misery. The rich engorge themselves way beyond anything seen in other wealthy, industrialized societies of the West. And yet, though the destitution is clear for all to see, recent research suggests that the story built around it is, at best, incomplete.

The accepted narrative was built on the back of pathbreaking research by Thomas Piketty, now at the Paris School of Economics, and the University of California at Berkeley's Emmanuel Saez, which established just how far inequality had been allowed to rip through the fabric of society.

Follow this author Eduardo Porter's opinions

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Subsequent work by economists David Autor at MIT, David Dorn at the University of Zurich and Gordon Hanson, now at Harvard's Kennedy School, documented how the surge in imports from China since roughly the early 1990s had devastated communities across the United States that relied on manufacturing for sustenance.

Princeton University scholars Anne Case and Angus Deaton made the case that these dynamics were deadly, producing an epidemic of suicides and overdoses that they called "deaths of despair."

"Inequality and death are joint consequences of the forces that are destroying the white working class," they wrote. "It is the deeper forces of power, politics and social change that are causing both the epidemic and the extreme inequality."

This narrative is consistent with a constellation of data documenting the poor health of American society - from the nation's short life expectancy and high infant mortality rate to a poverty rate that exceeds that of other wealthy democracies.

And yet, the straightforward story might be wrong.

Critics have left some dents in the "deaths of despair" thesis: The availability of opioids, they point out, does a better job explaining the overdose mortality patterns than socio-economic despair. The mortality trends look different when adjusted by age. The rise in mortality among the less educated is mostly about cardiovascular disease, not suicide, alcoholism or drug abuse. And it afflicts a small sliver of deeply disadvantaged Americans, not the working class writ large.

The accepted story of America's intertwined ills has been challenged from a more fundamental front by new research suggesting inequality might not have evolved as most of us thought. The most prevalent story among economists, following the seminal work by Piketty and Saez, has argued that the rising concentration of income in the hands of the richest Americans has drastically shrunk everybody else's share of the pie.

But a recently published alternative estimate, by economists Gerald Auten of the Treasury Department and David Splinter of Congress's Joint Committee on Taxation, suggests that the share of income going to the top has remained roughly flat since 1960.

Inequality has increased, for sure. Yet once they include the effect of redistribution, adding taxes and government programs such as food stamps, Medicaid, Social Security and the like, the academics find that the poorest one-fifth of Americans drew roughly the same share of income in 2019 that they did about 60 years ago.

The new analysis has fueled a scholarly mini brawl over the true depth of American inequality. And it raises a prickly but critical question: How can we explain the fraying of the social contract if inequality hasn't soared? The inverse can't be true: It's tough to fit the short life expectancy, the suicides, the extreme obesity and the high incarceration rates in a story in which inequality is tamed and desperation isn't deadly. It leaves the United States littered with a trail of scattered ailments without the connective tissue of a narrative.

In an honest effort to find a workable new synthesis, here's a suggestion: Inequality might not cause these symptoms on its own. Instead, many of America's social maladies stem from the strategies it has chosen to mitigate the lopsided distribution of income, which leave its citizens singularly vulnerable.

The U.S. government doesn't do much to affect how the market allocates prosperity. Unions are weak; the federal minimum wage is becoming irrelevant; retraining is patchy. If trade or a sudden technological shift makes swaths of the workforce redundant, there is next to nothing out there to help workers back on their feet.

Redistribution, then, must do most of the work. The U.S. government raises considerably less in taxes than do other Western democracies. But it does so more progressively, taxing people at the top most heavily. At the bottom, it largely provides help in cash. The earned income tax credit, for instance, is one of the most critical tools for lifting the income of low-wage workers. Poor families with children benefit from a child tax credit that puts money in their pockets.

Europeans are more aggressive about reshaping prosperity. Their unions are more powerful. Worker councils protect workers from swings in the market. Labor laws are more protective of incumbent workers.

European redistribution, moreover, is not just larger but different. European governments raise money more regressively, relying heavily on consumption taxes that take bigger bites out of the pockets of the poor. They use much of the money to pay for rich, often universal, welfare programs such as universal health care and housing, child care and early child education.

These institutional differences can help explain the chasm between workers' experience on either side of the North Atlantic. In Europe, bigger job and wage protections have gone some way to prevent the spread of precarious, low-pay work, which has become prevalent across the U.S. service sector. And their redistribution might have also done a better job at preventing deeper social maladies.

Cash is not a bad tool. An economist will tell you it is, in fact, the best - with more power to increase welfare than any government service. Still, research suggests that poverty hurts economic decision-making. This could make a case for governmental paternalism in the European vein: redistribution aimed at some specific, desirable outcomes rather than just supporting the income of the poor.

Europe's strategy is not without costs. Labor protections there shift unemployment onto the young. Still, it seems indisputable that European workers - and those in other rich economies - are surviving global capitalism without as many casualties, even though they have less money. The earned income tax credit might be a good tool for supporting the income of vulnerable Americans, but it has proved unable to mitigate America's dysfunctions.

This might appear like some trivial academic brawl. But America's dysfunctions are convulsing its politics. They could end up convulsing the world. It makes sense to figure out where they come from and try to fix them.
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Opinion | America's social ills are not simply due to inequality and despair

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The way we tackle poverty is leading to worse social outcomes.

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Child Marriage -

"A child marriage is considered to be a form of forced marriage, given that one and/or both parties have not expressed full, free and informed consent."

source:

violence against women and girls

date:

november 25th, 2020

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Violence against women and girls is one of the world's most prevalent human rights violations, taking place every day, many times over, in every corner of the globe. It has serious short- and long-term physical, economic and psychological consequences on women and girls, preventing their full and equal participation in society. The magnitude of its impact, both in the lives of individuals and families and society as a whole, is immeasurable. Conditions created by the pandemic - including lockdowns, reduced mobility, heightened isolation, stress and economic uncertainty - have led to an alarming spike in domestic violence and have further exposed women and girls to other forms of violence, from child marriage to sexual harassment online. This FAQ provides an overview of the many forms of violence, along with other commonly used terms, that any gender equality activist should have in their vocabulary toolkit.

Gender-based violence

Gender-based violence (GBV) refers to harmful acts directed at an individual or a group of individuals based on their gender. It is rooted in gender inequality, the abuse of power and harmful norms. The term is primarily used to underscore the fact that structural, gender-based power differentials place women and girls at risk for multiple forms of violence. While women and girls suffer disproportionately from GBV, men and boys can also be targeted. The term is also sometimes used to describe targeted violence against LGBTQI+ populations, when referencing violence related to norms of masculinity/femininity and/or gender norms.

Violence against women and girls

Violence against women and girls is defined as any act of gender-based violence that results in, or is likely to result in, physical, sexual or mental harm or suffering to women and girls, including threats of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life. Violence against women and girls encompasses, but is not limited to, physical, sexual and psychological violence occurring in the family or within the general community, and perpetrated or condoned by the State.

Survivor of violence

The term survivor of violence refers to any person who has experienced sexual or gender-based violence. It is similar in meaning to "victim", but is generally preferred because it implies resilience.

No means No. Yes means Yes. Consent is an agreement between participants to engage in sexual activity or enter into marriage. It must be freely and actively given and cannot be provided by someone who is under the influence of drugs or alcohol or by someone underage. Consent is specific, meaning that consent to one act does not imply consent to any others, and reversible, meaning that it may be revoked at any time.

Domestic violence

Domestic violence, also called domestic abuse or intimate partner violence, is any pattern of behavior that is used to gain or maintain power and control over an intimate partner. It encompasses all physical, sexual, emotional, economic and psychological actions or threats of actions that influence another person. This is one of the most common forms of violence experienced by women globally.

Domestic violence can include the following.

Economic violence

Economic violence involves making or attempting to make a person financially dependent by maintaining total control over financial resources, withholding access to money, and/or forbidding attendance at school or employment.

Psychological violence

Psychological violence involves causing fear by intimidation; threatening physical harm to self, partner or children; destruction of pets and property; "mind games"; or forcing isolation from friends, family, school and/or work.

Emotional violence

Emotional violence includes undermining a person's sense of self-worth through constant criticism; belittling one's abilities; name-calling or other verbal abuse; damaging a partner's relationship with the children; or not letting a partner see friends and family.

Physical violence

Physical violence involves hurting or trying to hurt a partner by hitting, kicking, burning, grabbing, pinching, shoving, slapping, hair-pulling, biting, denying medical care or forcing alcohol and/or drug use, or using other physical force. It may include property damage.

Sexual violence

Sexual violence involves forcing a partner to take part in a sex act when the partner does not consent. See more about sexual violence below.

Learn more: The signs of relationship abuse and how to help

Femicide refers to the intentional murder of women because they are women, but may be defined more broadly to include any killings of women or girls. Femicide differs from male homicide in specific ways. For example, most cases of femicide are committed by partners or ex-partners, and involve ongoing abuse in the home, threats or intimidation, sexual violence or situations where women have less power or fewer resources than their partner.

Honor killing

Honor killing is the murder of a family member, usually a woman or girl, for the purported reason that the person has brought dishonor or shame upon the family. These killings often have to do with sexual purity, and supposed transgressions on the part of female family members.

Sexual violence

Sexual violence is any sexual act committed against the will of another person, either when this person does not give consent or when consent cannot be given because the person is a child, has a mental disability, or is severely intoxicated or unconscious as a result of alcohol or drugs.

Sexual violence can include the following.

Sexual harassment

Sexual harassment encompasses non-consensual physical contact, like grabbing, pinching, slapping, or rubbing against another person in a sexual way. It also includes non-physical forms, such as catcalls, sexual comments about a person's body or appearance, demands for sexual favors, sexually suggestive staring, stalking, and exposing one's sex organs.

Rape is any non-consensual vaginal, anal or oral penetration of another person with any bodily part or object. This can be by any person known or unknown to the survivor, within marriage and relationships, and during armed conflict.

Corrective rape

Corrective rape is a form of rape of perpetrated against someone on the basis of their sexual orientation or gender identity. It is intended to force the victim to conform to heterosexuality or normative gender identity.

Rape culture

Rape culture is the social environment that allows sexual violence to be normalized and justified. It is rooted in patriarchy and fueled by persistent gender inequalities and biases about gender and sexuality.

Learn more: 16 ways you can stand against rape culture

Human trafficking

Human trafficking is the acquisition and exploitation of people, through means such as force, fraud, coercion, or deception. This heinous crime ensnares millions of women and girls worldwide, many of whom are sexually exploited.

Female genital mutilation

Female genital mutilation (FGM) includes procedures that intentionally alter or cause injury to the female genital organs for non-medical reasons. It is classified into four major types, and both the practice and the motivations behind it vary from place to place. FGM is a social norm, often considered a necessary step in preparing girls for adulthood and marriage and typically driven by beliefs about gender and its relation to appropriate sexual expression. It was first classified as violence in 1997 via a joint statement issued by WHO, UNICEF and UNFPA.

Child marriage

Child marriage refers to any marriage where one or both of the spouses are below the age of 18. It is a violation of the Universal Declaration of Human Rights, which states that "marriage shall be entered into only with the free and full consent of the intending spouses." Girls are more likely to be child brides, and consequently drop out of school and experience other forms of violence.

Online or digital violence

Online or digital violence against women refers to any act of violence that is committed, assisted or aggravated by the use of information and communication technology (mobile phones, the Internet, social media, computer games, text messaging, email, etc) against a woman because she is a woman.

Online violence can include the following.

Cyberbullying

Cyberbullying involves the sending of intimidating or threatening messages.

Non-consensual sexting

Non-consensual sexting involves the sending of explicit messages or photos without the recipient's consent.

Doxing involves the public release of private or identifying information about the victim.

Change starts with you.

Take action today to help end violence against women. Find out how ►

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Frequently asked questions: Types of violence against women and girls

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Violence against women is one of the world's most prevalent human rights violations, taking place every day, many times over, in every corner of the globe. This FAQ provides an overview of the many forms of violence against women and girls, along with other commonly used terms.

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Jacob St Amant - "Going to private school won’t make a difference to your kid’s academic scores" - The Conversation

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Jacob St Amant - "Going to private school won’t make a difference to your kid’s academic scores" - The Conversation

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Date February 20, 2022

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Going to private school won’t make a difference to your kid’s academic scores

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There is endless debate about whether private schools add value to students’ academic results. But research consistently shows when socioeconomic factors are taken into account, this is not the case.

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People of color expect to experience discrimination during health care visits, survey finds

Source: CNN News

Date: December 5th 2023

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People of color often expect to experience discrimination during health care visits, survey finds | CNN

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A trip to the doctor's office can be stressful, but many people of color in the US say they also expect to experience discrimination while seeking health care, according to a KFF Survey on Racism, Discrimination and Health released Tuesday.

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Abandon "Race." Focus on Racism

National Library of Medicine, September 7th 2021

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Abstract

The concept of "race" emerged in the 1600s with the trans-Atlantic slave trade, justifying slavery; it has been used to justify exploitation, denigration and decimation. Since then, despite contrary scientific evidence, a deeply-rooted belief has taken hold that "race," indicated by, e.g., skin color or facial features, reflects fundamental biological differences. We propose that the term "race" be abandoned, substituting "ethnic group" while retaining "racism," with the goal of dismantling it. Despite scientific consensus that "race" is a social construct, in official U.S. classifications, "Hispanic"/"Latino" is an "ethnicity" while African American/Black, American Indian/Alaska Native, Asian/Pacific Islander, and European American/White are "races." There is no scientific basis for this. Each grouping reflects ancestry in a particular continent/region and shared history, e.g., the genocide and expropriation of Indigenous peoples, African Americans' enslavement, oppression and ongoing disenfranchisement, Latin America's Indigenous roots and colonization. Given migrations over millennia, each group reflects extensive genetic admixture across and within continents/regions. "Ethnicity" evokes social characteristics such as history, language, beliefs, customs. "Race" reinforces notions of inherent biological differences based on physical appearance. While not useful as a biological category, geographic ancestry is a key social category for monitoring and addressing health inequities because of racism's profound influence on health and well-being. We must continue to collect and analyze data on the population groups that have been racialized into socially constructed categories called "races." We must not, however, continue to use that term; it is not the only obstacle to dismantling racism, but it is a significant one.

Keywords: racism, race, racialization, racial classification, racial/ethnic classification, public health monitoring, public health research

Introduction and Overview

Racism is a public health crisis requiring bold action on many fronts. One of those fronts, perhaps surprisingly, is semantic and conceptual in nature; this does not, however, mean that the issue is unimportant or abstract. Language has power. Words can matter. Words have meaning that can inspire, promote, condone, justify, or inhibit actions. Words are tools we use to build or reinforce a shared understanding. Take the word "race," for example. The concept of "race" emerged in the late seventeenth century, with the rise of the Transatlantic slave trade, and was used to justify slavery by regarding Africans as innately and biologically inferior ( 1- 7). "Race," as construed since then and now, refers to the classification of humans based on phenotype-observable physical differences-which are assumed to reflect inherent biological differences. "Race" has been used to justify the exploitation, denigration, and decimation of groups of people throughout our history. Even when those using the term underscore the distinction between phenotype and genotype (underlying genetic makeup), "race" still is and will continue to be implicitly conceptualized as biological. This paper's aim is to initiate discussion of what will undoubtedly be a controversial proposal: to abandon the term "race" as a way of categorizing humans, while retaining the term "racism" as a necessary tool for dismantling it.

Instead of "race" we propose that "ethnic group" or "ethnicity" be used to encompass what are now commonly referred to as "racial or ethnic groups," an approach that has prevailed in much of Europe for decades. We propose continued use of the terms African American/Black, American Indian/Alaska Native, Asian American, European American/White, Latino/Hispanic, Native Hawaiian/Other Pacific Islander, but explicitly denoting them as ethnic groups, reflecting geographic origin and ancestry, not "races," which intrinsically connotes biological differences. All of these terms denote geographic ancestry in a particular continent or other large region of the world and the shared history and language, beliefs, and/or customs that often accompany geographic ancestry. The concept of ethnic group or ethnicity has been criticized at times as too ill-defined and broad to be useful, given the diversity within each group;( 8, 9), ideally we would monitor and study much smaller ethnic groupings according to clear, explicit criteria. These large geographic ancestry groupings are, however, at a minimum, critically important to continue to monitor and study, principally because of racism, which holds profound, which holds profound implications for health and well-being. These groupings reflect-albeit very roughly-how people are perceived and treated.

Using "ethnic groups" to encompass not only those groups that have traditionally been referred to as such, but also those now called "races" or "racial groups" is more consistent with science than the current approach. The scientific consensus about "race" today is that it is a fundamentally social rather than biological construct. The differences in superficial secondary characteristics such as skin color and hair texture across different "racial" groups do not correlate with underlying fundamental biological differences ( 10- 13). Given human migrations over tens of thousands of years, each group defined by geographic ancestry reflects extensive genetic admixture across and within continents/regions. For example, Baharian et al. ( 14) estimated that African Americans today have approximately 16.7% European ancestry; Solovieff et al. ( 15) concluded that African Americans have "from 20 to 30% admixture with Europeans."

Despite this knowledge, in official U.S. public health classifications, "Hispanic" or "Latino" is regarded as an ethnic group, whose members can be of any "race," while African American/Black, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, and European American/White are classified as "races." There is no scientific basis for considering African Americans, Indigenous peoples, Asian Americans, Native Hawaiians/Pacific Islanders, and European Americans to be "races," not ethnic groups, while viewing Hispanics/Latinos as an ethnic group but not a "race." All of these groupings reflect geographic ancestry. The continued distinction between "race" and "ethnicity" only serves to underscore the implication that "race" reflects biological differences.

The continued use of the term "race" is by no means the only or even the principal obstacle to addressing racism. It is, however, a significant obstacle because it is irremediably imbued with scientifically unfounded but nevertheless tenacious notions of biological differences and hierarchy which have long served to justify exploitation and oppression. The use of "race," even by those who abhor racism, tends to reinforce those notions. Reflecting commonly held beliefs, ethnicity is consistently defined as a social or cultural characteristic, while race is defined as biological and/or based on physical traits (which implicitly reflecting biological differences) ( 16- 21).

We are not the first to propose abandoning use of the word "race" for classifying humans and substituting "ethnicity." Ashley Montagu proposed substituting ethnicity for race in the field of physical anthropology in the 1940s ( 22). Bradby ( 9) argued that in the face of the dangers of using "race," "the most helpful strategy is to reject the term "race," but to retain the words 'racism' and 'racialization' and to use the term 'ethnicity'." Furthermore, as noted earlier, in much of Europe today the words "race" or "racial" rarely appear and official statistics do not report on "race." The term "ethnicity" is used to encompass characteristics that in the United States would span both "race" and ethnicity ( 8, 23, 24). This well-established approach that has prevailed across multiple decades-abandoning the term "race" and substituting ethnicity-was adopted in Europe after World War II, in horrified reaction to the genocide of 6 million Jews, who the Nazis regarded as a "race" ( 25, 26). Unfortunately, when they ceased using the term "race," a positive action, the Europeans also ceased any official collection of routine data on the groups formerly called "races;" they now lack crucial evidence for routine monitoring of racism and its social and health effects ( 27). This is an error we must not commit.

While not useful as a biological category, "race" as currently categorized-African American/Black, American Indian/Alaska Native, Asian, European American/White, Native Hawaiian/Pacific Islander, along with Latino/Hispanic "ethnicity"-is a vitally important social category for monitoring, understanding, and intervening on differences in health ( 28). This is true because of racism's profound influence on health and well-being. Each of these geographic groupings reflects ancestral origin in a particular continent or other large region of the globe. These geographic groupings are of great social-and therefore health-meaning and significance principally because they reflect differences in how people are perceived and treated both currently and historically. They reflect experiences of racism. They reflect shared history, e.g., the genocide and expropriation of the lands of Indigenous peoples; African Americans' enslavement, oppression and disenfranchisement under Jim Crow laws and ongoing violations of civil rights; and Latin America's Indigenous roots and colonization by Spain and Portugal.

Because of the profound impact of racism on health and well-being, we must continue to collect data on these socially constructed categories that have been called "races." We should not, however, continue to unintentionally or intentionally racialize people -i.e., to regard people as if they represent fundamentally distinct groups-by using that term. In the next section, we further examine the concept of "race," how it has been used, and the implications.

The Concept of Race

Is there just one race-the human race? Or are there intrinsically different groups of humans who are biologically distinct from each other? Despite extensive scientific evidence to the contrary, there has been a long-standing, widely held, deeply rooted, and unfounded belief that "race," as reflected by skin color, hair texture, facial features and other superficial secondary physical characteristics, reflects fundamental biological differences. That is a convenient idea to hold if one wants to justify treating some people as undeserving of equal rights, autonomy, and respect.

In colonial America, the notion of race arose and not coincidentally co-evolved with the emerging trans-Atlantic slave trade in the late 1600s; it was used to justify the enslavement and brutal treatment of Africans ( 1- 7). Globally, the separation of groups into fundamentally different, "superior" and "inferior" races has been foundational to justify both slavery and genocide ( 29). These notions also have been used to justify discriminatory laws, policies, and practices that deny equal rights and opportunities based on "race." These discriminatory structures include the "Jim Crow" laws in formerly Confederate states that prevailed for almost 100 years after legal slavery officially ended ( 1, 30). They also include the lesser-known but also long-standing "Black Laws" denying the rights of Black people in Northern states, which predated and served as a model for Jim Crow laws ( 31). Pro-slavery doctors used pseudoscience to explain Black-White differences in anatomy and disease as innate and evidence of Black inferiority. Pro-slavery politicians amplified this pseudoscience to argue against abolition ( 4, 32).

Reflecting the belief in an underlying biological basis for "race," official birth and death records and other health data in the United States have long been reported separately by age, sex, and "race." Because age and sex do indeed reflect fundamental biological differences, this reporting practice implicitly tends to reinforce the erroneous notion of "race" as a biological construct. While changing "race" to "ethnic group" would not eliminate this conceptual problem, it may be a small but important step in helping us to reconceptualize superficial physical difference as reflective of social and cultural diversity rather than biology.

If race were biologically based, one might expect to find consistency in classification and consensus about some standard set of mutually exclusive racial groups. The concept of race has been very fluid, however, and operationalized in different ways at different times, which in itself suggests its fundamentally socially constructed nature. For example, the Nazi movement in twentieth century Germany regarded Jewish people as a "race;" the rationale for their mass extermination was that they were an inferior "race" that should be eliminated to avoid contaminating the superior race, Aryans ( 29). In the United States during the late nineteenth/early twentieth centuries, many economically struggling White people felt threatened by an influx of immigrants willing to work for lower wages. Immigrants from Ireland, Italy, and Poland were widely regarded as being from "races" distinct from and inferior to "Whites," i.e., the more established European immigrants ( 33, 34). The more established immigrants asserted their group's superiority, beginning to express political power through various institutions (e.g., labor unions) and actions (e.g., race riots). Over time, the ethnic identities of the more recently arrived European immigrants were subsumed into "Whiteness," and members of these groups came to benefit from the legal, political, and social advantages enjoyed by White people in the United States ( 33, 34).

Reflecting changing social, economic, and political forces over time in the United States, "races" have been officially categorized in various ways. The earliest U.S. Census surveys distinguished only Whites, all other free persons, and slaves. Later Census racial categories included White (European ancestry), Black (African ancestry), or Mulatto (mixed), and, only in the 1890 Census, "Quadroon" and "Octoroon," classifying people by their percentage of African ancestry. Census racial categories have changed and expanded to reflect emancipation, immigration, social movements, and political pressure ( 35, 36). Currently, the established "racial" categories for reporting federal statistics in the U.S. are African American/Black, American Indian/Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, and European American/White. Each of these groupings corresponds to ancestry in a continent or other large geographic region ( 37).

Since 1980, the U.S. Census and vital statistics have collected data on a separate field in addition to "race," to specify whether a person is of "Hispanic origin." This information is officially referred to as a measure of Hispanic ethnicity and not of race ( 38, 39). This implies that while African Americans, Asian Americans, European Americans (Whites), and Indigenous Americans each constitute a separate race, Latinos or Hispanics do not, they are an "ethnic group." This classification implicitly reinforces a notion of race as biological, in contrast to ethnicity, which is social. Based on recognition that these two categories are not distinct, the term "race/ethnicity" is often used instead of either term alone. In practice, health research and reporting frequently classify people into five mutually exclusive categories referred to as "racial/ethnic" groups, based on the continent or other large geographic region of their ancestry. This is often done by creating a category for Latinos/Hispanics regardless of "race" and restricting each of the "racial" groups to non-Latinos/Hispanics in each of those groups. This widespread practice reflects the lack of conceptual coherence of the distinction between "race" and "ethnic group. "Latino/Hispanic" denotes Latin American origin/ancestry ("Latino" is arguably a more appropriate term than "Hispanic," given that many Latin Americans-for example, those from Brazil, Latin America's largest country-are not Spanish-speaking, and people from Spain-who one would expect to be included as "Hispanics"-are Europeans).

Another example of the fundamentally social nature of "racial" classifications comes from a study examining the health advantage of Whiteness. Jones et al. ( 40) studied how self-reported health varied depending on whether it was examined in relation to survey respondents' "socially assigned race," meaning, the "race" that other people generally assumed them to be, or the respondents' self-identified "race." They found considerable differences in health based on self-identified vs. socially assigned "race." The investigators found that, among respondents who self-identified as Black, Hispanic, or multi-racial, the prevalence of excellent or good self-reported health was significantly higher among those who were perceived by others to be White than among those of the same self-identified "race" who were not perceived by others to be White; the same pattern was seen for American Indians but was not statistically significant. A 2020 review of 18 studies of associations between a range of health indicators and socially assigned "race" reported that most studies have found an association between socially assigned "race" and health ( 41). These research findings, along with a large body of other accumulated knowledge, support the realization that it is the social experience of living in bodies perceived to be of different "races," which society treats differently, rather than fundamental biological differences between people of different "races," that generally drives differential health outcomes.

Studies of human genetics have shown repeatedly that there is more genetic variation among people with the same geographic ancestry than there is between groups of people with different geographic ancestry ( 42). The scientific consensus about racial categories today is that they are fundamentally social rather than biological constructs ( 10- 13). Craig Venter, head of Celera, the private genetics company that partnered with the National Institutes of Health on the Human Genome Project, stated in a White House briefing on the Human Genome Project in June of 2000 (and note the use of the term "ethnicity" vs. "race"):

"The method used by Celera has determined the genetic code of five individuals. We have sequenced the genome of three females and two males, who have identified themselves as Hispanic, Asian, Caucasian or African American. We did this sampling not in an exclusionary way, but out of respect for the diversity that is America, and to help illustrate that the concept of race has no genetic or scientific basis. In the five Celera genomes, there is no way to tell one ethnicity from another."

This does not imply that there cannot be any genetic differences among people in groups with different geographic ancestry. Rather, it means that differences in superficial secondary physical characteristics such as skin color, facial features, or hair texture do not define biologically distinct groups. Neither do differences in some gene frequencies, nor any of the isolated, highly specific genetic differences that have been found across different geographic ancestry groups define biologically distinct groups. The racialization of sickle cell disease is an instructive example. If a person inherits the sickle cell mutation from both parents, sickle-shaped (deformed) hemoglobin is produced, which, when exposed to low levels of oxygen, damages red blood cells, leading to "acute vasoocclusive events, hemolytic anemia, organ damage and failure, and an average lifespan reduction in developed countries of more than three decades" ( 15). While the mutation is most common among people with ancestry in three areas of West Africa, it is not common among people from other areas of Africa, furthermore, it is also seen among people with Mediterranean, Middle Eastern or Indian ancestry, regions which, like West Africa had a high prevalence of malaria. The mutation came to be frequent in those regions because it was protective against malaria, giving an evolutionary advantage to carriers (people with only one affected gene). This highly specific genetic difference among African Americans from three areas of West Africa and people from the Mediterranean, Middle East, and India, compared to most Africans and Europeans arose because of evolutionary pressures. It does not occur among African Americans from most parts of Africa-only those from specific areas in West Africa, and it occurs among people with non-African ancestry. Despite these undisputed facts, sickle cell disease has been racialized; this isolated genetic difference does not define a "race." Another example is Tay-Sachs disease among Jewish people of Northern European ancestry, the greater frequency of the mutation that produces Tay-Sachs disease does not make Jewish people a biologically distinct group.

It should be noted, furthermore, that even when there are isolated genetic differences between groups of people with different geographic ancestry, those differences may not be expressed (i.e., the genes may not have their potential effects on the body) unless people are exposed to certain environmental influences, including stressors, which are shaped by social forces. Social experiences often control whether or not genetic differences are expressed or suppressed, this underscores the social nature of "racial" categories. The next section discusses how racism affects health.

Racism and Its Effects on Health

Racism is a fundamental social determinant of racial disparities in health ( 43). It is a system of "race"-based power, rooted in notions of inherent racial group superiority and inferiority, that systematically, pervasively, and unjustly privileges "Whites" and oppresses "non-Whites" ( 44). It manifests at multiple levels (internalized, interpersonal, institutional, and structural or systemic) and involves direct, indirect, individual, and group-based exposures across the life course ( 45, 46). Whether blatant, subtle, or ambiguous, racism is a particularly visceral threat to well-being because it denigrates a core aspect of a person's identity that is ever-visible and unchanging.

Racism produces racial discrimination, i.e., "race"-based unfair treatment. Although racial discrimination is no longer legal, socioeconomic, and health inequities along racial lines persist because of deeply rooted, unfair systems and structures that continue to operate to sustain the legacy of formerly overtly discriminatory practices, policies, laws, and beliefs. At times, these systems and structures operate unintentionally, but nevertheless effectively, to produce and sustain racial discrimination.

An extensive body of literature demonstrates large and pervasive disparities in health adversely affecting African Americans/Black people ( 47, 48), American Indians/Alaska Natives ( 49), Hispanics/Latinos ( 50), some Asian subgroups ( 51), Native Hawaiians/Pacific Islanders ( 52- 54), and immigrants compared to Whites ( 47, 48, 51, 55). These differences are seen for a wide range of health indicators and across the lifespan ( 56- 59). The largest and most consistent racial/ethnic health disparities generally have been observed when comparing Black people and (when data are available) American Indians with Whites, although Latinos and some Asian/Pacific Islander groups have worse health than Whites on some measures. What explains these pervasively and repeatedly observed differences in health across a wide array of health indicators?

Racism can damage the health of people of color through many different causal pathways. An extensive and growing body of scientific research indicates how diverse experiences of racism likely play a fundamental role in producing racial/ethnic inequities in health by activating multiple causal pathways. Some of these pathways are complex and long, often playing out over lifetimes and even generations. The complexity and length of the causal pathways often makes it difficult to detect their origins, that is, the underlying but hidden fundamental causes. The term "embodiment" has been used by several scholars within and outside the health field to describe how racism in its myriad forms ultimately produces damages to health, which are then misinterpreted as signs of underlying biological differences among racialized groups ( 60, 61).

Although the focus here is on how racism can damage the health of people of color, it is important to note that racism is likely to damage the health and well-being of virtually the entire society in which it operates. The support for this hypothesis comes from research on social inequality in general rather than on racism specifically: A convincing case has been made that social inequality damages the health of societies overall ( 62, 63), largely by undermining social cohesion ( 64).

Based on the literature, the figure (above) depicts a series of sequential general steps through which racism is thought to produce racial disparities in health. These general steps represent the basic skeleton of innumerable specific pathways, which are indicated by the factors listed in each box as non-exhaustive) examples.

The first box on the left in Figure 1 represents the beginning of the general sequence. The common beginning or source of all the potential causal pathways is systemic or structural racism, the unjust systems or structures that systematically put people of color at a disadvantage in multiple domains. Systemic or structural racism reflect underlying differences in power and social values in a society. The relevant systems or structures include laws, policies (written and unwritten), entrenched institutional practices, and the pervasive, deeply rooted beliefs that condone, promote, and perpetuate the systems and structures. Examples of systemic or structural racism in the past include: slavery and the laws and beliefs that supported it; Jim Crow laws and the systematic use of terror to enforce them; and redlining and the discriminatory implementation of the G.I. Bill and low-interest FHA loans that enabled many White people of modest means to become homeowners but were given to very few Blacks ( 65, 66). These historical systems and structures set the stage, establishing the inequitable patterns and providing a firm basis for their perpetuation including: disenfranchisement via gerrymandering and voter suppression; racial residential segregation; unfair lending practices depriving people of color of the opportunity to own a home or to start or expand a business, thereby closing off opportunities to accumulate wealth; the dependence of schools on local property taxes, ensuring that schools in areas that lack wealth are under-resourced; and mass incarceration of men of color based on biased policing and sentencing.

The 2nd box from the left in Figure 1 represents the next general step in the sequence: the direct products of systemic or structural racism which are unfair treatment and differential access to resources and opportunities. Arguably, the borders between this 1st box on the left and the 2nd box should be depicted as porous, as many examples of systemic or structural racism not only produce but in themselves constitute unfair treatment and lack of access to resources and opportunities. Examples of unfair treatment and lack of access to resources and opportunities that are generally due to systemic or structural racism include: both interpersonal and internalized racism; less favorable treatment in hiring, retention, promotion, and pay; the socioeconomic disadvantage that results from being trapped in low-opportunity segregated areas with substandard schools, poor services, and scant hope of escaping unrelenting financial hardship; and the permanent stigmatization of people who have been incarcerated, permanently blocking ex-prisoners from employment opportunities, thus ensuring ongoing financial hardship for the ex-prisoners, their families, and communities.

As depicted in the third box from the left, the unfair treatment and differential access to resources and opportunities result not only in exposure to health-harming conditions (such as toxic environmental hazards and chronic stress), but also limited access to conditions that are health-promoting, such as good schools, a nutritious diet, green spaces, bicycle lanes, being able to afford a gym membership, and quality medical care.

These health-harming (or lack of health-promoting) exposures or experiences in turn trigger biological mechanisms that produce ill health (the fourth box). These biological mechanisms include, for example, inflammation and alterations in the functioning of the immune system that are known to lead to chronic diseases, such as heart disease, stroke, and diabetes. The biological mechanisms include neuroendocrine processes triggered by chronically stressful experiences (such as experiencing discrimination or persistent financial hardship). These neuroendocrine processes result in the body's production of hormones (like cortisol and other substances) that can, if present at persistently high levels over time, lead to inflammation and dysfunction of the immune system, both of which contribute to susceptibility to chronic disease and premature aging ( 67).

This section of the paper has sought to demonstrate that, although further research is needed, there is now considerable evidence of how racism sets in motion a range of phenomena that damage health in multiple domains. The damage is inflicted by unfair treatment and differential access to resources and opportunities, which produce health-harming exposures (and lack of health-promoting experiences), which in turn trigger an array of physiologic processes that directly produce ill health. Racial differences in health overwhelmingly reflect racism.

Concluding Remarks

The concept of "race" had its origins in the slave trade. It is time to abandon it. The concept was used to justify slavery by implying that those who were enslaved were fundamentally different and inferior beings. Today "race" continues to reinforce false and weaponized notions of inherent biological differences based on physical appearance. It should be used rarely and only inside quotation marks when necessary to make a point about its historical usage. "Ethnic group" or "ethnicity" should be substituted, consistent with the social characteristics that often accompany geographic ancestry. The feasibility of using "ethnic group" or "ethnicity" in place of "race" is suggested by the fact that this has been standard practice in Europe since soon after the end of World War II. Has the fact that Europe has largely substituted ethnic group for race ended racism there? Of course not. But it does mean that racism is not being perpetually and unnecessarily reinforced by the use of a term that inevitably evokes biological difference. Wherever this approach is adopted, if it is, vigilance will be needed to detect and halt conscious or unconscious attempts to redefine ethnicity as biological. Abandoning "race" will not in itself end racism or even help us to focus more on racism; we have called for focusing on racism to make it clear that abandoning the term "race" is part of a struggle against racism; and that the term "racism" must be retained. Abandoning "race" should, however, remove one ubiquitous and not inconsequential source of constant reinforcement of racism. Abandoning "race" should be part-admittedly a small part-of a strategy to intensify and broaden actions against racism. While abandoning "race," we should not only retain the term "racism" but mount more intensive efforts to identify and dismantle it. In particular, we need to focus on systemic or structural racism, often invisible but posing the greatest barriers to justice and health because it is the root source of the varied manifestations of racial discrimination observed in multiple domains.

While not useful as a biological category, geographic ancestry-i.e., African American, American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, European American, and Latino/Hispanic-is a key social classification for monitoring and addressing health inequities. These geographic ancestry groups are very broad, with great heterogeneity within each. To address this, we ideally would monitor and study much smaller and better-defined ethnic groups. Because of racism's profound influence on health and well-being, however, we must, at a minimum, continue to collect and analyze data on the population groups that have been racialized into socially constructed categories called "races." It is unfortunate that in Europe, abandoning the term "race" has not been accompanied by routine monitoring of health and well-being according to markers of the ethnic groups that are relevant to racism. We must continue to monitor differences in health and well-being according to geographic ancestry. We must not, however, continue to use the term "race." It is not the only obstacle to dismantling racism, but it is a significant one, one that amplifies the damage every time it is used, even when used by those who actively struggle against racism. It is beyond the scope of this paper to discuss the implications of the use of "Black" and "White" in ongoing population monitoring and research, important and complex issues are involved there as well.

Bhopal ( 16) offered the following statement "for reflection and debate": "The term "race" should be used with caution for its history is one of misuse and injustice." Like Bhopal, we offer these thoughts to stimulate reflection and debate; however, we advocate not merely using "race" with caution but abandoning it entirely because it inherently lends itself to perpetuating falsehoods that promote serious harm and injustice. The public health crisis produced by racism requires fighting vigorously in many arenas, including conceptual and semantic spheres. Abandoning "race" is not a panacea but may be a useful adjunct to other crucial efforts to dismantle racism in all of its forms.

Author Contributions

PB wrote the first draft after discussion with TP. TP added substantially to that and made revisions. All authors consulted closely with each other and participated in final revisions.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

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Articles from Frontiers in Public Health are provided here courtesy of Frontiers Media SA

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The concept of "race" emerged in the 1600s with the trans-Atlantic slave trade, justifying slavery; it has been used to justify exploitation, denigration and decimation. Since then, despite contrary scientific evidence, a deeply-rooted ...

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"Trail Date Set for White Supremacist Who Targeted Black Shoppers at a Buffalo Supermarket"

Source- USNEWS

Date-2/2/24

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US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics, business, health, and education.

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"Senators unveil bipartisan bill to impose tougher asylum and border laws."

Source- NBC news

Date- 2/4/24

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Senators unveil bipartisan bill to impose tougher asylum and border laws

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The deal is the result of months of negotiations between Senate Republicans and Democrats, as well as the Biden administration. It’s not clear it will pass Congress.

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"White men have controlled women’s reproductive rights throughout American history – the post-Dobbs era is no different"

Source - The Conversation

Date - September 1, 2023

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U.S. Rep. Cori Bush, a Democrat from Missouri, after participating in an abortion rights sit-in on July 19, 2022, in Washington. Anna Moneymaker/Getty Images

More than a year after the Supreme Court ended federal protection for abortion rights in the United States, disagreements over abortion bans continue to reverberate around the country. Candidates sparred over the idea of a federal abortion ban during the Aug. 23, 2023, Republican presidential debate. And abortion is likely to figure prominently in the November 2023 contest for a seat on the Pennsylvania Supreme Court.

When the U.S. Supreme Court overturned Roe v. Wade in June 2022, removing women's federal constitutional right to get abortions and giving states the power to pass laws about the legality of the procedure, the 6-3 vote was by a four white men, one Black man and a white woman majority.

Since that decision - Dobbs v. Jackson Women's Health Organization - more than 1,500 state legislators, who are overwhelmingly white men, have voted for full or partial abortion bans.

This is not the first period in U.S. history when white men have exercised control over women's right to bear - or not bear - children, including during slavery. Then, it was a matter of numbers. The more people they enslaved, the more money white male enslavers could earn either from selling the enslaved or from the forced labor of the enslaved. White men controlled people's reproductive rights during the 20th century, too, with the American eugenics movement.

From the late 1800s until the 2000s, white proponents of eugenics - the selective breeding of people - tried to determine who was fit or unfit to have children. While the American eugenics movement affected people of other races and ethnic backgrounds, as well as men, it was particularly harmful to Black women who, data from 1950 to 1966 shows, were sterilized at " three times the rate of white women and more than 12 times the rate of white men."

During both periods, Black women and their health bore the brunt of the consequences of white men's control.

As a researcher who specializes in the history of race and racism in the U.S., I study historical issues related to race, gender and social justice.

Enslaved women forced to reproduce

African midwives, imported and enslaved as early as the 1600s, attended to the birthing needs of the enslaved and enslavers until the beginning of the 19th century.

But, after 1808, enslavers in the United States could no longer legally import enslaved people. With this shift, enslavers stepped up the forced breeding of enslaved women. White men raped the Black women and girls they enslaved, and then enslaved the children born from those rapes. White men also forced the Black women and Black men they enslaved to have sex with one another to generate more babies, who would be born into slavery.

This was a systemic way of ensuring enslaved women bore more children, which would increase profits for their enslavers.

Because the Black midwives and enslaved women often were blamed for or suspected of using birth control and abortions to resist forced pregnancy and the enslavement of their offspring, enslavers turned increasingly away from midwives and to white male doctors to figure out why nearly half of enslaved infants were stillborn or died within their first year of life and why so many enslaved women were infertile. These doctors also helped with difficult births.

In the two decades after 1810, the population growth rate of the enslaved averaged about 30%, despite the ban on slave importation. This was just under the 1800 to 1809 average of 31.6% which was a century high.

In the 1800s, as the slave population increased, profits in cotton did too. And after the legal importation of slaves ended, the value of Black women of childbearing age increased significantly. The forced breeding of these enslaved women was linked to the profitability of southern economies.

Eugenics and control over women's bodies

Eugenicists believed that increased breeding by white people, whom they assumed had high IQs, would benefit American society. But people who did not embody their idea of racial perfection, such as Black people, Native Americans, certain immigrants, poor white people and people with disabilities, should be sterilized - typically via tubal ligation and vasectomy.

In this debunked pseudo-science, eugenicists often used intelligence tests to determine who was fit or unfit to reproduce and to predict who would commit crimes, end up in poverty or have children who were mentally ill or intellectually disabled. And they worked to incorporate their ideas into state laws.

Thirty-two states, between 1907 and 1937, enacted forced sterilization mandates to prevent births by people eugenicists considered socially inadequate.

State-mandated procedures resulted in the coerced sterilization of women, particularly African American, Native American and Hispanic American women, and those from Southern and Eastern Europe.

Beginning in 1948 with President Harry Truman's executive order to integrate the military, which extended to other areas, including education, employment and commerce, sterilization rates for Black women increased. For example, in North Carolina, which had the country's third-highest sterilization rate, far more women than men were forcibly sterilized. And in the 1960s, Black women in the state made up 65% of the women sterilized, while only making up 25% of the population.

Between 1930 and 1970, close to 33% of the women in Puerto Rico, a U.S. territory, were forcibly sterilized. In California, between 1997 and 2003, 1,400 female inmates, mostly Black, were forcibly sterilized.

The post-Dobbs era

White nationalists and some right-wing politicians in the U.S. see the nation's demographic changes as dangerous. The Census Bureau projects that in the 2040s, non-Hispanic white people will no longer make up a majority of the U.S. population. The nation's racial and ethnic makeup will then be what some call " majority-minority." Those projections scare racists, who believe in a conspiracy about white people being destroyed, which they label the great replacement theory because they fear losing social, political and economic power.

There is no way to know if this theory factored into the majority's votes in the Dobbs decision, but the argument that not enough white people are being born has been a common historical thread in the American anti-abortion movement.

But, while believers in the great replacement conspiracy want white women to have more babies, actual anti-abortion decisions like the Dobbs ruling harm Black women more than any other group. Black women represent 39% of the country's abortion patients, but many live in communities that have limited access to family planning clinics. And they have disproportionately higher rates of complications during pregnancy.

As a result, Black women - who experience higher maternal complications and mortality rates - will be forced to give birth to more babies.

This is another period in the country in which the reproductive health decisions made by mostly white men will harm Black women.

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White men have controlled women's reproductive rights throughout American history - the post-Dobbs era is no different

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In the US, white men have long had the power to make decisions about women's reproductive health care. Those decisions have often been especially harmful to Black women.