RACIAL ATTITUDES OF PREK-12 AND POSTSECONDARY EDUCATORS · 2017-08-30 · RACIAL ATTITUDES OF PREK-12 AND POSTSECONDARY EDUCATORS 5 Background Dimensions of Racial Attitudes The term

Running Head: RACIAL ATTITUDES OF PREK-12 AND POSTSECONDARY EDUCATORS

Racial Attitudes of PreK-12 and Postsecondary Educators: Descriptive Evidence from

Nationally-Representative Data

David M. Quinn

[email protected]

University of Southern California

3470 Trousdale Pkwy

Los Angeles, CA 90089

Quinn, D.M. (2017). Racial attitudes of prek-12 and postsecondary educators: Descriptive

evidence from nationally-representative data. Educational Researcher.

DOI: 10.3102/0013189X17727270

The final, definitive version can be found at:

http://journals.sagepub.com/doi/full/10.3102/0013189X17727270

Acknowledgments: I am grateful to Celia Gomez, North Cooc, Kenneth Shores, and my

anonymous reviewers for comments on drafts of this article. Any errors or omissions are my

own.

mailto:[email protected]

http://journals.sagepub.com/doi/full/10.3102/0013189X17727270

RACIAL ATTITUDES OF PREK-12 AND POSTSECONDARY EDUCATORS

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Running Head: RACIAL ATTITUDES OF PREK-12 AND POSTSECONDARY EDUCATORS

Abstract

PreK-12 and postsecondary educators’ racial attitudes have important consequences for students’

learning and development, yet we know little about educators’ racial attitudes overall, how their

attitudes might differ from those of non-educators, or how attitudes might be changing over time.

I investigate these questions using the nationally-representative General Social Survey. Some

educators hold worrisome racial attitudes; yet compared to non-educators, educators are less

opposed to governmental equalizing efforts, give more politically liberal explanations for racial

inequalities, express less negative racial stereotypes, and report less social distance and collective

resentments towards minoritized groups. Many educator/non-educator differences were

explained by demographics, particularly education level. Time trends mostly show progress in

Americans’ racial attitudes, with generally similar trends for educators and non-educators.

Keywords: educator racial attitudes, racial beliefs, racial stereotypes, General Social Survey

(GSS)


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Racial Attitudes of PreK-12 and Postsecondary Educators: Descriptive Evidence from

Nationally-Representative Data

Across preK-12 and postsecondary settings, the racial attitudes of educators have

important consequences for the learning and developmental outcomes of students. Through a

variety of mechanisms, teachers and school administrators who hold racial prejudices, negative

racial stereotypes, or cold feelings toward different racial groups, can hinder students’ academic

growth, negatively impact their self-perception, and ultimately limit their educational and life

opportunities (Benner & Graham, 2011; Farkas, 2003; Gregory, Skiba, & Noguera, 2010). Such

educators can also influence students, either explicitly or implicitly, to develop negative attitudes

toward racial groups other than their own (Sue, Lin, Torino, Capodilupo, & Rivera, 2009). At

the opposite side of the spectrum, educators can help prepare students to combat structural

racism and to develop positive attitudes toward people from different racial groups

(Frankenberg, 2012; Tatum, 1992). Yet despite their importance, we know little about the racial

attitudes of educators in the United States as a whole. Virtually all research on teachers’ racial

attitudes has been qualitative and small-scale, and we lack a general population-level descriptive

picture of the racial attitudes of educators (Frankenberg, 2012).1

In this study, I use nationally-representative data from the General Social Survey (GSS)

to investigate several dimensions of US educators’ racial attitudes. Specifically, I: (1) document

preK-12 and postsecondary educators’ racial attitudes and examine how these attitudes differ

from those of the general population of non-educators, (2) test whether attitudes, and differences

in attitudes between educators and non-educators, have changed over the past several decades,

and (3) investigate the extent to which educator/non-educator attitude differences can be

explained by demographic variables.


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Background

Dimensions of Racial Attitudes

The term “racial attitudes” refers to the beliefs, stereotypes, and affective orientations that

people hold regarding different racial groups (Bobo, Charles, Krysan, & Simmons, 2012). These

attitudes are important because they mediate a range of behaviors by those who hold them, from

overtly discriminatory actions to more subtle racial slights (Fazio, 2001; Wilson, Lindsey, &

Schooler, 2000). Racial beliefs and stereotypes are conceptually distinct, but share overlapping

features (Devine, 1989). Beliefs are “propositions that are endorsed and accepted to be true”

(Devine, 1989, p. 5), while stereotypes are sometimes thought of as the “cognitive component of

prejudiced attitudes” (Devine, 1989, p5). In other words, someone can be aware of the existence

of a racial stereotype without believing it to be true. Nevertheless, stereotypes can be

automatically activated in the minds of people who do not endorse the stereotypes and cause

them to unconsciously behave as if they do believe them. In such cases, it is necessary for

people to intentionally mobilize non-prejudiced beliefs if they are to override an automatic

stereotype-influenced response (Devine, 1989). Similarly, a person’s beliefs about a racial group

may differ from that person’s affective orientation – or feelings of closeness or distance – toward

the group.

How Educators’ Racial Attitudes Affect Students

Educators’ racial beliefs. Social scientists are often interested in racial beliefs related to

three categories: 1) racial principles, or beliefs regarding the ways in which race relations should

be governed, 2) beliefs about the sources of social inequalities by race, and 3) views on

government policy initiatives related to race (Bobo et al., 2012). Each type of belief has unique

relevance in education settings.


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First, it is self-evident that educators who subscribe to explicitly racist principles, such as

forced racial segregation, are liable to discriminate against minoritized students (Farkas, 2003).

The second category of racial beliefs – beliefs about the sources of social inequalities – is of

interest because teachers will have less reason to invest effort in educating minoritized students if

they believe that inequalities are the result of inherent racial differences in ability (e.g., Jensen,

1969). Relatedly, teachers who understand how structural racism contributes to inequality will

be more likely to incorporate themes into their lessons that help students navigate prejudice, and

educate White students on such issues (Tatum, 1992).

Educators’ beliefs about government policy initiatives such as affirmative action are of

interest for at least two reasons. To begin with, these beliefs reflect the extent to which

educators value efforts to overcome inequality and the extent to which educators believe

inequality is due to malleable social structures (as opposed to inherent racial differences).

Secondly, opposition to such initiatives can reflect a lack of appreciation for the role that

prejudice plays in reproducing inequality (West, 2001), and can manifest as racial

microagressions in the classroom (Sue et al., 2007). For example, in some contexts, expressing

the attitude that “the most qualified person should get the job, regardless of race” can send the

hidden message that people of color are less qualified than Whites (Sue et al., 2007; Sue et al.,

2009). Similarly, advocating for color-blind policies or claiming reverse discrimination can be

examples of “color-blind racism” (Bonilla-Silva, 2006), or the “denial of racial reality” category

of microaggression (Sue et al., 2009).

Educators and racial stereotypes. Educators’ intelligence- and motivation-related

stereotypes can affect student learning through teacher expectancy effects, in which teachers’

expectations lead to self-fulfilling prophesies (Farkas, 2003; McKown & Weinstein, 2008;


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McKown & Weinstein, 2003; Babad, 1993; Babad, Bernieri, & Rosenthal, 1991; Weinstein,

Marshall, Sharp, & Botkin, 1987; McKown & Weinstein, 2003; Wheeler & Petty, 2001).

Teachers’ negative stereotypes can also induce stereotype threat, in which students’ fears of

confirming a negative intellectual stereotype about their group inhibits performance or learning

(Taylor & Walton, 2011; Wheeler & Petty, 2001). Finally, biased teachers may mis-identify

minoritized students for special education services, which can have deleterious effects on

students’ learning (Skiba, Simmons, Ritter, Gibb, Rausch, Cuadrado, & Chung, 2008).

Stereotypes about groups’ proclivities toward violence can influence the way in which

educators interpret students’ behaviors and the disciplinary practices they use with them.

Evidence suggests that Black students tend to get disciplined for more subjective infractions,

such as “disrespectful behavior” or acting “disruptively,” while White students tend to get

disciplined for more objective infractions (Skiba, Michael, Nardo, & Peterson, 2002). Educators

who hold stereotypes of Black students as violence-prone will be more likely to interpret Black

students’ behaviors as threatening and hence dispense discipline (Ferguson, 2000).

Stereotypes can also affect the behaviors of educators who do not endorse the stereotype,

through “implicit associations” or “implicit biases” (Warikoo, Sinclair, Fei, & Jacoby-Senghor,

2016). While measuring implicit bias is beyond the scope of this study, the explicit stereotypes

measured in this study are relevant to whether educators attempt to identify and counteract

automatically-activated stereotypes.

Educators’ affective orientations. Affective orientations can be divided into three sub-

dimensions: 1) “socioemotional evaluations,” or how close people feel to members of other

racial groups, 2) “social distance feelings,” or peoples’ attitudes about social contact across

races, and 3) “collective resentments,” or attitudes about whether minoritized groups are trying


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to advance themselves according to a different set of rules compared to Whites (Bobo et al.,

2012). Educators’ orientations in these areas are important because their feelings of comfort and

positive versus negative affect toward different racial groups will influence their demeanor and

level of warmth when interacting with students and families from those groups. Such cues are

often detectable (Dovidio, Kawakami, & Gaertner, 2002), and can inhibit the development of

trust or closeness, and communicate a lack of interest or confidence in students (Babad, 1993).

Consequently, educators’ negative affective orientations can harm students through expectancy

effects, stereotype threat triggers, and other microaggressions.

Historical Context

Given the many ways in which educators’ racial attitudes can affect students, it is worth

understanding the attitudes held by preK-12 and postsecondary educators, and whether these

attitudes have changed over time. The main source of data on Americans’ racial attitudes is the

General Social Survey (GSS), which has surveyed nationally representative US samples since

1972.

Researchers have used the GSS to track how racial attitudes of the general public have

changed over time. Using available GSS data through 2008, Bobo and colleagues (2012) found

that support for legalized racial segregation declined, but White Americans’ support for

governmental racial-equalizing programs has been consistently low (though support has been

somewhat higher for programs aimed at improving human capital). White Americans have

trended away from biological explanations for social inequalities by race, but have preferred

cultural explanations (such as between-group differences in motivation or willpower) over

structural explanations (such as discrimination). Some similar trends arose among Black

Americans, who have increasingly cited cultural explanations for inequalities, and have trended


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away from supporting government interventions for combating racial inequality in

socioeconomic outcomes.

Alongside these developments in the general public’s racial attitudes, the racial context of

education has changed. In the early 1990s, the Supreme Court issued three rulings that began

dismantling post-Brown school desegregation orders, leading to the re-segregation of schools

(Reardon, Grewal, Kalogrides, & Greenberg, 2012). Over this same period, the share of

minoritized students grew, further contributing to the increase in racial segregation between

schools (Fiel, 2013). Concurrently, the demographics of the teaching force changed, though not

as rapidly as the student population; in 1987, 87.6% of teachers were White, and 82.7% were

White in 2011 (Ingersoll, Merrill, & Stuckey, 2014). We know little about how educators’ racial

attitudes may have changed within this dynamic context, or how the trends may have differed for

educators and non-educators.

Educators’ racial attitudes. While national research on educators’ racial attitudes is

scant, several relatively small-scale studies have documented the racial attitudes of students in

teacher preparation programs. Across these studies, a common theme is that teacher candidates

report little exposure to cultures or groups other than their own, and often feel unprepared to

teach diverse groups of students (Gomez, 1993; Hollins & Torres-Guzman, 2005). Studies have

documented teacher candidates expressing the view that racism no longer exists (Ahlquist,

1991), and reporting feelings of discomfort toward working with parents from backgrounds

different from their own (Martin & Williams-Dixon, 1994, as cited in Hollins & Torres-Guzman,

2005). Teaching candidates have also communicated the belief that racial statements should be

ignored, and that minorities tend to overreact to racial statements (Martin & Williams-Dixon,

1994, as cited in Hollins & Torres-Guzman, 2005). Because these studies are small in scale,


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relatively outdated, and specific to teacher candidates, it is difficult to know the extent to which

these results generalize to the broader US population of educators today. Additionally, these

studies provide no information about postsecondary educators, attitude differences between

educators and non-educators, or trends over time.

Attitudes of educators versus non-educators. While educators’ absolute levels of

prejudice matter irrespective of how their attitudes differ from those of non-educators,

comparing the racial attitudes of educators and non-educators is worthwhile for several reasons.

First, the comparison facilitates interpretation of educator attitudes by providing a reference

point. More substantively, the comparison helps us understand how students may contextualize

the racial attitudes of educators, and how the attitudes students encounter from adults in school

may differ from the attitudes they encounter outside school. For example, if we were to learn

that 45% of educators believe that racial differences in income are primarily due to racial

differences in work ethic, this would be important in its own right because it tells us about the

attitudes students may be exposed to in school. If we were to further learn that 80% of the

general population held this belief, this might suggest that students would be more likely to

encounter positive messages in school compared to outside school; however, if we were to learn

that 10% of the general population held this belief, this would suggest the opposite – that

attending school may negatively affect the types of racial attitudes students encounter.

Educator/non-educator comparisons are also informative given the policymaking process.

Teachers, through the organized political power of teachers’ unions, have more influence on

education policy than do members of the general public (Peterson, Henderson, & West, 2014).

Given that education policy often has implications for racial equity, it is worth knowing how the


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racial attitudes of those who influence education policy (i.e., educators) compare to the attitudes

of others (i.e., the general public) who could conceivably be exerting more policy influence.

The racial attitudes of educators may differ from those of the general population for at

least three reasons. First, personal racial attitudes may motivate some people to enter the field of

education; some educators are driven by a desire to ensure that students from marginalized

backgrounds have access to high-quality education. Secondly, educators’ on-the-job experiences

may influence their racial attitudes, for better or worse (Frankenberg, 2012). Finally,

demographics are known to predict racial attitudes (Bobo et al., 2012), and the demographics of

educators differ from those of the general population. Consequently, differences in racial

attitudes between educators and non-educators may exist as a reflection of these demographic

variables (Frankenberg, 2012). Similarly, the demographics and education levels of preK-12 and

postsecondary educators differ along dimensions that predict racial attitudes (postsecondary

educators being more highly educated and more male), suggesting potential heterogeneity in

attitudes across educator subgroups.

Summary and Research Questions

The racial beliefs, stereotypes, and affective orientations held by educators have

important consequences for students. However, little is known about the racial attitudes of preK-

12 and post-secondary educators as a whole, or how these attitudes differ from those of the

general public. In this study, I use nationally-representative data from the General Social Survey

to answer the following research questions:

1) What are the racial attitudes of preK-12 and postsecondary educators in the United

States? How do their attitudes differ from those of the general population, and from one

another?


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2) What are the racial attitude trends over time, and do trends differ for educators and non-

educators?

3) To what extent do demographics explain educator/non-educator differences in attitudes or

attitude trends?

Methods

Data

I use data from the GSS (Smith, Marsden, & Hout, 1972-2014), which regularly

interviews nationally-representative samples of non-institutionalized adults (over 18). These

data include information on interviewees’ occupations, which allows me to identify preK-12

educators, postsecondary educators, and non-educators. See online Appendix A for detail on the

data and occupation coding.

Outcomes. I examine responses on 34 survey items from the GSS that relate to racial

attitudes. Inspired by Bobo and colleagues’ (2012) schema, I organize items into the three

attitude dimensions described above: beliefs, stereotypes, and affective orientation. The GSS

introduces and removes items over time; consequently, survey years for each item vary (I include

all possible years for each item; see Appendix A). Each dimension subsumes several sub-

dimensions, with a mix of binary (yes/no) items and Likert-type items. All stereotype items

employ a 7-point response scale on which one extreme indicates that the respondent believed that

“almost all” people in a given group have a particular trait (e.g., hardworking), while the other

extreme indicates that the respondent believed that “almost all” members of the group have the

opposite trait (e.g., lazy) (4 indicates neutrality). See online Appendix B for supplementary

analyses combining items from each sub-dimension into composites (pattern of results is

unchanged).


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Analytic Plan

To answer research questions 1 and 2 for the various Likert-type outcomes, I begin by

fitting the following weighted least squares model (applying sampling weights):

𝑌𝑖 = 𝛼(𝑃𝑟𝑒𝐾12𝑖) + ∑𝛽𝑗(𝑃𝑟𝑒𝐾12𝑖 × 𝑌𝐸𝐴𝑅𝑖) + 𝛿(𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖) + ∑𝜂𝑗(𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖 ×

𝑌𝐸𝐴𝑅𝑖) + ∑𝛾𝑗𝑌𝐸𝐴𝑅𝑖 + 𝜖𝑖 (1)

where 𝑃𝑟𝑒𝐾12𝑖is a binary indicator for whether person i is a preK-12 educator, 𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖is a

binary indicator for whether person i is a postsecondary educator, ∑𝛾𝑗𝑌𝐸𝐴𝑅𝑖 is a vector of

dummy variables indicating the year in which the respondent was included in the survey, and

(𝑃𝑟𝑒𝐾12𝑖 × 𝑌𝐸𝐴𝑅𝑖) and (𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖 × 𝑌𝐸𝐴𝑅𝑖) are vectors of interactions between the YEAR

dummies and the PreK12 and PostSec dummies, respectively. These interaction terms allow me

to test whether trends differ between the general population and preK-12 or postsecondary

educators (RQ2b); specifically, I test whether the coefficients for (𝑃𝑟𝑒𝐾12𝑖 × 𝑌𝐸𝐴𝑅𝑖) are

jointly zero, as well as whether the coefficients for (𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖 × 𝑌𝐸𝐴𝑅𝑖) are jointly zero.

Additionally, I test whether the difference between each of these groups and the general

population differs in the first and last survey years. If I reject the null on both tests for either

subgroup (at 𝛼 = .05), I report trends separately by subgroup. If I fail to reject the null on these

tests, I reduce to the more parsimonious model: 2

𝑌𝑖 = 𝛼(𝑃𝑟𝑒𝐾12𝑖) + 𝛿(𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖) + ∑𝛾𝑗𝑌𝐸𝐴𝑅𝑖 + 𝜖𝑖 (2)

To answer research questions 1 and 2 for binary outcomes, I use logistic regression to

model the probability that person i responded affirmatively (1=Y, 0=N) to survey item 𝑌

(applying sampling weights):

𝑃(𝑌𝑖 = 1|𝑿𝑖) =1

1+exp(−(𝛼(𝑃𝑟𝑒𝐾12𝑖)+𝛽(𝑃𝑜𝑠𝑡𝑆𝑒𝑐𝑖)+∑𝛾𝑗𝑌𝐸𝐴𝑅𝑖)) (3)


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where variables are as defined above. In order to document opinion trends over time (RQ2a), I

obtain model-predicted proportions of non-educators responding affirmatively in the base year

and test the significance of the difference between this proportion in the base compared to the

final survey year. In order to determine whether attitude trends differed for educators and non-

educators (RQ2b), I test whether educator/non-educator differences are significantly different in

the base year and final year. If this difference-in-difference is significant for either comparison

(preK-12 vs. non-educators, or postsecondary vs. non-educators), I report model-derived

educator-general population probability differences separately for the base and final years.3 If

the marginal effects are statistically equivalent over time, I report the overall model-derived

differences in proportions answering affirmatively across years (holding the year dummies

constant at their means).4

I answer my third research question by refitting all models shown above with the

following control variables: 1) binary indicator for whether the respondent was male, 2) vector of

dummy variables representing respondent race (the GSS includes the categories White, Black,

and “other”), 3) vector of dummies representing which fifth of the income distribution the

respondent belongs to, 4) vector of dummies representing which fifth of the age distribution the

respondent belongs to, and 5) vector of dummies indicating respondent’s highest degree earned

(less than high school, high school degree, junior college, bachelor’s, or graduate degree). From

these models, I report adjusted educator-general population differences, and adjusted trends

where applicable.

In all models, I apply the WTSSALL sampling weight and adjust standard errors to

account for the fact that the GSS is not a simple random sample.

Results


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Racial Attitudes

In Tables 1-3, I present results separately for items relating to racial beliefs, stereotypes,

and affective orientation, respectively (see online Appendix C for descriptive statistics). In each

table, rows are survey items; the first column describes the item and gives the first and most

recent year the item was included on the GSS. The second and third columns show estimated

preK-12/general population and postsecondary/general population differences, respectively; the

fourth and fifth columns show these adjusted differences. The sixth column shows the estimated

change on the item for the general population, from the first survey year to the most recent. The

seventh column shows the model-predicted value for the general population in the first survey

year. The final column shows the unweighted sample sizes by occupation type. If an item

showed evidence of differing time-trends by occupation, four rows were devoted to that item in

the table: one row presenting the expected value for non-educators in the first survey year along

with the change in expected value from first to final survey year, a second row presenting the

adjusted and unadjusted educator/non-educator differences in the first survey year, a third row

presenting these values for the final survey year, and a fourth row presenting the change in

educator/non-educator differences from the first to final survey year.

Beliefs. In Table 1, I present the results for each of the three sub-dimensions of racial

beliefs.

<Insert Table 1>

Racial principles.

RQ1: Attitudes and attitude differences. In general, educators prefer less government

involvement in race relations and public racial discourse than do non-educators. These

educator/non-educator differences tend to be larger for those in postsecondary, rather than preK-


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12, positions. Educators were more likely to believe a racist should be allowed to teach at a

college (54% of preK-12, 67% of postsecondary, compared to 49% of non-educators in the final

survey year), and less likely to believe a racist book should be removed from the library (27% of

preK-12 educators endorsing removal, 15% of postsecondary educators, and 35% of non-

educators). PreK-12 educators were slightly less likely than non-educators to support a law

allowing homeowners to refuse to sell their homes to African Americans.

RQ2: Time trends. For all items, trends were similar for educators and non-educators,

with increasing support over time for allowing a racist to teach at a college, decreased support

for a law allowing homeowners to refuse to sell to African Americans, and no significant change

on the other items.

RQ3: Adjusted differences. After controlling for demographics, most educator/non-

educator differences completely disappear.

Explanations for inequality.

RQ1: Attitudes and attitude differences. Across survey years, educators were often more

likely than non-educators to believe that inequalities between Black and White Americans in

jobs, income, and housing were mainly due to structural, rather than biological or cultural,

factors. Postsecondary educators were more likely than non-educators to believe that inequality

was mostly due to discrimination (in 2014, 51% of postsecondary educators, 38% of preK-12

educators, and 35% of non-educators favored this explanation). Educators were less likely to

believe inequalities were due to in-born ability differences by race (in 2014, 3% of

postsecondary, 4% of preK-12, and 8% of non-educators favored this explanation). Educators

were more likely than non-educators to explain inequalities by insufficient educational

opportunities for African Americans (with 72% of postsecondary, 56% of preK-12, and 42% of


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non-educators choosing this explanation in 2014). Finally, educators were less likely than non-

educators to attribute inequalities to a lack of motivation among African Americans (with 20% of

postsecondary, 31% of preK-12, and 46% of non-educators choosing this explanation in 2014).

RQ2: Time trends. Americans were less likely over time to endorse any of the

explanations for racial inequalities offered by the GSS, and many trends differed slightly for

educators and non-educators. Some opinion differences became smaller over time, including the

degree to which educators were less likely to believe inequities were due to in-born ability

differences by race (in 1985, 9% of postsecondary educators, 11% of preK-12 educators, and

21% of non-educators favored this explanation, compared to 3% of postsecondary, 4% of preK-

12, and 8% of non-educators in 2014) and the degree to which educators were less likely to

attribute inequalities to lack of motivation among African Americans (in 1985, 30% of

postsecondary educators, 44% of preK-12 educators, and 60% of non-educators favored this

explanation, compared to 20% of postsecondary, 31% of preK-12, and 46% of non-educators in

2014). Over time, all groups became less likely to explain inequalities by insufficient

educational opportunities for African Americans, but this drop was least pronounced for

postsecondary educators (in 1985, 80% of postsecondary educators, 67% of preK-12 educators,

and 53% of non-educators favored this explanation, compared to 72% of postsecondary, 56% of

preK-12, and 42% of non-educators in 2014).

RQ3: Adjusted differences. After adding controls, these opinion differences disappeared

between non-educators and preK-12 educators. For postsecondary educators, the differences

mostly narrowed but remained significant.

Government policy initiatives.


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RQ1: Attitudes and attitude differences. Compared to non-educators and preK-12

educators, postsecondary educators tended to express less opposition to government policy

initiatives aimed at helping African Americans. Postsecondary educators were less opposed to

preferential hiring and promotion of Black Americans, and more neutral on the question of

whether the government has a responsibility to help raise living standards for Black Americans.

Postsecondary and preK-12 educators were more likely than non-educators to believe that it is

“not very likely” that a White person would not get hired or promoted while an equally or less-

qualified Black person would (non-educators tended to believe this was “somewhat likely”). No

group supported government assistance to ethnic minorities to preserve their customs and

traditions.

RQ2: Time trends. Over time, Americans became slightly less opposed to preferential

hiring for African Americans (from 1994 to 2014) and slightly less likely to believe that a White

person would be turned down for a job/promotion that an equally or less qualified Black person

would get (from 1990 to 2014). Americans also became slightly less supportive of government

assistance for preserving the customs of ethnic minorities. There was no evidence that

educator/non-educator belief differences changed over time.

RQ3: Adjusted differences. After adding controls, the attitude differences between

postsecondary educators and non-educators on preferential hiring and government obligations

narrowed slightly. The sign of the difference between preK-12 educators and non-educators

switched on the question of whether a White person would be turned down for a job/promotion

(with preK-12 educators significantly more likely to believe that this would happen compared to

similar non-educators).


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Racial beliefs summary. Compared to non-educators, educators tend to give more

politically liberal explanations for racial inequalities, and postsecondary educators express less

opposition to governmental equalizing efforts (though they are generally still opposed). Some

educators hold problematic racial beliefs, yet overall these comparisons suggest that students

may be less likely to encounter negative racial attitudes from adults in school compared to

outside school. When freedom of speech conflicts with anti-racist values, educators were more

likely than non-educators to side with speech, raising questions about how educators might

address troubling race speech in their classrooms, and how this might affect students.

Racial Stereotypes. In Table 2, I present the results for racial stereotypes.

<Insert Table 2>

RQ1: Attitudes and attitude differences. On average, educators rate African Americans

as being neutral on all three stereotype dimensions (hardworking vs. lazy, intelligent vs.

unintelligent, violence-prone vs. not violence-prone). This contrasts the general public, who

rated African Americans as more lazy and more violence-prone than did educators (unadjusted;

ratings on intelligence were statistically equivalent across sub-groups). Educators and non-

educators alike rated Asian Americans as being more hard-working than not, and educators did

so to a greater extent than did non-educators. Similar to non-educators, educators rated Asians

as being more intelligent than not and less violent than not. For Hispanics, preK-12 educators

(like non-educators) were close to neutral on the stereotype dimensions. Compared to non-

educators, postsecondary educators tended to believe that Hispanics were more hardworking,

more intelligent, and less violence-prone (small postsecondary sample sizes should be noted).

Educators rated Whites as neutral on intelligence and work ethic, in contrast to non-educators

who viewed Whites as slightly more intelligent than not and slightly more hardworking than not.


20

All groups perceived Whites as being not violence-prone, and postsecondary educators did so to

a greater extent than non-educators and preK-12 educators.

RQ2: Time trends. For minoritized groups, stereotypes became either less negative or

more positive over time. In contrast, stereotypes of Whites were more positive in 1990, and

became somewhat more neutral over time (by 2000 or 2014, depending on the item). The gaps

between educators and non-educators remained statistically constant across years.

RQ3: Adjusted differences. After adding controls, differences between preK-12

educators and non-educators statistically disappeared (or became marginally significant);

postsecondary educator/non-educator differences disappeared or narrowed.

Stereotype summary. Overall, educators tended to rate all groups neutrally on the

stereotype dimensions (except that they rated Asians more positively than neutrally), and

expressed less negative racial stereotypes (compared to non-educators) toward minoritized

groups. This could mean that educators, on average, will be motivated to resist negative racial

stereotypes.

Affective orientation. In Table 3, I present the results for each of the sub-dimensions of

affective orientation.

<Insert Table 3>

RQ1: Attitudes and attitude differences. Educators reported significantly less collective

resentment toward African Americans than did non-educators, and the difference was larger for

postsecondary educators. Specifically, educators expressed more disagreement with the

statements “African Americans shouldn’t push themselves where they’re not wanted” and

“African Americans should work their way up without special favors.”


21

Educators also reported less social distance from minoritized groups than did non-

educators. Specifically, educators were more likely to report favoring living in a neighborhood

that was half Black, half Asian, or half Hispanic (with non-educators responding closer to neutral

in the final survey year). Educators responded more favorably to the idea of a family member

marrying an Hispanic spouse, compared to non-educators who were mostly neutral or slightly

more positive than neutral in the final survey year of 2014. Postsecondary, but not preK-12,

educators were more open to the idea of a family member marrying a Black spouse. Finally,

educators were more likely to report that someone in their family brought a friend of a different

race home for dinner over the past few years.

RQ2: Time trends. Time trends in affective orientation were mostly positive, with

respondents reporting somewhat lower levels of collective resentment toward African Americans

over time and more cross-racial social relations. Many of the differences between educators and

non-educators were constant.

RQ3: Adjusted differences. With controls, the educator/non-educator differences in

collective resentments disappeared for preK-12 educators and narrowed for postsecondary

educators. The educator/non-educator differences in the probability of having had a different-

race dinner guest appear to have been driven by demographics. Differences in openness to living

in a diverse neighborhood or having a family member marry a Black, Hispanic, or Asian spouse

narrowed slightly after controls were added (and mostly retained significance).

Affective orientation summary. Compared to non-educators, educators reported less

social distance from minoritized groups and less collective resentment. Educators may therefore

be slightly more comfortable, compared to non-educators, in building positive relationships with

students from backgrounds different from their own.


22

Discussion

These results show that some educators hold racial attitudes that can be detrimental to

students’ learning and development. At the same time, educators tend to express less negative,

or more positive, racial attitudes compared to non-educators. Educators were less likely to report

holding certain negative racial stereotypes, and expressed more social closeness and less

collective resentment toward other races or minoritized groups. Educators were also more likely

to explain the existence of racial inequalities by structural factors rather than biological or

cultural factors, and were less opposed to government policy interventions aimed at decreasing

inequalities. When the value of free speech or individual freedom conflicted with racially

tolerant views, however, educators were more likely than non-educators to side with individual

freedoms. Many of the attitude differences between educators and non-educators were partially

or fully explained by demographics, particularly education level (see Appendix D).

Limitations

Before discussing these findings further, it is worth noting some important limitations.

First, survey questions such as these are likely to contain social desirability bias, in that

respondents may be self-conscious about expressing opinions they fear will be perceived as

racially intolerant. If educators and non-educators are differentially influenced by social

desirability bias, this would also bias estimates of educator/non-educator differences. With these

caveats in mind, I proceed with consideration of these findings’ implications.

Implications

Attitudes and attitude differences.

Racial beliefs. In the final survey year (2014), an estimated 4% of preK-12 educators

and 3% of postsecondary educators reported believing that inequalities were mainly due to


23

African Americans having less in-born ability to learn. Though these percentages are small, it is

disturbing to consider that some students are being exposed to teachers or administrators who

hold such a belief. These figures have been on the decline (down from 11% and 9% of preK-12

and postsecondary educators in 1985), providing hope that the numbers will soon go to zero.

In 2014, 31% of preK-12 educators believed that inequalities were mainly a result of

African Americans lacking motivation or willpower to pull themselves up out of poverty, and

20% of postsecondary educators believed this. Considering the mechanisms discussed earlier

through which such a belief can negatively impact students, these are high numbers. While

educators are more likely than non-educators to believe that inequalities are due to African

Americans not having adequate educational opportunities (with 56% of preK-12 and 72% of

postsecondary educators expressing this belief in 2014, compared to 42% of non-educators),

students would be better off if fewer educators explained inequalities through racial differences

in motivation or willpower, and if more perceived education as playing a corrective role.

Educators tend to be opposed to preferential hiring and promotion for African Americans

(though postsecondary educators are less opposed than others), and are, on average, between

being opposed and neutral regarding the question of whether the government has an obligation to

help improve living standard for African Americans. As mentioned earlier, such attitudes from

educators can lead to the perpetration of microagressions of the “colorblind racism” variety.

Additionally, these attitudes may indicate that educators take an ahistorical view of modern

inequalities, lacking an appreciation for the ways in which various governmental policies have

advantaged Whites over Blacks throughout US history to perpetuate inequality (Wilson, 2009),

or for the difficulty of abating discrimination without enforceable corrective measures (West,

2001). If educators’ attitudes are indeed rooted in misunderstandings, minoritized students and


24

White students alike would benefit by having teachers with deeper knowledge of White privilege

and structural racism.

Stereotypes. Taking responses on the stereotype questions at face value, it is reassuring

that educators do not hold explicit negative racial stereotypes. While these results by no means

imply that educators are free of negative implicit biases (and other research would predict that

they are not [Banaji & Greenwald, 2013]), the lack of explicit prejudice is important for

motivating educators to monitor and evaluate biases in their behaviors rooted in implicit

associations. However, it is also worth noting that descriptively, educators gave more positive

ratings to White students than Black students. Relative to White students, then, educators may

hold negative stereotypes of Black students, which can lead to differential treatment that

maintains White students’ advantages.

Asians were the one group for whom educators gave ratings closer to one side than to

neutral, with ratings of more hardworking than lazy and more intelligent than unintelligent.

Again, this could lead to teachers’ differential treatment of Asian students compared to Black or

White students. For Asian students, it raises the concern that students’ awareness of these

stereotypes can be detrimental when students’ self-expectations do not match the stereotype (Sue

et al., 2009; Wheeler & Petty, 2001).

Affective orientation. PreK-12 educators showed some signs of collective resentment,

falling between “agree” and “neutral” on their reaction to the statement, “Other minorities

overcame prejudice and worked their way up. Blacks should do the same without special favors.”

Agreement with this statement suggests a lack of understanding of the ways in which the

experiences of Black Americans have differed historically from the experiences of other minority

groups (such as various European immigrant groups), and the ways in which slavery, Jim Crow,


25

and other state-sponsored (or private) forms or racism continue to affect the opportunities

available to Black Americans today (Wilson, 2009). A lack of understanding of these issues can

lead people to over-emphasize the role of individual traits in determining one’s life outcomes,

and to draw incorrect inferences about inherent differences across racial groups.

Interpreting trends. In most cases, the rates of racial attitude change over time did not

differ for educators and non-educators. In the instances in which they did, the differences in

rates of change were either small or due to non-educators having much less tolerant, or more

negative, racial attitudes compared to educators in early survey years, followed by steeper

declines in these intolerant attitudes. It is therefore likely not the case that different types of

people are entering the field of education over time, or that the experiences of teachers in more

recent years have caused them to become more progressive while on the job compared to

teachers in earlier decades.

Differences and adjusted differences. As discussed above, the attitude differences

between educators and non-educators mostly disappeared or diminished after controlling for

demographic and educational background variables; for most items, it was education that

explained the attitude differences (see Appendix D). What does this mean for students?

Assuming that the attitudes of non-educators do not differ depending on whether they have

school-age children, this result suggests that overall across the sample, when students have

parents or adult figures with education levels similar to their teachers (i.e., bachelor’s degree or

above), it is more likely that the teachers’ racial attitudes will be similar to the parents’. In

contrast, when students’ parents or adult figures have lower education levels compared to their

teachers (i.e., less than a bachelor’s degree), students may be more likely to be exposed to more


26

liberal racial attitudes at school than at home. Again, these results express the overall pattern in

the sample – more research is needed to explore the ways in which this may vary by racial group.

While respondent race did not explain much of the educator/non-educator attitude

differences, the reason has to do with the positive correlation between the being White and being

an educator. Consistent with previous research (e.g., Bobo et al., 2012), supplemental analyses

show meaningful and consistent differences by race across the items analyzed here, with

respondents from minoritized groups (compared to White respondents) expressing less racial

stereotypes and more positive affective orientation toward minority groups. This suggests an

additional benefit of increasing the share of educators from minoritized backgrounds (Cherng &

Halpern, 2016) – namely, exposing fewer students to negative racial attitudes.

Conclusion

This study represents an initial step toward a greater understanding of the racial attitudes

of educators in the US. Results show that educators are not free from problematic racial

attitudes. In most dimensions, however, educators have more positive, or less negative, racial

attitudes compared to non-educators. Generally, the educator/non-educator differences can be

explained by educational attainment. Through future research, we must develop a greater

understanding of how interventions can raise educators’ awareness of problematic attitudes, how

these attitudes affect their students, and how educators can take control of monitoring and

improving their attitudes.


27

Notes

1Hinojosa and Moras (2009) used data from the GSS to investigate the racial attitudes of

teachers. The present study offers the following methodological and substantive contributions

beyond their study: 1) I use all available years of data (HJ use 1994-2004), 2) I model the effects

of time (HJ pool years to conduct ANOVA), and 3) I use the full sample (HJ compare White K-

12 teachers to non-teachers with at least 16 years of education).

2 Sensitivity analyses using ordered logit models for the Likert-type outcomes show largely

consistent results in terms of direction and significance (some comparisons showing significant

differences in weighted least squares show marginally significant differences in ordered logit).

3Note that in this main effects logistic regression model, educator/non-educator differences can

vary over time in the probability scale, but not in the log-odds scale. In most cases, interactions

between the educator dummies and the year dummies were not jointly significant for the logit

models; since my interest is in the probability scale, however, I focus on the significance of the

difference-in-difference for predicted probabilities (as opposed to logits). Magnitude and

significance of educator/non-educators differences replicated with linear probability models

(inference regarding whether gaps differed by year varied in some cases).

4 To obtain all predicted means and mean differences, I use the margins command in Stata; to

test for the significance of model-predicted differences, I use lincom.


28

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32

Table 1.

Unadjusted and Adjusted Racial Beliefs of Educators and Non-educators, and Belief Trends over Time.

Unadjusted

Differences Adjusted Differences

Gen. Pop. Expected

Values Ns

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop.

Diff

(SE)

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop.

Diff

(SE)

Change

over

time

(last yr –

1st yr)

(SE)

First

Incl. Yr

(SE)

pK-12

PS

Gen.

Pop.

Racial Principles

Should a person who believes blacks are

genetically inferior be allowed to teach at a

college? (0=N, 1=Y) (1976-2014)

.059***

(.016)

.184***

(.034)

-.02

(.018)

.056

(.038)

.061**

(.023)

.424

(.018)

1295

231

25648

Should a book claiming that blacks are inferior

be removed from the public library? (0=N, 1=Y)

(1976-2014)

-.082***

(.014)

-.2***

(.029)

.032~

(.018)

-.069

(.042)

-.035

(.028)

.384

(.022)

1306

233

25758

Should people prejudiced against any racial or

ethnic group be allowed to hold public meetings?

(0=N, 1=Y) (2004-2014)

.074

(.052)

.232~

(.122)

-.047

(.052)

.038

(.122)

.042

(.026)

.375

(.015)

140

22

2095

Support law allowing homeowner to refuse to

sell home to African Americans, as opposed to

law preventing homeowner from discriminating

(or no law at all)? (0=N, 1=Y) (1978-2014)

-.037*

(.017)

-.038

(.041)

.017

(.019)

.02

(.046)

-.336***

(.024)

.585

(.02)

991

185

20284

Explanations for Social Inequality

On the average African-Americans have worse

jobs, income, and housing than white people. Do

you think these differences are . . .


33

Mainly due to discrimination? (0=N, 1=Y)

(1985-2014)

.03

(.019)

.16***

(.043)

0

(.02)

.137**

(.044)

-.092***

(.025)

.438

(.019)

962

180

19462

Because most Af. Am. have less in-born ability

to learn? (0=N, 1=Y)

-.125***

(.015)

.205

(.013)

1985

-.098***

(.015)

-.12***

(.033)

-.004

(.02)

-.028

(.046) 991

183

19710 2014

-.041***

(.007)

-.049***

(.013)

-.002

(.009)

-.013

(.02)

2014-1985 Difference .057*** .073** .002 .016

Because most Af. Am. don't have the chance

for education that it takes to rise out of poverty?

(0=N, 1=Y)

-.111***

(.026)

.534

(.021)

1985

.131***

(.017)

.262***

(.032)

-.002

(.02)

.13**

(.043) 985

180

19719 2014

.137***

(.019)

.293***

(.04)

-.002

(.02)

.14**

(.051)

2014-1985 Difference .006~ .03* 0 .01

Because most Af. Am. don't have the

motivation or will power to pull themselves up out

of poverty? (0=N, 1=Y)

-.134***

(.026)

.596

(.017)

1985

-.156***

(.019)

-.297***

(.041)

-.023

(.02)

-.158***

(.047) 957

179

19271 2014

-.148***

(.017)

-.261***

(.032)

-.023

(.02)

-.15***

(.042)

2014-1985 Difference .008* .034** 0 .008

Government Policy Initiatives


34

For or against preferential hiring and promotion

of blacks? (1=strongly oppose, 4=strongly favor)

(1994-2014)

.011

(.045)

.497***

(.131)

-.039

(.045)

.41**

(.13)

.111*

(.049)

1.65

(.039)

652

104

12332

Does govt. have obligation to help improve

blacks' living standards? (1=no special treatment,

5=govt should help) (1975-2014)

.101*

(.039)

.667***

(.093)

-.031

(.041)

.465***

(.097)

-.093

(.069)

2.456

(.052)

1176

215

23559

How likely that white person not get a

job/promotion while equally or less qualified

black person does? (1=not v. likely, 3=v. likely)

(1990-2014)

-.054~

(.032)

-.189**

(.068)

.067*

(.033)

-.024

(.071)

-.214***

(.037)

1.935

(.031)

661

128

12327

Ethnic minorities should be given govt.

assistance to preserve customs and traditions

(1=agree strongly; 5=disagree strongly) (1996-

2014)

.108

(.098)

.027

(.199)

.022

(.095)

-.107

(.204)

.106*

(.051)

3.592

(.038)

171

29

2966

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Separate models were fit for each survey item

(logistic regression models for binary items and weighted least squares for non-binary items; all models incorporate appropriate sampling weights and adjust

standard errors based on primary sampling unit). Items for which educator/non-educator differences varied over time appear over 4 rows; items for which

educator/non-educator differences did not vary over time appear on one row. Date ranges shown for items are first and most recent year on survey, but additional

years may be included (see Appendix E). Gen. pop. expected values show the predicted probabilities (for binary outcomes) or predicted means (for non-binary

outcomes) for non-educators in the first survey year, and the mean change from the first to most recent survey year. Unadjusted differences are the differences in

predicted probabilities or predicted means between educators and non-educators. Adjusted differences control for: vector of dummy variables indicating

respondent educational attainment (with possibilities for less than high school, high school degree, junior college, bachelor’s degree, graduate degree), binary

indicator for respondent gender, vector of GSS race dummy variables (with possibilities for black, white, and other race), vector of dummy variables representing

which fifth of the income distribution respondent falls in, and vector of dummy variables representing which fifth of the age distribution the respondent falls in.

Adjusted predicted probabilities hold controls constant at their means.


35

Table 2.

Unadjusted and Adjusted Racial Stereotypes Held by Educators and Non-educators, and Stereotype Trends over Time.

Unadjusted Differences Adjusted Differences

Gen. Pop.

Expected Values Ns

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

Change

over

time

(last yr –

1st yr)

(SE)

First

Incl.

Yr

(SE)

pK-12

PS

Gen.

Pop.

Stereotypes

Af. Am. tend to be hard-working or lazy?

(1=lazy, 7=hardworking) (1990-2014)

.138**

(.043)

.361***

(.105)

.038

(.044)

.265*

(.104)

.277***

(.067)

3.595

(.058)

700

123

13298

Af Am. tend to be unintelligent or intelligent?

(1=unintelligent, 7=intelligent) (1990-2014)

.039

(.046)

.076

(.112)

.046

(.05)

.131

(.117)

.289***

(.055)

3.969

(.047)

634

114

12096

Af. Am. tend to be violence prone or not?

(1=not violent prone, 7=violent) (1990-2000)

-.312**

(.107)

-.888***

(.233)

-.114

(.113)

-.700**

(.23)

-.159*

(.064)

4.671

(.045)

107

24

2110

Asians tend to be hard-working or lazy?


.344***

(.103)

.462*

(.2)

.097

(.109)

.132

(.216)

.209**

(.071)

4.658

(.054)

163

32

3095

Asians tend to be unintelligent or intelligent?


.011

(.124)

.148

(.254)

-.117

(.133)

.072

(.26)

.212**

(.067)

4.511

(.052)

103

24

1987

Asians tend to be violence prone or not?


.092

(.119)

-.427~

(.256)

.215~

(.124)

-.183

(.267)

-.117~

(.065)

3.748

(.05)

104

24

1972


36

Hispanics tend to be hard-working or lazy?


.055

(.097)

.547*

(.228)

.077

(.097)

.51*

(.24)

.539***

(.073)

3.815

(.051)

164

31

3146

Hispanics tend to be unintelligent or

intelligent? (1=unintelligent, 7=intelligent)

(1990-2000)

.143

(.12)

.557*

(.25)

.071

(.129)

.486~

(.265)

.115*

(.056)

3.919

(.048)

104

24

2024

Hispanics tend to be violence prone or not?


-.23~

(.121)

-.632**

(.228)

-.088

(.127)

-.496*

(.23)

-.138*

(.065)

4.426

(.048)

106

24

2020

Whites tend to be hard-working or lazy?


-.098*

(.04)

-.138

(.107)

-.037

(.043)

-.079

(.113)

-.421***

(.053)

4.852

(.04)

701

123

13369

Whites tend to be unintelligent or intelligent?


-.08~

(.046)

-.288**

(.105)

.069

(.05)

-.105

(.102)

-.345***

(.056)

4.902

(.043)

636

115

12135

Whites tend to be violence prone or not?


.138

(.113)

-.456*

(.229)

.166

(.118)

-.384

(.239)

.207**

(.064)

3.63

(.049)

108

24

2114

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Separate models were fit for each survey item; all

models incorporate appropriate sampling weights and adjust standard errors based on primary sampling unit). Gen. pop. expected values show the predicted

means for non-educators in the first survey year, and the mean change from the first to most recent survey year. “Hardworking vs. lazy” and “violence-prone vs.

not” items were reverse-coded from original. Unadjusted differences are the differences in predicted means between educators and non-educators. Date ranges

shown for items are first and most recent year on survey, but additional years may be included (see Appendix E). Adjusted differences control for: vector of

dummy variables indicating respondent educational attainment (with possibilities for less than high school, high school degree, junior college, bachelor’s degree,

graduate degree), binary indicator for respondent gender, vector of GSS race dummy variables (with possibilities for black, white, and other race), vector of

dummy variables representing which fifth of the income distribution respondent falls in, and vector of dummy variables representing which fifth of the age

distribution the respondent falls in.


37

Table 3.

Unadjusted and Adjusted Racial Affective Orientation of Educators and Non-educators, and Trends in Affective Orientation over

Time.


Gen. Pop. Expected

Values Ns

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

pK-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

Change

over time

(last yr –

1st yr)

(SE)

First

Incl. Yr

(SE)

pK-12

PS

Gen.

Pop.

Affective/Socioemotional Evaluations

In general, how close do you feel to

Blacks? (1=very close; 9=not close at all)

(1996-2014)

-.126

(.092)

-.222

(.19)

-.112

(.088)

-.325~

(.186)

-.695***

(.109)

4.576

(.085)

591

114

10894

Collective Resentments

Af. Am. shouldn't push themselves where

they're not wanted. (1=disagree strongly;

4=agree strongly) (1980-2002)

-.418***

(.047)

-.694***

(.097)

-.007

(.049)

-.316**

(.107)

-.753***

(.052)

2.888

(.033)

527

77

10502

Other minorities overcame prejudice and

worked their way up. Blacks should do the

same without special favors. (1=disagree

strongly; 5=agree strongly) (1994-2014)

-.367***

(.06)

-.957***

(.152)

-.002

(.061)

-.489**

(.148)

-.17**

(.06)

4.036

(.042)

681

111

12813

Social Distance


38

During last few years, anyone in your

family brought friend who was (opposite

race) home for dinner? (0=N, 1=Y)

.164***

(.027)

.29

(.018)

1980

.088***

(.021)

.162**

(.052)

.014

(.021)

.082

(.052) 669

114

14057 2006

.099***

(.022)

.171***

(.049)

.016

(.024)

.09

(.055)

2006-1980 Difference .011*** .011* .002 .009*

Favor living in neighborhood where half

of neighbors were…

Black? (1=strongly oppose; 5=strongly

favor) (1990-2014)

.171***

(.047)

.279**

(.096)

.119*

(.048)

.268**

(.103)

.51***

(.054)

2.641

(.04)

652

117

12341

Asian? (1=strongly oppose; 5=strongly

favor) (1990-2000)

.333***

(.087)

.48*

(.196)

.211*

(.095)

.378~

(.201)

.469***

(.043)

2.696

(.028)

111

24

2106

Hispanic? (1=strongly oppose; 5=strongly

favor) (1990-2000)

.435***

(.053)

2.601

(.04)

1990

.089

(.149)

.48*

(.217)

.041

(.153)

.411~

(.224) 111

24

2127 2000

.503***

(.13)

.519~

(.296)

.382**

(.138)

.422

(.307)

1990-2000 Difference -.414* -.039 -.341~ -.011

How feel about close relative or family

member marrying...


39

Af. Am? (1=strongly oppose; 5=strongly

favor) (1990-2014)

.077

(.054)

.32**

(.119)

-.018

(.054)

.301**

(.115)

1.088***

(.06)

2.262

(.049)

654

117

12368


favor) (1990-2014)

.879***

(.045)

2.536

(.03)

1990

.319~

(.179)

.655***

(.13)

.256

(.169)

.583***

(.139) 484

88

8644

2014

.035

(.12)

.082

(.256)

-.052

(.121)

-.04

(.255)

1990-2014 Difference .285 .573* .308 .622*

Hispanic? (1=strongly oppose; 5=strongly

favor) (1990-2014)

.165**

(.054)

.351**

(.124)

.101~

(.056)

.291*

(.124)

.817***

(.05)

2.608

(.039)

483

88

8656

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Separate models were fit for each survey item

(logistic regression models for binary items and weighted least squares for non-binary items; all models incorporate appropriate sampling weights and adjust

standard errors based on primary sampling unit). Items for which educator/non-educator differences varied over time appear over 4 rows; items for which

educator/non-educator differences did not vary over time appear on one row. Date ranges shown for items are first and most recent year on survey, but additional

years may be included (see Appendix E). Gen. pop. expected values show the predicted probabilities (for binary outcomes) or predicted means (for non-binary

outcomes) for non-educators in the first survey year, and the mean change from the first to most recent survey year. Unadjusted differences are the differences in

predicted probabilities or predicted means between educators and non-educators. Adjusted differences control for: vector of dummy variables indicating

respondent educational attainment (with possibilities for less than high school, high school degree, junior college, bachelor’s degree, graduate degree), binary

indicator for respondent gender, vector of GSS race dummy variables (with possibilities for black, white, and other race), vector of dummy variables representing

which fifth of the income distribution respondent falls in, and vector of dummy variables representing which fifth of the age distribution the respondent falls in.

Adjusted predicted probabilities hold controls constant at their means.


40

Supplementary Online Material

Online Appendix A: GSS Data and Occupation Coding.

For over four decades, the National Opinion Research Center has interviewed Americans

on a wide variety of social issues through the General Social Survey, for the purpose of

documenting and explaining trends in attitudes, behaviors, and attributes of American society.

The GSS uses a multi-stage sampling procedure, though some of the design specifics have varied

across years (see Smith et al., 2015, Appendix A). From 1972 to 2004, each survey drew an

independent sample of English-speakers who were 18 years or older and living in non-

institutional settings. Since 2006, non-English-speaking Spanish-speakers have been included in

the target population (Smith et al., 2015). In the present study, I exclude these respondents from

the sample for population consistency over time (results are robust to inclusion or exclusion).

Educator occupation coding. The GSS codes interviewees’ occupations according to

census classifications. These classifications have changed over time, and the GSS used the 1970

census occupation codes for survey years 1972-1990, the 1980 occupation codes for survey years

1988-2010, and the 2010 occupation codes for 2012 and 2014. For each coding scheme, I

identified codes indicating preK-12 and postsecondary educators and created a consistent binary

indicator variable for each group of educators across waves (see Table A1 for a listing the

occupation codes included under each educator label for each classification system, along with

the overall sample sizes for each category).

I excluded childcare workers, non-postsecondary adult education workers, and “not

elsewhere classified” instructors. The 1970 occupation codes distinguish between administrators

in preK-12 and postsecondary settings; the 1980 and 2010 codes do not make this distinction.

For these years, I classify administrators as preK-12 educators because this is where the majority


41

of education administrators fall (approximately 80% for 1970 codes). For the 3 survey years that

included both 1970 and 1980 occupation codes in the data set, 4074 of 4135 cases had consistent

codes across schemes; for the 61 cases that disagreed, I chose the more recent code.

Outcomes. In some cases, I reverse-coded or collapsed response categories on outcome

variables to facilitate interpretation. Some items were asked only of white respondents in early

years but were asked of all respondents in later years; for these items, I included only the years in

which all groups were surveyed, so as to maintain a consistent target population over time.

Finally, I excluded any items that were removed from the survey prior to the year 2000.

Following Bobo et al. (2012), I treat all “I don’t know” responses as missing data (these

responses are relatively rare, ranging from 0% to 9% across outcomes, median=2%).


42

Table A1.

Educator Census Occupation Codes and (Unweighted) Sample Sizes.

1970 Census Occupation Codes n 1980 Census Occupation Codes n

Postsecondary Postsecondary Agriculture teachers 3 agriculture and forestry teachers 1

Art, drama, and music teachers 14 art, drama, and music teachers 19

Biology teachers 4 biological science teachers 5

Business and commerce teachers 8 business, commerce, and marketing teachers 4

Chemistry teachers 5 chemistry teachers 3

Coaches and physical education teachers 7 computer science teachers 3

Economics teachers 5 earth, environmental, and marine science teachers 6

Education teachers 4 economics teachers 6

Engineering teachers 2 education teachers 3

English teachers 13 engineering teachers 6

Foreign language teachers 10 english teachers 28

Health specialties teachers 20 foreign language teachers 15

Home economics teachers 3 health specialties teachers 10

Law teachers 1 history teachers 7

Mathematics teachers 7 home economics teachers 2

Miscellaneous teachers, college and university 9 law teachers 3

Physics teachers 4 mathematical science teachers 19

Psychology teachers 3 medical science teachers 2

School administrators, college 19 natural science teachers, n.e.c. 2

Social science teachers, n.e.c. 7 physical education teachers 2

Sociology teachers 2 physics teachers 3

Teachers, college and university, subject not specified 17 political science teachers 5

Trade, industrial, and technical teachers 8 postsecondary teachers, subject not specified 56

Pre-K-12 psychology teachers 7

Prekindergarten and kindergarten teachers 54 social science teachers, n.e.c. 2

Elementary school teachers 483 social work teachers 3

Secondary school teachers 297 teachers, postsecondary, n.e.c. 6


43

Teacher aides, exc. school monitors 85 theology teachers 2

School administrators, elementary and secondary 65 trade and industrial teachers 5

2010 Census Occupation Codes Pre-K-12

Postsecondary administrators, education and related fields 170

postsecondary teachers 44 teachers, prekindergarten and kindergarten 104

Pre-K-12 teachers, elementary school 621

education administrators 20 teachers, secondary school 328

preschool and kindergarten teachers 20 teachers, special education 43

elementary and middle school teachers 80 teachers' aides 94

secondary school teachers 52

special education teachers 15

teacher assistants 26 Note. n.e.c.=not elsewhere classified.


44

Online Appendix B: Composite Outcomes.

In Table B1, I present the results to analyses that use composites for groups of survey

items, organized by the dimensions and sub-dimensions described in the main text. I created

composites by generating scores for the first principal component from a principal component

analysis (PCA) using relevant items for each sub-dimension. For composites based on a

collection of binary items, I used a tetrachoric correlation matrix as the basis for the PCA. All

composite scores were standardized to mean 0 and sd 1 before analysis. Models used for the

analyses are the same as those used for the individual items (see main text).

I report results using the individual items, as opposed to the composites, as my preferred

main outcome analyses in the main text for three primary reasons. First, composites can only be

interpreted meaningfully as relative scores; unlike the scales of the original survey items, they do

not have a metric that allows us to interpret the racial attitudes of educators in any absolute

sense. Second, the composite analyses mask interesting information about responses on

individual items. Finally, because the GSS changes survey items over time, it is not possible to

combine all of the items from each sub-dimension into an overall composite; creating the

composites requires dropping some items entirely and, in many cases, greatly reduces sample

size.


45

Table B1.

Analyses using Composite Outcomes.


Gen. Pop. Expected

Values Ns

K-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

K-12 -

Gen. Pop.

Diff

(SE)

PS-Gen

Pop. Diff

(SE)

Final Svy

Yr - First

Svy Yr

(SE)

First Incl.

Yr

(SE)

K-12

PS

Gen.

Pop.

Racial Beliefs

Inequality due to structures over

individual traits (1985-2014)

.315***

(.039)

.703***

(.078)

.009

(.04)

.359***

(.075)

.033

(.058)

-.025

(.047)

907

171

18220

Individual rights trump anti-racist ideals

(1976-2014)

.12**

(.044)

.467***

(.083)

-.098*

(.046)

.118

(.084)

.024

(.052)

.001

(.041)

705

135

13877

Stereotypes

Stereotypes of blacks (lower=more

negative) (1990-2000)

.261*

(.101)

.788**

(.247)

.152

(.095)

.713**

(.247)

.176***

(.051)

-.12

(.043)

105

24

2070

Stereotypes of Hispanics (lower=more

negative) (1990-2000)

.139

(.107)

.647*

(.262)

.071

(.11)

.532~

(.275)

.316***

(.051)

-.182

(.042)

103

24

1970

Stereotypes of Asians (lower=more

negative) (1990-2000)

.112

(.098)

.373~

(.221)

-.064

(.098)

.133

(.233)

.22***

(.058)

-.107

(.044)

102

24

1918


46

Stereotypes of whites (lower=more

negative) (1990-2000)

-.18*

(.084)

-.004

(.244)

-.074

(.093)

.17

(.25)

-.19***

(.051)

.096

(.04)

106

24

2086

Affective Orientation

Racial resentments (1994-2002)

-.466***

(.092)

-.74***

(.198)

-.006

(.088)

-.415*

(.19)

-.122

(.076)

.094

(.054)

147

23

2959

Positive affective orientation toward

blacks (1990-2014)

.131**

(.046)

.306***

(.085)

.056

(.046)

.29***

(.087)

.802***

(.054)

-.585

(.043)

650

116

12283


Asians (1990-2000)

.348***

(.095)

.657***

(.192)

.193~

(.099)

.505**

(.193)

.648***

(.048)

-.332

(.029)

110

24

2086


Hispanics (1990-2000)

.257*

(.101)

.576**

(.177)

.152

(.107)

.457*

(.177)

.58***

(.055)

-.307

(.038)

110

24

2106

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Outcomes are PCA-derived indices, standardized to

mean 0 and SD 1. Outcomes analyzed using weighted least squares. All models include appropriate sampling weights and adjustments to standard errors based

on primary sampling units (see main text for additional model information). Adjusted models control for: vector of dummy variables indicating respondent

educational attainment (less than high school, high school degree, junior college, bachelor’s degree, graduate degree), binary indicator for respondent gender,

vector of GSS race dummy variables (with possibilities for black, white, and other race), vector of dummy variables representing which fifth of the income

distribution respondent falls in, and vector of dummy variables representing which fifth of the age distribution the respondent falls in.


47

Appendix C: Descriptive Statistics.

In Table C1, I present weighted descriptive statistics for non-educators, preK-12

educators, and postsecondary educators on background variables. As seen in the table and

discussed in the main text, educators are, on average, more white, more educated, and have

higher incomes compared to the average non-educator. PreK-12 educators are more female than

non-educators, and postsecondary educators are slightly more male than non-educators.


48

Table C1.

Weighted Descriptive Statistics by Occupation Type (pooled across survey years).

Non-educators PreK-12 Educators Postsecondary Educators

M SD N M SD N M SD N

Family Income in Constant $ 35119.7 30737.63 47092 44617.53 32305.13 2430 49743.18 36003.3 411

Gender (Male=1, Female=0) 0.494 0.5 47092 0.228 0.42 2430 0.549 0.498 411

Age 44.145 16.327 47092 46.524 15.406 2430 47.121 15.617 411

White (Y=1, N=0) 0.82 0.384 47092 0.854 0.353 2430 0.885 0.319 411

Black (Y=1, N=0) 0.13 0.337 47092 0.105 0.307 2430 0.043 0.202 411

Other Race (Y=1, N=0) 0.049 0.216 47092 0.041 0.199 2430 0.072 0.259 411

Occupational Prestige Score 38.2 13.246 21115 58.046 8.407 1006 75.507 6.642 174

Father's occupational prestige score

(1970) 39.333 12.496 18020 43.302 13.43 905 46.875 14.232 162

Father's occupational prestige score

(1980) 43.333 12.462 20098 46.102 13.232 1149 50.735 15.279 195

Father's years of education 10.669 4.238 33767 11.665 4.245 2001 12.994 4.417 354

Mother's years of education 10.853 3.631 39417 11.721 3.45 2225 12.484 3.321 376

Years of education 12.794 2.943 47047 16.446 2.148 2428 18.058 2.203 411

Highest degree: Less than High School

(Y=1, N=0) 0.197 0.398 47092 0.006 0.077 2430 0.003 0.059 411

Highest degree: High School (Y=1,

N=0) 0.553 0.497 47092 0.175 0.38 2430 0.068 0.252 411

Highest degree: Jr. College (Y=1,

N=0) 0.06 0.237 47092 0.036 0.187 2430 0.02 0.141 411

Highest degree: Bachelor's (Y=1, N=0) 0.137 0.343 47092 0.432 0.495 2430 0.168 0.375 411

Highest degree: Graduate degree (Y=1,

N=0) 0.053 0.224 47092 0.351 0.477 2430 0.74 0.439 411


49

Online Appendix D: Results Adjusting for Full Set of Controls vs. Education Only.

Table D1.

Racial Beliefs, Stereotypes, and Affective Orientations of Educators and Non-educators, Adjusting for Full Set of Demographic

Variables, and Education Level Only.

Adjusting for all

Demographic Variables

Adjusting only for

Education Level Ns

K-12 - Gen.

Pop. Diff

PS-Gen

Pop. Diff

K-12 -

Gen. Pop.

Diff

PS-Gen Pop.

Diff

K-12

PS

Gen. Pop.

Racial Principles

Should there be law against inter-marriage of whites and blacks?

(0=N, 1=Y)

1980

.056*

(.026)

-.121~

(.063)

.075**

(.027)

-.128

(.084) 820

141

17024 2002

.022*

(.011)

-.041*

(.02)

.034*

(.014)

-.051~

(.031) 2002-1980 Difference -.035* .079~ -.04** .077

Should a person be allowed to make a speech in your community

claiming that blacks are inferior? (0=N, 1=Y) (1976-2014)

-.053**

(.018)

.064

(.04)

-.09***

(.018)

.049

(.042)

1315

233

25997

Should a person who believes blacks are genetically inferior be

allowed to teach at a college? (0=N, 1=Y) (1976-2014)

-.02

(.018)

.056

(.038)

-.047**

(.017)

.046

(.038)

1295

231

25648


50

Should a book claiming that blacks are inferior be removed from

the public library? (0=N, 1=Y) (1976-2014)

.032~

(.018)

-.069

(.042)

.056**

(.018)

-.062

(.044)

1306

233

25758

Should people prejudiced against any racial or ethnic group be

allowed to hold public meetings? (0=N, 1=Y) (2004-2014)

-.047

(.052)

.038

(.122)

-.061

(.049)

.039

(.129)

140

22

2095

Support law allowing homeowner to refuse to sell home to African

Americans, as opposed to law preventing homeowner from

discriminating (or no law at all)? (0=N, 1=Y) (1978-2014)

.017

(.019)

.02

(.046)

.011

(.019)

.037

(.046)

991

185

20284

Explanations for Social Inequality

On the average African-Americans have worse jobs, income, and

housing than white people. Do you think these differences are . . .

Mainly due to discrimination? (0=N, 1=Y) (1985-2014)

0

(.02)

.137**

(.044)

.023

(.02)

.138**

(.044)

962

180

19462

Because most Af. Am. have less in-born ability to learn? (0=N,

1=Y)

1985

-.004

(.02)

-.028

(.046)

-.003

(.021)

-.011

(.057) 991

183

19710 2014

-.002

(.009)

-.013

(.02)

-.002

(.011)

-.006

(.03) 2014-1985 Difference .002 .016 .002 .005


51

Because most Af. Am. don't have the chance for education that it

takes to rise out of poverty? (0=N, 1=Y)

1985

-.002

(.02)

.13**

(.043)

.013

(.02)

.134**

(.043) 985

180

19719 2014

-.002

(.02)

.14**

(.051)

.013

(.02)

.143**

(.05) 2014-1985 Difference 0 .01 0 .009

Because most Af. Am. don't have the motivation or will power to

pull themselves up out of poverty? (0=N, 1=Y)

1985

-.023

(.02)

-.158***

(.047)

-.031

(.02)

-.141**

(.048) 957

179

19271 2014

-.023

(.02)

-.15***

(.042)

-.031

(.02)

-.135**

(.044) 2014-1985 Difference 0 .008 0 .005


For or against preferential hiring and promotion of blacks?

(1=strongly oppose, 4=strongly favor) (1994-2014)

-.039

(.045)

.41**

(.13)

-.024

(.047)

.381**

(.136)

652

104

12332

Does govt. have obligation to help improve blacks' living

standards? (1=no special treatment, 5=govt should help) (1975-2014)

-.031

(.041)

.465***

(.097)

.002

(.042)

.475***

(.096)

1176

215

23559

How likely that white person not get a job/promotion while equally

or less qualified black person does? (1=not v. likely, 3=v. likely)

(1990-2014)

.067*

(.033)

-.024

(.071)

.077*

(.034)

-.01

(.069)

661

128

12327


52

Ethnic minorities should be given govt. assistance to preserve

customs and traditions (1=agree strongly; 5=disagree strongly)

(1996-2014)

.022

(.095)

-.107

(.204)

.049

(.101)

-.009

(.197)

171

29

2966

Stereotypes

Af. Am. tend to be hard-working or lazy? (1=lazy, 7=hardworking)

(1990-2014)

.038

(.044)

.265*

(.104)

.05

(.045)

.229*

(.109)

700

123

13298

Af Am. tend to be unintelligent or intelligent? (1=unintelligent,

7=intelligent) (1990-2014)

.046

(.05)

.131

(.117)

.069

(.049)

.111

(.115)

634

114

12096

Af. Am. tend to be violence prone or not? (1=not violent prone,

7=violent) (1990-2000)

-.114

(.113)

-.700**

(.23)

-.197~

(.112)

-.666**

(.23)

107

24

2110

Asians tend to be hard-working or lazy? (1=lazy, 7=hardworking)

(1990-2000)

.097

(.109)

.132

(.216)

.038

(.106)

.072

(.214)

163

32

3095

Asians tend to be unintelligent or intelligent? (1=unintelligent,


-.117

(.133)

.072

(.26)

-.102

(.122)

.02

(.262)

103

24

1987

Asians tend to be violence prone or not? (1=not violent prone,

7=violent) (1990-2000)

.215~

(.124)

-.183

(.267)

.226~

(.12)

-.204

(.262)

104

24

1972


53

Hispanics tend to be hard-working or lazy? (1=lazy,

7=hardworking) (1990-2000)

.077

(.097)

.51*

(.24)

-.009

(.098)

.453~

(.233)

164

31

3146

Hispanics tend to be unintelligent or intelligent? (1=unintelligent,


.071

(.129)

.486~

(.265)

.064

(.124)

.43

(.268)

104

24

2024

Hispanics tend to be violence prone or not? (1=not violent prone,

7=violent) (1990-2000)

-.088

(.127)

-.496*

(.23)

-.14

(.124)

-.445~

(.231)

106

24

2020

Whites tend to be hard-working or lazy? (1=lazy, 7=hardworking)

(1990-2014)

-.037

(.043)

-.079

(.113)

-.023

(.042)

-.056

(.112)

652

104

12332

Whites tend to be unintelligent or intelligent? (1=unintelligent,


.069

(.05)

-.105

(.102)

.083~

(.048)

-.078

(.106)

1176

215

23559

Whites tend to be violence prone or not? (1=not violent prone,

7=violent) (1990-2000)

.166

(.118)

-.384

(.239)

.108

(.114)

-.447~

(.236)

661

128

12327


In general, how close do you feel to Blacks? (1=very close; 9=not

close at all) (1996-2014)

-.112

(.088)

-.325~

(.186)

-.179~

(.094)

-.278

(.195)

591

114

10894



54

Af. Am. shouldn't push themselves where they're not wanted.

(1=disagree strongly; 4=agree strongly) (1980-2002)

-.007

(.049)

-.316**

(.107)

-.036

(.049)

-.226*

(.103)

527

77

10502

Other minorities overcame prejudice and worked their way up.

Blacks should do the same without special favors. (1=disagree


-.002

(.061)

-.489**

(.148)

-.018

(.062)

-.44**

(.151)

681

111

12813

Social Distance

During last few years, anyone in your family brought friend who

was (opposite race) home for dinner? (0=N, 1=Y)

1980

.014

(.021)

.082

(.052)

.017

(.02)

.051

(.049) 669

114

14057 2006

.016

(.024)

.09

(.055)

.02

(.023)

.058

(.054) 2006-1980 Difference .002 .009* .003 .007

Favor living in neighborhood where half of neighbors were…

Black? (1=strongly oppose; 5=strongly favor) (1990-2014)

.119*

(.048)

.268**

(.103)

.144**

(.048)

.234*

(.1)

652

117

12341

Asian? (1=strongly oppose; 5=strongly favor) (1990-2000)

.211*

(.095)

.378~

(.201)

.235*

(.093)

.336~

(.203)

111

24

2106


55

Hispanic? (1=strongly oppose; 5=strongly favor) (1990-2000)

1990

.041

(.153)

.411~

(.224)

.02

(.15)

.343

(.219) 111

24

2127 2000

.382**

(.138)

.422

(.307)

.424**

(.14)

.406

(.299) 1990-2000 Difference -.341~ -.011 -.404* -.063

How feel about close relative or family member marrying...

Af. Am? (1=strongly oppose; 5=strongly favor) (1990-2014)

-.018

(.054)

.301**

(.115)

.008

(.056)

.223~

(.123)

654

117

12368

Asian? (1=strongly oppose; 5=strongly favor) (1990-2014)

1990

.256

(.169)

.583***

(.139)

.233

(.177)

.526***

(.136) 484

88

8644

2014

-.052

(.121)

-.04

(.255)

-.039

(.121)

-.021

(.256)

1990-2014 Difference .308 .622* .272 .547~


56

Hispanic? (1=strongly oppose; 5=strongly favor) (1990-2014)

.101~

(.056)

.291*

(.124)

.11*

(.056)

.27*

(.128)

483

88

8656

Racial Beliefs

Inequality due to structures over individual traits (1985-2014)

.009

(.04)

.359***

(.075)

.045

(.04)

.338***

(.076)

907

171

18220

Individual rights trump anti-racist ideals (1990-2000)

-.098*

(.046)

.118

(.084)

-.16***

(.045)

.099

(.085)

705

135

13877

Stereotypes

Stereotypes of blacks (lower=more negative) (1990-2000)

.152

(.095)

.713**

(.247)

.183~

(.103)

.661**

(.25)

105

24

2070

Stereotypes of Hispanics (lower=more negative) (1990-2000)

.071

(.11)

.532~

(.275)

.046

(.109)

.49~

(.272)

103

24

1970

Stereotypes of Asians (lower=more negative) (1990-2000)

-.064

(.098)

.133

(.233)

-.093

(.094)

.096

(.232)

102

24

1918

Stereotypes of whites (lower=more negative) (1994-2002)

-.074

(.093)

.17

(.25)

-.036

(.085)

.173

(.248)

106

24

2086


57

Affective Orientation

Racial resentments (1990-2014)

-.006

(.088)

-.415*

(.19)

-.062

(.093)

-.245

(.193)

147

23

2959

Positive affective orientation toward blacks (1990-2000)

.056

(.046)

.29***

(.087)

.082~

(.048)

.234**

(.087)

650

116

12283

Positive affective orientation toward Asians (1990-2000)

.193~

(.099)

.505**

(.193)

.209*

(.099)

.455*

(.202)

110

24

2086

Positive affective orientation toward Hispanics (1978-2010)

.152

(.107)

.457*

(.177)

.156

(.109)

.413*

(.183)

110

24

2106

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Binary outcomes are analyzed using logistic

regression; non-binary outcomes analyzed using weighted least squares. All models include appropriate sampling weights and adjustments to standard errors

based on primary sampling units (see main text for additional model information). Adjusted models control for: vector of dummy variables indicating respondent





58

Online Appendix E.

Table E1.

Weighted Descriptive Statistics for Outcome Variables by Occupation Type (pooled across survey years).

Non-educators Pre-K -12 Educators

Postsecondary

Educators

M SD N M SD N M SD N Years on survey

On the average African-Americans have

worse jobs, income, and housing than

white people. Do you think these

differences are . . . Mainly due to discrimination? (0=N,

1=Y) (1985-2014)

0.38 19462 0.41 962 0.54 180 1985 1986 1988 1989

1990 1991 1993 1994

1996 1998 2000 2002

2004 2006 2008 2010

2012 2014

Because most Af. Am. have less in-

born ability to learn? (0=N, 1=Y)

0.13 19710 0.06 991 0.05 183 1985 1986 1988 1989

1990 1991 1993 1994

1996 1998 2000 2002

2004 2006 2008 2010

2012 2014


chance for education that it takes to rise

out of poverty? (0=N, 1=Y)

0.47 19719 0.61 985 0.76 180 1985 1986 1988 1989

1990 1991 1993 1994

1996 1998 2000 2002

2004 2006 2008 2010

2012 2014


motivation or will power to pull

themselves up out of poverty? (0=N, 1=Y)

0.53 19271 0.37 957 0.25 179 1985 1986 1988 1989

1990 1991 1993 1994

1996 1998 2000 2002

2004 2006 2008 2010

2012 2014

Af. Am. shouldn't push themselves where

they're not wanted. (1=disagree strongly;

4=agree strongly) (1980-2002)

2.41 1.10 10502 1.98 1.01 527 1.67 0.86 77 1980 1982 1984 1985

1994 1996 1998 2000

2002


59

Other minorities overcame prejudice and

worked their way up. Blacks should do the

same without special favors. (1=disagree


3.98 1.19 12813 3.61 1.32 681 3.02 1.43 111 1994 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Af. Am. tend to be hard-working or lazy?


3.78 1.18 13298 3.92 0.96 700 4.14 1.00 123 1990 1994 1996 1998

2000 2002 2004 2006

2008 2010 2012 2014

Af Am. tend to be unintelligent or

intelligent? (1=unintelligent,


4.20 1.03 12096 4.25 0.92 634 4.28 1.03 114 1990 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Af. Am. tend to be violence prone or not?

(1=not violent prone, 7=violent) (1990-

2000)

4.59 1.33 2110 4.28 1.04 107 3.70 1.12 24 1990 2000

Asians tend to be hard-working or lazy?


4.81 1.34 3095 5.16 1.16 163 5.25 1.10 32 1990 1994 2000

Asians tend to be unintelligent or



4.62 1.26 1987 4.64 1.08 103 4.77 1.19 24 1990 2000

Asians tend to be violence prone or not?


2000)

3.69 1.22 1972 3.78 1.05 104 3.26 1.23 24 1990 2000

Hispanics tend to be hard-working or

lazy? (1=lazy, 7=hardworking) (1990-

2000)

4.03 1.34 3146 4.11 1.16 164 4.61 1.18 31 1990 1994 2000

Hispanics tend to be unintelligent or



3.98 1.12 2024 4.12 0.99 104 4.54 1.18 24 1990 2000

Hispanics tend to be violence prone or

not? (1=not violent prone, 7=violent)

(1990-2000)

4.36 1.22 2020 4.12 1.04 106 3.72 1.13 24 1990 2000

Whites tend to be hard-working or lazy?


4.52 1.10 13369 4.41 0.92 701 4.38 1.02 123 1990 1994 1996 1998

2000 2002 2004 2006

2008 2010 2012 2014


60

Whites tend to be unintelligent or



4.64 1.11 12135 4.56 0.93 636 4.36 0.98 115 1990 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Whites tend to be violence prone or not?


2000)

3.73 1.22 2114 3.88 1.12 108 3.28 1.11 24 1990 2000

For or against preferential hiring and

promotion of blacks? (1=strongly oppose,

4=strongly favor) (1994-2014)

1.69 0.95 12332 1.70 0.96 652 2.19 1.17 104 1994 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Does govt. have obligation to help

improve blacks' living standards? (1=no

special treatment, 5=govt should help)

(1975-2014)

2.42 1.26 23559 2.52 1.18 1176 3.07 1.30 215 1975 1983 1984 1986

1987 1988 1989 1990

1991 1993 1994 1996

1998 2000 2002 2004

2006 2008 2010 2012

2014


job/promotion while equally or less

qualified black person does? (1=not v.

likely, 3=v. likely) (1990-2014)

1.84 0.72 12327 1.78 0.70 661 1.64 0.68 128 1990 1994 1996 1998

2000 2002 2004 2006

2008 2010 2012 2014

Ethnic minorities should be given govt.

assistance to preserve customs and

traditions (1=agree strongly; 5=disagree

strongly) (1996-2014)

3.56 1.03 2966 3.63 1.12 171 3.59 1.05 29 1996 2004 2014

Should there be law against inter-marriage

of whites and blacks? (0=N, 1=Y)

0.17 17024 0.09 820 0.02 141 1980 1982 1984 1985

1987 1988 1989 1990

1991 1993 1994 1996

1998 2000 2002

Should a person who believes blacks are

genetically inferior be allowed to teach at

a college? (0=N, 1=Y) (1976-2014)

0.46 25648 0.52 1295 0.65 231 1976 1977 1980 1982

1984 1985 1987 1988

1989 1990 1991 1993

1994 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Should a book claiming that blacks are

inferior be removed from the public

library? (0=N, 1=Y) (1976-2014)

0.34 25758 0.26 1306 0.15 233 1976 1977 1980 1982

1984 1985 1987 1988

1989 1990 1991 1993


61

1994 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

Should people prejudiced against any

racial or ethnic group be allowed to hold

public meetings? (0=N, 1=Y) (2004-2014)

0.39 2095 0.47 140 0.62 22 2004 2014

Should a person be allowed to make a

speech in your community claiming that

blacks are inferior? (0=N, 1=Y) (1976-

2014)

0.63 25997 0.67 1315 0.81 233 1976 1977 1980 1982

1984 1985 1987 1988

1989 1990 1991 1993

1994 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014

During last few years, anyone in your

family brought friend who was (opposite

race) home for dinner? (0=N, 1=Y)

0.34 14057 0.44 669 0.51 114 1980 1982 1984 1985

1987 1988 1989 1990

1991 1993 1994 1996

2006

Support law allowing homeowner to

refuse to sell home to African Americans,

as opposed to law preventing homeowner

from discriminating (or no law at all)?

(0=N, 1=Y) (1978-2014)

0.38 20284 0.33 991 0.32 185 1978 1980 1983 1984

1986 1987 1988 1989

1990 1991 1993 1994

1996 2004 2006 2008

2010 2012 2014

Favor living in neighborhood where half

of neighbors were…

Black? (1=strongly oppose; 5=strongly

favor) (1990-2014)

3.03 1.01 12341 3.21 1.00 652 3.30 0.94 117 1990 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014


favor) (1990-2000)

2.93 0.94 2106 3.28 0.92 111 3.41 0.96 24 1990 2000

Hispanic? (1=strongly oppose;


2.82 1.00 2127 3.15 1.09 111 3.32 0.89 24 1990 2000


62

How feel about close relative or family

member marrying...

Af. Am? (1=strongly oppose;


3.02 1.22 12368 3.13 1.14 654 3.34 1.08 117 1990 1996 1998 2000

2002 2004 2006 2008

2010 2012 2014


favor) (1990-2014)

3.20 1.05 8644 3.41 0.97 484 3.56 0.90 88 1990 2000 2004 2006

2008 2010 2012 2014

Hispanic? (1=strongly oppose;


3.21 1.07 8656 3.40 1.00 483 3.56 0.92 88 1990 2000 2004 2006

2008 2010 2012 2014

In general, how close do you feel to

Blacks? (1=very close; 9=not close at all)

(1996-2014)

4.26 2.03 10894 4.12 1.94 591 4.00 1.77 114 1996 1998 2000 2002

2004 2006 2008 2010

2012 2014


63

Online Appendix F. Tables Including p-value for Test of Differences Between PreK-12 and Postsecondary Educators.

Table F1.

Unadjusted and Adjusted Racial Beliefs of Educators and Non-educators, and Belief Trends over Time.

Gen. Pop.

Expected

Values Unadjusted Differences Adjusted Differences Ns

1st Yr

[Last-1st]

pK-12 - GP

(SE)

PS-GP

(SE)

p,

H0:

pK-

12=PS

pK-12 - GP

(SE)

PS-GP

(SE)

K-12

PS

GP

Racial Principles

Should there be law against inter-marriage of whites

and blacks? (0=N, 1=Y)

.277

[-.184***]

1980

-.118***

(.018)

-.242***

(.032) .064

.056*

(.026)

-.121~

(.063)

820

141

17024

2002

-.045***

(.008)

-.082***

(.012) .238

.022*

(.011)

-.041*

(.02)

2002-1980 Difference .074*** .163*** -.035* .079~

Should a person be allowed to make a speech in

your community claiming that blacks are inferior?

(0=N, 1=Y) (1976-2014)

.647

[-.031]

.043**

(.015)

.183***

(.031) <.001

-.053**

(.018)

.064

(.04)

1315

233

25997

Should a person who believes blacks are genetically

inferior be allowed to teach at a college? (0=N, 1=Y)

(1976-2014)

.424

[.061**]

.059***

(.016)

.184***

(.034) <.001

-.02

(.018)

.056

(.038)

1295

231

25648


64

Should a book claiming that blacks are inferior be

removed from the public library? (0=N, 1=Y) (1976-

2014)

.384

[-.031]

-.082***

(.014)

-.2***

(.029) .002

.032~

(.018)

-.069

(.042)

1306

233

25758

Should people prejudiced against any racial or

ethnic group be allowed to hold public meetings?

(0=N, 1=Y) (2004-2014)

.375

[.042]

.074

(.052)

.232~

(.122) .18

-.047

(.052)

.038

(.122)

140

22

2095

Support law allowing homeowner to refuse to sell

home to African Americans, as opposed to law

preventing homeowner from discriminating (or no law

at all)? (0=N, 1=Y) (1978-2014)

.585

[-.336***]

-.037*

(.017)

-.038

(.041) .98

.017

(.019)

.02

(.046)

991

185

20284

Explanations for Social Inequities

On the average African-Americans have worse jobs,

income, and housing than white people. Do you think

these differences are . . .

Mainly due to discrimination? (0=N, 1=Y) (1985-

2014)

.438

[-.092***]

.03

(.019)

.16***

(.043) .002

0

(.02)

.137**

(.044)

962

180

19462

Because most Af. Am. have less in-born ability to

learn? (0=N, 1=Y)

.205

[-.125***]

1985

-.098***

(.015)

-.12***

(.033) .554

-.004

(.02)

-.028

(.046) 991

183

19710 2014

-.041***

(.007)

-.049***

(.013) .569

-.002

(.009)

-.013

(.02)

2014-1985 Difference .057*** .073** .002 .016


65

Because most Af. Am. don't have the chance for

education that it takes to rise out of poverty? (0=N,

1=Y)

.534

[-.111***]

1985

.131***

(.017)

.262***

(.032) p<.001

-.002

(.02)

.13**

(.043) 985

180

19719 2014

.137***

(.019)

.293***

(.04) p<.001

-.002

(.02)

.14**

(.051)

2014-1985 Difference .006~ .03* 0 .01

Because most Af. Am. don't have the motivation or

will power to pull themselves up out of poverty? (0=N,

1=Y)

.596

[-.134***]

1985

-.156***

(.019)

-.297***

(.041) .001

-.023

(.02)

-

.158***

(.047) 957

179

19271 2014

-.148***

(.017)

-.261***

(.032) .002

-.023

(.02)

-.15***

(.042)

2014-1985 Difference .008* .034** 0 .008


For or against preferential hiring and promotion of

blacks? (1=strongly oppose, 4=strongly favor) (1994-

2014)

1.65

[.111*]

.011

(.045)

.497***

(.131) <.001

-.039

(.045)

.41**

(.13)

652

104

12332

Does govt. have obligation to help improve blacks'

living standards? (1=no special treatment, 5=govt

should help) (1975-2014)

2.456

[-.093]

.101*

(.039)

.667***

(.093) <.001

-.031

(.041)

.465***

(.097)

1176

215

23559


66


job/promotion while equally or less qualified black

person does? (1=not v. likely, 3=v. likely) (1990-

2014)

1.935

[-.214***]

-.054~

(.032)

-.189**

(.068) .064

.067*

(.033)

-.024

(.071)

661

128

12327

Ethnic minorities should be given govt. assistance to

preserve customs and traditions (1=agree strongly;

5=disagree strongly) (1996-2014)

3.592

[.106*]

.108

(.098)

.027

(.199) .714

.022

(.095)

-.107

(.204)

171

29

2966

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Binary outcomes are analyzed using logistic regression;

non-binary outcomes analyzed using weighted least squares. All models include appropriate sampling weights and adjustments to standard errors based on primary

sampling units (see main text for additional model information). Adjusted models control for: vector of dummy variables indicating respondent educational

attainment (less than high school, high school degree, junior college, bachelor’s degree, graduate degree), binary indicator for respondent gender, vector of GSS race

dummy variables (with possibilities for black, white, and other race), vector of dummy variables representing which fifth of the income distribution respondent falls

in, and vector of dummy variables representing which fifth of the age distribution the respondent falls in.


67

Table F2.

Unadjusted and Adjusted Racial Stereotypes of Educators and Non-educators, and Stereotype Trends over Time.

Gen. Pop.

Expected Values Unadjusted Differences Adjusted Differences Ns

1st Yr

[Last-1st]

pK-12 -

GP

(SE)

PS-GP

(SE)

p,

H0:

pK-

12=PS

pK-12 -

GP

(SE)

PS-GP

(SE)

K-12

PS

GP

Af. Am. tend to be hard-working or lazy? (1=lazy,


3.595

[.277***]

.138**

(.043)

.361***

(.105) .045

.038

(.044)

.265*

(.104)

700

123

13298

Af Am. tend to be unintelligent or intelligent?


3.969

[.289***]

.039

(.046)

.076

(.112) .751

.046

(.05)

.131

(.117)

634

114

12096

Af. Am. tend to be violence prone or not? (1=not

violent prone, 7=violent) (1990-2000)

4.671

[-.159*]

-.312**

(.107)

-.888***

(.233) .023

-.114

(.113)

-.700**

(.23)

107

24

2110

Asians tend to be hard-working or lazy? (1=lazy,


4.658

[.209**]

.344***

(.103)

.462*

(.2) .596

.097

(.109)

.132

(.216)

163

32

3095

Asians tend to be unintelligent or intelligent?


4.511

[.212**]

.011

(.124)

.148

(.254) .62

-.117

(.133)

.072

(.26)

103

24

1987

Asians tend to be violence prone or not? (1=not


3.748

[-.117~]

.092

(.119)

-.427~

(.256) .067

.215~

(.124)

-.183

(.267)

104

24

1972

Hispanics tend to be hard-working or lazy?


3.815

[.539***]

.055

(.097)

.547*

(.228) .042

.077

(.097)

.51*

(.24)

164

31

3146


68

Hispanics tend to be unintelligent or intelligent?


3.919

[.115*]

.143

(.12)

.557*

(.25) .113

.071

(.129)

.486~

(.265)

104

24

2024

Hispanics tend to be violence prone or not? (1=not


4.426

[-.138*]

-.23~

(.121)

-.632**

(.228) .107

-.088

(.127)

-.496*

(.23)

106

24

2020

Whites tend to be hard-working or lazy? (1=lazy,


4.852

[-.421***]

-.098*

(.04)

-.138

(.107) .729

-.037

(.043)

-.079

(.113)

652

104

12332

Whites tend to be unintelligent or intelligent?


4.902

[-.345***]

-.08~

(.046)

-.288**

(.105) .061

.069

(.05)

-.105

(.102)

1176

215

23559

Whites tend to be violence prone or not? (1=not


3.63

[.207**]

.138

(.113)

-.456*

(.229) .018

.166

(.118)

-.384

(.239)

661

128

12327

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Outcomes analyzed using weighted least squares. All

models include appropriate sampling weights and adjustments to standard errors based on primary sampling units (see main text for additional model

information). Adjusted models control for: vector of dummy variables indicating respondent educational attainment (less than high school, high school degree,

junior college, bachelor’s degree, graduate degree), binary indicator for respondent gender, vector of GSS race dummy variables (with possibilities for black,

white, and other race), vector of dummy variables representing which fifth of the income distribution respondent falls in, and vector of dummy variables

representing which fifth of the age distribution the respondent falls in.


69

Table F3.

Unadjusted and Adjusted Racial Affective Orientation of Educators and Non-educators, and Trends in Affective Orientation over

Time.

Gen. Pop.

Expected

Values Unadjusted Differences Adjusted Differences Ns

1st Yr

[Last-1st]

pK-12 -

GP

(SE)

PS-GP

(SE)

p,

H0:

pK-

12=PS

pK-12 -

GP

(SE)

PS-GP

(SE)

K-12

PS

GP


In general, how close do you feel to Blacks? (1=very

close; 9=not close at all) (1996-2014)

4.576

[-.695***]

-.126

(.092)

-.222

(.19) .647

-.112

(.088)

-.325~

(.186)

591

114

10894


Af. Am. shouldn't push themselves where they're not

wanted. (1=disagree strongly; 4=agree strongly) (1980-

2002)

2.888

[-.753***]

-.418***

(.047)

-.694***

(.097) .006

-.007

(.049)

-.316**

(.107)

527

77

10502

Other minorities overcame prejudice and worked their

way up. Blacks should do the same without special

favors. (1=disagree strongly; 5=agree strongly) (1994-

2014)

4.036

[-.17**]

-.367***

(.06)

-.957***

(.152) <.001

-.002

(.061)

-.489**

(.148)

681

111

12813

Social Distance

During last few years, anyone in your family brought

friend who was (opposite race) home for dinner? (0=N,

1=Y)

.29

[.164***]

1980

.088***

(.021)

.162**

(.052) .148

.014

(.021)

.082

(.052)


70

2006

.099***

(.022)

.171***

(.049) .165

.016

(.024)

.09

(.055)

669

114

14057

2006-1980 Difference .011*** .011* .002 .009*

Favor living in neighborhood where half of neighbors

were…

Black? (1=strongly oppose; 5=strongly favor) (1990-

2014)

2.641

[.51***]

.171***

(.047)

.279**

(.096) .315

.119*

(.048)

.268**

(.103)

652

117

12341

Asian? (1=strongly oppose; 5=strongly favor) (1990-

2000)

2.696

[.469***]

.333***

(.087)

.48*

(.196) .493

.211*

(.095)

.378~

(.201)

111

24

2106

Hispanic? (1=strongly oppose; 5=strongly favor)

(1990-2000)

2.601

[.435***]

1990

.089

(.149)

.48*

(.217) .135

.041

(.153)

.411~

(.224) 111

24

2127 2000

.503***

(.13)

.519~

(.296) .959

.382**

(.138)

.422

(.307)

1990-2000 Difference -.414* -.039 -.341~ -.011

How feel about close relative or family member

marrying...

Af. Am? (1=strongly oppose; 5=strongly favor)

(1990-2014)

2.262

[1.088***]

.077

(.054)

.32**

(.119) .064

-.018

(.054)

.301**

(.115)

654

117

12368


71

Asian? (1=strongly oppose; 5=strongly favor) (1990-

2014)

2.536

[.879***]

1990

.319~

(.179)

.655***

(.13) .115

.256

(.169)

.583***

(.139) 484

88

8644 2014

.035

(.12)

.082

(.256) .866

-.052

(.121)

-.04

(.255)

1990-2014 Difference .285 .573* .308 .622*

Hispanic? (1=strongly oppose; 5=strongly favor)

(1990-2014)

2.608

[.817***]

.165**

(.054)

.351**

(.124) .169

.101~

(.056)

.291*

(.124)

483

88

8656

~p<.10, *p<.05, **p<.01, ***p<.001 Note. pK-12=preK-12 educators; PS=post-secondary educators; GP=general population of non-educators. Binary outcomes are analyzed using logistic

regression; non-binary outcomes analyzed using weighted least squares. All models include appropriate sampling weights and adjustments to standard errors

based on primary sampling units (see main text for additional model information). Adjusted models control for: vector of dummy variables indicating respondent





72

References (Appendices)

Smith, T.M., Marsden, P.V., Hout, M., Son, J., Schapiro, B., Mai, N., & Milliff, A., (2015).

GENERAL SOCIAL SURVEYS, 1972-2014 CUMULATIVE CODEBOOK.

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