The effects of sexism and racism on American migrants' hourly wages
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The effects of sexism and racism on American migrants’ hourly wages Citation Fernandes, Ines Pedro. 2022. The effects of sexism and racism on American migrants’ hourly wages. Bachelor's thesis, Harvard College. Permanent link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37372695 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA Share Your Story The Harvard community has made this article openly available. Please share how this access benefits you. Submit a story . Accessibility
The effects of sexism and racism on American
migrants’ hourly wages
∗
Inês Fernandes
Abstract
In this paper, we study how sexist and racist beliefs, as reported in the General
Social Survey affect the hourly wages of American whites and blacks whose state of
work differs from their state of birth. Ordinary least squares and Two-stage least
squares estimates show that higher levels of sexism and racism where individuals
were born (background sexism) negatively impact the hourly wages of black and
white men and women but that sexism (racism) particularly affects black women’s
(white and black men’s) wages. We argue that background sexism influences wages
through internalized sexist beliefs during individuals’ formative years. particularly
for black women, and internalized racism a more negative factor for white and black
men’s hourly wages. Finally, we find that white women’s wages benefit the most
from or are the least negatively impacted by higher levels of sexism and racism
in this demographic’s state of work (market sexism and racism). Moreover, black
women are the demographic whose wages are negatively impacted to the greatest
extent by gender prejudices in their state of work.
1 Introduction
Although black feminist theorists had discussed the unique oppressions that black women
experience as a virtue of both their gender and race (e.g. Anzaldua, 1987; Lorde, 1984),
the term “intersectionality” was only coined in 1989 by Crenshaw, to note that black
women’s intersecting gender and racial identities explained instances of discrimination
against this demographic in the absence of discrimination against black men and white
women (Crenshaw, 1989). Intersectionality theory has become a popular framework
∗
I would like to thank Dev Patel for valuable comments on earlier methodologies of this paper.
through which to study the ways in which systems of oppression or discrimination over-
lap to create distinct experiences and outcomes for individuals with multiple non-default
identities (e.g., whiteness, maleness, heterosexuality, able-bodiedness).
In particular, the theory has become increasingly well integrated in social sciences studies,
but, relative to fields such as Gender Studies, Sociology, or Psychology, Economics has
lagged behind in its use of intersectionality theory when studying historically oppressed
groups. Economic studies that examine the labor market outcomes of individuals with
intersecting, non-default gender and race identities, such as black and Hispanic women are,
by definition, intersectional (e.g., Daly, Hobijn, and Pedtke, 2020; Bayard et al., 1999).
However, very few empirical or theoretical studies of discrimination in the economics
literature examine the role that intersecting discriminatory forces or prejudices have on
groups with multiple non-default identities1 , opting to measure or model, for example,
how racism affects black men (e.g., Charles and Guryan, 2008) and how sexism affects
white women (e.g., Charles, Guryan, and Pan, 2018) but not how both forces influence
the outcomes of black women.
Although this choice is usually made for simplicity or due to data constraints, it might
also reflect the fact that some identities (i.e. male and white) are default social judgments
(e.g., Merrit Harrison, 2006) as well as “intersectional invisibility”, whereby individuals
with non-default race and gender identities (e.g., black women) go largely unnoticed2
(Purdie-Vaughns Eibach, 2008; Sesko Biernat, 2010).
The U.S. gender and racial wage gaps are sizable, and the white-black earnings gap is
estimated to have widened over the last 30–40 years (Daly, Hobijn, and Pedtke, 2020).
Further, the white-black wage gap is estimated to be smaller for women than for men and
persist even after controlling for standard human capital and ability variables (e.g., test
scores, years in schooling, work experience) and other controls (e.g., age, marital status,
region, and urban residence), suggesting that the intersection of factors related to gender
and race partly mediates observed differences in pay between demographic groups.
Charles and Guryan (2008) estimate that racial animus among whites helps explain one-
fourth of the white-black male wage gap. Charles, Guryan, and Pan (2018) document
considerable cross-state differences in white men and women’s labor market outcomes
(among them, hourly wages) that have persisted for decades and are not explained by
the usual aforementioned controls. The authors further find that these persistent resid-
ual cross-state gaps between white men and women’s labor market outcomes are partly
explained by differences in the distribution of sexist beliefs, as defined above, across U.S.
states. Both studies find evidence in support of Becker’s [1957] 1971) model of employer
taste-based discrimination, suggesting that observed white-black male and female-male
white wage gaps are partly attributable to employers’ preference for hiring and paying
higher wages to white and/or male workers, rather than factors such as statistical dis-
crimination or human capital differences between these groups.
1
One exception is Lahey and Oxley (2018), which studies the effects of race on employment discrimi-
nation over the lifecycle of black and white men and women.
2
Black women’s “invisibility” expresses itself in multiple ways. As one example, in addition to a
lack of research focusing on this demographic, black women are less likely to have their own statements
attributed to them in a group conversation and less likely to have their face recognized (Sesko Biernat,
2010).
2This paper extends the work in Charles, Guryan, and Pan (2018) to study racism and
the wages of black workers, in addition to sexism and white workers. To this end, we
measure the relative influences of state-level sexist and racist beliefs on American whites’
and blacks’ hourly wages, with a particular emphasis on black women, thus applying
an intersectional framework to the study of discrimination. Sexist and racist beliefs are
measured by indices constructed in Charles, Guryan, and Pan (2018) and Charles and
Guryan (2008), which use General Social Survey (GSS) responses to items that elicit
attitudes towards women’s social/family roles and political abilities and attitudes towards
interracial marriage, interracial social proximity, and blacks’ political abilities and rights,
respectively.
We restrict the analysis to employed American citizens, between 25 to 64 years old in the
American Community Survey (ACS), who work in a state different from that of their birth
state, in order to measure the extent to which state-level sexism and racism influences their
hourly wages through two different channels: early exposure to sexist/racist beliefs in their
state of birth (background sexism and racism) and market discrimination or social norms
in their state of work (market sexism and racism). The former effectively constitutes
internalized sexism and racism and accounts for the influence that sexist beliefs in the
place where individuals grow up restrict their access to resources during their formative
years and lead them to make certain early human capital investments and college major
or occupational choices that later influence their wages. The latter channel captures
gender and racial attitudes that change individuals’ behavior, for example, their behavior
in the workplace, as a response to gender and racial social norms in their labor markets,
which potentially affects their wages. Additionally, it captures employer, coworker, and
customer discriminatory attitudes towards blacks and/or women that apply upward or
downward pressure on their wages.
Moreover, we collapse individual observations into (state of birth, state of residence,
gender, race, year) cells and study the relation between American whites and blacks’ state-
level mean hourly wages, and racism and sexism in their state of birth using ordinary least
squares (OLS) regressions, which should yield unbiased results, given that individuals are
randomly assigned a place of and thus a level of sexism at birth. However, given the
endogeneity of the sexism and racism levels that Americans choose when they decide to
work in a state different from their state of birth and the potential tendency of individuals
with certain traits to sort into locations with particular levels of sexism and/or racism,
we study the relation between white and blacks’ mean state-level hourly wages and racist,
sexist beliefs in their state of work by writing the mean levels of labor market sexism
and racism as functions of inter-state geographical distances and historical migration
patterns, as in Charles, Guryan, and Pan (2018), and using two stage least squares (TSLS)
regressions, with these variables as instruments for sexism and racism in individuals states
of work.
We find that state-level background sexism and racism are associated with lower hourly
wages for both men and women, regardless of race, although the estimated effects are
small and mostly statistically insignificant. Further, the magnitude of the effect that one
standard deviation increases in background sexism have on American migrants’ hourly
wages, relative to one standard deviation increases in the levels of background racism is
systematically larger.
3Internalized sexism as a product of exposure to gender prejudices in one’s state of birth
seems to be of greater detriment to women’s hourly wages, particularly for black women,
and internalized racism a more negative factor for white and black men’s hourly wages.
Restricting our sample to white women and men, we observe that women’s wages are
penalized to a greater (smaller) extent by the prevalence of sexist (racist) beliefs in their
state of birth. We further note that black women suffer a higher (lower) wage penalty
due to background sexism (racism), relative to black men.
Notably, the effects of background and market sexism on mean state-level wages seem
to be considerably distinct for all demographic groups considered in this paper, with the
latte being systematically larger. We conclude that internalized sexism/racism and social
norms or discrimination suffered in one’s labor market influence wages through different
channels, with the latter being a stronger influence on wages.
A key take-away from our results is that the effects of sexism and racism for one de-
mographic always occur in relation to another demographic group: racism is positively
associated with mean hourly wages for white men but negatively associated with wages
for black men, when we restrict our analysis to men. Moreover, when the sample is re-
stricted to blacks, racism benefits men more so than it does women and, when the sample
is restricted to whites, racism seems more detrimental to men’s wages. These patterns
could be indicative of the fact that, due to occupational segregation, these four groups do
not compete equally for higher wages. We refer to summary statistics on college major,
occupation, and industry segregation by gender and race to explore this hypothesis but
note that this is only meant as a non-rigorous, exploratory exercise and that we do not
have enough information to assess which groups compete for better jobs or wages in this
paper.
Finally, we find that, among whites and among women, white women’s wages benefit
the most from or are the least negatively impacted by higher levels of sexism and racism
in this demographic’s state of work. Moreover, among blacks and among women, black
women are the demographic whose wages are hurt most by gender prejudices.
This paper makes several contributions to the current literature on wages, race, and
labor market discrimination. First, we simultaneously study and compare the outcomes
of white women, white men, black men, and black women, a demographic not as often
studied. Second, we analyze our results on the relation between sexist/racist beliefs
and white and blacks’ outcomes using insights not only from the economic literature
but from intersectionality theory, black feminist theory, gender studies, and sociology.
Specifically, we connect observed gender and racial differences in the association between
hourly wages and sexism/racism to explanations that feature in social science fields other
than economics: racial differences in ideals of femininity that fit white women better than
black women, differences in the upbringing of black boys and girls, and differences in the
workplace experiences and rise to management job positions of black men and women.
The remainder of the paper is structured as follows. Section 2 reviews the relevant lit-
erature on labor market discrimination, on the basis of sex and/or race, trends in wages
and occupational choice of whites, blacks, men and women as well as the workplace ex-
periences of black women and men, and how they relate to ideals of masculinity and
femininity. Section 3 presents the mathematical and empirical frameworks that inform
4the main analyses in the paper, as adapted from Charles, Guryan, and Pan (2018). Sec-
tion 4 presents summary statistics for the main sample in our paper and discusses the
construction of the racism and sexism indices presented here. Section 5 presents and
discusses our empirical results. Section 6 concludes by discussing the limitations of the
paper and potential extensions of the present work.
2 Literature review
2.1 Theories, sources, and consequences of discrimination
In Economics literature, employer discrimination is usually categorized as taste-based or
statistical discrimination. As pointed out in Becker ([1957] 1971), some of the possible
sources driving discrimination against certain demographics are: employer, coworker, and
customer discrimination.
One clear indication of employer sex discrimination in hiring is the use of job descriptions
targeting applicants with a specific gender, which has been made illegal in certain countries
(Card, Colella, and Lalive, 2021). Where prejudice or preferences are less apparent,
researchers have identified and measured employer discrimination through a number of
empirical strategies, the best-known of which are “audit” and “correspondence studies”.
In the former, the resumes of fictitious, similarly well-qualified white and black or male
and female job candidates are submitted for review, with employers usually favoring white
applicants (Bendick, 2007). These studies may also involve comparing the outcomes of
similar actors applying to the same job.
“Correspondence studies” address the concern that “audit” studies might lead to biased
results, because, researchers cannot match white and black resumes or actors playing out
as job applicants perfectly, and, thus, employers’ hiring decisions in these experiments
might be based on real differences between the candidates that researchers do not perceive.
In “correspondence studies”, researchers submit matched resumes for review that differ
only by applicants’ name, which is supposed to signal the candidate’s gender or race.
These studies also measure a preference for white candidates (Bertrand and Mullainathan,
2004), but, like “audit” studies, the fictitious job candidates in these experiments are likely
not matched according to important characteristics such as class. Indeed, Jacquemet and
Yannelis (2012) find considerable variation in callback rates across names within race,
reflecting the fact that different names associated with blacks and whites are correlated
with socioeconomic classes that makes them more or less attractive to employers.
In addition to to these types of studies, researchers have taken advantage of natural
experiments and within-firm variation to measure employer discrimination. For example,
Goldin and Rouse (2000) find that female job candidates are considerably more likely to
be hired into symphony orchestras when they switch to blind auditions, suggesting that,
previous to blind auditions, employers discriminated against female musicians based on
their observed gender. Giuliano, Levine, and Leonard (2009) compare the hiring decisions
of white and black managers at the same outlet of a large U.S. retailer, finding that white
managers hired more white workers relative to their black counterparts, evidence of these
5employers’ preference for own-race hires.
Employer discrimination can, in reality, reflect not employers’ own gender or racial animus
but rather their employees’ prejudices, as they pressure employers to make discriminatory
decisions. Empirical evidence offers both supports and contradicts this hypothesis. In
support of coworker discrimination, Giuliano, Levine, and Leonard (2009) find that work-
ers, especially whites and Asians, are less likely to quit when more coworkers have the
same race/ethnicity. Hedegaard and Tyran (2018) calculate that, when given information
about the productivity of coworkers with Danish-sounding and Muslim-sounding names,
secondary school students chose to work with someone with the same ethnicity and lose
8 percent of their earnings for two days. By contrast, Bygren (2010) provides evidence
of workers’ preference for greater gender diversity at the workplace, finding that, in a
matched sample of Swedish firms and workers, workers are less likely to leave a given
establishment when they have more coworkers of the opposite sex.
Another channel that may contribute to employers’ discriminatory decision-making is
customer prejudice against workers with certain characteristics. Although multiple em-
pirical studies have identified evidence of customer discrimination in settings where there
is no/little customer-worker interaction or, on the other end of the spectrum, very inti-
mate interaction, such as in customers’ interactions with sex-workers , we review research
that has investigated the more common settings of retail stores and online transactions
with individual sellers.
Customer prejudice may express itself through a distaste of interactions with workers of
a given race or gender, customers’ desire to interact with workers similar to them, and,
related to this last point, their belief that shared race, gender, or language improves the
quality of the firm’s product/services, as presented by the worker.
Using data from a large U.S. retailer with multiple outlets, Leonard, Levine, and Giuliano
(2010) find that, in areas with a larger proportion of whites, having more black employees
slightly reduces sales, but having more Hispanics slightly increases them. They also find
that having more Asian workers when the proportion of individuals nearby speaking only
Asian-Pacific languages is high increases sales. Similarly, Combes et al. (2016) show that,
in jobs with customer contact, a higher proportion of French residents is associated with a
larger increase in the employment gap between African and French workers more so than
in jobs without such contact.
Customer discrimination may also stem from a perception of products as being of lesser
quality or trustworthiness when advertised or sold by workers with certain characteristics.
For example, buyers are less likely to make an offer to purchase a portable digital music
player offered by a black person (Doleac and Stein, 2013). Similarly, Arab sellers and
buyers faced discrimination in an online market for used automobiles in Israel (Zussman,
2013).
2.1.1 Taste-based discrimination
The canonical Becker model of employer discrimination ([1957] 1971) assumes that em-
ployers have a preference for particular groups of job applicants or employees, that is
6they are prejudiced against other groups. Under this model, the observation that female
and/or black job applicants who differ from male or white applicants in no other relevant
characteristic but gender and race are less likely to be offered a job or receive lower wages
is justified by employers’ preference for male and/or white employees.
In the simplest version of Becker’s ([1957] 1971) canonical model of employer discrimina-
tion, employers’ dislike for female and/or black workers consists of a fixed disutility cost
that is incurred whenever a female and/or black worker is hired. This additional cost of
hiring a worker from these demographics groups, effectively raises the female/black wages
that prejudiced employers face.
Prejudiced employers thus require a fixed level of compensation to hire a female and/or
black worker, rather than a male and/or a white one. If the female-male (black-white)
wage gap exceeds this compensating differential, the employer hires only women (blacks);
otherwise, the employer hires only men (whites).
The Becker model of employer discrimination ([1957] 1971) suggests a market equilibrium
where firms that engage in taste-based discrimination are driven out of the market until
wage differentials between equally productive workers are eliminated, because prejudiced
employers will pay more for or hire less qualified workers, with their preferred gender or
race and therefore have fewer profits. By contrast, non-discriminatory firms, will be more
profitable because they can hire productive workers at relatively low wages. Moreover, as
the less prejudiced firms expand and all-white firms contract, in equilibrium, demand for
and thus the relative wages of women/blacks increase and the male-female, white-black
wage differentials are driven to zero.
Further, given that female/black labor is cheapest to the least prejudiced employers in the
market, they are hired first by these employers. Female/black wages are determined by the
prejudice of the most prejudiced employer with whom these groups interact in equilibrium
- the “marginal discriminator” - whose levels of prejudice are below average. One other
consequence of female and black workers’ segregation into non-discriminatory firms is that
gender or racial prejudice in the right tail of the employer prejudice distributions should
not affect gendered or racial wage differences as much as higher prejudice in the left tail
of the prejudice distribution.
The aforementioned equilibrium would not be possible if the market becomes ineffective
in allocating black workers to the least prejudiced firms, however.
A major difficulty in empirically identifying and measuring taste-based discrimination is
the fact that most individuals will not admit to their discriminatory behavior nor disclose
their prejudices against certain groups of people. Nonetheless, multiple studies have been
able to identify taste-based discrimination, by testing the predictions of Becker’s models
or via field experiments.
Charles and Guryan (2008) test the predictions of a simple version of the Becker ([1957]
1971) taste-based discrimination model using data from the GSS and the U.S. Census. In
this model, for any given level of wages, blacks are hired by the least prejudiced employers
in the market, for which the cost of hiring blacks is lower, and whites are hired by the
most prejudiced. In equilibrium, the wages of black and white workers are equal, and the
7marginal employer is indifferent between hiring black and white workers; the prejudice
of the marginal discriminator is equal to the additional disutility cost of hiring a black
worker. Employers more prejudiced than the marginal discriminator hire only whites;
those less prejudiced than the marginal discriminator hire only blacks, and the markets
for both black and white workers clear.
Therefore, the authors note, the model predicts that (1) the marginal discriminatory
employer can affect blacks’ labor market outcomes more than employers with the average
level of prejudice; (2) the number (or fraction) of blacks in the workforce is negatively
related to racial wage gaps, with prejudice held constant, because; (3) prejudice in the
right tail of the employer prejudice distribution should not matter for racial differences
whereas higher prejudice in the left tail of the prejudice distribution should affect racial
wage gaps, because the market segregates blacks from the most prejudiced whites.
The authors use questions from the GSS, such as whether the respondent opposes interra-
cial marriage or would not vote for a black president, to create a “prejudice index.” They
then estimate the tenth, fiftieth, and ninetieth percentile of racial prejudice in each state.
Consistent with Becker’s theory and thus taste-based discrimination, they find that the
tenth percentile of racial prejudice best predicts the racial wage gap.
Charles, Guryan, and Pan (2018) test similar predictions of a Beckerian model of sex
discrimination, finding evidence that female workers’ labor market outcomes are mainly
determined by the prejudices of men with the median level of sexism (as measured by
their index), who are presumably the “marginal discriminators” in these female workers’
labor markets, since they are the most sexist men with whom they interact. The findings
thus provide evidence in favor of taste-based discrimination against female workers in the
U.S., as modelled by Becker.
2.1.2 Statistical discrimination
Statistical discrimination is discrimination based on valid statistical inference when indi-
vidual information is unknown to the person who discriminates. When a characteristic
like gender or race is correlated with unobserved or imperfectly observed productivity, em-
ployers may use known group-level information about the characteristic to update their
prior estimated productivity of any individual from that group. For example, consider a
scenario where employers wish to fill in vacancies requiring high social skills and are un-
able to judge individual job candidates’ productivity but can accurately know that women
score higher in interpersonal ability tests. Employers would hire more female applicants
and discriminate against male applicants, some of which might have similar social skills
as their female counterparts.
An important distinction should be made between statistical discrimination and inaccu-
rate statistical discrimination. The former presupposes real differences between groups
of workers or job applicants, whereas the latter describes a situation where employers
misjudge or are misinformed about the true abilities of different groups of employees and
thus engage in invalid statistical inference about individual workers belonging to a given
group.
8The key difference between taste-based and inaccurate statistical discrimination is that
the latter stems from an honest lack of information about employees’ true individual
abilities and can thus be rectified when employers acquire this knowledge. Taste-based
discrimination can either stem from animus towards certain groups of workers or reflect in-
valid statistical inferences that are not corrected by the provision of accurate information.
Thus, for example, someone who, after being presented with a large body of evidence,
believes women are less capable of holding leadership roles in business organizations, can
be said to discriminate based on prejudice.
A very crucial implication of theoretical models of statistical discrimination is that pro-
viding accurate information about workers’ characteristics that are correlated with gender
or race is expected to reduce discrimination against these workers. For example, if em-
ployers perceive the mathematical abilities of female job candidates to be worse than
those of male, they will discriminate against all female candidates, even those with good
mathematical abilities, so providing employers with the actual math test scores of job
candidates can increase employers’ willingness to hire female applicants.
Multiple empirical studies have found evidence in favor of this particular prediction, there-
fore lending support to the theory of statistical discrimination. For instance, Wozniak
(2015), noting that blacks are more likely than whites to have been imprisoned for drug
offenses, finds that drug testing, which provides employers’ with individual-specific in-
formation on drug consumption and thus reveals that some black job applicants which
employers might have thought consumed drugs, in fact, do not, increased employers’
willingness to hire blacks.
Additionally, Agan and Starr (2018) find that legislation that forbids firms from asking job
applicants about their criminal background reduces callbacks of black male applicants,
relative to comparable whites. This observation supports the statistical discrimination
theory, suggesting that, employers prefer applicants without a criminal record and, in the
absence of individual-specific information, rely on the knowledge that blacks are more
likely to have a criminal record than whites to statistically infer that most of their black
job applicants have faced criminal charges in the past and therefore not offer them a
job. Similarly, Bartik and Nelson (2019) find that the prohibition of the use of credit
reports in hiring reduced black employment, given that it presumably increased statistical
discrimination against this demographic.
Finally, Law and Marks (2009) find that, despite minorities’ relatively lower pass rates
in occupational licensing exams, occupational licensing increases the share of minority
workers in an occupation, which suggests that it provides employers and customers with
information that corrects their low estimates of individual minority licensed workers’
abilities.
2.2 Race, gender, wages, and occupation
The U.S. gender and racial wage gaps are sizable, and the white-black earnings gap is
estimated to have widened over the last 30–40 years (Daly, Hobijn, and Pedtke, 2020).
Such gender and race gaps are known to persist even after controlling for standard human
9capital and ability variables (e.g., test scores, years in schooling, work experience) and
other controls that influence wages (e.g., age, marital status, region, and urban residence).
For concreteness, black men earned 31 percent less annually conditional on employment
than white men in 2010 (Lang and Spitzer, 2020). Relative to white women, black female
workers also earn less, but the differential is only about half that for males (Daly, Hobijn,
and Pedtke, 2020). Indeed, black-white wage differentials have been estimated to be
lower for women, which could be due to the strong positive relation between skill and
employment among black but not white women (Neal, 2004). However, Neal (2004) also
argues that the true black-white gap in potential wages is larger than has been measured
and estimates that the median black-white gap in log potential wages among women in
the National Longitudinal Survey of Youth is approximately -0.25.
Studying the labor market outcomes of black and white women from 1940 to 2014, Collins
and Moody (2017) find that women, regardless of race, significantly increased their la-
bor force participation in this period, with white women’s labor force participation rates
catching up to those of black women by 1990. Moreover, black-white wage and occupa-
tional gaps decreased considerably from 1940 to 1980, after which the wage gap between
black and white women widened. The authors further conclude that differences in human
capital are an important driver of the black-white wage gaps observed from 1940 to 2014.
Indeed, there is wide agreement in the literature that black-white wage differentials are
partly due to observed skill differences such as years and quality of education (O’Neill,
1990; Trejo, 1997). Hellerstein and Neumark (2008) find that only a small portion of
racial segregation in the workplace is driven by education differences between black and
white workers, but that a considerable fraction of ethnic (white-Hispanic) segregation in
the workplace can be attributed to differences in language proficiency, providing evidence
in support of segregation at the intersection of (in this case English language) skills and
ethnicity.
One other important driver of black-white and male-female wage gaps is segregation along
race and gender lines3 (e.g., Carrington Troske, 1998a; Bayard et al., 1999). On this latter
point, Blau and Kahn (2017) document a rise in the relative importance of occupation and
industry in explaining the gender wage gap from the 1980s to 2010s. Moreover, greater
segregation between black and white men than between black and white women accounts
for one-third to one-half of the higher black-white wage gap for men (Bayard et al., 1999).
Although occupation and industry segregation help explain gender and racial wage gaps,
segregation into particular establishments and jobs within establishments have been found
to contribute more to wage differentials (Bayard et al., 1999). Goldin (2014) argues that
some jobs disproportionately reward individuals who are willing to work long hours and
satisfy restricting requirements (e.g. deadlines, schedules), and, as female workers tend
to value work flexibility, women and men sort across workplaces and job positions such
that, within any given occupation, men earn higher-wages. The increasing measured
returns to working long hours across education groups and occupations (Kuhn and Lozano,
2008; Cortes and Pan, 2016a) might thus partly explain the observed slow-down of the
convergence of the gender pay gap in the 2000s and 2010s (Cha and Weeden, 2014).
3
Female occupations tend to pay less than male occupations with similar measured characteristics
(Blau and Kahn, 2017).
10As university affiliation and college majors determine graduates’ occupations and given
the heterogeneity in college majors’ monetary returns, racial and gender segregation into
particular universities and fields of study are expected to contribute to female-male and
black-white wage gaps.
Hinrichs (2015) finds evidence of decreasing racial segregation in higher education from
the 1960s to the 2010s, driven by the declining share of blacks attending historically black
colleges/universities. Although there is considerable similarity in choice of college major
across racial groups, there also seem to be important differences. For example in 2012/13,
business administration, psychology, nursing, and biology were four of the top five majors
for both black and white students, but social work appeared on the list only for black
students. Moreover, elementary education, history, and marketing feature only on white
students’ list of top-ten majors (Hinrichs, 2015).
Similarly, differences in choice of college majors between male and female students have
been estimated to partly account for their different educational and career choices (Xie
and Shauman, 2003; Barres, 2006) as well as a sizable portion of observed gender wage
gaps (Paglin and Rufolo, 1990; Brown and Corcoran, 1997). The relation between gender
gaps in college major choice and wages is due to the statistical observation that female
graduates tend to study fields with low-returns whereas male graduates study high-return
fields, such as STEM disciplines. As one example, in 1999-2000, 13% of female recipients
of bachelor’s degrees in the U.S. majored in education, compared to 4% of male grad-
uates, and only 2% of women majored in engineering, compared to 12% of men (2001
Baccalaureate and Beyond Longitudinal Study).
Finally, among other factors that contribute to racial and gender wage gaps, we point
out the four mechanisms discussed in Daly, Hobijn, and Pedtke (2020): early-career wage
gaps, same-job wage increases, job switching, and job transitions. First, the authors note
that, on average, black workers, particularly men, initiate their careers at lower wage
levels than white workers with similar education levels. Second, although wage gains
over the years are similar for white and black individuals who remain at the same job
(though lower for black women), blacks tend to switch jobs more often, and wage increases
are lower for black, male job switchers, relative to job stayers in the same demographic.
Finally, on average, black workers have more frequent and longer spells of part-time work
or non-employment, which presumably hamper their career wage growth.
2.3 Intersectionality: race, masculinity, and femininity
In addition to the aforementioned drivers of race and gender wage gaps, racist and sexist
preferences lead employers to discriminate against white and black men and women on
the basis of taste (Charles and Guryan, 2008; Charles, Guryan, and Pan, 2008). These
gendered and racial preferences might be partly connected to employers’ normative views
of masculinity and femininity, which dictate how each of these four demographic groups
should behave at the workplace.
There is wide agreement in the literature that black men and women are judged as more
masculine than same-sex white individuals. Sex categorization errors are more com-
11mon for black women than other race/sex combinations, meaning that black female faces
are more likely than white women to be classified as men (Goff, Thomas, and Jackson,
2008). Black women are also more likely to be stereotyped as louder, ruder and more
talkative, aggressive, and argumentative than white women by non-Latino whites (Rasin-
ski and Czopp, 2010; Weitz and Gordon, 1993). The latter are more often stereotyped
as “sensitive”, “independent”, or “family-oriented” (Donovan, 2011), which are aligned
with normative views of femininity in the U.S. Indeed, ideals of femininity4 center on
adherence to norms of innocence, purity, and virtue that are historically associated with
whiteness (Goff Thomas, and Jackson, 2008; Hampton et al., 2008; Harris-Perry, 2011;
Kulig Cullen, 2017), and which are in stark opposition to the stereotypes of criminality
and threat more often assigned to black women (Thiem et al., 2019).
The dominant stereotypical images of black women outlined in the literature that are
marked by traits stereotypically associated with white femininity - submission, house-
work, and dependency - are also associated with welfare dependency: the mammy (obese,
of dark complexion, and submissive); the jezebel (highly sexual, prone to unfaithfulness,
illegitimate children, and welfare dependency (hooks, 1992)); and, more recently, the wel-
fare queen (living alone with her children, who does not wish to work and is content living
off the state (Collins, 1990)). The one stereotypical image of black women pointed out by
scholars that is not prone to welfare dependency and may thus be seen in the workforce
- the “Sapphire” (sassy, bossy, loud, and opinionated) - is also described as possessing
positive masculine characteristics that become negative when in excess (e.g., sassiness as
exaggerated pride and straightforwardness; bossiness as exaggerated leadership) and that
gender-normative employers are thus unlikely to seek in female workers.
In interview studies (e.g., Smith et al. (2019), interviewing senior-level black female man-
agers and executives in different industries), whereas women, regardless of race, mention
several difficulties to their career success, black female interviewees frequently mention
additional struggles that are related to intersecting gender and racial stereotyping (Coc-
chiara et al., 2006; Giscombe and Mattis, 2002). For example, they discuss being stereo-
typed as “the angry black woman” and as intellectually inferior at work as well as the
prevention/coping mechanisms they adopt to mitigate the effects of stereotyping on their
careers, besides code-switching, which blacks in general employ in the work-place (Dickens
Chavez, 2018). They also report feeling less integrated in their workplaces, having their
authority questioned, despite holding senior positions, and report exclusion from informal
networks, a lack of sponsors and role models of the same racial group, as well as a lack of
high-visibility assignments to a greater extent than other female workers (Holder et al.,
2015).
Moreover, multiple experiments show that college students tend to attribute black women’s
anger to internal rather than external factors and evaluate their performance and leader-
ship capabilities less favorably (e.g., Motro et al., 2021), attitudes that are likely applicable
to employers too.
By contrast, although in interview studies black men report being punished for mis-
takes and having to meet higher expectations than white coworkers, they also report
greater support from other black workers and using visibility as the only or one of the
4
Eurocentric ideals of feminine beauty are also biased against black women Shaw (2005).
12few black workers in their establishment as an opportunity to showcase achievements and
professional capabilities, an advantage that black women might not enjoy (Wingfield and
Wingfield, 2014).
3 Mathematical and Empirical Frameworks
The methodology in this paper is adapted from Charles, Guryan, and Pan (2018).
Following these authors’ framework, let b ∈ {b1 , b2 , ..., b50 } denote the birth state of Amer-
ican men and women who have migrated within the U.S. and let w ∈ {w1 , w2 , ..., w50 }
denote their state of work. Moreover, assume that men and women’s occupational choice
is determined by the following factors: θb , individual productive traits such as work ex-
perience and skills; ϕw , characteristics of the labor market in their state of work, such as
unemployment rates; and racist and sexist norms in their states of birth and work. For
the purposes of this analysis, “migrant Americans” are individuals who work in a state
that is different from their state of birth, regardless of whether their state of work is also
their state of residence.
In this framework, the prevalence of sexist and racist beliefs among individuals in migrant
Americans’ state of birth and work affect their occupational choice through two channels:
background social norms and social norms in their state of work and market discrimi-
nation. Background social norms, Nb , refer to the sets of gendered social and cultural
conventions prevalent in migrants’ state of birth, which they presumably assimilate and
emulate during their formative years and thus continue to affect their traits, preferences,
and choices throughout adult life. Moreover, background social norms can lead individ-
uals and institutions in American migrants’ state of birth to provide blacks and whites,
boys and girls with different resources and education or cause them to make different
human capital investments in their youth, which affects their choice of field of study and
consequently occupation in their adulthood, as individuals tend to choose to specialize
in fields of study and jobs that match their skills. In turn, social norms in one’s state of
work, Nw , are beliefs, conventions, and practices in the labor and economic markets in
which American migrants’ currently work. Lastly, labor market gender and/or racial dis-
crimination, Dw , is the amount by which other individuals’ actions reduce the economic
returns that an adult American of a given gender and/or race receives from market ac-
tivity, relative to those of another individual, identical in every aspect but gender and/or
race, in the same occupation.
The aforementioned social norms and labor market discrimination are functions of sexist
and racist beliefs in the relevant locations and markets, among other factors. Denoting
sexist and racist beliefs in American migrants’ state of birth (work) by Sb and Rb (Sw and
Rw ), respectively, and the mean overall sexism and racism in their state of birth (work)
by S̄b and R̄b (S¯w and R¯w ), respectively, we thus have that:
Nb = δ b S̄b + ω b R̄b + ϵ1 (1)
13Nw = δ r S¯w + ω r R¯w + ϵ2 (2)
Dw = β s S¯w + β r R¯w + ϵ3 (3)
where ϵ1 , ϵ2 , ϵ3 are error terms with mean zero, capturing all factors unrelated to sexism
that determine state-level social norms and market discrimination. In this paper, we will
refer to sexism and racism in one’s state of birth (work) as background (market) sexism
or racism.
The above equations express that prevailing sexist and racist beliefs in American migrants’
state of birth (background sexism) affects their current occupational choice because such
social norms have permanent or long-lasting effects in their latent traits, preferences, and
skills. The parameter δ b (ω b ) thus captures the effect of mean background sexism on
migrants’ current occupational choices. Similarly, market sexism and racism encompass
sexist and racist social norms as well as employer, coworker, and customer discrimination
to which migrant Americans are exposed in their state of work. The effect of market
sexism and racism on individuals’ occupational choice is thus captured by the parameters
δ w and β s (ω w and β r ).
The hourly wages of an American migrant i, Yi , are given by the sum of θb and ϕw ,
which measure the gender and race-neutral factors of individual human capital and state-
level labor market characteristics respectively, as well as gender and racial norms and
discrimination. We thus have:
Yi = Dw + Nw + Nb + θb + ϕw (4)
or, substituting from the equations that describe social norms and market discrimination
as functions of sexism and racism:
Yi = (β s + δ w )S¯w + δ b S̄b + (β r + ω w )R¯w + ω b R̄b + θb + ϕw + ϵ (5)
where ϵ is a random, mean-zero error term.
In this paper, we estimate the parameters δ w , δ b , ω w , ω b , β s and β r .
3.1 Background sexism
We begin by estimating the following variations of equation 5, using OLS regressions:
bw
yt,g = δsb (gender × S̄b ) + ωsb (gender × R̄b ) + stateG,w + θb + ϵ (6)
14bw
yt,r = δrb (race × S̄b ) + ωrb (race × R̄b ) + stateR,w + τb + ϵ (7)
where stateG,w is a vector of state of work and gender-specific state of work fixed effects;
stateR,w is a vector of state of work and race-specific state of work fixed effects. The
vector of personal attributes θb includes the controls: region of birth and gender-specific
year effects; the vector of personal attributes τb includes the controls: region of birth and
race-specific year effects. Gender and race in this paper are restricted to man/woman and
black/white.
Equation 6 is estimated when our analysis is restricted to blacks or whites, and equation
7 is estimated when our sample is restricted to men or women. The outcomes studied
are mean log hourly wages (residualized of age and highest education level achieved and
at the state of birth-state of work pair level) for individuals of gender g (race r), who
bw bw
migrate from state b to state w in year t, yt,g (yt,r ).
Background sexism and racism potentially changes the human capital investments made
into boys and girls, blacks and whites, during their formative years and are, therefore,
likely to be correlated with American migrants’ years of schooling. To avoid issues of
multicolinearity, the baseline OLS models in equations ?? and 7 does not control for edu-
cation. Under this specification, the estimated coefficient on our background sexism index
captures the effect that sexist and racist beliefs in Americans’ state of birth have on their
hourly wages due to their influence on these Americans’ early education. The regressions
that estimate the effects of residential sexism on occupational choice, however, control for
highest education level achieved, since individuals’ school attendance is determined prior
to their exposure to market sexism and racism and likely influences their hourly wages in
ways that are not related to prejudices in their labor markets.
3.2 Residential sexism
Importantly, American individuals who work in a state different from their birth state
different state select the level of market sexism and racism to which they become exposed
directly or indirectly, by selecting other labor market characteristics that are correlated
with sexist and racist beliefs in their state of work. For this reason, market sexism and
racism are likely to be endogenous in an OLS regression with specifications given by
equations similar to 6 or 7, which would yield a relationship between market prejudices
and wages that reflects the fact that migrants with certain traits θb , ϕw are more likely
to willingly choose to work in that market, in addition to the desired causal effect of
prejudices on wages. To avoid capturing the effects of systematic sorting, we use Two
Stage Least Squares (TSLS) regressions.
Following Charles, Guryan, and Pan (2018), we assume that the level of market sexism
and racism chosen by migrants born in state b are a function of the background sexism
Sb and racism Rb they experienced during their formative years and the fixed costs of
migration that all migrants incur when moving between locations that can be written as:
15S̄w (b) = γ s S̄b + ρs1 Z1s (∆bj ) + ρs2 Z2s (κbj ) + θb + ϕw + ξbw
s
(8)
R̄w (b) = γ r R̄b + ρr1 Z1r (∆bj ) + ρr2 Z2r (κbj ) + θb + ϕw + ξbw
r
(9)
where the ξbw terms are random error terms. The terms Z1 , Z2 are functions that depend
on fixed costs of migration and are the source of exogenous variation in the TSLS analysis.
The variable ∆bj is a vector measuring the distance between the population weighted
centroid of migrants’ birth state b and that of every other state. The relative distance
between states is a fixed migration cost which reflects the fact that it is more difficult to
work in a state that is farther from one’s birth state, regardless of one’s individual traits
or the characteristics of the two labor markets (Boustan et al. (2010); Ortega and Peri
(2014)). The term Z1s (∆bj ) (Z1r (∆bj )) in equation 8 (9) is thus the fraction of the expected
market sexism (racism) for migrants moving from state b to state r that is attributable
to the exogenous geographical distance between U.S. states.
The portion of migrants’ chosen levels of sexism and racism in their place of work that is
attributable to exogenous geographical distance between U.S. states is given by:
X S¯j
Z1s (∆bj ) = (10)
b̸=j
∆bj
and
X R̄j
Z1r (∆bj ) = (11)
b̸=j
∆bj
To assess the validity of this assumption, we plot a histogram of the distances between
the states of birth and work of Americans who work in a different state from where they
were born in Figure 1. The histogram shows that internal American migrants tend to
work in states geographically closer to their state of birth, confirming our hypothesis that
inter-state physical distance is a fixed cost of internal migration in the U.S. As expected,
inter-state migration does not decline perfectly with distance between birth and work
states, because factors other than inter-state distance determine migrants’ choice of labor
market.
The variable κbj is the share of individuals born in state b who worked in a different state
j in 1990, the first year included in this paper’s data analysis, and thus a proxy for the
pre-existing migrant enclaves that individuals in our analysis encountered in their places
of work.
Existing literature on international migration shows that the “enclaves” formed by previ-
ous migrants from a given initial location affect the ease with which later generations of
migrants settle in that destination, as it is easier to migrate to places where one will meet
16Figure 1: Distance between States of Work and States of Birth Among U.S. Internal
Migrants
others from one’s place of origin. We thus assume that migrant enclaves represent a sec-
ond fixed cost of migration and that historical patterns of migration between U.S. states
are a plausible exogenous determinant of the state internal American migrants choose to
as their place of work.
The portion of migrants’ chosen levels of sexism and racism in their place of work that is
attributable to exogenous historical migration is given by:
X
Z2s (κbj ) = S¯j × κbj (12)
b̸=j
and
X
Z2r (κbj ) = R̄j × κbj (13)
b̸=j
In sum, equation 8 (9) is the first-stage regression for the endogenous variable Sw¯(b)
(Rw¯(b)), where Z1s (∆bj ) and Z2s (κbj ) (Z1r (∆bj ) and Z2r (κbj )) are instrumental variables
whose contribution to migrants’ chosen level of sexism (racism) in their labor market is
captured by ρs1 and ρs2 (ρr1 and ρr2 ). The TSLS regression performed on the system of
equations given by 5, 8-12 allows us to estimate the effect of market sexism and racism
on occupational choice, given by β s + δ w and β r + ω w in equation 5.
174 Data and descriptive statistics
4.1 American Community Survey
We study hourly wages over the period from 1990 and every year from 2000 to 20195 ,
using data from the American Community Survey (ACS). The analysis is restricted to
American citizens who work in a state different from their state of birth, which are referred
to as “internal migrants” or simply “migrants” throughout the paper, between the ages
of 25 and 64 years old, to exclude individuals who are pursuing an educational degree or
retired. Each of our regression models further restricts the analysis by gender or race, as
mentioned in the previous section and will be clear in the tables of results.
Moreover, rather than study individual outcomes, we collapse the more than 7 million
individual observations on adult migrants to (state of birth x state of work x gender x
race x year) cells, yielding between around 16,000 to 25,000 observations to be used in the
regression analysis, depending on demographic group, once empty cells are dropped. The
main labor market outcome studied in this paper is thus mean log hourly wages, at the
(state of birth x state of work x gender x race x year) cell level. The reasoning behind this
choice was that, if we had chosen to analyze individual-level outcomes, the pairs of U.S.
states in our original ACS sample with relatively large populations and/or more traffic
of migrant workers would disproportionately affect our results. By working with state of
birth-state of work pairs, we do not weigh pairs with high migrant mobility too heavily
in the analysis.
Table 1 presents cross-state summary statistics of mean yearly6 wages at the (state of
birth, state of work, gender, race) cell level and the equivalent residual means for each of
the demographic groups of interest to this paper: white women, black women, white men,
and black men, for which data on wages is available in the ACS. Residual yearly wages
control for gender and race-specific year effects, state fixed effects, age, and highest level
of education attained.
The highest to lowest mean yearly wages in our sample are those of: white men, black
men, white women and black women, and there is considerably more variation in the
yearly wages of black men, relative to other groups. The difference between the mean
yearly wages of white and black women (men) over the entire time period we study was
4,215 (13,816) dollars, and the difference between the mean yearly wages of white (black)
women and men was 19,873 (7,879) dollars, corroborating the usual observation that the
white-black wage gap tends to be larger for men.
Notably, after we control for gender and race-specific year effects, state fixed effects, age,
and higher level of education attained, we observe that, whereas the mean yearly wages
of women do not significantly change, white and black men’s mean yearly wages become
very similar to the mean yearly wages of women in their racial group. We may thus
conclude that gender wage gaps within racial groups may be driven by the schooling and
5
Even though ACS data is available for the year 2020, we do not include it in our sample to avoid
considerations of how the COVID-19 pandemic affected hourly wages or the ways in which sexism and
racism influence workers’ wages.
6
Future drafts of this paper will present summary statistics on hourly wages instead.
18Table 1: Summary Statistics on Yearly Wages and Residual Yearly
Wages, by Gender and Race
White women Black women White men Black men
Mean yearly wages
Mean 41,531 39,709 61,404 47,588
SD 24,295 26,513 32,170 42,544
Regression-adjusted mean yearly wages
Mean 43,151 38,936 42,125 36,474
SD 12,999 15,550 13,194 15,607
Observations 16,135 7,708 16,333 8,086
The table reports summary statistics using the sample of whites and blacks,
aged 25 to 64, from the 1990 and 2000-2019 waves of the ACS. Regression-
adjusted results control for gender and race-specific year effects, state fixed
effects, age, and higher level of education attained. The sexism index was
normalized to have a mean of 0 and standard deviation 1 across the 44
states for which the index was constructed from GSS data; the racist index
was constructed similarly from GSS data and is available for all 50 states.
resources that men receive, possibly by virtue of background and market sexism.
4.2 Racism index
The state-level index of racial prejudice used in this paper was kindly shared by the authors
of Charles and Guryan (2008). These authors constructed it using respondents’ answers
to 21 questions of the General Social Survey (GSS) on multiple waves of the questionnaire
(1972–2004) that captured several dimensions of racial animus, among which: (1) “Do you
think there should be laws against marriages between blacks and whites?”; (2) questions
about whether one would object to sending one’s kids to a school that had few/half/mostly
black students; (3) “If your party nominated a black for president, would you vote for
him if he were qualified for the job?”; and (4) “Agree? White people have the right to
keep black people out of their neighborhoods and blacks should respect that right?”7
The authors restricted the data to answers from whites aged 18 and older and re-coded
responses so that higher values corresponded to higher levels of prejudice. They then
normalized the prejudice measure by subtracting off from individual responses to each
GSS question the mean of the responses to that question in 1977 (the year in which the
largest number of prejudice questions were asked) and divided by the standard deviation
of answers measured in the first year the question was asked. For questions not asked in
1977, a linearly interpolated mean was subtracted from respondents’ answers, instead of
the mean in 1977.
Formally, let dit denote respondent i’s response in year t to prejudice question k. For
7
See the appendix of Charles and Guryan (2008) for the full list of questions used to construct the
index.
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