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# Making the Case for Racial Mobility

Saperstein Aliya

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Saperstein Aliya. Making the Case for Racial Mobility. LIEPP Working Paper, 2017, 69. hal-01592895

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LABORATOIRE INTERDISCIPLINAIRE
D'ÉVALUATION DES POLITIQUES PUBLIQUES

LIEPP Working Paper

September 2017, n°69

Making the Case for Racial Mobility

Aliya Saperstein

Stanford University

asaper@stanford.edu

www.sciencespo.fr/liepp

© 2017 by the author. All rights reserved.

How to cite this publication :

Saperstein Aliya, Making the Case for Racial Mobility, Sciences Po LIEPP Working Paper n°69, 2017-08-31.
# Making the Case for Racial Mobility*

Aliya Saperstein

By definition, in order to study social mobility one needs to focus on characteristics that can change. Traditionally, social scientists have focused on class mobility or geographic mobility rather than mobility along other axes of social inequality, such as race or gender, because the latter characteristics are typically treated as fixed.<fnref n="1" /> Indeed, to many, the very idea of racial mobility will seem like an oxymoron. If race is a characteristic one inherits – we are what we are because of what our biological parents were before us, and their biological parents were before them, and so on – then race can be ascribed at birth and would remain fixed throughout one’s lifetime. A person’s race would never change because there is no way to change one’s biological parents. This is the commonsense understanding of race in the United States, and it has been for several hundred years.

Yet, as many scholars have argued, people do not simply inherit their race from their parents, in part because clear-cut and consistent categorical differences are not inscribed in our genes.<fnref n="2" /> The idea that distinct races were and are present among the human species is instead a social invention and, even from a sociopolitical standpoint, there is little agreement about the criteria for dividing one “race” from another, let alone the names and numbers of relevant racial ca-

<footnote>* This paper is part of a book manuscript in preparation for the Russell Sage Foundation.</footnote>

<footnote n="1">¹ See, e.g., Arminio (2010), Persell and Gerdes (2008). Interestingly, neither Linton (1936) nor Parsons (1940), who popularized the distinction between ascribed and achieved characteristics in the study of stratification, list “race” as an example of an ascribed characteristic. Parsons (1940: 848) seemed to think of ascribed and achieved characteristics as the ideal typical ends of a spectrum: “Concrete qualities range all the way from certain basic things altogether beyond personal control, such as the facts of sex and age, to those which are mainly achievements.” See, also, Parsons (1964) for his revised approach to the study of stratification.</footnote>

<footnote n="2">² Some genetic traits cluster along geographic lines more than others (e.g., sickle cell anemia), but even these traits – and their associated alleles – are not limited to a single population that also maps directly on to a widely recognized racial category. Indeed, very few alleles are “private,” meaning they are found in only one specific population and no others. See, e.g., Rosenberg (2011) and Feldman and Koenig in Markus and Moya (2010). Although some have claimed that self-identified race can be useful in predicting health and disease, in terms of genetics, it is a best a proxy (for geographic ancestry) of a proxy (for a given allele) for any such outcomes.</footnote>
tegories. Within the same country, ideas about who (or what) should
be counted when people are classified by race not only change over
time but also often differ by region. For example, in the United
States, when laws banning interracial marriage were common, who
was to be kept from marrying whom differed from one state to the
next.<fnref n="3" /> Although there may have been widespread agreement at the
end of the 19th century that “whites” and “Negros” should not wed,
there were varying opinions about at what point someone with both
“white” and “Negro” ancestry stopped being on one side of the sup-
posed racial divide and was assumed to belong on the other. Thus, in
both a social and a legal sense, one’s race could change when cros-
sing state lines or country borders.

If researchers believe this and other evidence that it is possible for a
person's race to change – and, perhaps more importantly, that such
changes are likely to be related to their experience of social inequali-
ty – then what remains to be done is to make the potential for racial
mobility explicit in how we study processes of racial categorization
and stratification. Current methods of accounting for race in most
social scientific research treat it as a fixed input, an exogenous va-
riable that comes from outside the stratification system to shape indi-
vidual life chances. Rather than treating race in this way, as a static
characteristic analogous to a person's year of birth, I argue race
should be studied the same way social scientists study occupational
sorting or marital transitions – as a system of status categories people
can move into and out of at different points in their lives, which also
have implications for their mobility along other dimensions of social
status. This does not mean that everyone's race is subject to change.
Some people will remain single their entire lives, or will remain in
the same social class, and sometimes even the same occupation, as
their parents; that does not keep us from considering social class,
occupation, and marital status to be characteristics that can change

<footnote n="3">³ See Pascoe (2009), Jenks (1916).</footnote>
over the course of a person's life. So, too, should be the case with race.

Put another way, racial mobility does not have to be widespread to be worthy of study. It simply has to help us better understand the relationship between race and inequality. A racial mobility perspective does that by treating racial categorization not as exogenous but *endogenous* to the American stratification system. How you are categorized by race can shape a whole host of experiences including where you live, where you go to school, and whom you date or marry, to how much you earn, and how often you get stopped by the police. Those experiences will, in turn, shape how you identify and how other people are likely to classify you by race in the future. This conceptual shift not only recognizes that a person's race is not necessarily fixed, it also allows race to be both a determinant and a consequence of life chances.

# I. The analogy to social mobility

The key empirical ingredient from a racial mobility perspective is to have measures of race for the same people from at least two different points in time. Instead of assuming everyone has a single fixed race (or multiple fixed races), some people will have races that do not change, while others will have different single races, or switch back and forth between single and multiple races, over the course of their lives. The aim of a racial mobility perspective is to understand not only whose race is likely to change and whose is not, but also to understand when, and in what direction, changes in racial categorization are most likely to occur.

Scholars of social mobility in the classic sense of occupational or educational attainment have similar aims. Thus, rather than invent new terminology or empirical tools for the purposes of studying racial mobility, I propose borrowing concepts and tools from research
on social mobility in the hopes that using them will help shed new light on processes of racial categorization and inequality. In doing so, I do not mean to imply that the analogy between racial and social mobility is a perfect one, or that empirical approaches to studying social mobility do not come with their own set of limitations.<fnref n="4" /> My hope is that these tools can provide a productive shift in perspective that, like the twist of a kaleidoscope, will help to reveal new patterns and relationships, and advance our understanding of both the social construction of race and the dynamic processes that underlie persistent racial inequality in the United States and elsewhere.

## *Racial origins and racial destinations*

Sociological studies of social mobility begin by considering people's social origins, their “circumstances of early life,” which can include everything from their parents' education and their family structure to the conditions in the neighborhood where they grew up.<fnref n="5" /> Mobility scholars are interested in the extent to which this package of individual, familial, and contextual attributes either facilitates or constrains people's opportunities and life outcomes, shaping their social destinations. Social mobility is high when destinations are only loosely coupled to a person's origins, and both upward and downward mobility are possible. Social mobility is low when people end up in the same class or strata they started in, with little chance to move up – or down – the social ladder. In addition to studying why certain individuals are more likely to experience social mobility than others at a given point in time, trends in levels of mobility in a society also can be studied over time, and societies can be compared to one another, to understand what predicts the relative stability or fluidity of their class structure.<fnref n="6" />

<footnote n="4">⁴ See, e.g., Swift (2004), Hout (2015).</footnote>

<footnote n="5">⁵ Hout (2015:28).</footnote>

<footnote n="6">⁶ The classic studies of social mobility, among men, in the United States are Blau and Duncan (1967) and Featherman and Hauser (1978). For a more contemporary study that does not exclude women, see</footnote>
Now imagine a similar approach that focuses on changes in racial categorization. Racial mobility would be low when people are consistently classified in the same category over the course of their life, and high when measures of race from early in life differ from those in later in life even for the same people. Much like with social mobility in terms of class, some racial origins would facilitate mobility while others would constrain it.<fnref n="7" /> Racial mobility in general also might be more common under certain political or economic conditions, or in some places and times, relative to others.<fnref n="8" /> Overall patterns of mobility would vary by the classification scheme employed, and the relative levels of upward and downward mobility would depend in part on how one chose to rank the relevant categories – just as they do with occupation or class categories instead of race categories.<fnref n="9" /> All of the promise, and the pitfalls, of studying class mobility would apply to studying racial mobility, as well.

To document individual racial mobility, one could take starting out and ending up in a racial category literally, and compare how people are classified on their birth and their death certificates. When John Hahn and colleagues first did this comparison in the early 1990s they found that nearly 4 percent of racial classifications did not match between birth and death, even for a cohort of U.S. infants who died before they were one year old.<fnref n="10" /> Alternatively, to study intergenerational mobility, the measure of *origins* could be how one’s parents identified by race, with the *destinations* representing how one racial-ly identified in adulthood. In general, for intragenerational studies,

<footnote>Beller (2009). See also Campbell (1983), Ganzeboom et al. (1991), Erickson and Goldthorpe (2002) and van Leewen and Maas (2010) for reviews.</footnote>

<footnote n="7">⁷ See, e.g., Waters (1990) on how some Americans have more “ethnic options” than others in terms of claiming particular identities.</footnote>

<footnote n="8">⁸ c.f. Wimmer (2008)</footnote>

<footnote n="9">⁹ For debates on how to characterize class structure and rankings, see Erickson and Goldthorpe (1992) and Wright (2005).</footnote>

<footnote n="10">¹⁰ Hahn et al (1992). They found inconsistent racial classification between birth and death ranged from 1.2 percent among whites at birth to greater than 50 percent among infants classified as Filipino or Japanese at birth. However, Hahn and colleagues were not interested in racial mobility *per se*; they were concerned primarily with what they saw as issues of data quality for identifying racial health disparities.</footnote>
racial origins and destinations can be any two comparisons for the
same measure of race at different points in time, whether self-
identified or as classified by others.<fnref n="11" />

Available data

Survey data that includes repeated measures of race for the same
individuals is not (yet) common in the United States, but it also is not
as hard to come by as one might think – particularly given the stan-
dard assumption that a person’s race does not, and should not,
change over time. Many longitudinal studies that follow the same
people year after year, or across multiple survey waves, collect racial
information at multiple points in time. This data might not always be
included in public-use datasets, discussed in research briefs, or be
clearly highlighted in user’s manuals and codebooks, but it does
exist.

I illustrate many of my points using data from the 1979 National Longitudinal Survey of Youth (NLSY), which includes the race of respondents, as recorded by the survey interviewers, each time the respondent was interviewed from 1979 to 1998. For many respondents, this yields a series of 17 racial classifications, the longest string for the same individuals that I have found to date. Other data, such as linked census samples and the more recent National Longitudinal Study of Adolescent to Adult Health (which includes up to three racial classifications at present), also provide promising opportunities for studying racial mobility with existing resources.

There are limitations to using survey data that was not collected with
the study of racial mobility in mind. The biggest concern for resear-
chers is when repeated measures of race exist because there was a
need to change how racial information was collected. This makes it

<footnote n="11">¹¹ Studies of racial mobility could also examine changes in reflected appraisals, or how people think others classify them. For a discussion of different dimensions, or measures of race, and their utility for studying discrimination and inequality see Roth (2010) and Saperstein, Kizer, and Penner (2016).</footnote>
difficult to distinguish a “real” change in racial categorization from
the change in the measure, a cloud that hangs over many studies of
self-identification that use existing data.<fnref n="12" /> A similar problem can
occur when studies link administrative records to each other, or to
other survey data, and each data source had a different way of recor-
ding race.<fnref n="13" /> Some changes or differences in how racial categoriza-
tions were collected, such as adding or subtracting categories, can be
seen as an opportunity to study “structural” features of racial mobili-
ty, as I discuss in detail below. However, I generally limit my ana-
lyses to repeated measures of race for which none of the key features
of data collection changed – neither the numbers of the categories,
nor their names, or any instructions that might have been provided. I
do so in the hopes of providing the strongest evidence available that
racial mobility is a real feature of the experience of race in the United
States.

The mobility table

With a conceptual distinction between racial origins and destinations, and data that has repeated measures of race in hand, researchers still need methods to both display and analyze the resulting patterns of stability and change. The simplest descriptive tool for comparing social origin and destination categories is called a *mobility table*. The rows of the cross-tabulation reflect the person's *origin*, while the columns reflect the person's *destination*. The cells that fall along the main diagonal of the mobility table represent stability: people whose origins and destinations are one and the same. The cells that fall off the main diagonal represent mobility: people whose origins and des-

<footnote n="12">¹² See, e.g., Liebler et al. (2014), Saperstein and Penner (2012). Many such studies frame differences in data collection as an opportunity to study how the survey mode or context (e.g., Harris and Sim 2002), or changes in the available categories (Brown, Hitlin and Elder 2007), affect reporting. But the fact remains that there are few if any published studies of changes in racial self-identification where the measurement of race itself remained stable over time.</footnote>

<footnote n="13">¹³ This could happen, for example, if a survey recorded race through self-identification and it was matched to hospital intake data where race was recorded by a combination of self-identification and classification by a nurse or other member of the hospital staff.</footnote>
tinations differ. In classic studies of social mobility, such tables
might compare occupational categories describing people’s first jobs
and their current jobs to determine whether they had experienced any
mobility since they started working and, if so, whether their career
trajectory suggested they were heading up or down the socioecono-
mic ladder.<fnref n="14" />

The same type of table can be used with repeated measures of race to
answer similar questions about racial mobility.<fnref n="15" /> For example, if so-
meone were classified as white in a particular year, how likely are
they to be classified as white again the next year? Table 1 sum-
marizes paired comparisons from the NLSY data on racial classifica-
tion to answer this question and help visualize what patterns of racial
mobility looked like in the United States in the 1980s and 1990s. The
rows represent the previous year's classification, or the person's ra-
cial origin, and the columns represent the current year's classifica-
tion, or the person's racial destination. With a measure of race at just
two points in time, the frequencies in the table would be based on the
numbers of persons in our study; because the NLSY contains as ma-
ny as 17 years of classification data, the frequencies are based on
person-years instead of persons. So, a respondent who was inter-
viewed in 10 different years is counted in this table 10 times, once
for each year they participated in the survey.

If race were a fixed characteristic, then we would expect to see all
the observations along the main diagonal of a racial mobility table –
indicating that there was no mobility. Instead, in Table 1, there are
cases that fall off the main diagonal, which suggests, at minimum,
that there is not complete unanimity about who is a member of which

<footnote n="14"><sup>14</sup> See Hout (1983) for more details on methods of analysis using mobility tables.</footnote>

<footnote n="15">¹⁵ Demographers and others familiar with transition matrices might prefer to use them to represent and project probabilities of classification and the resulting population distributions. See, e.g., Van de Gaer et al. (2001); also, Caswell (1989) on matrix population models in general and Montgomery (2011) for an application of demographic population projection that includes probabilities of racial “switching” in black-white-mulatto classification systems.</footnote>
racial category in the United States. There are two other important
points to note from this racial mobility table:

1) all the cells in the table have non-zero values,
which means that changes in racial classification
occur in all possible directions

2) some patterns of mobility are more common than others

Reading across the rows of Table 1 indicates that, on average over
the course of the survey, people who were classified as white in one
year were classified as white the next year 96 percent of the time.
When they were not classified as white again the next year, they
were most likely to be classified as “other.” The reverse was also
true: people who were classified as “other” in one year but not classi-
fied as “other” the next year, were most likely to have their classifi-
cation changed to white. People who were classified as black were
the most likely to be consistently classified from one year to the next
(98 percent), but when their classification did change it was more
likely to change to white (1.3 percent) than to “other” (.5 percent).
Put another way, on average, more than 1 percent of current blacks
had white origins (554 of 43,309) and 0.5 percent of current whites
had black origins (553 of 108,315).

Studies of occupational mobility tend to find that mobility between a
given origin and destination is more likely when it represents a “short
distance” move between categories that are more proximate in the
overall hierarchy.<fnref n="16" /> Other transitions are rare, or non-existent, when
the formal and informal qualifications for being a member of one
category compared to the other are most distinct – as in the unlikely
prospect of rising from seasonal farm work to becoming the Chief
Executive Officer of a Fortune 500 company. In terms of racial mo-
bility, Table 1 suggests there is considerable overlap between percep-
tions of who is “white” and who is “other,” which might be taken to

<footnote n="16">¹⁶ Blau and Duncan (1967), Featherman and Hauser (1978).</footnote>
imply that those two categories are closer in the racial hierarchy than “white” and “black.” However, transitions between “black” and “white” also are more common than transitions between “black” and “other,” which complicates inferences about the potential ordering of the categories.

<table><caption>Table 1. Racial classification mobility in the 1979 NLSY</caption><caption>Racial destination</caption><thead><tr><th></th><th>White</th><th>Other</th><th>Black</th><th>Total</th></tr></thead><tbody><tr><th scope="row">Racial origin</th><td></td><td></td><td></td><td></td></tr><tr><th scope="row">White</th><td>103,721<br>96%</td><td>4,036<br>3.7%</td><td>554<br>0.5%</td><td>108,311<br>100%</td></tr><tr><th scope="row">Other</th><td>4,041<br>45%</td><td>4,708<br>53%</td><td>205<br>2.3%</td><td>8,954<br>100%</td></tr><tr><th scope="row">Black</th><td>553<br>1.3%</td><td>199<br>0.5%</td><td>42,550<br>98%</td><td>43,302<br>100%</td></tr></tbody><tfoot><tr><th scope="row">Total</th><td>108,315<br>67%</td><td>8,943<br>6%</td><td>43,309<br>27%</td><td>160,567<br>100%</td></tr></tfoot></table>

Source: 1979 National Longitudinal Survey of Youth.
In addition to revealing the existence of racial mobility, using the classic tools of social mobility studies, such as a *mobility table*, also invites a deeper engagement with the similarities, differences, and interrelations between racial and social mobility. For example, it could be fruitful to think of racial categorization as analogous to the matching process between employees, employers, and available jobs more generally, except that it is conducted continuously in everyday interactions: each of us has our own idea of where we belong, best fit, or want to end up in the existing system of racial categories, and we strive to be perceived that way by others; at the same time, each racial category has its own formal and informal criteria for membership that some of us will meet to varying degrees based, at least in part, on our social and racial origins. Also, like an occupational career, some people might start out in one racial category and end up in another, but bounce around quite a bit in between, while others experience a clear shift in their racial trajectory following key life transitions such as leaving school, or partnering and starting a family. Thinking through how racial mobility and social mobility might be related further underscores that, from a racial mobility perspective, asking whether race can change and for whom it changes are only the first of many questions worth exploring.

## II. Tracing racial trajectories

A mobility table is best suited to summarizing and comparing the social – or racial – hierarchy at two points in time, and it provides an important first look at whose positions remain stable and whose do not. However, limiting racial mobility to an either/or phenomenon obscures the full picture. With measures of race at more than two points in time, for the same individuals, patterns emerge not only in whose race changes, but also in the direction and timing of those changes. This attention to *racial trajectories* is another key to taking a racial mobility perspective. The goal is not to identify whether race
is fixed or fluid, it is to try to understand when and in what direc-
tion(s) mobility is most likely to occur.

Table 2 illustrates how a focus on the existence of fluidity, by itself,
would miss more complex patterns of racial mobility. Each row in
the table presents the most common racial classification trajectory for
the various levels of fluidity observed in the NLSY. The trajectories
are represented with a letter for each racial classification in each
year: "W" for white, "B" for black and "O" for other. The first row
represents stability: these respondents have 17 Ws because they were
consistently classified as white by NLSY interviewers across 17
years of observations. The remaining rows all present examples of
fluidity, with respondents having anywhere from one to as many as
10 racial classifications that differ from their modal classification.<fnref n="17" />

The example trajectories shown are those that appear most fre-
quently, but they do not necessarily reflect the only pattern of classi-
fication possible for each level of fluidity, and thus should not be
considered representative. There are hundreds of different permuta-
tions that I could have selected to display for many of these cases.
For example, the trajectories OWWWWWWWWWWWWWWWW
and BBBBBWBEBEBEBEBEBEBB each represent a single discrepant
racial classification, but the 11 people in the NLSY who share the
latter trajectory – they are recorded as “black” in every survey year
except 1985, when each of these people was recorded as “white” –
likely have a very different experience of race than the 19 people
who were classified as “other” in the very first year of the survey but
were seen as white every year thereafter. Similarly, the trajectory that
represents four discrepant classifications in Table 2 –
WWWWOOOOOOOOOOOO, four years as “white” at the very
beginning of the survey, followed by 13 years of classification as

<footnote n="17">¹⁷ These trajectories also underscore that focusing solely on “changes” can be misleading depending on how many years of data one has and when differences – or discrepancies – as I have labeled them in Table 2 occur. A discrepant classification at the beginning or the end of a time series represents just one change, while one that occurs in the middle counts as two: a change to the new category, and a change back.</footnote>
"other" – suggests a clear shift in how the respondents were perceived from one racial category to another. However, other classification trajectories with four discrepant years depict different patterns, such as having a few anomalous years sprinkled throughout (WWOWWWWWOWWWWWOWWOW) or exhibiting a period of instability followed by a return to the original classification (BBBBBBWBBOOOBBBBBB).

<table><caption>Table 2. Racial mobility trajectories, by level of fluidity</caption><thead><tr><td>No. of discrepant classifications</td><td>Example racial classification trajectories</td></tr></thead><tbody><tr><td>0</td><td>WWWWWWWWWWWWWWWW</td></tr><tr><td>1</td><td>OWWWWWWWWWWWWWWW</td></tr><tr><td>2</td><td>WWWWWWWWWWWWWWOW</td></tr><tr><td>3</td><td>WWWWWWWWOWWWWWOO</td></tr><tr><td>4</td><td>WWWWOOOOOOOOOOOOO</td></tr><tr><td>5</td><td>WWWOWWWOWWWWOOWOW</td></tr><tr><td>6</td><td>WOOWWWWOOOOWWWWWWO</td></tr><tr><td>7</td><td>OWWWWWWWOOOOOOOWWW</td></tr><tr><td>8</td><td>OWOWWWWWOOOOOOOWWW</td></tr><tr><td>9</td><td>BBOWWWWWWWOOOOOOO</td></tr><tr><td>10</td><td>WWOOOOWWBBBOBOBWO</td></tr></tbody></table>

Source: 1979 National Longitudinal Survey of Youth.
This variation in classification trajectories highlights the importance of moving beyond the simple dichotomy of stability and fluidity in favor of a more complex notion of *racial mobility*.<fnref n="18" /> The patterns of fluidity are not consistent from one example to the next, and are unlikely to be explained by the same factors. When discrepant classifications are few and far between, scattered one year here and another year there, they could reflect “noisy” and relatively random fluctuation, as might be explained by every once in a while being interviewed by an unusual interviewer who has different ideas about race than her colleagues, or by the presence of occasional coding mistakes in the data – as when an interviewer or data coder meant to record 1 for “white” but accidentally entered 3 for “other.” In contrast, there are also patterns that include clear shifts from one classification to another, or a period of fluidity followed by a return to stability. These repeated strings of classifications imply changes that are occurring in a particular direction, and thus are unlikely to be explained by coding mistakes, or the occasional quirky interviewer. Instead, they raise the question: What else might be changing along with these changes in racial classification?

### III. Explaining racial mobility

There are a number of factors identified by previous research, across individual, interactional, and institutional (or structural) levels of analysis, that might help explain patterns of racial mobility. These could range from a person's physical characteristics and family ancestry, to how the interviewer and respondents relate to one another during the interview, to which racial categories are available at a given point in time. Although I tend to focus on individual-level pre-

<footnote n="18">¹⁸ See also Telles and Paschel (2014) on moving beyond “fixed vs. fluid” discussions of race.</footnote>
dictors of racial mobility, it is important to keep insights – and alternative explanations – from other levels of analysis in mind.<fnref n="19" />

Structural mobility

In studies of social mobility, *structural mobility* occurs when there is a change in the overall system of stratification that has implications for social position a given person is likely to occupy. Examples from research on trends in U.S. occupational mobility highlight the decline of farm labor, and later manufacturing, accompanied by the rise of professional and technical service work, as important drivers of upward mobility during the mid-20th Century. As sectors of the economy grow and shrink, one's likelihood of acquiring or retaining a job in a particular sector shift accordingly, sometimes regardless of the relevant skills or experience one might possess. Many children of farmers and factory workers needed to find other avenues for employment over the course of the 20th Century, and increasing numbers of jobs for relatively high-status professionals and managers opened up more opportunities at the top of the occupational hierarchy than had existed in the past.<fnref n="20" />

A similar story can be told about structural racial mobility in places that pursued strategies of “whitening.” A number of countries, from Argentina to Australia, implemented immigration policies – ranging from enticements to restrictions – aimed at increasing their “white” populations during the 20th Century. In many cases, these policies were accompanied by rhetoric of racial uplift that also encouraged intermarriage and assimilation according to “white” ideals.<fnref n="21" /> In part because offspring of mixed unions – the children of parents who were perceived to be of different races – could be classified in the lighter of their parental categories, this produced populations that

<footnote n="19">¹⁹ See Saperstein, Penner and Light (2013).</footnote>

<footnote n="20">²⁰ See, e.g., Featherman and Hauser (1978).</footnote>

<footnote n="21">²¹ See Graham (1990), Wade (1997), and Telles and Sue (2009).</footnote>
became “whiter” over time.<fnref n="22" /> In contrast, the “one-drop rule” in the U.S., according to which anyone with any known African ancestry was to be classified as black, effectively encouraged the opposite scenario: from one generation to the next, more positions were created at the bottom of the racial hierarchy rather than the top.<fnref n="23" />

Other structural changes that affect prospects for both inter- and in-
tragenerational mobility include the creation of entirely new catego-
ries, or the elimination of old ones. The advent of recent jobs such as
computer programmers and IT professionals generates social mobili-
ty as people join those occupations from other sectors, or take those
positions as their first jobs despite having parents in other occupa-
tions. Similarly, adding racial categories to the U.S. census, such as
Native Hawaiian or Other Pacific Islander (in 2000), or removing
categories, such as “Hindu” (after 1940), rearranges the existing po-
pulation into different configurations.<fnref n="24" /> This type of mobility is illus-
trated in Table 3. In its first wave, the National Longitudinal Study of
Adolescent to Adult Health (Add Health) asked interviewers to clas-
sify respondents into five racial categories: “White,” “Black or Afri-
can-American,” “Asian or Pacific Islander,” “American Indian or
Alaska Native,” and “Other.” By Wave 3, when interviewers were
asked to classify respondents again, the survey dropped the category
“Other.” This forced a change in race for everyone who had been
labeled as “Other” initially, creating structural racial mobility regar-
dless of any changes in the characteristics of the respondents
themselves.<fnref n="25" /> Seventy-six percent of Wave 1 “Others” became
“White,” and the overall sample shifted from 63 percent to 68 per-
cent “White.”

<footnote n="22">²² See, e.g., Loveman and Muniz (2007).</footnote>

<footnote n="23">²³ See Davis (2001), Williamson (1995).</footnote>

<footnote n="24">²⁴ See Snipp (2003) for more detail on historical changes in U.S. census racial categories.</footnote>

<footnote n="25">²⁵ Additional mobility might also have been generated by moving the interviewer's classification from the middle of the survey (immediately following the respondent's self-identification) to the end of the survey. See Hitlin, Brown, and Elder (2006).</footnote>
<table><caption>Table 3. Structural racial mobility in Add Health, Waves 1 to 3</caption><thead><tr><th></th><th colspan="4">Destination</th><th></th></tr><tr><th>Origin</th><th>White</th><th>Black</th><th>American Indian</th><th>Asian</th><th>Total</th></tr></thead><tbody><tr><td>White</td><td>9,332<br>98%</td><td>39<br>0.4%</td><td>97<br>1.0%</td><td>71<br>0.7%</td><td>9,539<br>100%</td></tr><tr><td>Black</td><td>49<br>1%</td><td>3,310<br>98%</td><td>8<br>0.2%</td><td>6<br>0.2%</td><td>3,373<br>100%</td></tr><tr><td>American Indian</td><td>84<br>43%</td><td>8<br>4%</td><td>86<br>44%</td><td>16<br>8%</td><td>194<br>100%</td></tr><tr><td>Asian</td><td>61<br>6%</td><td>5<br>0.5%</td><td>17<br>2%</td><td>1,008<br>92%</td><td>1,091<br>100%</td></tr><tr><td>Other</td><td>714<br>76%</td><td>72<br>8%</td><td>63<br>7%</td><td>95<br>10%</td><td>944<br>100%</td></tr><tr><td>Total</td><td>10,240<br>68%</td><td>3,434<br>23%</td><td>271<br>2%</td><td>1,196<br>8%</td><td>15,141<br>100%</td></tr></tbody></table>

Source: National Longitudinal Study of Adolescent to Adult Health.
This type of structural mobility is what most people have in mind when they think about race as being “socially constructed.” The racial categories that are considered appropriate or relevant often differ both over time and across countries. In this way, a person’s race might change on paper, or in the eyes of their beholders, without anything else about them changing. Indeed, survey researchers would describe the Add Health example in Table 3 as a *methodological artifact*, a change brought about by a change in how the data was collected rather than a change in the experiences of respondents in their everyday lives.

Although racial mobility of the structural variety is worth studying in its own right, my focus is on changes in racial classification that cannot be explained by changes in the available categories, or other changes in how the data was collected. This is what makes the racial mobility in the NLSY, shown in Tables 1 and 2 so intriguing. The categories of “White,” “Black” and “Other,” are more limited than the categories that appear in Add Health, but they remained exactly the same every year over a span of as many as 17 observations. Also, the interviewers’ classifications always occurred at the same time in the survey and their instructions never changed.<fnref n="26" /> Thus, structural mobility or methodological artifacts are unlikely to explain why some people’s classifications changed and some did not, or why people experienced racial mobility in the particular directions that they did. The consistency of data collection also leaves open the possibility that the changes in classification do capture meaningful differences in people’s experiences of race and inequality in the U.S.

<footnote n="26">²⁶ Admittedly, the instructions to interviewers on how to racially classify respondents did not change in part because there weren’t any offered after the original screening was completed in 1978. The biggest change in survey administration that might have affected patterns of classification occurred in 1994 when the NLSY switched to computer-assisted interviewing, and the interviewers began recording their responses directly into laptop computers rather than circling or writing them on the questionnaires and having the responses entered into a computer by someone else. Mobility tables comparing racial classification changes between 1992 and 1993 to those that occurred between 1993 and 1994 do not exhibit unique patterns of mobility or levels any higher (or lower) than that found between other pairs of years during survey.</footnote>
## Racial performance and presentation of self

Much of the daily experience of race – and inequality – in the contemporary United States is shaped by interpersonal interactions. Even at the height of Jim Crow and legally mandated racial segregation, it was real estate agents, waiters and their customers, train conductors and their passengers, county marriage licensing clerks, and other local bureaucrats who made the moment-to-moment decisions about who should be denied service, who should buy houses where, and who should be allowed to marry whom – all based on their perceptions of someone’s race.<fnref n="27" /> Scholars of inequality have demonstrated that decisions about allocating resources, rewards and punishments continue to be colored by racial perceptions today, in everything from hiring decisions to jury sentencing, sometimes even when people are not aware they are taking race into account.<fnref n="28" />

At the same time, race scholars have increasingly embraced a conception of race as an interactional accomplishment, or a type of performance, highlighting how people “do” race much in the same way that West and Zimmerman famously described people as “doing gender.”<fnref n="29" /> People who are socialized in American society learn the expectations that go along with particular racial classifications. Certain behaviors, clothing, hairstyles, and other cues become racially coded and can serve to steer categorization in line with preconceived notions of how members of a given race should look or act. Rap music, hoodies, and dreadlocks become “black.” Being shy, having dark straight hair, and doing well in math become “Asian.” Feathers and long braids cue “American Indian,” and so on.

Together these perspectives offer another explanation for why it is reasonable to expect a person’s race to change over time, or differ across contexts, and for that mobility to have implications for social

<footnote n="27">²⁷ See, e.g., Pascoe (2009, chapter 5)</footnote>

<footnote n="28">²⁸ See, e.g., Bertrand and Mullainathan (2004), Rattan et al (2012).</footnote>

<footnote n="29">²⁹ See West and Fenstermaker (1995); also Wilkins (2004), Markus and Moya (2010).</footnote>
inequality. Because Americans have shared scripts for what an “au-
thentic” racial performance looks like, people can purposefully try to
present themselves in racially stereotypical ways in the hopes of
being so classified. When certain opportunities are closed off to
members of a given race, whether they be jobs, citizenship, or mar-
riage partners it creates a material incentive for people to try to
“pass” and be accepted in a more advantaged category. However,
such performances might not always be convincing to their intended
audience, or people might choose to “pass” in some instances and not
others.<fnref n="30" /> Thus, if we followed them over time, we might expect some
people to bounce back and forth between racial categories, or make a
clear move from one racial category to another.

A limitation of using secondary survey data to study racial mobility
is not being present to witness the interaction that led to a given ra-
cial classification, and being unable to speak to either the intervie-
wers' or the respondents' intentions during their meeting. In face-to-
face encounters, interviewers would have access to a range of cues,
both physical and contextual, that might affect how they view res-
pondents by race. Some of those cues might be consciously selected,
manipulated, or otherwise presented by respondents to help direct the
racial classification of themselves or their family members. But this
also could be the case for people who are classified in the same racial
category year after year. Consistent and convincing performances
over a period of time will yield racial stability; the presence of racial
performance and the presence of racial mobility are not necessarily
one and the same.

The presence of racial mobility in a longitudinal survey could be the
result of catching someone in the act – so to speak – either of begin-
ning a performance aimed at a new racial destination, or of cons-
ciously ending a performance and returning to their racial origin.
Mobility also could be the result of an unintentional a break in per-
formance, a slip-up, of sorts, that suddenly changed the interviewer’s

<footnote n="30"><sup>30</sup> See, Goffman (1959) and Hobbs (2014).</footnote>
interpretation. Identifying which of these scenarios might be occur-
ring and when is beyond the scope of my research. Instead, I aim to
use survey data to do what it does best: reveal the broader patterns
generated by each individual, interactional negotiation of racial ca-
tegorization. By examining which characteristics predict racial mobi-
lity, we might come to better understand the ingredients of suc-
cessful (or unsuccessful) racial performances. We might also be able
to infer the presence or absence of intention by whether racial mobi-
lity tends to be upward or downward. But a fuller explication of
these processes also awaits future ethnographic research undertaken
with a racial mobility perspective in mind.<fnref n="31" />

*Categorical ambiguity*

Another common explanation for why some people are categorized
one way one year and another way the next year is that they would
be difficult for anyone to classify based on commonly used racial
cues, such as physical appearance, name, or accent. For example,
some people might have an unusual combination of characteristics –
such as dark skin and light-colored eyes, or light skin and blonde hair
but also a stereotypically non-white name – that makes them chal-
lenging for others to classify in a single racial category. From the
perspective of racial performance and passing, such people might be
the most able to exercise some control over how they are perceived:
by hiding or downplaying certain characteristics, and emphasizing
others. However, explanations of racial mobility that point to catego-
rical ambiguity as a contributing factor tend to do so less to highlight
the role of individual agency in shaping racial perceptions and more
to draw attention to the inadequacy of the classification scheme and
its inability to capture the full range of human variation.<fnref n="32" /> From this
perspective, it is because of a limited number of categories, or a lack
of clarity in how to assign them, that different observers might come

<footnote n="31"><sup>31</sup> See Wilkins (2004) and Morris (2007) for important work along these lines.</footnote>

<footnote n="32">³² See, e.g., the work of anthropologist Marvin Harris on racial classification in Brazil.</footnote>
to different conclusions based on the same racial cues. Alternatively, the same person might be inconsistent in applying the categories, even when interpreting the same racial cues, over time. In this way, *ambiguity* in how someone could be categorized would manifest as *fluidity* when repeated racial classifications for the same person were compared.

This explanation for changes in racial classification is an important factor to consider. Unfortunately, most large-scale survey data includes little information on the kinds of factors most people assume are related to categorical racial ambiguity. Neither the NLSY nor census data include any information about the respondent's appearance. For reasons of confidentiality, neither the NLSY nor Add Health includes respondents' names. Instead, the best survey researchers can do is use proxies for whether or not people might be difficult to classify – including whether or not they were born in in another country, whether they describe their race or ancestry using multiple categories, or whether they select particular racial or ancestry categories that do not fit as well into the survey's simplified racial classification scheme. If the survey includes multiple measures of self-identification, we can also use whether the respondents changed how they identified to try to capture either their own difficulty in selecting racial categories for themselves, or a calculated attempt to (also) change how other people perceive them.

Table 4 shows the levels of racial fluidity for NLSY respondents with and without some of these potentially ambiguity-producing characteristics. It is very clear from these frequencies that people who identified as having a Hispanic origin in 1979, who are not native-born Americans, or who changed their self-identification in broad racial terms between 1979 and 2002 are more likely to experience changes in racial classification. However, each of these potentially ambiguous characteristics are not equally likely to result in changes in racial classification, and people without these characteristics do not universally experience racial stability. In fact, people who re-
ported mixed or multiracial origins in 1979 are actually somewhat less likely to have fluid racial classifications than their non-multiracial counterparts.

<table><caption>Table 4. Individual ambiguity and racial fluidity, NLSY</caption><thead><tr><th></th><th>Percent with<br>at least one<br>change in<br>classification</th><th>Avg. number<br>of discrepant<br>classifications</th><th>N</th></tr></thead><tbody><tr><td>Reported multiracial origins in 1979</td><td>19%</td><td>0.5</td><td>1379</td></tr><tr><td>Did not report multiracial origins in 1979</td><td>21%</td><td>0.7</td><td>11258</td></tr><tr><td>Reported a Hispanic origin in 1979</td><td>87%</td><td>3.5</td><td>1978</td></tr><tr><td>Did not report a Hispanic origin in 1979</td><td>8%</td><td>0.2</td><td>10552</td></tr><tr><td>Not born in the United States</td><td>70%</td><td>2.6</td><td>870</td></tr><tr><td>Born in the United States</td><td>17%</td><td>0.5</td><td>11767</td></tr><tr><td>Changed racial self-id</td><td>45%</td><td>1.7</td><td>1544</td></tr><tr><td>Did not change racial self-id</td><td>18%</td><td>0.6</td><td>6090</td></tr></tbody></table>

Source: 1979 National Longitudinal Survey of Youth.
Table 5 presents a comparison of similar characteristics from Add Health. Several important differences between these data and those in Table 4 should be noted.<fnref n="33" /> First, the Add Health comparison is between just two waves of the survey (Waves 3 and 4) because they are the most similar in terms how racial classifications were recorded and which categories were offered. With only one comparison between two years of data, the likelihood that a given person would experience a change in their racial classification is much lower than in the NLSY. Nevertheless, a similar pattern emerges in terms of who is more likely to experience such changes. People who reported a Hispanic origin, identified themselves using multiple race categories, or were not born U.S. citizens, all have higher frequencies of racial fluidity than their presumably less racially ambiguous peers. The second major difference between the two surveys is that Add Health also includes a measure of the respondents' skin color. According to this measure, people with light brown or medium brown skin, as perceived by their Wave 3 interviewer, are the most likely to be classified by race differently in different years.<fnref n="34" /> But overall in Add Health, as in the NLSY data, the absence of potentially ambiguous characteristics does not completely rule out the possibility of racial mobility.

<footnote n="33">³³ Another difference between the two surveys relates to how self-identification was measured between NLSY and Add Health, and might explain why reporting multiple origins in NLSY is less predictive of experiencing fluidity than reporting multiple races in Add Health. The 1979 question in the NLSY referred to “origin or descent” and might have prompted people to report racial “mixing” that was more distant in their family tree. The Add Health question was a census-style racial identification measure. The Add Health cohort is also 20 years younger, and includes members of the post *Loving v. Virginia* “biracial baby boom.” It is also important to note the different distinctions between being born in the United States and being born a U.S. citizen (which includes people born abroad to American parents), and the difference in the racial composition of the foreign-born populations between the two cohorts.</footnote>

<footnote n="34">³⁴ It is important to note that interviewers recorded the respondents’ skin color after they recorded their racial classification (and also after they heard the respondents identify themselves by race earlier in the survey). Thus the recorded skin color could be influenced, in part, by the racial classification rather than the other way around.</footnote>
<table><caption>Table 5. Individual ambiguity and racial fluidity, Add Health</caption><thead><tr><th></th><th>Percent inconsistently<br>racially classified</th><th>N</th></tr></thead><tbody><tr><td>Identified using multiple races in Wave 3</td><td>16%</td><td>520</td></tr><tr><td>Did not report multiple races in Wave 3</td><td>4%</td><td>12,450</td></tr><tr><td>Reported a Hispanic origin in Wave 3</td><td>15%</td><td>2,038</td></tr><tr><td>Did not report a Hispanic origin in Wave 3</td><td>2%</td><td>10,935</td></tr><tr><td>Not born a U.S. citizen</td><td>13%</td><td>798</td></tr><tr><td>Born a U.S. citizen</td><td>4%</td><td>12,195</td></tr><tr><td colspan="3"><em>Perceived skin color in Wave 3:</em></td></tr><tr><td>Black</td><td>2%</td><td>801</td></tr><tr><td>Dark brown</td><td>3%</td><td>874</td></tr><tr><td>Medium brown</td><td>7%</td><td>1,319</td></tr><tr><td>Light brown</td><td>15%</td><td>1,783</td></tr><tr><td>White</td><td>2%</td><td>8,210</td></tr></tbody></table>

Source: National Longitudinal Study of Adolescent Health, Waves 3 and 4.
Another implication of a categorical ambiguity perspective is that racial mobility would be eliminated if we had better measures of race with clearer, more accurate categories. However, in comparing the results from the NLSY and Add Health, it seems that having more racial categories than “white,” “black,” and a vague, residual category “other,” does not eliminate the presence of racial mobility. Overall, the year-to-year change in racial classification is very similar between the NLSY (6%) and Add Health (5%), despite the latter having more – and more specific – racial categories (i.e., “American Indian or Alaska Native” and “Asian or Pacific Islander” instead of “other”). More categories might clarify the classifications of some people while suddenly casting doubt on others that seemed obvious when there were fewer options from which to choose. Changes to the numbers or names of categories alone also likely do little to change the underlying logic of what “race” means in the United States.<fnref n="35" />

Given the results in Tables 4 and 5, I argue that the existence of some ambiguity about a person's racial classification is a necessary but not sufficient condition for racial mobility. Complex or seemingly contradictory racial cues are strong predictors of who is most likely to experience a change in racial classification. A lack of instructions or explicit criteria for category assignment also promotes inconsistent classifications. However, the presence of doubt or uncertainty alone does not ensure the presence of fluidity. I could be unsure about someone's race and still repeatedly decide to classify that person as white every time we meet. A relatively constant source of racial ambiguity, such as being born outside the U.S. or having multiracial

<footnote n="35">³⁵ I argue the same would be true if racial classifications included a category for “Hispanic or Latino,” as the U.S. census bureau is currently testing for its self-identification question in 2020. If these surveys had offered interviewers such a category it certainly would decrease the presence of fluidity for respondents who report Hispanic origins. However it would not eliminate mobility entirely, nor I suspect would it erase the relationship between social status and racial classification that I have explored in my research (see Saperstein and Penner 2016). It is also important to keep in mind that people do not consistently report their Hispanic origins over time, and that whether one claims “Hispanic” as an identity – as opposed to a question of ancestry or descent – is partly determined by other factors such as their social status and whether they are married to someone who identifies as Hispanic. See, e.g., Duncan and Trejo (2011).</footnote>
ancestry, also cannot explain when changes in racial classification
are likely to occur. These factors are less helpful in understanding the
particular direction a change in classification is likely to take, as
well, and whether a change from white to black is explained by the
same factors as a change from black to white. Those are the crucial
questions, from a racial mobility perspective: categorical ambiguity
aside, what factors are likely to “tip the scales,” resulting in a change
in classification in one direction or another?

Status and hierarchy

For answers to why racial mobility occurs in the particular directions
it does we need to look beyond the obvious characteristics of an-
cestry and physical appearance. Those factors have long been central
to how U.S. racial distinctions were understood in commonsense
terms, but research on racial categorization, including 19th Century
trials that involved questions of racial determination, demonstrate
that who one's parents are and how one looks are not the only cha-
racteristics that get taken into account when asked on which side of a
racial boundary someone “belongs.” For example, as legal historian
Ariela Gross has shown, Americans have long considered how
people are accepted in their community – where they live and work;
with whom they associate; in short, whether they do the things
people of a particular race are expected to do – when deciding how to
classify them.36 All of those factors are subject to change over the
course of a person’s life, which could help explain why a person’s
race is subject to change, too.

The belief that race is simply a biological distinction that one inhe-
rits, that it is summarized by our ancestry and reflected in our faces,
also masks the more important function that race has served in U.S.
society. The idea of races among humans was invented not as a neu-
tral recognition of difference, but as an index of perceived worth, a

<footnote n="36">³⁶ Gross (2008).</footnote>
ranking of people as allegedly superior or inferior, intended to justify unequal treatment.<fnref n="37" /> Long after the end of slavery, access to jobs, the right to vote or serve on a jury, to own land, and at times even the right to set foot in the United States were all circumscribed by race. Although exclusion, subjugation, segregation, and discrimination on the basis of race are no longer legal in the U.S., vestiges of the old racial regimes remain.<fnref n="38" /> Assigning someone a racial classification has never been a straightforward read of physical characteristics, nor is it a simple distillation of their family tree. It was and continues to be, in part, a judgment about where they stand in American society; a window into how they are likely to be perceived and treated by others. Only by asking what predicts racial mobility in particular directions can we begin to assess the role that social status has played, and continues to play, in shaping racial categorization and maintaining racial inequality.

Of course, there are a number of mechanisms that maintain racial inequality in the United States. Racial mobility is not the only one, or even the most influential one. Nevertheless, when we acknowledge and study racial mobility, we are forced to confront important aspects of how race comes to matter in people's lives, including predictors of racial perceptions and categorization that are often obscured in more conventional accounts of racial inequality.

<footnote n="37">³⁷ See, e.g., Frederickson (2002), Smedley (2007), and Omi and Winant (1994).</footnote>

<footnote n="38">³⁸ Some have argued that racial domination is not in fact in our past, and that the only real change in race relations and inequality in America has been a change in tactics. See Wacquant (2002).</footnote>
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USPC

Université Sorbonne
Paris Cité

Le LIEPP (Laboratoire interdisciplinaire d'évaluation des politiques publiques) est un laboratoire d'excellence (Labex).

Ce projet est distingué par le jury scientifique international désigné par l'Agence nationale de la recherche (ANR).

Il est financé dans le cadre des investissements d'avenir.

(ANR-11-LABX-0091, ANR-11-IDEX-0005-02)

www.sciencespo.fr/liepp

Directeurs de publication :
Bruno Palier & Etienne Wasmer

Sciences Po - LIEPP
27 rue Saint Guillaume
75007 Paris - France
+33(0)1.45.49.83.61
liepp@sciencespo.fr

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