Reflection paper
Wealth Inequality Among Immigrants: Consistent Racial/Ethnic Inequality in the United States
Matthew A. Painter II1 • Zhenchao Qian2
Received: 2 February 2015 / Accepted: 24 February 2016 / Published online: 3 March 2016
� Springer Science+Business Media Dordrecht 2016
Abstract Wealth is a strong indicator of immigrant integration in U.S. society. Drawing on new assimilation theory, we highlight the importance of racial/ethnic
group boundaries and propose different paths of wealth integration among U.S.
immigrants. Using data from the Survey of Income and Program Participation and
quantile regression, we show that race/ethnicity shapes immigrant wealth inequality
across the entire distribution of net worth, along with immigrants’ U.S. experience,
such as immigrant status, U.S. education, English language proficiency, and time
spent in the United States. Our results document consistent racial/ethnic inequality
among immigrants, also evidenced among the U.S. born, revealing that even when
accounting for key aspects of U.S. experience, wealth inequality with whites for
Latino and black immigrants is strong.
Keywords Race/ethnicity � Immigrants � U.S. experience � Wealth inequality
Introduction
Immigrants move to the United States for a variety of reasons, including the pursuit
of opportunities to improve their financial well-being (e.g., Portes and Rumbaut
2006; Smith and Edmonston 1997). A growing body of literature has focused on
& Matthew A. Painter II mpainter@uwyo.edu
Zhenchao Qian
zhenchao_qian@brown.edu
1 Department of Sociology, University of Wyoming, 411 Ross Hall, 1000 E. University Avenue,
Laramie, WY 82071, USA
2 Department of Sociology, Brown University, 206 Maxcy Hall, Providence, RI 02912, USA
123
Popul Res Policy Rev (2016) 35:147–175
DOI 10.1007/s11113-016-9385-1
wealth as an indicator of financial well-being in an effort to understand how
immigrants integrate into U.S. society (e.g., Akresh 2011; Hao 2004, 2007; Painter
2013, 2014; Painter and Qian Forthcoming). This focus is important because wealth
signifies a unique set of resources that reflect financial attitudes and behaviors as
well as priorities, goals, and values (Hao 2007). Together, the various investments
within a financial portfolio represent a pool of resources that can be used to meet
short- and long-term needs and provide a number of additional financial advantages
(e.g., return on investment, investment collateral, transferability) (Keister 2000b,
2005). How much wealth immigrants possess provides valuable insight into their
financial well-being and into how well they are integrating into U.S. society.
If immigrants possessed characteristics that mirrored those of the U.S.
population, we would expect immigration to have little influence on wealth
inequality in the United States because the wealth of immigrants would resemble
that of the native born (Hao 2007). Immigrants, however, are mostly racial/ethnic
minorities, with some characteristics that facilitate integration into U.S. society
while others serve as barriers. The central contribution of this paper is to explore
how racial/ethnic realities in the United States provide differential opportunities and
constraints for immigrants of different racial/ethnic groups. We move beyond a
single summary measure of wealth inequality and use quantile regression techniques
to offer a more complete picture of how immigrants’ race/ethnicity affects wealth
inequality across the distribution of net worth. This is important because a
disproportionate share of immigrants are concentrated near the bottom of the U.S.
socioeconomic ladder (e.g., Lichter et al. 2005; Smith and Edmonston 1997).
Quantile regression broadens the understanding of wealth inequality by focusing on
racial/ethnic wealth disparities at various points of the wealth distribution.
To understand how race/ethnicity affects immigrants’ integration into U.S.
society, we draw on new assimilation theory to provide insight into contemporary
immigrant patterns of incorporation (Alba and Nee 2005). In this reformulation of
classical assimilation theory, race/ethnicity is considered a social boundary
embedded in a variety of social, economic, and cultural differences not only at
the individual level but also in social institutions. While social, financial, and human
capital are important for immigrants’ integration into U.S. society, their race/
ethnicity remains an important predictor of wealth. We expect immigrant racial/
ethnic wealth inequality to resemble the patterns of their native-born counterparts
because racial/ethnic realities in the United States provide differential opportunities
and constraints to both immigrants and natives of different racial/ethnic groups.
Our second contribution helps understand how immigrants’ U.S. experiences,
including immigrant status, U.S. education, English language proficiency, and time
spent in the United States, affects their integration into U.S. society. Specifically, we
explore how immigrant status, such as naturalization or legal permanent residency,
affects immigrants’ financial well-being. We then turn to two indicators of
immigrants’ human capital and assess how acquiring U.S. education and greater
English language proficiency ease immigrants’ transition into U.S. society. Last, we
investigate the time immigrants have spent in the United States. The longer
immigrants live in the United States, the more familiar they become with U.S.
customs, financial institutions, and savings/investment opportunities, and thus have
148 M. A. Painter II, Z. Qian
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more wealth (Zhang 2003). We expect that immigrants’ characteristics will affect
wealth differently depending upon where immigrants lie, all else being equal, along
the wealth distribution.
We examine wealth inequality by using data from the 2001 and 2004 panels of
the Survey of Income and Program Participation (SIPP), national representative data
of the non-institutionalized U.S. population. They are well-suited for this study
because they contain detailed migration and financial information. Following Hao
(2004, 2007), we expand the concept of immigrant financial well-being to include
wealth. SIPP collects rich information on assets and debts held at the time of the
survey, which include immigrants’ pre-migration financial resources and wealth
accumulated in the United States. This means that we are unable to exclude
immigrant wealth holdings held abroad at the time of arrival. Fortunately, we are
able to control for pre-immigration characteristics, including foreign educational
attainment and legal permanent resident (LPR) status at arrival, which are strong
proxies of wealth at the time of arrival. In sum, we make a unique contribution in
this study and underscore the consistency of racial/ethnic inequality among both
immigrants and the native born by examining broad racial/ethnic differences at
multiple points of the wealth distribution.
Conceptual Framework
Assimilation, Immigrant Integration, and Racial/Ethnic Realities
Assimilation theory has long been used to understand immigrant experiences in the
United States. It captures the process where the distinctiveness of immigrants’
country of origin gradually diminishes over time as later generations and those who
lived in the United States for a long time adopt cultural patterns of the majority
population (Gordon 1964). This theory explains successfully the experiences of
European immigrants and their descendants in the United States at the turn of the
twentieth century. At the time of arrival, the first generation European immigrants
were highly diverse from an ethnic, cultural, and/or economic standpoint
(Hirschman 2005). Yet, over time, ethnic distinctions faded, European immigrants
and their descents achieved socioeconomic parity with earlier-arriving European
immigrants, and they are all now grouped—and generally group themselves—into a
white racial category (Alba 1990; Perlmann and Waldinger 1997).
Classical assimilation theory has been criticized, however, in a number of ways,
including its inability to address the continued salience of race/ethnicity among
contemporary immigrants (for a summary of the criticism, see Alba and Nee 2005,
pp. 3–5). The persistent and highly institutionalized racial/ethnic inequality in the
United States suggests that immigrants of various racial/ethnic minority back-
grounds are unlikely to follow the path of the descendants of European immigrants
who arrived to the United States at the turn of the twentieth century. In their
reformulation of assimilation theory, Alba and Nee (2005) argue that social
boundaries based on race/ethnicity are so deeply rooted and rigid that attitudes and
behaviors are formed based on individuals’ positions in a racial/ethnic hierarchy.
Wealth Inequality Among Immigrants… 149
123
This suggests that racial/ethnic minority immigrants may not integrate well into
U.S. society because they are subject to similar—if not more severe—prejudices
and discriminations compared to their native-born counterparts, regardless of their
socioeconomic position at the time of arrival.
In the United States, historically rampant prejudice and discrimination against
racial/ethnic minorities, especially African Americans, has created tremendous
wealth gaps among racial/ethnic groups (Oliver and Shapiro 2006). Racial/ethnic
group boundaries serve as strong barriers to integration because of the highly
institutionalized racial/ethnic inequality in the United States (Omi and Winant
1994). Even without contemporary discrimination and prejudice, historical and
cumulative racial/ethnic inequalities are unlikely to disappear. Yet, contemporary
discrimination and prejudice persist, although they have become more covert and
elusive. Racial/ethnic minorities continue to face obstacles in education, employ-
ment, housing, and credit and consumer markets (e.g., Logan 2011; Pager and
Shepherd 2008; White and Glick 2011). Such structural barriers create and reinforce
racial/ethnic boundaries, which will affect racial/ethnic minority immigrants as
well. Immigrants’ integration thus depends not only on their social, financial, and
human capital, but also on opportunities and constraints available to them given
their position within existing racial/ethnic structures. In the end, immigrants’
integration hinges on how well their native-born racial/ethnic counterparts fare in
U.S. society.
In fact, racial/ethnic minority immigrants may face worse challenges than their
native-born counterparts, which influence their job opportunities, social networks,
and, ultimately, asset attainment (Hao 2007; Portes and Rumbaut 2006; Waldinger
1996; Waters 1999). Studies on wealth (Hao 2004, 2007; Painter 2013; Painter and
Qian Forthcoming) as well as education, earnings, and residential and intermarriage
patterns among immigrants (e.g., Alba et al. 1999; Borjas 1994; Kao and Thompson
2003; Qian and Lichter 2007) underscore that racial/ethnic minority immigrants are
often doubly disadvantaged, lagging behind their native-born counterparts and white
immigrants, and to a large extent, native-born whites.
Yet, Alba and Nee’s conceptualization of new assimilation theory views racial/
ethnic boundaries as more flexible over time because such boundaries can be
crossed, blurred, and/or shifted. Depending on their racial/ethnic position, some
immigrants may integrate more easily into U.S. society than others. For example,
Alba and Nee (2005, p. 132) note that the perception of racial distinctiveness
between whites and both Asian and lighter-skinned Latino immigrants has already
changed, which suggests that their group boundaries may serve as less of an
impediment for their integration into U.S. society. Meanwhile, Alba and Nee (2006,
p. 133) call the black–white divide the ‘‘most intractable racial boundary,’’ which
continues to limit the incorporation of black immigrants (see also Portes and Zhou
1993). Indeed, Waters (1999) shows that West Indian immigrants strive to maintain
their ethnic identity as a way of distinguishing themselves from black Americans
and to help facilitate upward mobility. In the end, however, ‘‘race as a master
status… overwhelms the identities of the immigrants and their children, and they are seen as black Americans’’ (Waters 1999, p. 8). In sum, the salience of race/
150 M. A. Painter II, Z. Qian
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ethnicity—particularly in terms of the black–white divide—shapes immigrants’
integration patterns and influences wealth inequality in the United States.
Race/Ethnicity and Wealth
Since the racial/ethnic status of both immigrants and the native born is strongly
related to wealth, it is essential to briefly review this literature in order to shed light
on how race/ethnicity affects wealth for both the native born and immigrants.
Broadly, our study builds on a growing body of research that examines immigrant
wealth inequality. Some of this work has examined specific immigrant character-
istics, including country of origin (Akresh 2011; Cobb-Clark and Hildebrand 2006c;
Hao 2004) or place of education (Painter 2013). Other work looked at racial/ethnic
inequality for immigrants with positive wealth (Hao 2007) or among LPRs (Painter
and Qian Forthcoming). Below, we focus our review on research that examines
racial/ethnic differences in overall wealth or net worth.
Asians
A growing body of research examines Asian wealth inequality. Among the native
born, there is mixed evidence with Asian Americans having more (Hao 2004;
Painter 2013), less (Campbell and Kaufman 2006; Hao 2007), or equivalent (Painter
2013) wealth as native-born whites, though educational attainment and ethnic
diversity may explain these discrepancies. By race/ethnicity, with the exception of
Japanese immigrants, Asian immigrants have less wealth than white immigrants
(Hao 2004; Painter and Qian Forthcoming).
Blacks
Most of the literature on racial/ethnic wealth inequality has focused on the black–
white divide. Research overwhelmingly demonstrates that native-born blacks have
less wealth than native-born whites (e.g., Blau and Graham 1990; Conley 1999; Hao
2004, 2007; Keister 2000a, 2004; Killewald 2013; Oliver and Shapiro 2006; Smith
1995). This pattern holds for immigrants as well (Hao 2004; Painter 2013; Painter
and Qian Forthcoming).
Latinos
The wealth literature has increasingly focused on Latino financial well-being. For
net worth, native-born Latinos are less wealthy than native-born whites (Campbell
and Kaufman 2006; Cobb-Clark and Hildebrand 2006a, b; Hao 2004, 2007; Painter
2013; Smith 1995). A similar pattern is evident when comparing Latino immigrants
to whites, whether they are fellow immigrants or native born (Hao 2004, 2007;
Painter 2013; Painter and Qian Forthcoming). Among Latinos, there is some mixed
evidence with some research reporting that there is no nativity effect (Painter 2013),
while other research finds that Latino immigrants have a lower level of wealth than
native-born Latinos (Hao 2007).
Wealth Inequality Among Immigrants… 151
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Immigrants’ U.S. Experience
Immigrants arrive to the United States with different levels of educational
attainment, socioeconomic status, and U.S.-based social networks. Human,
financial, and social capital at the time of arrival determine their initial starting
position, shape their U.S. experiences, and influence their socioeconomic achieve-
ment and wealth attainment (Nee and Sanders 2001). In this section, we discuss four
dimensions of U.S. experience: immigrant status, U.S. education, English language
proficiency, and time spent in the United States. Each of these factors reflects
immigrant integration in U.S. society and affects their integration and wealth. We
first describe each dimension and then develop a link to immigrants’ financial well-
being.
For immigrant status, we distinguish between legal permanent residency and
naturalization. Immigrants can live in the United States permanently by applying for
LPR status in two main ways: adjustment or new arrival. Adjusted immigrants have
often lived in the United States for a number of years with a non-immigrant status
before they apply for LPR status. New arrival immigrants apply for LPR status in
their home country; however, some may actually live in the United States before
returning to their home country to file and receive their LPR paperwork. LPR
immigrants have the same rights and responsibilities as citizens except that they
have no voting rights and lack access to jobs that require U.S. citizenship (Massey
and Bartley 2005).
Immigrants are eligible to naturalize after living 5 years in the United States and
spouses of U.S. citizens, military personnel, and minor children of naturalized
citizens are eligible for naturalization sooner (U.S. Citizenship and Immigration
Services 2013). 1
With naturalization, immigrants gain the right to vote, the ability to
sponsor adult relatives for migration, full Social Security benefits, access to a U.S.
passport, and eligibility for educational programs, certain employment opportuni-
ties, and jury duty responsibilities (Jasso and Rosenzweig 1990; Massey and Bartley
2005; Yang 1994). Immigrants do encounter costs associated with naturalization,
which may encourage immigrants to maintain their status as LPRs. For example,
unless dual citizenship is permitted, immigrants may lose citizenship in their
country of origin, which may result in the forfeiture of access to public benefits
(e.g., retirement funds, public health care), restricted travel, and/or constrained
home country employment prospects (Van Hook et al. 2006; Yang 1994).
Additionally, naturalization is a complex, time-consuming, and expensive process
that requires financial resources, the ability to navigate bureaucracy, and satisfactory
English language and civics proficiency (Alvarez 1987; Gilbertson and Singer 2003;
Van Hook et al. 2006; see also North 1987). We expect naturalized citizens to have
similar levels of wealth as the native born. In part, this is due to lowered educational
and occupational barriers as naturalization is associated with better jobs, greater
wage growth, and higher earnings (Bratsberg et al. 2002; Chiswick and Miller
1 Other requirements for naturalization include immigrants’ physical presence in the United States for set
time periods (for some paths to naturalization), good moral character, English and civics knowledge, and
attachment to the Constitution (U.S. Citizenship and Immigration Services 2013).
152 M. A. Painter II, Z. Qian
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2002). Similarly, LPR immigrants are expected to have lower levels of wealth than
citizens (whether native born or naturalized). This is also due to their more tenuous
tie to U.S. society.
Research consistently finds that foreign education, relative to U.S. education, is
associated with worse financial well-being in the United States, either in terms of
earnings (Aly and Ragan 2010; Bratsberg and Ragan 2002; Kaushal 2010; Kim and
Sakamoto 2010; Schoeni 1997; Tao 2010, 2011; Tong 2010; Zeng and Xie 2004) or
wealth (Hao 2007; Painter 2013). 2
Foreign education is devalued in the United
States for a number of reasons, including a (perceived or actual) lower quality of
education in source countries (Bratsberg and Ragan 2002; Friedberg 2000; Schoeni
1997; Zeng and Xie 2004), difficulty in transferring certain majors and/or degrees
(Basran and Zong 1998; Bratsberg and Ragan 2002; Friedberg 2000; Grant and
Nadin 2007), and/or discrimination by U.S. employers or a lack of familiarity with
how to assess the quality and/or level of education (Butcher 1994; Chiswick 1978;
Greeley 1976). 3
Lower earnings reduce immigrants’ ability to save, invest, and
attain wealth.
Obtaining U.S. education increases immigrants’ educational attainment, helps
immigrants overcome the barriers associated with foreign education, and contributes
to higher wealth (Hao 2007; Painter 2013). U.S. education can produce improved
financial well-being in several ways. For one, it serves to upgrade and/or
authenticate education received in the country of origin, which helps immigrants
transfer their source-country specific skills to the U.S. labor market (Bratsberg and
Ragan 2002). Additionally, beyond the credential itself, colleges and universities
provide valuable job search resources, including access to recruitment networks,
internships, and job fairs. Further, a U.S. education improves immigrants’ English
language proficiency, increases their contact with U.S. culture, and encourages
interactions with U.S. institutions, in particular financial establishments (Chiswick
1978; Hao 2007). This creates opportunities for immigrants to acquire U.S.-specific
skills and information (Friedberg 2000; Kaushal 2010).
English language proficiency helps improve wealth for several reasons. First,
greater command of English indirectly affects wealth through better job access with
potentially higher income (e.g., Chiswick and Miller 2002; Hall and Farkas 2008;
Tainer 1988). Second, English language ability directly affects wealth through
participation in formal U.S. financial institutions, where greater command of the
language allows for more familiarity with the customs of these institutions, easier
communication with financial personnel (e.g., bank employees, financial advisors,
investment brokers), and more comfort within financial settings (Paulson et al.
2 We are aware of several exceptions. Stewart and Hyclak (1984) find no difference in earnings for
immigrants’ pre- and post-migration schooling. There is evidence that higher education is rewarded
among Arab immigrants (Aly and Ragan 2010). Nurses educated abroad earn higher wages in the United
States than U.S.-educated nurses, which reflects the number of nurses with experience working in
hospitals or in English-speaking countries (Huang 2011). 3
There is likely important variation by source country in the devaluation of foreign education as
immigrants from countries that commit more resources to education and/or have comparable educational
systems to the United States likely experience a better transition of their skills and educational credentials
(Bratsberg and Ragan 2002; Schoeni 1997).
Wealth Inequality Among Immigrants… 153
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2006). It aids in immigrants’ acquisition of investment knowledge and strategies
(Hao 2007). Our expectation, therefore, is that greater English language proficiency
will result in higher financial well-being, which is supported by findings that wealth
increases with immigrants’ English proficiency (Painter 2013).
Last, the length of time immigrants have resided in the United States is an
important factor for integration and correlated with the factors discussed above.
Greater lengths of stay provide opportunities for immigrants to gain proficiency in
English, learn local customs and develop knowledge of economic, social, and
political institutions, and adopt new ideas and practices (e.g., Alba and Nee 2005;
Bass and Casper 2001; Glick 2000; Gordon 1964). More time means more
experience with housing markets, which increases the likelihood of buying a home
(Glick 2000). Similarly, immigrants’ familiarity with financial institutions would be
essential for improved financial well-being. More time in the United States lets
immigrants build social networks, which may provide immigrants with a number of
resources to help improve their financial well-being, including help searching for
and obtaining financial information and knowledge about particular types of
accounts and/or investments (Chang 2005). Indeed, research on immigrant wealth
consistently finds that length of time in the United States increases financial well-
being among immigrants (Akresh 2011; Hao 2004; Painter 2013; Zhang 2003).
Data and Methods
Data
This study uses data from the 2001 and 2004 panels of the SIPP, a continuous series of
national multistage-stratified panels of the U.S. civilian non-institutionalized popu-
lation that interviews all household members 15 years old and over. Respondents are
interviewed every four months over the duration of the panel (3 years for the 2001
panel; 2.5 years for the 2004 panel) with interviews designed around a core set of
questions with rotating topical modules. SIPP data are especially valuable for
immigrant studies because the large sample size yields a relatively substantial sample
of immigrants and, in particular, racial/ethnic minorities. SIPP is also ideal to analyze
wealth because it collects extensive financial information (Cobb-Clark and Hilde-
brand 2006a, b, c; Hao 2004, 2007; Painter 2013). 4
From the larger SIPP data files, we created a cross-sectional dataset by using
select waves from each panel. We did this by combining the core files with the
Wave 2 (Migration History) and Wave 3 (Assets and Liabilities) topical modules for
both the 2001 and 2004 panels. We also used information from a third module in the
2001 panel because English language proficiency questions are located in Wave 8
(Adult Well-Being). 5
4 The quality of the asset and debt data in SIPP have been examined elsewhere (Czajka et al. 2003; Hao
2007) with Hao (2007) providing a detailed explanation of the advantages and disadvantages of the
various SIPP wealth measures. She notes that the SIPP wealth data compare favorably to the Survey of
Consumer Sciences, which is considered the benchmark data to study U.S. wealth. 5
These questions are included in the Wave 2 topical file in the 2004 data.
154 M. A. Painter II, Z. Qian
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The analytic sample included native born and immigrant adults living in the United
States. We excluded Native Americans 6
and respondents from U.S. territories 7
due to
small sample sizes. With these restrictions, the analytic sample size was 70,947 and
included 2098 non-Latino Asians, 9243 non-Latino blacks, 5861 Latinos, and 53,745
non-Latino whites. In addition, we subset the data by nativity, giving us a subsample of
immigrants (N = 7319) and native born (N = 59,910). 8
SIPP used a sequential hot deck procedure to impute missing data. This procedure
matched a respondent with missing information to a donor respondent according to
multiple categories including sex, race, age, and marital status. The missing
information for the respondent was then replaced with the donor’s valid data. This
resulted in no missing data within waves; therefore, the only source of missing data in
SIPP arose when respondents entered or exited a panel between waves (U.S. Census
Bureau 2001, pp. 13-15–13-17). Merging multiple waves within a panel thus
introduced missing data. For respondents who exited the SIPP sample, but remained in
the population represented by the sample, one strategy SIPP recommended was
multiple imputation (U.S. Census Bureau 2001, pp. 13-20–13-21). We imputed
missing data using SAS Proc MI to create five datasets. Analyses were conducted with
SAS Proc Quantreg and final results were returned using SAS Proc MIAnalyze.
Measures
Net Worth
SIPP contains detailed information on asset and debt holdings in the United States.
Net worth is measured as the US$2004 value of assets less debts. Assets include the
value of financial investments, such as checking and savings accounts, bonds,
stocks, and Individual Retirement Accounts (IRAs). Also included are the value of
non-financial holdings, such as homes, automobiles, real estate, and other valuable
possessions. The value of these assets is weighed against total debts, such as those
from credit cards, hospital bills, mortgages, and property liens.
Explanatory Variables
We use five sets of explanatory variables. First, race/ethnicity is classified as non-
Latino white (reference), non-Latino Asian, non-Latino black, and Latino. 9
Second,
we include five dichotomous variables for immigrant status: native born (reference),
naturalized citizen, LPR status at arrival, adjustment to LPR status, and a residual
category. 10
For the immigrants-only subsample, naturalized citizens are the
6 Native Americans included American Indians, Aleutians, and Eskimos.
7 U.S. Territories included American Samoa, Guam, Puerto Rico, and the Virgin Islands.
8 The nativity subsamples do not total the amount of the full analytical sample. This is due to respondents
entering or exiting the SIPP sample between waves. See the missing data discussion for details. 9
For the rest of the paper, we shorten the label for racial/ethnic groups by dropping ‘‘non-Latino.’’ 10
The residual category includes students, certain refugees/asylees, and undocumented immigrants,
among others.
Wealth Inequality Among Immigrants… 155
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reference category. Third, U.S. education is measured with a dichotomous variable
that indicates immigrants’ (and the native born’s) place of education or where they
completed their last degree (1 = completed last degree in the United States). For
English language proficiency, we identify whether respondents are native English
speakers (reference) or speak English ‘‘not at all,’’ ‘‘not well,’’ ‘‘well,’’ or ‘‘very
well.’’ Last, we include a measure of immigrant’s length of residence in the United
States (age at survey less age at arrival). Notably, the U.S. experience variables are
omitted from the analyses of the native-born subsample.
Control Variables
We use several controls from the life cycle. Continuous variables include age and its
square, household size, and income (logged). We account for gender with a
dichotomous variable (1 = female). Educational attainment is measured as no high
school (reference), high school, some college, college degree, and advanced degree.
Marital status is captured with dichotomous variables: married (reference category),
never married, separated, divorced, or widowed. For place of residence, we include
a variable for urban/rural residency (rural is the reference category) and a set of four
dichotomous variables that capture the U.S. Census regions: Northeast (reference
category), Midwest, South, and West. Last, we also control for respondents’
participation in a particular panel with a dichotomous variable (1 = 2004 panel).
Analytical Approach
To model net worth, we use quantile regression analysis. In recent years, the
empirical quantile regression literature ‘‘makes a persuasive case for the value of
going beyond models for the conditional mean’’ (Koenker and Hallock 2001,
p. 151). Wealth variables have many outliers, especially in the higher tail of the
distribution, and these outliers affect the results of OLS regression. Quantile
regression provides more robust estimates to outliers than the mean. Median
regression, for example, estimates the 50th percentile and minimizes the sum of
absolute residuals. This minimization equates the number of positive and negative
residuals and assures the same number of observations above and below the median
(Koenker and Bassett 1978). Further, quantile regression is robust to normality
assumption violations as it puts more emphasis on the distribution in close
proximity around a particular quantile—like the median—rather than areas of the
distribution that are further away from the quantile (Hao and Naiman 2007,
pp. 41–42). In this way, median regression provides insight into the financial well-
being of immigrants and the native born in the middle of the wealth distribution,
rather than estimates of the ‘‘average’’ respondent which are affected by influential
observations. Further, as the median is a measure of central tendency, median
regression provides an appropriate comparison to other studies that analyze wealth
with conventional regression techniques (see Hao and Naiman 2007, p. 56).
Another advantage of quantile regression is that it provides a more complete
assessment of the effects of covariates across the conditional distribution of net
worth (at given quantiles). Therefore, we explore other types of quantile regression
156 M. A. Painter II, Z. Qian
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by setting the threshold, s, to deciles in order to explore the relationship between our covariates and wealth at different points on the conditional distributions. This
analysis provides important insights about racial/ethnic inequality at different points
of the conditional distribution of net worth.
Figure 1 provides insight into the advantages of using quantile regression to analyze
wealth inequality. 11
This figure presents wealth values for the full sample and each
racial/ethnic group at select percentiles, including the median. 12
Here, we chose deciles
to mirror our modeling strategy. As Fig. 1 demonstrates, among the least wealthy, there
is relatively little wealth inequality; however, gaps among racial/ethnic groups emerge
and then widen across the wealth distribution. This pattern supports a quantile regression
approach as the presence and amount of racial/ethnic wealth inequality differs
depending upon the particular quantity of net worth. A single summary measure of
wealth, like the mean, would miss such distinct patterns. Substantively, Fig. 1 shows
that race/ethnicity matters greatly among more wealthy individuals, but its effect is
muted among the less wealthy. This calls for a close examination of how and why race/
ethnicity results in different patterns of wealth inequality across the wealth distribution.
We use a variable-nested modeling approach to explore how immigrants’ U.S.
experience affects immigrant wealth inequality. We introduce four models to examine
net worth (Table 2). Model 1 introduces the race/ethnic variables. Model 2 adds the
measures of immigrant status. Model 3 includes the rest of the U.S. experience
variables. Model 4 is the full model, with controls. To explore the relationship between
immigrants’ U.S. experience and wealth inequality over the conditional wealth
distribution, Table 3 presents quantile regression results by conditional decile.
Tables 4 and 5 present quantile regression results by conditional decile for the
immigrant and native-born subsamples. 13
Here, we focus on the overall pattern of
racial/ethnic wealth inequality and do not directly compare the coefficients from the
unique conditional wealth distributions. In addition, we test for equality of coefficients
within the same model (Clogg et al. 1995; see also Paternoster et al. 1998). Together,
these analyses demonstrate the consistency of racial/ethnic wealth inequality and
show that racial/ethnic minority immigrants, like their same-race/co-ethnic native-
born counterparts, experience barriers to acquiring wealth in the United States.
Results
Descriptive Results
Table 1 presents descriptive statistics. Median wealth for the full sample is $66,915.
Whites have the highest median wealth ($92,917), followed by Asians ($70,275).
Blacks and Latinos have similar median wealth with $8425 and $9080, respectively.
11 Appendix Table 6 contains the values used to create Fig. 1.
12 In additional analyses not shown here, we used the same approach to compare the pattern displayed in
Fig. 1 to the wealth distributions of immigrants and the native born. The patterns by nativity status were
similar to that presented in Fig. 1. 13
To conserve space, Tables 3, 4, 5 present coefficients and significance levels for the explanatory
variables. Full results are available from the authors upon request.
Wealth Inequality Among Immigrants… 157
123
Table 1 Means and standard deviations, SIPP 2001 and 2004, N = 70,947
Total Asian Black Latino White
Outcome variable
Net worth a —
median value
$66,915 $70,275 $8425 $9080 $92,917
Explanatory variables
Race/ethnicity
Asian 0.03 – – – –
Black 0.13 – – – –
Latino 0.08 – – – –
White 0.76 – – – –
Immigrant status
Native born 0.89 0.21 0.93 0.49 0.95
Naturalized
citizen
0.05 0.46 0.04 0.18 0.03
LPR at arrival 0.03 0.17 0.02 0.16 0.01
Adjusted to LPR
status
0.01 0.06 0.01 0.07 0.00
Other immigrant
status
0.02 0.10 0.01 0.10 0.00
Place of education—U.S. degree
Immigrants only 0.39 0.40 0.46 0.38 0.40
English language proficiency
Native speaker 0.90 0.36 0.95 0.37 0.96
Immigrants only 0.32 0.25 0.56 0.20 0.48
Very well 0.05 0.31 0.03 0.26 0.02
Immigrants only 0.20 0.35 0.22 0.14 0.20
Well 0.02 0.18 0.01 0.11 0.01
Fig. 1 Net worth for full sample and racial/ethnic groups, by select percentiles
158 M. A. Painter II, Z. Qian
123
Table 1 continued
Total Asian Black Latino White
Immigrants only 0.15 0.20 0.11 0.14 0.13
Not well 0.02 0.10 0.01 0.17 0.01
Immigrants only 0.22 0.16 0.08 0.33 0.13
Not at all 0.01 0.04 0.00 0.09 0.00
Immigrants only 0.11 0.04 0.03 0.19 0.05
U.S. duration 2.03 (7.66) 14.11 (12.61) 1.03 (4.99) 9.05 (12.81) 1.10 (6.30)
Immigrants only 13.79 (10.49) 10.71 (8.25) 12.71 (9.77) 14.94 (9.99) 14.22 (12.47)
Control variables
Education
No high school
degree
0.13 0.10 0.19 0.38 0.10
High school
degree
0.28 0.17 0.31 0.27 0.29
Some college 0.33 0.22 0.35 0.25 0.34
College degree 0.16 0.30 0.09 0.06 0.18
Advanced degree 0.09 0.21 0.05 0.03 0.10
Household characteristics
Age 49.34 (16.93) 44.66 (14.90) 48.08 (16.26) 41.86 (14.71) 50.55 (17.09)
Female 0.52 0.42 0.64 0.48 0.51
Household size 2.58 (1.49) 3.06 (1.64) 2.62 (1.57) 3.53 (1.86) 2.46 (1.38)
Income a
$44,180
($57,910)
$64,114
($73,128)
$29,329
($37,793)
$37,888
($42,477)
$46,641
($60,928)
Marital status
Married 0.52 0.66 0.32 0.58 0.55
Never married 0.17 0.19 0.31 0.20 0.15
Separated 0.03 0.02 0.07 0.06 0.02
Divorced 0.16 0.08 0.17 0.11 0.16
Widowed 0.11 0.05 0.13 0.05 0.12
Residency
Northeast 0.17 0.22 0.15 0.11 0.18
Midwest 0.25 0.13 0.18 0.10 0.29
South 0.37 0.20 0.59 0.36 0.34
West 0.20 0.46 0.07 0.43 0.19
Urban 0.76 0.93 0.85 0.87 0.73
2004 panel 0.53 0.49 0.53 0.46 0.54
N 70,947 2098 9243 5861 53,745
Some columns may not total 1.0 due to rounding. Standard deviation in parentheses a
US$2004
Wealth Inequality Among Immigrants… 159
123
Table 2 Median regression estimates for net worth (in thousands), SIPP 2001 and 2004, N = 70,947
Model 1 Model 2 Model 3 Model 4
Explanatory variables
Race/ethnicity (ref = white)
Asian -22.482
(6.513)*
-28.343
(5.846)*
-20.374
(6.445)**
-17.041
(5.173)**
Black -86.063
(1.057)***
-83.849
(0.964)***
-82.847
(1.201)***
-39.411
(1.194)*** a,b
Latino -85.289
(1.208)***
-77.560
(1.388)***
-70.118
(1.772)***
-18.415
(2.084)***
Immigrant status (ref = native born)
Naturalized citizen – 33.993
(3.835)***
1.813 (5.307) -21.353
(5.414)***
LPR at arrival – -11.602
(1.420)***
-24.303
(2.588)***
-33.863
(3.822)***
Adjusted to LPR
status
– -8.708
(2.487)***
-23.969
(3.682)***
-33.720
(5.935)***
Other immigrant
status
– -16.270
(1.071)***
-26.308
(3.121)***
-34.303
(5.147)***
Place of education (ref = foreign degree)
U.S. degree – – 2.502 (2.321) 17.132 (3.221)***
English language proficiency (ref = native speaker)
Very well – – -6.186 (2.570)* -1.843 (2.516)
Well – – -10.486
(2.101)***
-11.756 (4.579)*
Not well – – -14.011
(1.938)***
-22.567
(3.175)***
Not at all – – -19.306
(2.616)***
-30.295
(4.195)***
U.S. duration – – 1.185
(0.180)***
0.537 (0.142)***
Control variables
Education (ref = no high school)
High school – – – 24.859 (1.773)***
Some college – – – 38.038 (1.871)***
College degree – – – 93.949 (3.224)***
Advanced degree – – – 151.905
(5.020)***
Household characteristics
Age – – – 4.844 (0.225)***
Age, squared – – – -0.017
(0.002)***
Female (ref = male) – – – -4.271
(1.142)***
Household size – – – 3.630 (0.459)***
Income (logged) – – – 2.094 (0.161)***
160 M. A. Painter II, Z. Qian
123
Almost 90 % of the sample are native born and an additional 5 % are naturalized
citizens. For the remaining categories, 3 % received their LPR status at arrival while
1 % adjusted to LPR status later. By race/ethnicity, the vast majority of black and
white respondents are native born and relatively few are LPRs. In contrast to these
two groups, the majority of Asians are either naturalized (46 %) or LPR (23 %).
Almost half of Latinos are native born, with similar proportions of naturalized
citizens and immigrants with LPR status at arrival (18 and 16 %, respectively).
Last, for the remainder of the U.S. experience variables, a substantial proportion
of immigrants complete their education in the United States (39 %). By race/
ethnicity, there is relatively similarity in the attainment of U.S. education with black
immigrants having a slightly higher frequency of completion. For English language
proficiency, half of the immigrants in the sample are either native English speakers
(32 %) or speak the language ‘‘very well’’ (20 %). In contrast, approximately one-
third of the immigrants speak English ‘‘not well’’ or ‘‘not well at all.’’ More than
half of black immigrants speak English as a native language, but only 20 % of
Latino immigrants and 25 % of Asian immigrants are native speakers. Latinos are
least proficient in English. For U.S duration, the average duration is almost 14 years
and Asian immigrants have been in the United States for the least amount of time
while Latinos and whites have the longest duration of residence.
Table 2 continued
Model 1 Model 2 Model 3 Model 4
Marital status (ref = married)
Never married – – – -39.416
(1.492)***
Separated – – – -57.736
(2.122)***
Divorced – – – -67.203
(1.465)***
Widowed – – – -65.385
(2.195)***
Residence (ref = northeast)
Midwest – – – -8.135
(1.755)***
South – – – -15.473
(1.630)***
West – – – 3.171 (2.081)
Urban (ref = rural) – – – 10.932 (1.220)***
2004 panel (ref = 2001
panel)
– – – 7.345 (1.123)***
Intercept 94.661*** 93.830*** 93.171*** -133.669***
a Significantly different (p \ 0.05, two-tailed) from ‘‘Asian’’ coefficient
b Significantly different (p \ 0.05, two-tailed) from ‘‘Latino’’ coefficient
* p \ 0.05; ** p \ 0.01; *** p \ 0.001, two-tailed
Wealth Inequality Among Immigrants… 161
123
T a b le
3 C
o e ffi
c ie
n ts
a n d
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h (i
n th
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n d s)
, S
IP P
2 0 0 1
a n d
2 0 0 4 , N =
7 0
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s
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th
R a c e /e
th n ic
it y
(r e f =
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- 3 2 .8
1 0 *
- 4 4 .9
3 0
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c k
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- 1 2 .6
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a ,b
- 2 1 .9
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a ,b
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a ,b
- 3 9 .4
1 1 * * *
a ,b
- 4 8 .2
9 1 * * *
a ,b
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a ,b
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a ,b
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st a tu
s (r
e f =
n a ti
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)
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im m
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st a tu
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2 4 * * *
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- 5 1 .9
5 8 *
P la
c e
o f
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(r e f =
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3 .8
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6 9 *
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sh la
n g u a g e
p ro
fi c ie
n c y
(r e f =
n a ti
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sp e a k e r)
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w e ll
- 3 .5
4 0
- 3 .2
3 4 *
- 4 .4
7 6 * *
- 3 .6
1 5
- 1 .8
4 3
- 0 .8
4 1
- 0 .3
6 6
- 3 .4
2 5
7 .8
4 6
W e ll
- 7 .5
0 6
- 7 .7
9 2 * * *
- 1 0 .4
3 1 * * *
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0 7 * * *
- 1 1 .7
5 6 * * *
- 1 5 .3
2 4 * *
- 1 1 .2
9 0
- 5 .7
1 5
- 9 .3
3 3
N o t
w e ll
- 1 2 .4
9 4 * * *
- 1 0 .0
6 6 * * *
- 1 5 .5
7 4 * * *
- 1 8 .0
8 8 * * *
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1 5 * * *
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0 4
N o t
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a ll
- 1 4 .6
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U .S
. d u ra
ti o n
0 .1
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0 .2
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1 .5
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M o
d e ls
in c lu
d e
th e
c o
n tr
o l
v a ri
a b
le s
d e sc
ri b e d
in th
e te
x t
a n
d d
is p
la y
e d
in M
o d e l
4 ,
T a b
le 2
a S
ig n
ifi c a n
tl y
d if
fe re
n t
(p \
0 .0
5 ,
tw o
-t a il
e d
) fr
o m
‘‘ A
si a n
’’ c o
e ffi
c ie
n t
b S
ig n
ifi c a n tl
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t (p \
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fr o m
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162 M. A. Painter II, Z. Qian
123
Median Wealth by Race/Ethnicity and U.S. Experience
To provide insight into two dimensions of immigrants’ U.S. experience, we graph
wealth by race/ethnicity and immigrant status. Several patterns stand out in Fig. 2.
First, there is a clear contrast between the wealth of Asians and whites and that of
blacks and Latinos. The wealth inequality between Asians/whites and blacks/
Latinos is larger for native-born and naturalized citizens while the gap is smallest
among immigrants with LPR status at arrival and those with a non-LPR status.
Second, with the exception of Asians, there is a wealth divide between citizens—
either native born or naturalized—and immigrants. As shown in Fig. 2, naturalized
citizens have the highest median wealth within each racial/ethnic group, followed
by the native born for every group except for Asians. Last, there are differences in
wealth by immigrant status within racial/ethnic groups. Among immigrants,
adjusted LPRs have a higher median wealth value, followed by LPRs at arrival, and
then by other immigrants.
Median Quantile Regression Results
Table 2 contains four variable-nested models estimated with median regression.
Together, these models document the endurance of racial/ethnic wealth inequality,
after accounting for important dimensions of the U.S. experience. Model 1
introduces the race/ethnicity variables. In comparison to whites, all racial/ethnic
minorities are associated with less wealth. The smallest wealth inequality is between
Asians and whites with Asians having $22,482 less wealth. The wealth gap between
both blacks and Latinos and whites is much larger. The model predicts that these
groups have more than $85,000 less wealth than whites.
$0
$20
$40
$60
$80
$100
$120
$140
Total Asian Black La�no White
M ed
ia n
N et
W or
th (i
n th
ou sa
nd s)
Na�ve-born Naturalized ci�zen LPR at arrival LPR via adjustment Other immigrant
Fig. 2 Median net worth by race/ethnicity and immigrant status
Wealth Inequality Among Immigrants… 163
123
Model 2 adds the immigrant status variables. 14
With the introduction of these
variables, the racial/ethnic coefficients change relatively little. For the immigrant
status variables, naturalized citizens are advantaged relative to the native born with
a wealth premium of $33,993. In contrast, immigrants have less wealth. In
comparison to the native born, wealth inequality is greatest for immigrants with a
non-LPR status ($16,270), followed by LPRs at arrival ($11,602) and adjusted LPRs
($8708).
Model 3 introduces the remainder of the U.S. experience variables and their
addition only slightly changes the racial/ethnic and immigrant status results from the
previous models. The most notable change is that the coefficient for naturalized
citizens is no longer significant, which signals wealth parity between this group and
the native born. Immigrants who are not native English language speakers
experience wealth disadvantage ranging from $6186 for those who speak English
‘‘very well’’ to $19,306 for those who speak English ‘‘not at all.’’ Time spent in the
United States leads to greater wealth: each additional year produces a $1185
increase in wealth.
Model 4 is the full model with controls. With the addition of the control
variables, the coefficients for the race/ethnic and immigrant status variables are
reduced (substantially so for blacks and Latinos), but remain significant.
Beginning with race/ethnicity, t-tests for the equality of coefficients suggest a
three-tiered racial/ethnic hierarchy. Whites have the most wealth and both Asians
and Latinos occupy the second tier. These groups have more than $17,000 less
wealth than whites. Blacks are at the bottom of the racial/ethnic wealth hierarchy
and have $39,411 less wealth. For the immigrant status variables, the wealth gap
between the native-born and naturalized citizens is the smallest ($21,353) while
immigrants are clustered closely together, independent of their LPR or non-LPR
status. A U.S. education is advantageous relative to a foreign education with a
wealth premium of $17,132. Speaking English with proficiency below ‘‘very
well’’ is related to less wealth, ranging from $11,756 less wealth for immigrants
who speak English ‘‘well’’ to $30,295 for immigrants who speak English ‘‘not at
all.’’ Last, for U.S. duration, the model predicts $537 more wealth for each
additional year of residency.
Together, the results are largely in line with expectations. The wealth gaps
between whites and both Asians and blacks support the racial wealth inequality
expectations from the literature; however, that the wealth inequality between
Latinos and whites is similar to the Asian/white contrast is not expected. For the
other U.S. experience variables, the results generally reflect our expectations:
U.S. education and duration lead to greater wealth while immigrants and those
with lesser English language proficiency have lower wealth.
14 In supplemental analyses, we explored interactions between the race/ethnicity and immigrant status
variables. The results suggested that there was little variation by immigrant status within racial/ethnic
groups.
164 M. A. Painter II, Z. Qian
123
Quantile Regression Results by Conditional Decile
We now explore how race/ethnicity and immigrants’ U.S. experience affects wealth
inequality across the entire conditional wealth distribution. Model 4 is re-estimated
with threshold, s, set to deciles. Racial/ethnic wealth inequality is consistent at most points of the conditional
wealth distribution. The three-tiered hierarchy identified from median regression
(see Table 2) holds for most of the conditional wealth distribution. It is only at the
10th and 90th percentiles that a different pattern emerges. At the top of the
conditional wealth distribution, Asians and whites and then blacks and Latinos have
equivalent wealth. At the bottom of the conditional wealth distribution, a black/non-
black wealth inequality is present.
For immigrants, wealth inequality between the native-born and naturalized
citizens is evident across most of the conditional wealth distribution. Among
immigrants, there appears to be little distinction between LPR and non-LPR
immigrants at the bottom half of the conditional wealth distribution. Above the
median, there is more variation by LPR status.
The remaining explanatory variables largely reflect the pattern identified at the
median. U.S. education has a wealth premium, an advantage that grows across the
conditional wealth distribution. Those who speak English ‘‘very well’’ are generally
associated with a level of wealth equivalent to native English speakers. English
proficiency below this level results in lower wealth. Last, the models predict that
time spent in the United States generates wealth over most of the wealth
distribution; however, above the 70th percentile there is no relationship between
U.S. duration and wealth.
Quantile Regression Results by Conditional Decile—Immigrant Subsample
Table 4 displays results from the same models as Table 3 for the immigrant
subsample. 15
Overall, the results show that race/ethnicity plays an important role for
immigrant wealth inequality. In contrast to the full sample (see Table 3), Asian
immigrants have an equivalent level of wealth as white immigrants across most of
the conditional wealth distribution. Black and Latino immigrants consistently have
less wealth than white immigrants with blacks having the least wealth. T-tests for
the equality of coefficients indicate that black and Latino immigrants have
equivalent levels of wealth above the 70th percentile of the conditional wealth
distribution.
For the other explanatory variables, immigrants are associated with less wealth
than naturalized citizens for most of the conditional wealth distribution. Similarly,
lower levels of English proficiency are consistently associated with less wealth.
Longer durations in the United States produce positive wealth returns. Counter to
expectations, the association between U.S. education and wealth is inconsistent.
15 The one difference is that the reference group for immigrant status is the native born in Table 3 and
naturalized citizens in Table 4.
Wealth Inequality Among Immigrants… 165
123
T a b le
4 C
o e ffi
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n ts
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IP P
2 0
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4 ,
im m
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ly su
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s
1 0
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0 th
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0 th
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in th
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x t
a n
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la y e d
in M
o d e l
4 ,
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ifi c a n tl
y d
if fe
re n
t (p \
0 .0
5 ,
tw o
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o m
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si a n
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, tw
o -t
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166 M. A. Painter II, Z. Qian
123
Overall, the racial/ethnic hierarchy in financial well-being observed among
immigrants is most similar to the pattern observed with the larger sample for blacks
and Latinos and is less consistent with Asians. While the exact ordering of the
racial/ethnic groups is slightly different, it is clear that race/ethnicity plays an
important role for wealth inequality among immigrants as among the native born.
Quantile Regression Results by Conditional Decile—Native-Born Subsample
Table 5 presents results for the native-born sample. Here, the pattern of racial/ethnic
inequality is quite similar to that presented for immigrants in Table 4. Native-born
Asians have equivalent wealth as native-born whites across the entire conditional
wealth distribution. T-tests for equality of coefficients indicate that blacks and
Latinos have distinct levels of wealth from the 20th to the 70th percentiles, with the
racial/ethnic gap with whites greater for blacks. Overall, these results document the
consistency of racial/ethnic inequality across the conditional wealth distribution
with the racial/ethnic inequality patterns observed among the native born
resembling those of immigrants.
Discussion and Conclusion
Immigrants move to the United States, at least in part, to pursue a higher standard of
living and improve their financial well-being (Portes and Rumbaut 2006). Social
scientists have long been interested in immigrants’ economic integration into U.S.
society, but much of the previous research focuses on immigrants’ low income and
poverty (e.g., Lichter et al. 2005; Smith and Edmonston 1997). A relatively new
aspect of scholarly interest in immigrant financial well-being is wealth or net worth.
Given that economic mobility is often the primary goal for immigrants in the United
States, wealth—or the lack thereof—is a strong indication of immigrant integration
in U.S. society (Farley 1996; Kritz and Gurak 2001).
We examined racial/ethnic wealth inequality across the full conditional
distribution of net worth. This approach allowed us to assess the effect of race/
ethnicity on wealth, given other characteristics, among those with differing amounts
of financial resources. To understand the implications of race/ethnicity for
immigrant integration, we used new assimilation theory to acknowledge that race/
ethnicity is a social boundary, which hinders minority immigrants’ ability to
integrate into U.S. society no matter where they fall on the conditional wealth
continuum. Immigrants’ incorporation across the conditional wealth distribution
depends not only on larger social structures and institutional constraints, but also on
social, economic, and cultural differences that are associated with race/ethnicity at
the individual level (Alba and Nee 2005; see also Omi and Winant 1994). In this
way, we posited that immigrants’ integration into U.S. society would reflect existing
racial/ethnic inequalities across the entire distribution of net worth.
In order to compare other studies that analyze the conditional mean using
conventional regression techniques, we began to explore immigrant and native-born
Wealth Inequality Among Immigrants… 167
123
T a b le
5 C
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n ts
a n d
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h (i
n th
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, S
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th 2
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it y
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in c lu
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c o n tr
o l
v a ri
a b le
s d e sc
ri b e d
in th
e te
x t
a n d
d is
p la
y e d
in M
o d e l
4 ,
T a b le
2 a
S ig
n ifi
c a n
tl y
d if
fe re
n t
(p \
0 .0
5 ,
tw o
-t a il
e d
) fr
o m
‘‘ L
a ti
n o
’’ c o
e ffi
c ie
n t
* p \
0 .0
5 ;
* * p \
0 .0
1 ;
* *
* p \
0 .0
0 1
, tw
o -t
a il
e d
168 M. A. Painter II, Z. Qian
123
racial/ethnic wealth inequality in the middle of the wealth distribution. We found
racial/ethnic inequality that corresponded to three tiers: whites, Asians/Latinos, and
blacks. These results align with a large body of research (e.g., Blau and Graham 1990;
Conley 1999; Hao 2004, 2007; Keister 2000a, 2004; Killewald 2013; Oliver and
Shapiro 2006; Smith 1995). Most of this research focuses on the black–white divide
and relatively few studies compare whites and either Asians or Latinos (e.g., Campbell
and Kaufman 2006; Hao 2004, 2007; Painter 2013; Painter and Qian Forthcoming).
We then demonstrated that racial/ethnic inequality is persistent over the whole
spectrum of the wealth distribution. For both natives and immigrants, we found that
blacks consistently had the least amount of wealth, and Asians and Latinos were at
the middle and had similar levels of wealth across most of the conditional wealth
distribution. The only variation from this pattern was at the bottom of the
conditional wealth distribution for Asians and Latinos and at the top of the
conditional distribution for Asians, indicating the absence of racial/ethnic disad-
vantage for these groups at these particular locations in the wealth distribution,
conditional on the other characteristics controlled in the model. Overall, racial/
ethnic wealth differentiation is consistent across the conditional wealth distribution.
We then analyzed immigrants and the native born separately. We focused on the
overall pattern of racial/ethnic inequality across the two conditional wealth
distributions. A clear racial/ethnic hierarchy was again present with blacks having
the least wealth of the racial/ethnic groups across the conditional wealth distribution.
A key difference was the equivalence in wealth between Asian and white natives and
the near equivalence between Asian and white immigrants. Along with the findings
based on both natives and immigrants, it is clear that Asian and white immigrants were
behind their native-born counterparts in wealth and there is a three-tiered hierarchy in
wealth for immigrant and native-born subsamples—Asians and whites on top, Latinos
in the middle, and blacks at the bottom. Together, these results demonstrate the
robustness of racial/ethnic wealth inequality, even when accounting for important
dimensions of the U.S. experience and other factors that influence wealth.
Clearly, racial/ethnic inequality affects immigrants as well as the native born,
highlighting the importance of racial/ethnic realities in the United States,
independent of other factors like nativity status, time in the United States, and
educational and linguistic resources. As far as we are aware, this is the first study to
examine the consistency of racial/ethnic inequality across the conditional wealth
distribution. This approach moves beyond the mean and explores how the
relationships among their concepts and variables of interest (do not) change across
the full distribution of their outcome variable.
Our second contribution focused on how other dimensions of immigrants’ U.S.
experiences help improve their financial well-being and integrate into U.S. society.
We included four indicators of U.S. experience: immigrant status, place of
education, English language proficiency, and time spent in the United States.
Overall, these factors affect wealth across the conditional wealth distribution and
explain some of the racial/ethnic wealth inequality. Yet, racial/ethnic differences in
net worth persist across the wealth continuum.
For the specific factors, naturalized immigrants in the United States, at least in
theory, should have access to the same resources, privileges, and rights as native-
Wealth Inequality Among Immigrants… 169
123
born citizens; however, we found that naturalized citizens had less wealth at most
points on the conditional wealth distribution and had wealth more similar to those of
other types of immigrants. While we cannot explore inheritances or remittances
with SIPP data, this difference could reflect naturalized immigrants’ (and their
families’) relative lack of wealth inherited across generations and even the financial
environments of their home countries prior to their migration. After all, many of
them had similar experiences as other immigrants because many did not naturalize
until they were adults. For the other dimensions of immigrants’ U.S. experiences,
when compared to native speakers, lower levels of English language proficiency
were associated with lower levels of financial well-being (Chatterjee and Kim 2011;
Fontes 2011; Kim et al. 2012; Painter 2013; but see Osili and Paulson 2008).
English language proficiency clearly helps immigrants improve their financial well-
being with better access to good jobs and U.S. financial institutions. (e.g., Chiswick
and Miller 2002; Hall and Farkas 2008; Tainer 1988). Last, U.S. education and time
spent in the United States were consistently related to improved financial well-
being, though U.S. education probably was more tied to educational attainment and
did not affect wealth for most of the conditional wealth distribution among
immigrants. These findings mostly reflect prior research and illustrate the value of
gaining U.S.-specific resources (e.g., Akresh 2011; Chatterjee and Kim 2011; Cobb-
Clark and Hildebrand 2006c; Kim et al. 2012; Hao 2007; Painter 2013).
Our research informs several policy recommendations. Overall, our research calls
attention to the importance of immigrants’ race/ethnicity for wealth inequality. For
racial/ethnic inequality, such policies would focus on the relative lack of wealth for
blacks and Latinos. Here, we echo the policy recommendations of Sykes (2003) who
called for the targeting of specific assets and investments in order to reduce racial/
ethnic wealth inequality. For example, policies that promote and facilitate home-
ownership—a key asset for wealth attainment—would contribute to the narrowing of
racial/ethnic wealth inequality. Other assets including non-primary home real estate,
savings accounts, and stocks/bonds would also benefit from policies that increase
access to and ownership of these key investments that all contribute to both diversified
and balanced portfolios. For immigration policy, our research demonstrates that
naturalization, U.S. education, and English language proficiency increase immigrants’
wealth. While it may be more difficult to formulate policies to increase immigrants’
educational attainment in the United States, policies that encourage naturalization and
help immigrants improve proficiency in English are more achievable goals.
Along with the contributions of this study, we need to acknowledge its
limitations. We do not have information on immigrants’ financial well-being at the
time of their arrival. This information would be valuable because it would provide
insight into the actual processes underlying wealth attainment, rather than provide
insight into levels of wealth at a single—and arbitrary—point in time. Although we
have information such as educational attainment and whether immigrants received
U.S. education as proxies of their pre-immigration socioeconomic position, SIPP
does not have direct information on pre-immigration wealth or forms-of-capital;
therefore, we cannot completely account for compositional differences—in terms of
social, human, and/or financial capital—across immigrants’ racial/ethnic groups as
these resources vary by national origin and/or race/ethnicity (see Alba and Logan
170 M. A. Painter II, Z. Qian
123
1993). Further, SIPP does not contain information on spouses and their wealth. We
control for marriage because it is an important factor for wealth. This control also
accounts for differences in marital composition between natives and immigrants
given that more immigrants are likely to be married than their native counterparts
(Qian 2013). Therefore, our results are likely robust to differences in marriage
composition, though it would be interesting for future research to explore how
wealth differences in nativity are affected by marriage and marital wealth. There is
also no information on remittances in the SIPP. Remittances reduce immigrants’
investment capacity in the United States, but may represent investment if
immigrants send money to purchase and/or maintain assets back in their home
country. The overall impact of remittances on wealth in the United States, however,
may actually be small. For example, research examining U.S. wealth attainment
among immigrants who recently received LPR status shows that less than 10 % of
immigrants report sending more than $500 in the past year to their home country
(Painter and Qian Forthcoming; Painter et al. 2016).
To close, immigrants move to the United States for an opportunity to expand
their life chances. Upon arrival, immigrants’ racial/ethnic status affects their ability
to achieve this goal. This paper shows that for some groups, namely, blacks and
Latinos, the U.S. social structure serves as a barrier to economic integration and
improved financial well-being. Other groups, like Asians, may encounter some
obstacles to wealth attainment, but they also are advantaged in key ways (e.g.,
educational attainment, socioeconomic status) that help facilitate their incorporation
into U.S. society. Overall, this study documents persistent racial/ethnic inequality,
revealing that even when accounting for key aspects of U.S. experience, wealth
parity with whites for racial/ethnic minorities is not attained. This suggests that the
very opportunities that immigrants pursue with their relocation to the United States
are stratified and this inequality may exist for quite some time.
Acknowledgments We thank Ariela Schachter for helpful comments on a previous draft. This research was supported in part by Grant R03HD058693 from the National Institute of Child Health and Human
Development (principal investigators: Zhenchao Qian and Matthew Painter).
Appendix
See Table 6.
Table 6 Net worth for full sample and racial/ethnic groups, by select percentiles
Percentiles
10th 20th 30th 40th 50th 60th 70th 80th 90th
Full sample -$1915 $1288 $9890 $32,926 $66,915 $109,528 $174,390 $278,621 $485,108
Asian $0 $2000 $10,100 $29,372 $70,275 $133,080 $215,660 $335,031 $514,842
Black -$4645 $0 $0 $2100 $8425 $25,251 $50,216 $88,257 $173,310
Latino -$4647 $0 $800 $3600 $9080 $24,790 $53,181 $98,798 $204,444
White -$853 $4560 $22,780 $54,815 $92,917 $142,835 $214,100 $326,680 $547,790
Wealth Inequality Among Immigrants… 171
123
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- c.11113_2016_Article_9385.pdf
- Wealth Inequality Among Immigrants: Consistent Racial/Ethnic Inequality in the United States
- Abstract
- Introduction
- Conceptual Framework
- Assimilation, Immigrant Integration, and Racial/Ethnic Realities
- Race/Ethnicity and Wealth
- Asians
- Blacks
- Latinos
- Immigrants’ U.S. Experience
- Data and Methods
- Data
- Measures
- Net Worth
- Explanatory Variables
- Control Variables
- Analytical Approach
- Results
- Descriptive Results
- Median Wealth by Race/Ethnicity and U.S. Experience
- Median Quantile Regression Results
- Quantile Regression Results by Conditional Decile
- Quantile Regression Results by Conditional Decile---Immigrant Subsample
- Quantile Regression Results by Conditional Decile---Native-Born Subsample
- Acknowledgments
- Appendix
- References