Sociology Mind
2012. Vol.2, No.1, 23-33
Published Online January 2012 in SciRes (http://www.SciRP.org/journal/sm) http://dx.doi.org/10.4236/sm.2012.21003
Copyright © 2012 SciRes. 23
23
Beyond the Shadow of White Privilege?: The Socioeconomic
Attainments of Second Generation South Asian Americans
Hyeyoung Woo1*, Arthur Sakamoto2, Isao Takei3
1Department of Sociology, Portland State University, Portland, USA
2Department of Sociology, Univ ersity of Texas, Austin, US A
3Department of International Relations, Nihon University, Mishima-City, Japan
Email: *hyeyoung@pdx.edu
Received June 7th, 2011; revised September 4th, 2011; accepted November 15th, 2011
Despite numerous studies of second generation minorities in recent years, South Asian Americans have
been largely ignored. Using the most recent data available from the US Census Bureau, we investigate the
socioeconomic attainments of second generation South Asian Americans. We find that their average lev-
els of education, wages, and occupational attainment exceed those of non-Hispanic whites. Contrary to
the “model minority myth” view, second generation South Asian Americans remain slightly advantaged
relative to non-Hispanic whites in terms of labor market success net of age, education, and region of resi-
dence. These results are also inconsistent with discussions of white privilege that emphasize the socio-
economic disadvantages of minorities with darker skin tones. Our results suggest that theories of race re-
lations should also incorporate South Asian Americans.
Keywords: South Asian Americans; White Privilege; Second Generation; Wages
Introduction
South Asian Americans and White Privilege
Prior research has not investigated the socioeconomic at-
tainments of second generation South Asian Americans. Be-
cause South Asian Americans are currently classified into the
Asian American category as defined by the US Census Bureau,
they have been included in the samples used to study Asian
Americans overall (Sakamoto & Furuichi, 2002). However,
South Asian Americans have rarely been considered separately
using multivariate statistical analysis with nationally represen-
tative data. Given their increasing population size and high
growth rate (Min, 2006), this omission from the literature needs
to be addressed. We still do not have adequate information
about how the socioeconomic attainments of South Asian Ame-
ricans may differ from those for non-Hispanic whites and Asian
Americans overall (see Sakamoto, Goyette, and Kim (2009) for
a recent review of the latter).
In addition to being a significant demographic group worthy
of investigation, South Asian Americans have a broader theo-
retical significance in terms of the debate on white privilege.
Although the literature on the latter is complex and highly
theoretical, a common assumption in many of these writings is
that racial and ethnic minorities with darker skin tones are dis-
advantaged in American society (Bonilla-Silva, 2001; Feagin,
2001; Omi & Winant, 1994). As discussed by Saenz and
Morales (2005), the “whiteness” literature emphasizes the ex-
tent to which whites gain socioeconomic advantages because of
“structural arrangements” that provide them greater opportuni-
ties in terms of college admissions, job interviews, improved
labor market rewards, and greater acceptance by whites in de-
sirable residential neighborhoods and schools. By contrast,
darker-skinned minorities are incorporated into subordinate
positions in the “racialized stratification system” due to their
“collective blackness” (Saenz & Morales, 2005: p. 173). Whites
will be always at the top of the social structure, and “a hierar-
chical racial order continues to shape all aspects of American
life” (Bonilla-Silva & Glover, 2004: p. 28).
According to Bonilla-Silva (1997), prejudice against persons
with darker skin tones has been so thoroughly ingrained into
American culture for so long that these negative attitudes can-
not be easily dismantled. Bonilla-Silva (1997: p. 475) argues
that American institutions have evolved with centuries of racist
relations so that racism must still be a significant feature of
American labor markets because “racialization develops a life
of its own”. As stated by Feagin and Vera (1995: p. 7), “White
racism can be viewed as the socially organized set of attitudes,
ideas, and practices that deny African Americans and other
people of color the dignity, opportunities, freedoms, and re-
wards that this nation offers white Americans”. Because whites
will probably become a numerical minority in the US by 2070,
whites need to preserve and consolidate their racial power by
continuing to denigrate darker-skinned persons and to maintain
the socioeconomic privileges of light-skinned persons (Bonilla-
Silva, 2003).
Our objective here is not, however, to summarize or review
the literature on white privilege, but to investigate the socio-
economic attainments of second generation South Asian Ameri-
cans which relate to one the most basic assumptions of that
literature. In doing so, we assume that South Asian Americans
have, at least on average, darker skin tones than white Ameri-
cans of European ancestry (i.e., non-Hispanic whites). For this
reason, the white privilege literature suggests the hypothesis
that South Asian Americans should face a net racial disadvan-
tage in their socioeconomic attainments. Assuming that they
tend to have darker skin tones (on average), South Asian Ameri-
*Corresponding author.
H. WOO ET AL.
cans are predicted to face fewer opportunities in the labor mar-
ket and are consequently hypothesized to have lower wages and
occupational attainment (on average) relative to non-His- panic
whites (i.e., persons with lighter skin tones) after controlling for
relevant educational attainment and other demographic factors
relating to l abor market outcomes.
In drawing out this hypothesis, we hasten to add and fully
recognize that skin tones vary considerably within the South
Asian and South Asian American communities. This fact is
reflected in the early history of South Asian Americans when
debates considered whether South Asians should be classified
as whites (Takaki, 1998: pp. 294-301; Kitano & Daniels, 2000:
p. 107) due to the fair complexions of at least some persons
from South Asia. Most of the early South Asian immigrants
were from northern India (i.e., Punjab) and they are sometimes
described as having light skin tones (Takaki, 1998). Although
the Thind decision handed down by the US Supreme Court in
1923 ruled against the legal treatment of South Asians as being
classified as white, the fact that many states had earlier ruled in
favor of categorizing South Asians as whites (Jensen, 1988) is
suggestive of light skin tones among at least some of the South
Asian immigrants especially at that t ime.
In our study, we do not have data on the skin tones of the re-
spondent nor are we aware of any socioeconomic data for the
US that includes such information. We therefore do not directly
test the white privilege hypothesis using data on skin color. We
can, however, indirectly investigate the hypothesis by making
the assumption that South Asian Americans have darker skin
tones than non-Hispanic whites albeit only in terms of an aver-
age (i.e., not in all individual cases). Nonetheless, because our
statistical analyses investigate average tendencies in the data
(i.e., using regression analysis which is in essence a multivari-
ate model of conditional averages), this on-average reasoning is
appropriate given our research methods.
Consistent with our major assumption, Stokowski et al.
(2007) identify a genetic basis for skin tone in the South Asian
population. Their results show that genetic polymorphisms
relating to three specific genes (i.e., SLC24A5, TYR, and
SLC45A2) explain a very large fraction of the melanin content
of skin across the South Asian individuals in their study. This
research was based on a volunteer sample of over 50 adults
who identified themselves as being of “South Asian descent”.
The results furthermore show that, based on the birthplaces of a
respondent’s four grandparents in terms of the specific South
Asian locale organized into regional areas (i.e., Punjab, Gujarat,
Ski Lanka, Bangladesh, etc.), variation in the level of melanin
content can be substantially explained. Stokowski et al. (2007:
pp. 1129-1130) also discuss how related research on samples of
the white population (who identify themselves as having a
European ancestry) tend to have notably different genetic pat-
terns that are associated with lower melanin content as com-
pared to their South Asian sample. Thus, the results of Sto-
kowski et al. (2007) are consistent with our underlying assump-
tion that South Asian Americans tend to have darker skin tones
than non-Hispanic whites at least on average.
Recent qualitative research by Dingra (2003) furthermore
finds that second generation South Asian Americans continue
to be generally viewed as being a non-white racial minority.
Even when they may be highly educated and employed in a
relatively high status occupation, second generation South
Asian Americans are generally seen by whites as having a
separate ethnic identity that needs to be curtailed and highly
controlled in a professional setting. When combined with the
findings of Stokowski et al. (2007) regarding the tendency for
darker skin tones on average, the results of Dingra (2003) un-
derscore the need to investigate the possibility of continuing
racial discrimination against second generation South Asian
Americans because they appear to be readily viewed by whites
as being a non-white racial minority.
Studying t he S oc i oe conomic Attainments of Second
Generation Asian Americans
The sociological literature on the socioeconomic attainments
of Asian Americans has often emphasized the view that this ra-
cial category faces discrimination in the labor market (Sakamoto,
Goyette, & Kim, 2009). Perhaps the most famous reference on
this topic is Hirschman and Wong (1984) who argued for the
“model minority myth” (MMM) view which states that “Asian
Americans approach socioeconomic parity with whites because
of their overachievement in educational attainment” (Hirschman
& Wong, 1984: p. 584). That is, Hirschman and Wong (1984)
contend that the average earnings and occupational attainments of
Asian Americans do not differ very much from those of whites.
However, because Asian Americans tend to have higher educa-
tional attainments than do whites, the labor market is said to be
actually discriminating against Asian Americans in as much as
they must make a higher investment in human ca pital in order to
obtain the same overall socioecono mic rewards as do whites. As
stated by Hirschman and Wong (1984: p. 602), “The apparent
equality between Asians and whites is largely a function of edu-
cational overachievement by Asians. If Asians experienced the
same proces s of stratification as white s, their educational creden-
tials would shift their (Asians) occupational and earnings levels
substantially above those of the majority population”.
A succinct summary of the MMM perspective is provided by
(Hurh & Kim, 1989: p. 512) who concluded that “our analyses
in the light of the principle of earnings equity indicate that the
success image (of Asian Americans) is largely a myth due to
labor market disadvantages and other related social problems”.
Similar conclusions are reached by Zhou and Kamo (1994),
Waters and Eschbach (1995), McCall (2001), Zhou (2004), and
Snipp and Hirschman (2005). According to Hirschman and
Snipp (2001: p. 634):
…the sources of the Chinese and Filipino disadvantage are
current residence, labor market positions, and unmeasured fac-
tors. Their potential disadvantage is reduced by their higher
levels of schooling. In fact, their educational advantage over
whites generates (all else equal) about a $5000 gain. Without
this educational “boost”, their economic situations would be
similar to the level of blacks, American Indians, and Hispan-
ics… These results—the persistence of race and ethnic differ-
entials in late twentieth-century America—challenge conven-
tional theories about the declining role of ascribed factors in the
American stratification system…
Though not extensively connected in prior re searc h, the MMM
view (as evident in the above study by Hirschman and Snipp
among others) seems to be broadly consistent with the white
privilege literature discussed above. Both approaches represent a
fundamental critique of American society in that they both point
to continuing racism as embedded in existing institutional struc-
tures including those pertaining to labor market outcomes.
In order to investigate these perspectives systematically with
2
4 Copyright © 2012 SciRes.
H. WOO ET AL.
empirical data, we study the most recent data from the US
Census Bureau. Due to the lack of a prior quantitative research
on South Asian Americans to build upon, we emphasize that
our investigation represents only a small first step towards a
broader literature that hopefully will eventually develop. While
a full blown statistical analysis of the entire South Asian
American community is ultimately desirable, space and data
constraints prevent us from engaging in a more extensive
analysis in this paper.
First, we limit our study to the second generation. Kibria’s
(2006) descriptive statistics indicate some significant socio-
economic differences between foreign-born and native-born
South Asian Americans. In addition, previous research on
Asian Americans finds that native-born Asian Americans sys-
tematically differ from their foreign-born immigrant counter-
parts in terms of labor market processes (Zeng & Xie, 2004). In
keeping with prior research, we follow the practice of including
in our analysis persons who are foreign born but who came to
the US at a young age (i.e., 12 years old or younger) and are
therefore schooled and socialized primarily in the US (Portes &
Rumbaut, 2005; Portes & Zhou, 1995). When there is a need to
be specific, we refer to this latter group as the “1.5 generation”.
Following the usual custom in this field, however, we use the
term “second generation” to include both the 1.5 generation as
well as the native-born offspring of foreign-born immigrants
(Farley & Alba, 20 02).
From a more substantive point of view, our specific research
objective is to estimate the net racial disadvantage for South
Asian Americans rather than assessing the various disadvan-
tages of being an immigrant. Immigrants are less familiar with
American labor market practices that may be further obfuscated
by cultural differences and reduced social networks (e.g.,
Duleep & Regets, 1997; Min, 1995). The quality of training
obtained in foreign universities is often of lower quality than
that obtained in US universities (Bratsberg & Ragan, 2002). As
noted by Sanders and Nee (1996: p. 232), “US employers are
ill-prepared to evaluate foreign-earned human capital” which
exacerbates skill transfer problems among immigrants. Al-
though South Asian immigrants are much more likely than
most other Asian immigrants to speak English well, a signifi-
cant proportion of South Asian immigrants nonetheless do not
(Reeves & Bennett, 2004: p. 11) and may therefore encounter
some language problems when entering the US labor market.
Finally, foreign-born immigrants of all racial backgrounds may
be disadvantaged in the labor market due to limitations associ-
ated with visa and non-citizenship restrictions.
By contrast, these sorts of labor market issues are virtually
absent or are at least trivial for most of the second generation.
The second generation is socialized and schooled primarily in
the US and is therefore more likely to be comparable to non-
Hispanic whites in terms of unmeasured aspects of labor market
qualifications such as fluency in English. For this reason, fo-
cusing on the second generation yields estimates of net wage
differentials that may be more confidently interpreted as repre-
senting a racial disadvantage per se rather than deriving from
some aspect of immigration that is not ad e q ua tely controlled for
in the statistical model. That is, the estimated wage disadvan-
tages would be more arguably associated with the persistence
of racial discrimination that is our main theoretical concern
stemming from the MMM view and discussions of white privi-
lege.
Although Kibria (2006) provides some descriptive statistics
for Sout h Asian Americ ans using recent data, her results do not
address the hypothesis that South Asian Americans face a sys-
tematic racial disadvantage net of their investments in educa-
tion and other labor force characteristics as claimed by the
MMM approach. One earlier study, however, used the 1980 US
Census data for native-born Asian Americans and reported that
the wages of South Asian American men were about 20% lower
(as compared to non-Hispanic whites) net of education and
other demographic factors (Duleep & Sanders, 1992: p. 421).
That result supports the MMM view for second generation
South Asian American men, but the study did not find similar
evidence for East Asian American (i.e., Chinese, Filipino, Japa-
nese and Korean) men (Duleep & Sanders, 1992: p. 421). These
findings suggest the possibility that second generation South
Asian American men may be somewhat different from East
Asian American in terms of labor market outcomes. If so, then
the darker skin tones among South Asian American men might
perhaps be considered as a potential factor.
On the basis of that prior study by Duleep and Sanders
(1992), we argue that the white privilege hypothesis—that mi-
norities with darker skin tones are significantly disadvantaged
in the US labor market relative to non-Hispanic whites—needs
to be seriously investigated for second generation South Asian
Americans. In the following, we seek to test this hypothesis
using a large sample size for more recent data as well as ex-
tending the analysis to South Asian American women who have
not been previously considered in prior literature. Multivariate
analysis is needed for the study of South Asian Americans be-
cause without it, systematic evidence on racial disparities in the
labor market cannot be rigorously assessed using nationally
representative data.
Data and Methods
We use the 2006, 2007 and 2008 American Community Sur-
vey (ACS) which are the most up-to-date data available with an
adequate sample size to study racial minorities. The ACS is
also one of the very few data sets that identifies South Asian
Americans rather than lumping them into the overall Asian
American category. In the following, we define the South Asian
American category as including persons who identified them-
selves as Asian Indian, Bangladeshi, Pakistani or Sri Lankan.
The large majority of our sample is, however, Asian Indian due
to the much larger demographic size of that South Asian group
in the US.1
Our study of socioeconomic attainments is focused on per-
sons aged 25 to 64 who had some employment in the labor
force during the year prior to the survey. The OLS regression
models of the hourly wage rate use the logarithmic transforma-
tion as the dependent variable which is standard in labor force
studies due to the positive skew in the distribution of wages
(Sakamoto & Furuichi, 1997). Additional regression models are
estimated which use as the dependent variable 4 broad occupa-
tional categories that are assumed to be hierarchically rewarded
(at least on average) in terms of the rewards typically associated
with the jobs in a given occupational category (e.g., earnings,
1Unfortunately, the sample sizes for Bangladeshi, Pakistani, and Sri Lankan
Americans were too small to treat them as separate categories in our statis-
tical analysis. We do not consider other South Asian groups (e.g., Burmese,
Maldivians, Nepalese) because they are more difficult to identify with the
ACS and because they are unlikely to be present in significant numbers in
these sample data especially in regard to our target population which is the
adult second-generation.
Copyright © 2012 SciRes. 25
H. WOO ET AL.
social status, desirable work conditions, etc.). Based on the
occupational codes that are available in the ACS, these 4 hier-
archical categories include: 1) management, business, finance,
computer, engineering, science, education, legal, community
service, arts, media, healthcare and technical occupations; 2)
service, sales, office and administrative support occupations; 3)
construction, extraction, installation, maintenance, repair, pro-
duction, transportation and material moving occupations; and 4)
farming, fishing and forestry occupations. Due to the ordinal
nature of these occupational categories when used as a de-
pendent variable, the appropriate statistical model is ordered
logistic regression. In keeping with the recommendation of
ACS technical documentation, all of our computed statistics
apply sampling survey we ights.
As noted above, we limit the analysis to the second genera-
tion. Although our data do not specifically include a variable to
identify generational status, we refer to native-born South
Asian Americans who are over 25 years of age as being second
generation because the vast majority of the South Asian popu-
lation are post-1965 immigrants (Min, 2006).2 As an additional
control variable, however, these data do permit us to identify
the 1.5 generation which we define as persons who were born
in another country but who came to the US at age 12 or
younger. Our statistical analysis is broken down by gender
because our main substantive focus is on identifying racial
differentials per se.
The independent variables for both the log-wage regression
and the ordered logistic regression include years of age, a
quadratic term for years of age, a dichotomous variable to indi-
cate whether 1.5 generation, a dichotomous variable to indicate
whether South Asian, a set of dichotomous variables to indicate
the highest level of schooling completed, and a set of dichoto-
mous variables to indicate region of residence in terms of the
major US Census Bureau divisions. Because Sakamoto, Go-
yette, and Kim (2009) discuss how regional mobility is higher
among Asian Americans due to their higher educational levels
which lead them to be more involved in particular labor mar-
kets that are more likely to be national in scope (e.g., college
professors), we estimate two versions of the regression models.
The first does not include the controls for region of residence
while the second does. This approach follows the recommenda-
tion of Sakamoto, Goyette, and Kim (2009) who question
whether current region of residence can be treated as an ex-
ogenous independent variable in regard to labor market out-
comes for more highly educated workers.
Related to this issue, we note that we seek to avoid the prob-
lem of what Sakamoto and Furuichi (1997: p. 183) refer to as
“over-controlling” in regression models. Including measures
such as occupational category or industrial sector as independ-
ent variables in the regression equations results in a model in
which the racial effects are net of the type of job (in terms of
occupation or industry). These results therefore indicate racial
inequality within jobs and do not assess racial inequality that
derives from racial differences in being assigned to different
jobs in the first place (i.e., racial inequality in job attainment).
Given the research objective of estimating the total level of
racial inequality that is generated by the labor market (i.e., both
within and between jobs), the earnings regression should only
include the human capital investments, credentials, productive
abilities and other endowments that the workers bring to the
labor market very early in their work careers so that these
characteristics are not themselves a reflection of discrimination
in the labor market.
Empirical Results
Statistical Findings for Men
Table 1 shows the descriptive statistics for non-Hispanic
white (hereafter “white”) and South Asian American (hereafter
“South Asian”) men.
They are broken down for the age range from 25 to 64 and
from 25 to 40. The results for the larger age range from 25 to
64 show that the mean age for white men is 43.4 while the
mean age for South Asian men is 32.5. This is a large differ-
ence of over ten years that undoubtedly reflects the fact that
most second generation South Asians are part of the post-
1965 immigration stream as described earlier. Table 1 also
shows that the standard deviation (i.e., variability) in age is
much larger for white than for South Asian men which com-
plicates the issue of providing adequate statistical controls in
multivariate analysis. Due to these results as well as our chief
research concern of estimating the net racial effect per se, our
focus in the following will be on the more comparable age
range of 25 to 40 among wh om the mean age for white men i s
32.6 while the mean age for South Asian men is 31.4 as
shown in Table 1.
Other results in Table 1 indicate that, among men aged 25 to
40, South Asians are much more likely than whites to have a
college or graduate degree; to reside in the Middle Atlantic or
Pacific regions; to be employed in the highest occupational
category; and to be 1.5 generation. Table 1 also shows that
South Asian men have higher average wages whether measured
in terms of actual dollars or log-dollars. The sample size for
South Asian men in the 25 to 40 age range is 2240 which is
generally adequate for multivariate analysis.
Table 3 shows the estimates for the ordered logistic regres-
sion of occupational attainment for men in the 25 to 40 age
range. The estimates are generally consistent with prior re-
search in that occupational attainment is notably increased by
higher educational attainment. Other positive effects in Table 3
include age and 1.5 generation. These estimated coefficients are
fairly similar in both Models 1 and 2 indicating that they are
not much affected by controlling for region.
Contrary to the expectations of the MMM view and white
privilege theory, however, the net racial effect for South Asian
men is actually positive as well as statistically significant at any
conventional level. This statistical advantage in occupational
attainment is evident in both Models 1 and 2 in Table 3. After
controlling for region in Model 2, South Asian men have a net
advantage over white men of 77% (i.e., a multiplicative change
in the odds ratio of 1.77). This finding indicates that, after con-
trolling for age, education, 1.5 generation, and region, South
Asian men have 77% higher odds of being employed in a
higher occupational category.
Table 5 shows the estimates for the regression of log-wage
for men in the 25 to 40 age range. The estimates are generally
consistent with prior research (Sakamoto & Furuichi, 1997). In
this case, the exponent of the coefficient refers to the percent-
age change in wages for a unit change in the independent vari-
able (Sakamoto & Furuichi, 1997). For example, the results in
odel 2 indicate that, relative to high school dropouts, a
2We also exclude persons who report multi-racial ancestry although they are
very small i n n umber in the case of a d ul t South Asian America ns. M
2
6 Copyright © 2012 SciRes.
H. WOO ET AL.
Copyright © 2012 SciRes. 27
Table 1.
Descriptive statistics for me n by ag e group.
Aged 25 to 64 Aged 25 to 40
White South Asian White South Asian
Variable Proportion Proportion Proportion Proportion
Native-Born 0.99 0.49 0.98 0.45
1.5 Generation 0.01 0.51 0.02 0.55
Education
Less than Hi gh School 0.06 0.03 0.07 0.03
High School 0.29 0.08 0.28 0.08
Some College 0.22 0.10 0.22 0.09
Associate Degree 0.08 0.05 0.09 0.05
Four Year College Degre e 0.23 0.38 0.24 0.38
More than College Degree 0.12 0.36 0.09 0.36
Region
New England 0.06 0.05 0.05 0.05
Middle Atlantic 0.13 0.28 0.13 0.29
East North Central 0.19 0.13 0.19 0.13
West North Ce nt ral 0.09 0.02 0.09 0.02
South Atlantic 0.18 0.16 0.18 0.16
East South C entral 0.07 0.02 0.07 0.02
West South Central 0.09 0.09 0.10 0.09
Mountain 0.07 0.03 0.08 0.03
Pacific 0.12 0.22 0.12 0.22
Aggregated Occupation Category 1a 37.11 67.85 34.61 68.49
Aggregated Occupation Category 2b 27.00 23.98 28.69 24.21
Aggregated Occupation Category 3c 35.31 8.04 36.06 7.18
Aggregated Occupation Category 4d 0.58 0.13 0.64 0.11
Mean SD Mean SD Mean SD Mean SD
Age 43.44 60.66 32.49 38.07 32.56 28.11 31.44 27.91
Age-Squared 2002.54 5312.36 1092.10 2819.11 1082.53 1830.26 1008.10 1793.54
Wage 28.46 165.57 34.11 220.27 23.57 136.84 33.81 213.79
Log-Wage 3.06 4.10 3.20 5.04 2.92 4.01 3.20 4.99
Sample Size 1,284,972 2400 472,550 2240
Notes: aCategory 1 includes management, business, finance, computer, engineering, science, education, legal, community service, arts, media, healthcare and technical
occupations; bCategory 2 includes service, sales, office and administrative support occupations; cCategory 3 includes construction, extraction, installation, maintenance,
repair, production, transportation and material moving occupations; dCategory 4 includes farming, fishing and forestry occupations.
worker with a bachelor’s degree has a wage that is 83% higher
(i.e., exp(0.604) = 1.83 or an 83% multiplicative change in the
wage) after controlling for the effects of age, 1.5 generation,
region, and race.
As was the case with occupational attainment in Table 3, the
findings in Table 5 indicate that South Asian men are advan-
taged in the labor market in regard to wages. The results for
Model 1 in Table 5 show that, without controlling for region,
the net advantage of being South Asian is 7.8%. In Model 2, the
net advantage declines slightly to 5.8% after controlling for
region. Both of these results are highly significant indicating
that these estimated net wage advantages for South Asian men
are very unlikely to be the results of random sampling error.
Statistical Findings for Women
Table 2 shows the descriptive statistics for white and South
Asian women. They are broken down for the age range from 25
to 64 and from 25 to 40. The younger age distribution for South
sian women relative to white women is just as evident in Ta- A
H. WOO ET AL.
Table 2.
Descriptive statistics for women by age group.
Aged 25 to 64 Aged 25 to 40
White South Asian White South Asian
Variable Proportion Proportion Proportion Proportion
Native-Born 0.99 0.49 0.98 0.46
1.5 Generation 0.01 0.51 0.02 0.54
Education
Less than Hi gh School 0.04 0.02 0.04 0.02
High School 0.26 0.07 0.21 0.06
Some College 0.23 0.09 0.23 0.09
Associate Degree 0.11 0.05 0.11 0.05
Four Year College Degre e 0.23 0.40 0.28 0.40
More than College Degree 0.13 0.37 0.13 0.38
Region
New England 0.06 0.04 0.06 0.04
Middle Atlantic 0.14 0.26 0.13 0.26
East North Central 0.19 0.15 0.19 0.16
West North Ce nt ral 0.09 0.02 0.09 0.02
South Atlantic 0.18 0.19 0.18 0.19
East South C entral 0. 06 0.02 0.07 0.02
West South Central 0.09 0.09 0.10 0.08
Mountain 0.07 0.03 0.01 0.03
Pacific 0.11 0.21 0.11 0.21
Aggregated Occupation Category 1a 45.14 70.64 46.20 71.12
Aggregated Occupation Category 2b 48.57 27.24 48.21 26.75
Aggregated Occupation Category 3c 6.11 2.03 5.42 2.03
Aggregated Occupation Category 4d 0.18 0.09 0.17 0.09
Mean SD Mean SD Mean SD Mean SD
Age 43.69 59.82 32.17 36.87 32.47 27.80 31.10 26.23
Age-Squared 2024.91 5237.78 1072.73 2758.89 1077.07 1808.35 986.22 1677.05
Wage 21.16 119.78 30.28 193.59 19.55 113.30 30.63 197.35
Log-Wage 2.79 3.88 3.11 4.56 2.73 4.01 3.12 4.57
Sample Size 1,213,192 2105 434,248 1964
Notes: aCategory 1 includes management, business, finance, computer, engineering, science, education, legal, community service, arts, media, healthcare and technical
occupations; bCategory 2 includes service, sales, office and administrative support occupations; cCategory 3 includes construction, extraction, installation, maintenance,
repair, production, transportation and material moving occupations; dCategory 4 includes farming, fishing and forestry occupations.
ble 2 as it was for South Asian men relative to white men in
Table 1. We will therefore, for the same reasons, focus on the
25 to 40 age range in our discussion.
For that age range, the results in Table 2 show that the edu-
cational advantage of South Asian women over white women is
even slightly greater than the educational advantage of South
Asian men over white men (which is shown in Table 1). Rela -
tive to white women, South Asian women are also more likely
to reside in the Middle Atlantic or Pacific regions, and to be
employed in the highest occupational category. South Asian
women are more likely to be 1.5 generation as was the case for
South Asian men.
Table 4 shows the estimates for the ordered logistic regres-
sion of occupational attainment for women in the 25 to 40 age
range. The estimates are again contrary to the expectations of
the MMM view and white privilege theory. Model 2 in Table 4
shows that the occupational advantage of South Asian women
over white women is 24% after controlling for age, education,
1.5 generation, and region. The net racial advantage in occupa-
tional attainment for South Asian women is thus for smaller
2
8 Copyright © 2012 SciRes.
H. WOO ET AL.
Table 3.
Estimates of order ed logit models for men aged 25 to 40.
Model 1 Model 2
Variable Estimate Odds Ratio SE Estimate Odds Ratio SE
Intercept 1 –3.198 *** 0.027 –3.085
*** 0.027
Intercept 2 –1.560 *** 0.027 –1.442
*** 0.027
Intercept 3 3.309 *** 0.027 3.439
*** 0.027
1.5 Generation (Native-Born) 0.248 *** 1.282 0.004 0.209
*** 1.233 0.004
South Asian (Non-H i sp anic White) 0.594 *** 1.810 0.008 0.572
*** 1.772 0.008
Age 0.031
*** 1.032 0.002 0.035
*** 1.036 0.002
Age-Squared 0.000
*** 1.000 0.000 0.000
*** 1.000 0.000
Education (Less than High Sc hool)
High School 0.434 *** 1.543 0.002 0.437
*** 1.547 0.002
Some College 1.343 *** 3.832 0.002 1.339
*** 3.816 0.002
Associate Degree 1.683 *** 5.381 0.003 1.683
*** 5.381 0.003
Four Year College Degre e 3.019 *** 20.463 0.002 3.014
*** 20.377 0.002
More than College Degree 4.356 *** 77.954 0.003 4.352
*** 77.606 0.003
Region (Pac i fic)
New England –0.097 *** 0.907 0.003
Middle Atlant ic –0.150 *** 0.861 0.002
East North Central –0.343 *** 0.710 0.002
West North Central –0.350 *** 0.705 0.002
South Atlantic –0.076 *** 0.927 0.002
East South Central –0.350 *** 0.705 0.002
West South Centra l –0.120 *** 0.887 0.002
Mountain –0.066
*** 0.936 0.002
–2LL 31,507,756 31,436,249
***p < 0.001. Source: 2006-2008 American Community Survey (N = 474,790). Note: Values in parentheses are reference categories.
than the net racial advantage in occupational attainment for
South Asian men as is shown in Table 3.
Table 6 shows the estimates of the log-wage regression for
women. In Model 1 without controlling for region, the net ad-
vantage for South Asian women is 17.0% while in Model 2, the
net advantage declines slightly to 15.0% after controlling for
region. Both of these results are highly significant. The net
racial advantage in wages for South Asian women as shown in
Table 6 is evidently larger than the net racial advantage in
wages for South Asian men as shown in Table 5. Thus, the net
racial advantage in occupational attainment for South Asian
women (i.e., Table 4) is smaller than the net racial advantage in
occupational attainment for South Asian men (i.e., Table 3) but
the net racial advantage in wages for South Asian women (i.e.,
Table 6) is larger than the net racial advantage in wages for
South Asian men (i.e., Table 5).
Discussions and Conclusion
According to Collins (1989), social theory differs from ide-
ology in several ways, but perhaps the most critical difference
is that social theory seeks to amend its explanations of social
phenomena by comparing empirical data to the predictions
derived from it. That is, social theory differs from ideology in
that the latter is much less concerned with systematically test-
ing whether its expectations about the empirical world are actu-
ally in fact ever observed. Social theory should therefore con-
stantly strive to be relevant by providing valid explanations of
the real world in the sense that its analytical predictions are
compared with the observed facts and then amended in order to
seek to coincide with them as much as possible.
In the foregoing, we have investigated the socioeconomic at-
tainments of second generation South Asian men using the
most recent data. The empirical results show that this group has
higher education, occupational attainment, and wages than
white men. Indeed, even after controlling for age, education,
1.5 generation, and region, South Asian men still remain
slightly advantaged over white men in terms of having 5.8%
higher wages and 77% higher odds for occupational attainment.
Similar results are evident for South Asian women although
their net advantage over white women is 15% higher wages and
24% higher odds for occupational attainment.
Copyright © 2012 SciRes. 29
H. WOO ET AL.
Table 4.
Estimates of or de red logit models for wom en aged 25 to 40.
Model 1 Model 2
Variable Estimate Odds Ratio SE Estimate Odds Ratio SE
Intercept 1 –4.518 *** 0.031 –4.487
*** 0.031
Intercept 2 –0.922 *** 0.031 –0.883
*** 0.031
Intercept 3 2.657 *** 0.031 2.699
*** 0.032
1.5 Generation (Native-Born) 0.071 *** 1.073 0.004 0.052 *** 1.054 0.004
South Asian (Non-H i sp anic White) 0.220 *** 1.246 0.010 0.217 *** 1.243 0.010
Age 0.135
*** 1.145 0.002 0.135 ** * 1.145 0.002
Age-Squared –0.002
*** 0.998 0.000 –0.002
*** 0.998 0.000
Education (Less than High Sc hool)
High School 0.518 *** 1.678 0.003 0.527 *** 1.693 0.003
Some College 1.171 *** 3.224 0.003 1.178 *** 3.247 0.003
Associate Degree 2.079 *** 7.996 0.003 2.097 *** 8.145 0.003
Four Year College Degre e 2.887 *** 17.932 0.003 2.898 *** 18.130 0.003
More than College Degree 4.293 *** 73.176 0.004 4.308 *** 74.321 0.004
Region (Pac i fic)
New England –0.018
*** 0.982 0.003
Middle Atlantic –0.054
*** 0.948 0.002
East North Central –0.200 *** 0.818 0.002
West North Central –0.112 *** 0.894 0.003
South Atlantic 0.055 *** 1.056 0.002
East South Central –0.096 *** 0.909 0.003
West South Centra l 0.163 *** 1.177 0.003
Mountain –0.036
*** 0.965 0.003
–2LL 21,410,633 21,375,590
***p < 0.001. Source: 2006-2008 American Community Survey (N = 436,212). Note: Values in parentheses are reference categories.
In terms of quantitative research, prior studies have almost
entirely ignored second generation South Asians. Similarly,
discussions of white privilege and the MMM view have not
explicitly noted the existence of second generation South
Asians. The results of our foregoing empirical analysis suggest,
however, that this demographic group defies the predictions of
both white privilege theory and the MMM view. That is, the
advantaged socioeconomic attainments of second generation
South Asians are exactly the reverse of what is predicted by
white privilege theory and the MMM view. By ignoring South
Asians, the proponents of these theories have conveniently
avoided having to confront such “inconvenient facts” (Weber,
1946: p. 147 [1922]).
In any event, our findings do not appear to support the gen-
eralization that second generation South Asians currently en-
counter a systematic socioeconomic disadvantage due to being
a minority group with darker skin tones (at least on average).
Our findings are thus not consistent with the application of
strong claims about a rigid “pigmentocracy” (Bonilla-Silva,
2003: p. 121) to second generation South Asians. Nor do we
find any evidence that, as predicted by the MMM view, second
generation South Asians must make a higher investment in
human capital in order to obtain the same labor market rewards
as whites. To the contrary, the reverse seems more likely to be
true as second generation South Asians are actually advantaged
over whites in regard to wages and occupational attainment.
Given the high levels of inequality that characterize these latter
two variables in the 21st century (Lemieux, 2006), this advan-
tage is not trivial.
We speculate that these results may in part reflect the in-
creasing significance of educational attainment for labor market
success in the 21st century. Kim and Sakamoto (2008) report a
47% relative increase in the explanatory power of basic educa-
tional levels in predicting wage inequality in recent decades
while the explanatory power of three-digit occupations declined.
Other studies indicate increases in the economic returns to col-
lege attainment (Card & DiNardo, 2002; Becker & Murphy,
2007) while Lemieux (2006) argues that notably high wages are
increasingly associated with postsecondary education. The fact
that second generation South Asians have higher levels of edu-
cational attainment than whites suggests the labor market ad-
vantage of the former demographic group may increasingly
3
0 Copyright © 2012 SciRes.
H. WOO ET AL.
Table 5.
Estimates of OLS regr ession of log-wage for men aged 25 to 40.
Model 1 Model 2
Variable Estimate SE EstimateSE
Intercept 0.278
*** 0.048 0.357*** 0.048
1.5 Generation (Native-Born) 0.046 *** 0.006 0. 027*** 0.006
South Asian (Non-H i sp anic White) 0.075 *** 0. 013 0. 056*** 0.013
Age 0.109
*** 0.003 0. 111*** 0.003
Age-Squared –0.001
*** 0.000 –0.001*** 0.000
Education (L ess than Hi gh School)
High School 0.165 *** 0. 004 0. 158*** 0.004
Some College 0.291 *** 0. 004 0. 282*** 0.004
Associate Degree 0.365 *** 0.005 0. 35 5*** 0.005
Four Year College Degre e 0.620 *** 0.00 4 0. 604*** 0.004
More than College Degree 0.806 *** 0.004 0. 788*** 0.004
Region (Pac i fic)
New England –0.025*** 0.005
Middle Atlantic –0.033*** 0.004
East North Central –0.116*** 0.003
West North Central –0.168*** 0.004
South Atlantic –0.114*** 0.003
East South Central –0.205*** 0.004
West South Centra l –0.138*** 0.004
Mountain –0.114*** 0.004
R-Squared 0.187 0.195
***p < 0.001. Source: 2006-2008 American Community Survey (N = 474,790).
Note: Values in parentheses are reference categories.
become secure as high educational attainment (i.e., a college
degree) is becoming a necessary prerequisite for mobility out of
the low-wage labor market (Card & DiNardo, 2006).
Some additional evidence suggests that second generation
Asian American men (including South Asians) are more highly
concentrated in science, technology, engineering and medical
(i.e., STEM) fields of study that tend to have higher earnings
(Kim & Sakamoto, 2010). Although the results are still pre-
liminary, such a pattern might be a potential explanation for the
advantage of South Asians over whites. Because STEM fields
of study tend to have higher labor market returns, the higher
wages and occupational attainment of South Asians would be
expected given their higher concentration in STEM areas. The
higher racial wage advantage for South Asian women in rela-
tion to South Asian men may reflect the lower gender differen-
tial in STEM concentration among South Asians than among
whites. The lower racial occupational advantage for South
Asian women in relation to South Asian men may reflect the
measurement limitations of our occupational typology if white
women are more highly concentrated in administrative white
collar positions (e.g., secretaries) that are ranked relatively
highly in our occupational classification. This issue is certainly
an appropriate area for future research.
Table 6.
Estimates of OL S regression of log-wage for women aged 25 to 40.
Model 1 Model 2
Variable Estimate SE EstimateSE
Intercept 0.308
*** 0.051 0. 396*** 0.051
1.5 Generation (Native-Born) 0.082 *** 0. 007 0. 061*** 0.007
South Asian (Non-H i sp anic White) 0.157 *** 0.01 4 0. 140*** 0.014
Age 0.098
*** 0.003 0. 100*** 0.003
Age-Squared –0.001
*** 0.000 –0.001*** 0.000
Education (L ess than Hi gh School)
High School 0.210 *** 0.00 5 0. 203*** 0.005
Some College 0.348 *** 0.00 5 0. 341*** 0.005
Associate Degree 0.524 *** 0.005 0. 515*** 0.005
Four Year College Degre e 0.759 *** 0.005 0. 74 4*** 0.005
More than College Degree 0.967 *** 0. 005 0. 944*** 0.005
Region (Pac i fic)
New England –0.022*** 0.005
Middle Atlantic –0.040*** 0.004
East North Central –0.138*** 0.003
West North Central –0.196*** 0.004
South Atlantic –0.108*** 0.004
East South Central –0.231*** 0.004
West South Centra l –0.161*** 0.004
Mountain –0.130*** 0.004
R-Squared 0.194 0.204
***p < 0.001. Source: 2006-2008 American Community Survey (N = 436,212).
Note: Values in parentheses are reference categories.
One control variable that is not explicitly included in our re-
gression models is being second generation itself which relates
to the issue of “immigrant optimism” (Kao & Tienda, 1995).
This idea suggests that the second generation may have high
socioeconomic attainments due to greater selectivity, effort,
ambition and motivation. Second generation children are fre-
quently reminded of the sacrifices that their parents have made
in order to come to America often for the purpose of obtaining
better socioeconomic opportunities. Immigrant parents who
lack US educational credentials may find that their own labor
market prospects are constrained, and may alternatively moti-
vate their children into becoming high achievers (Goyette &
Xie, 1999; Sakamoto, Goyette, & Kim, 2009).
This process of “immigrant optimism” may help to explain
the slight net advantage of being 1.5 generation that was evi-
dent in all of our regression models. The 1.5 generation may be
more likely to have more highly motivated immigrant parents
who have yet to have fully achieved the higher standard of
living that is more typical of American society. If so, such more
recent immigrant parents may more greatly influence their
children into becoming economically successful.3
3Note that the 1.5 generation effect and “immigrant optimism” are not re-
stricted to Asian Americans. Thus, the estimated effects of 1.5 generation
shown in the tables for the regression results apply equally to both whites
and South A s i ans.
Copyright © 2012 SciRes. 31
H. WOO ET AL.
An additional theme in this general literature is the selective
retention of traditional values and customs (sometimes known
as “segmented assimilation”) that might serve as resources for
upward mobility or improved socioeconomic attainments in the
more multicultural environment of contemporary America. As
stated by (Zhou, 1997: p. 994), “Asian subgroups selectively
unpack from their cultural baggage those traits suitable to the
new environment, such as two parent families, a strong work
ethic, delayed gratification, and thrift…”. Zhou (1997: p. 988)
also notes that by maintaining some traditional values and
norms, Asian American children may be better equipped to
counteract the “oppositional culture” and “poverty , poor schools,
violence, drugs, and a generally disruptive social environment”
in the inner city. The “segmented assimilation” perspective thus
suggests that limited or incomplete integration into American
society may actually improve the socioeconomic attainments of
Asian Americans.
Xie and Goyette (2004: p. 10) report, for example, that 53%
of recent cohorts of native born Asian Americans complete
college as compared to 30% among whites. This Asian Ameri-
can advantage in education may be in part facilitated by tradi-
tional Asian values and norms regarding family cohesiveness,
the parental control of children, and children’s sense of filial
piety towards accommodating their parents’ wishes (Goyette &
Xie, 1999; Sakamoto, Goyette, & Kim, 2009). While the selec-
tivity of Asian immigration towards persons who are more
highly educated plays an important role as well, every known
study on this issue finds that social class factors alone cannot
fully account for the higher educational attainments of Asian
American youth over white youth (Sakamoto, Goyette, & Kim,
2009). Thus, “segmented assimilation” processes including
“immigrant optimism” may be conducive towards high educa-
tional attainment among second generation Asian American
youth including South Asians.
In sum, our findings of higher socioeconomic attainments
among South Asians may perhaps be explained as deriving
from the selectivity of being second generation when combined
with Asian American family patterns that emphasize educa-
tional attainment and upward social mobility. If this interpreta-
tion is approximately accurate, then it would seem to be con-
sistent with Wilson’s (2009) more general view that ethnic
subcultures may sometimes interact with class positions to af-
fect the socioeconomic attainments of children. More research
on this complex issue is obviously clearly warranted.
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