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Diverging pathways to citizenship and immigrant integration in the U.S.

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Diverging pathways to citizenship and immigrant integration in the U.S.

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DIVERGING PATHWAYS TO CITIZENSHIP
AND IMMIGRANT INTEGRATION IN THE U.S.

by

Thai Van Le

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree

DOCTOR OF PHILOSOPHY
PUBLIC POLICY AND MANAGEMENT

December 2021

Copyright 2021 Thai Van Le

ii
Dedication

For those seeking refuge
and a place to belong beyond borders.

iii
Acknowledgements

Though it took several years of learning, research, and writing to produce this dissertation, it would
not have been possible without the guidance and support of several people. First and foremost, I
want to acknowledge and thank my committee of brilliant and kind mentors. My remarkable
advisor and committee chair, Juliet Musso, has played a vital role throughout my journey in
completing my dissertation and doctorate degree. Juliet has provided honest, constructive, and
encouraging feedback on my work, ultimately shaping my interests in research and policies that
promote social equity. I would not be the scholar I am without her. I would also like to express my
deep appreciation for Lisa Schweitzer who continues to challenge me to not only think more
critically but also more intentionally. Over the years, Lisa has opened my eyes to new ways of
thinking and understanding what justice means for different communities and their intersectional
identities. I will always be inspired by Lisa’s passion, radicalness, and fierceness in everything she
does. Next, I would like to give a shoutout to Manuel Pastor—the greatest hype man alive. Manuel
took me under his wing and connected me to so many opportunities that have contributed to my
growing work on immigrant integration and social justice. Though I cannot grow an impressive
moustache like him, I hope to one day become the prominent academic activist that he is. To my
committee: you all have always fought for me and reminded me that I belong. You always made
time to support me despite all the other demands on your time and attention. I owe a debt of
gratitude to you all for believing in me.
I would be remiss if I did not also recognize the faculty and staff members at USC who
have given me invaluable insight and support, including T.J. McCarthy, Dowell Myers, Pierrette
Hondagneu-Sotelo, Lavonna Lewis, and Julie Kim. Many thanks also to my peers who have
provided encouraging words and a space to speak candidly about our shared research interests and

iv
struggles. A special shoutout to Jocelyn Poe, Liane Hypolite, Ben Toney, Sean Angst, Cynthia
Barboza-Wilkes, and Marisa Turesky. I am eternally grateful for these connections and
friendships. Without them, completing my dissertation and doctorate degree would have been
more challenging and isolating.
A round of thanks to the Equity Research Institute (ERI) family. I have such a deep
appreciation for the phenomenal and important work that you all do in uplifting marginalized and
minoritized communities. The last few years learning and collaborating with everyone at ERI have
helped me grow as a mixed-methods researcher and data nerd. I would like to give an especially
important thank you to Justin Scoggins, Rhonda Ortiz, Edward Muna, and Dalia Gonzalez for their
continued patience and support during my time at ERI.
I would not be here today without my friends and family who continue to support me
socially, emotionally, and mentally. They have been keeping me levelheaded throughout these last
few years, reminding me that it is important to have a life outside of school and academia. A special
thank you to Diana “Lu” Aschner for her friendship, genuine interest in my research, and editing
skills. A heartfelt thank you to Koji Kitagawa for his patience, support, and unconditional love as
I continue to figure out my life.
Finally, I would like to acknowledge my parents on the small chance that they may
someday read this: Con có rất nhiều tình yêu và lòng biết ơn đối với bố mẹ — nhiều hơn những gì
con có thể diễn ra bằng chữ, bằng lời nói. Là những người nhập cư tại Hoa Kỳ từ lâu rồi, bố mẹ
đã phải chịu đựng và trải qua những khó khăn để mang lại cho con một cuộc sống tốt đẹp hơn.
Con đã được ăn, được học, được trưởng thành và lớn khôn do bố mẹ đã hy sinh cái tôi và sức khỏe
của mình. Con hy vọng là luận văn này sẽ có thể giúp những người nhập cư như bố mẹ tìm được sự
thân thuộc và công lý ở một đất nước mà bố mẹ yêu thương vô điều kiện.

v
Table of Contents

Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Abstract .......................................................................................................................................... ix
Introduction ................................................................................................................................... 1
Divergent Pathways to Citizenship: The Disparate Drivers and Barriers to Naturalizing in
the United States ......................................................................................................................... 14
Introduction ....................................................................................................................... 15
Benefits of Naturalization ................................................................................................. 16
Factors Impacting Naturalization ...................................................................................... 19
Individual Characteristics .................................................................................... 19
Place-based Attributes .......................................................................................... 22
Network Effects ..................................................................................................... 25
Hypotheses ........................................................................................................................ 26
Data, Methodology, and Model Specifications................................................................. 29
Estimating Who is Eligible to Naturalize and Who Recently Naturalized............ 30
Modeling Propensity to Naturalize ....................................................................... 33
Results ............................................................................................................................... 37
Individual Characteristics .................................................................................... 37
Place-based Attributes .......................................................................................... 39
Network Effects ..................................................................................................... 40
Discussion and Conclusion ............................................................................................... 42
Segmented Citizenship: The Racialized and Gendered Economic Gains of Naturalizing in
the United States ......................................................................................................................... 47
Introduction ....................................................................................................................... 48
Naturalization and Economic Assimilation ...................................................................... 51
Understanding Economic Outcomes Through a Critical Lens ......................................... 54
Data ................................................................................................................................... 56
Methodology and Model Specifications ........................................................................... 57
Estimating the Eligible-to-Naturalize and Recently Naturalized ......................... 58
Estimating the Economic Impact of Naturalization .............................................. 64
Results ............................................................................................................................... 66
Characteristics of the Eligible-to-Naturalize and the Recently Naturalized ........ 67
Propensity to Naturalize ....................................................................................... 69
Effect of Naturalization on Personal Total Income and Personal Wage/Salary
Earnings ................................................................................................................ 72
The Moderating Effects of Race and Gender ........................................................ 78
Discussion and Conclusion ............................................................................................... 82

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The Model Minority and Perpetual Foreigner Paradox: A Disaggregated Analysis of
Naturalization Among Asian American Immigrants .............................................................. 86
Introduction .........................................................................................................................87
An Overview of Historical Policies Shaping Asian American Immigration ..........................91
The Drivers of Naturalization and Intragroup Disparities .....................................................95
Individual Characteristics .......................................................................................95
Place-Based Attributes ............................................................................................98
Network Effects ..................................................................................................... 101
A Framework in Understanding the Asian American Immigrant Experience ...................... 104
Data, Methodology, and Model Specifications ................................................................... 107
The Eligible-to-Naturalize and Recently Naturalized Asian American Immigrants . 108
Estimating the Differential Drivers and Barriers to Naturalizing ........................... 110
Results .............................................................................................................................. 113
Individual Characteristics ..................................................................................... 113
Place-based Attributes........................................................................................... 115
Family Network Effects ......................................................................................... 116
Discussion and Conclusion ................................................................................................ 121
Conclusion and Policy Implications ........................................................................................ 126
References .................................................................................................................................. 130
Appendix .................................................................................................................................... 145

vii
List of Tables

Table 1.1: Comparing OIS and Le & Pastor estimates of the eligible to naturalize ............ 32
Table 1.2: Estimated effects on the odds of naturalization ..................................................... 41
Table 2.1: Comparing OIS estimates of the eligible to naturalize .......................................... 60
Table 2.2: Summary statistics of the eligible-to-naturalize and the recently naturalized.... 68
Table 2.3: Estimated effects on the odds of naturalization across subsamples ..................... 71
Table 2.4: Balancing table for propensity score analysis on total personal income. ............ 75
Table 2.5: Balancing table for propensity score analysis on personal wage/salary earnings.
....................................................................................................................................................... 76
Table 2.6: Results from propensity score methods estimating impact of naturalization on
total personal income (log) and personal wage/salary earnings (log). ................................... 78
Table 2.7: Estimates of the mediating impact of race and gender on the benefits of
naturalization on total personal income (log) and personal wage/salary earnings (log). ..... 81
Table 3.1: Estimated effects on the odds of naturalization across immigrant groups ....... 118
Table 3.2: Estimated effects on the odds of naturalization across immigrant groups ....... 119
Table 3.3: Estimated effects on the odds of naturalization across immigrant groups ....... 120

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List of Figures

Figure i: A diagram of themes and related frameworks in studying immigrant integration.
....................................................................................................................................................... 13
Figure 2.1: Statistical and computational process in determining eligible-to-naturalize and
recently naturalized sample. ...................................................................................................... 61
Figure 2.2: Overlap of observations by propensity to naturalize. .......................................... 73
Figure 2.3: Distribution of propensity scores across subsamples, unweighted and weighted.
....................................................................................................................................................... 74

ix
Abstract

In this dissertation, I explore how immigrant integration in the United States is not only uneven
but also racialized and gendered. I use a social equity framework to empirically explore the barriers
to naturalization through three essays: 1) Divergent Pathways to Citizenship: The disparate drivers
and barriers to naturalizing in the United States, 2) Segmented Citizenship: The racialized and
gendered economic value of naturalizing in the United States, and 3) The Model Minority and
Perpetual Foreigner Paradox: A disaggregated analysis of naturalization among Asian American
immigrants. These essays build on previous work that relies on traditional models of predicting
naturalization, including measures of human capital and place-based “push-and-pull” factors. My
dissertation expands on the current literature by examining how family effects shape pathways to
citizenship. Novel to this body of work, my dissertation empirically examines how the U.S.
immigration and citizenship regime’s criminalization of undocumented immigrants impact
immigrants’ access to naturalization, particularly those who are in mixed-status families, compared
to those with close ties to individuals who have already naturalized. Integrating a framework that
uplifts the tenets of critical race theory and intersectionality, I present analyses that are more
intentional in extrapolating how immigrant experiences in the U.S. are shaped by existing
structures of power that often marginalize immigrants, particularly women and the racially
minoritized.

In the first essay, we examine the unequal barriers that lead to disparate naturalization
outcomes in the U.S., contributing a novel analysis on family effects. Utilizing multivariate
regressions and conditional imputations, we isolate and empirically measure the impact both
naturalized and undocumented family members have on eligible immigrants’ propensities to

x
naturalize. The results show that having a naturalized spouse prior to one’s own naturalization is
associated with 72.4 percent greater odds of naturalizing whereas being married to a spouse who
is either a lawful permanent resident, U.S.-born, or undocumented reduces the odds of naturalizing
by 47.8 percent, 13.7 percent, and 85.7 percent, respectively. Similarly, having a naturalized adult
in the family other than a spouse improves the odds of naturalization by 39 percent, but having an
undocumented family member other than a spouse reduces the odds by 32.6 percent. Whereas
eligible immigrants with naturalized family members are more likely to improve their access to
citizenship through pooled resources and increased information sharing, eligible immigrants with
undocumented family members are more likely to avoid the naturalization process due to chilling
effects shaped by immigrant enforcement and policies that target close ties with liminal legality or
undocumented status.

In my second essay, I measure the economic gains associated with naturalization by
building on the methodological approach in the first essay. I apply a series of propensity score
methods and regression adjustments to measure the impact of naturalization on income and wage
earnings. This methodological approach addresses self-selection bias previous studies have
encountered. Findings show, on average, naturalization is associated with a 13.24 percent increase
in total personal income and 10.07 percent increase in wage/salary earnings within the first ten
years of gaining citizenship. These relative changes in economic outcomes vary in magnitude by
increment of time since naturalizing, race, and gender. I find that Black immigrants, Latinx
immigrants, and immigrant women see larger relative gains in income and earnings through
naturalization compared to their white and man/male counterparts. This leads to speculations of
how economic barriers facilitated by citizenship status are particularly racialized and gendered.

xi

In further extrapolating how pathways to citizenship are particularly racialized, I take a
disaggregated approach in my third essay in analyzing how Asian American immigrant groups
face different barriers to naturalization. There is increasing interest and importance in
understanding how Asian American immigrants are integrating as they continue to grow and
become a significant share of the electorate in local, state, and federal political arenas. Research
on Asian American immigrants and their political incorporation into the U.S. is often done in a
way that overlooks within-group heterogeneity. Like studies on Asian American political
participation, further work needs to be done to disaggregate the nuanced intragroup differences
with Asian American immigrants’ pathways to citizenship. In examining Asian American
immigrants’ paradoxical treatment as model minorities and perpetual foreigners, I explore how
disparities in individual, place-based, and family characteristics have diversely shaped their access
to naturalization. By disaggregating traditional models of predicting naturalization by subgroups,
I find heterogenous effects that provide further evidence supporting the need to have more nuanced
analyses of the Asian American immigrant experience. Notable empirical findings include how
immigrant groups with limited human capital turn to their social capital and networks to
supplement and improve their access to naturalization. These findings can help shape policies to
be more effective and equitable by addressing group-specific needs in facilitating integration and
representation of an increasingly diverse group of immigrants.

1
Introduction

With nearly 45 million foreign-born residents, approximately 15 percent of the country’s
population, the United States is home to more immigrants than any other country in the world
(Batalova, Hanna, and Levesque 2021). Immigrants are an integral part of society and our
understanding of immigrant integration in the United States is constantly changing as migration
patterns shift and research on the matter continues to evolve. Ongoing research on immigrant
communities has played a pivotal role in the formulation and implementation of consequential
policies that ultimately shape the livelihood of immigrants, subsequent generations, and entire
communities. Contributing to the immigrant integration literature, my dissertation focuses on
identifying the mechanisms that lead to the inequitable incorporation of immigrants as measured
by their uneven pathways to citizenship. Understanding the barriers to naturalization is
increasingly important as citizenship provides privileges that would help fully engage immigrants
socially, politically, and economically, such as the right to vote (Aptekar 2015; National
Academies of Sciences, Engineering, and Medicine 2015). Becoming a citizen in the United States
is a point of interest for many scholars, policymakers, and advocates as the immigrant population
will continue to grow and become more diverse while debates on immigrant rights become more
polarized and contentious. This dissertation serves to provide evidence and discussion on which
mechanisms policymakers and immigrant-serving organizations should consider to equitably
improve access to naturalization, particularly for the hard-to-reach immigrant population.
In this dissertation, I use a social equity framework to empirically explore the barriers to
naturalization through three essays: 1) Divergent Pathways to Citizenship: The disparate drivers
and barriers to naturalizing in the United States, 2) Segmented Citizenship: The racialized and
gendered economic value of naturalizing in the United States, and 3) The Model Minority and

2
Perpetual Foreigner Paradox: A disaggregated analysis of naturalization among Asian American
immigrants. These essays build on previous work that relies on traditional models of predicting
naturalization, including measures of human capital and place-based “push-and-pull” factors (e.g.,
Abascal 2017; Aptekar 2015; Johnson et al. 1999; Yang 1994). My dissertation expands on the
current literature by exploring the effects of social networks on naturalization and how such effects
are particularly gendered and racialized. Novel to this body of work, my dissertation empirically
examines how the U.S. immigration and citizenship regime’s criminalization of undocumented
immigrants impact immigrants’ access to naturalization, particularly those who are in mixed-status
families, compared to those with close ties to individuals who have already naturalized. Integrating
a framework that uplifts the tenets of critical race theory and intersectionality, I also present
analyses that are more intentional in extrapolating how immigrant experiences in the U.S. are
shaped by existing structures of power that often marginalize immigrants, particularly women and
those who are racially minoritized.
Considering the complex and racialized history of immigrant policies in the United States,
I ground my research on immigrant integration and pathways to citizenship within a social equity
framework. This framework critically interrogates existing disparities across immigrant groups as
a product of institutionalized inequities propagated by discriminatory policies and systematic
biases. This social equity framework centers issues of race/ethnicity, place, and power so that they
drive the theoretical and methodological approaches in studying immigrant integration and the role
of policy within these themes. Though I draw primarily from critical race theory and
intersectionality for my dissertation, I also include elements from Henri Lefebvre’s Right to the
City framework and concepts of domicile citizenship, which I plan to further develop and apply in
future research to extrapolate the role of hyper localized mechanisms in facilitating immigrant

3
integration and citizenship. Figure i shows how these themes and frameworks are positioned in
studying immigrant integration and pathways to citizenship.
Critical race theory is significant in interrogating how pathways to citizenship and other
forms of immigrant integration have been racialized. The centrality of critical race theory to this
dissertation acknowledges the historical truth that immigration is a race/ethnicity issue.
Exclusionary acts, anti-immigrant sentiment, and the disproportionate enforcement against Black
and Brown immigrants prove that immigrant integration and pathways to citizenship cannot be
comprehensively studied without critically incorporating the mediating and moderating roles of
race/ethnicity and institutionalized racism. Critical race theory argues that racialized power
relations and social positions that have been dictated by historical and existing policies are the core
mechanisms of existing inequities and disparities (Crenshaw et al. 1995; Garcia 1995; Romero
2008a). Through this paradigm, the distribution of power and social location on the basis of
race/ethnicity are understood to have historically contributed to the systemic marginalization and
subordination of racial minorities, including those who are immigrants. In other words, this model
of thought critically analyzes the enduring impact of intergenerational disadvantages that
predetermine one’s social position from birth as it is associated with one’s socially identified
race/ethnicity. A social equity framework with a critical race lens links the cumulative effects of
past and present policies that largely disadvantages racially marginalized immigrants to existing
mechanisms driving the cycle of racial disparities and inequalities in immigrant integration.
Examining immigration policies within a racialized historical context helps to deconstruct
the unequal integration outcomes across racial lines that have been predicated on intergenerational
and institutionalized disadvantages. I discuss this in further detail throughout the three essays
where I explore how the historical exploitation and disenfranchisement of Latinx and Asian

4
American immigrants in the United States is a consequence of racialized anti-immigrant sentiment
and is reproduced in immigration policies denying racially minoritized immigrants’ full
participation and membership to the very communities they build and call home. I use the tenets
of critical race theory to further problematize the U.S. immigration and citizenship regime as it
disproportionately targets racially minoritized immigrants. In barring citizenship and lawful
membership to the United States, the white majority deprives racial minorities rights to public
benefits and voting, a tried-and-true tradition in maintaining a racial hierarchy of power. Remnants
of the historical racial barriers to citizenship (e.g., the Naturalization Act of 1790, Chinese
Exclusion Act of 1882, the Naturalization Act of 1906, the Immigration Act of 1917) that limited
the right to naturalize to “free white persons” are still evident in today’s rhetoric on who is
deserving of citizenship and rights inclusive of “property, taxes, welfare, and the freedom of
movement across nation states” (Romero 2008a:27). From California’s Proposition 187 to the
explicit exclusion of undocumented immigrants from the COVID-19 stimulus package, legal status
and citizenship have often been used as an indirect qualifier to exclude disproportionately large
numbers of immigrants from communities of color.
Through a social equity framework and a critical race lens, citizenship and civic
engagement are avenues for marginalized communities to reclaim their voices and rights to spaces
in a hierarchy that has historically worked to ostracize them. To push for more opportunities and
resources for these communities to have an integral role in public discourse, policies need to be
formulated and framed in a way that accounts for generations of racial/ethnic oppression and
institutionalized barriers, such as unequal access to naturalization.
To supplement critical race theory, I integrate tenets of intersectionality to interrogate how
racially minoritized immigrants’ pathways to citizenship are shaped by their race/ethnicity and

5
immigration status. Kimberle Crenshaw’s (1990) work on intersectionality uplifts the
multidimensional aspect of social equity by emphasizing the interconnected nature of social
identities. This interconnectedness examines the overlapping systems of discrimination and
disadvantage felt across and within oppressed groups. Initially focused on the intersection of
gender and race (e.g., the social and political discrimination faced by Black women), theories of
intersectionality have come to encompass other socially-constructed identities including class,
nationality, and citizenship (Romero 2008b; Yuval‐Davis 2007). With this paradigm, I explore
how immigrants with multiple oppressed identities (e.g., women and racially minoritized
immigrants) have disproportionately higher barriers to naturalization.
The literature and scope of intersectionality and social equity are expanding and growing
as society encounters starker disparities and inequities that intersect identities of race, ethnicity,
nativity, citizenship status, gender, ability, sexuality, and socioeconomic class. We are in an era
where legal status reveals inter- and intragroup differences in privileges and barriers to upward
mobility. Legal status interacts with other social identities differently and should be explored
further to understand the multiple layers of barriers faced by mixed-status families and
undocumented immigrants from different groups. For example, Romero (2008b) utilizes an
intersectional framework to study how immigrant raids discriminately target low-income Latinx
immigrants from mixed-status households. Her analysis interrogates how enforcement policies
disproportionately impact low-income immigrants from Latin America who live in areas
stereotyped to be frequented by undocumented families, whereas Latinx immigrants who live in
middle-class neighborhoods are at lower risk of raids. This analysis highlights the vulnerability of
mixed-status families and their multiplicative disadvantages because of their immigration status,
class, and racial/ethnic identities. I integrate a similar framework throughout the three essays to

6
explore the chilling effects and unique barriers to naturalization immigrant women, immigrants of
color, and immigrants in mixed-status families face. In this dissertation, I operationalize chilling
effects as the side effects created by anti-immigrant policies and rhetoric, such as a climate of fear
and avoidance of services.
Through an intersectionality lens, policies can “mediate the tension between assertions of
multiple identity and the ongoing necessity of group politics” to deconstruct institutionalized
inequities attached to group membership beyond a single socially-constructed identity (Crenshaw
1990:1296). In studying immigrant integration and pathways to citizenship, the disadvantages and
uncertainties that come with undocumented or liminal status need to be a point of focus as they
are not equally experienced across immigrant groups. From a policy perspective, Hedström and
Ylikoski (2010) posit societal problems are multidimensional and result from multiple mechanisms
and their relations to each other. Policies then should focus on how these different factors and
identities must be addressed individually and congruently in any policy issue. For example, do
racial/ethnic privileges allow undocumented white immigrants more security and safety from
immigrant enforcement than undocumented immigrants of color who are more likely to be racially
profiled? Do class and race play a significant intersecting role in how undocumented immigrants
become civically engaged? Without intersectionality, policies aimed to improve access to
naturalization and immigrant integration may ignore within-group heterogeneity and specific
factors that disadvantage only a share of immigrants, such as Latinx immigrants and immigrant
women. Overlooking these nuanced experiences within the immigrant community then could lead
to policy failures that perpetuate inequities if not addressed.
To explore the role of hyper localized mechanisms and entities in shaping immigrant
integration and citizenship, I incorporate arguments from Right to the City and domicile

7
citizenship into this social equity framework. Right to the City, coined by Henri Lefebvre (1968)
and re-popularized by scholars like David Harvey (2003) and Varsanyi (2008), is a school of
thought garnering more attention as metropolitan areas are becoming more densely populated and
diverse, while also experiencing widening inequalities that further marginalize groups not seen as
politically powerful or economically elite. This paradigm argues for the equal right to access urban
life and spaces through public services, resources, and platforms to change the city beyond the
constraints of the state, politics, and capitalism (Harvey 2003; Lefebvre 1968). Through the earlier
interpretations of this framework, those who can claim social membership and legitimacy to the
“representations of space” and the “representational space” are equally shared across class. This
framework has since been used as the foundation in expanding such rights to the city to
marginalized groups beyond class divide, including communities of color and immigrants (Dikeç
and Gilbert 2002; Harvey 2003; McCann 1999; Varsanyi 2008). Such inclusion argued by the
Right to the City framework would thus provide alternative ways of defining citizenship including
domicile citizenship where “the relevant and important factor for citizenship acquisition is not
place per se, but the connections and bonds of association that one establishes by living and
participating in the life and work of the community” (Bauder 2012:187–88). Incorporating the jus
domicile principle into a social equity framework would push for local strategies in giving
immigrants the rights to public spaces, resources, and the ability to change their community despite
their legal status as defined by the U.S. immigration and citizenship regime.
Using this social equity framework with the principles of Right to the City and domicile
citizenship, I plan to explore how localities and regions can lower barriers to immigrant integration
and redefine citizenship. From local policies to regional social movements that provide immigrants
an informal avenue to change their community, these place-specific strategies and factors mediate

8
and moderate disparities in integration—particularly for those facing barriers due to their legal and
citizenship status. The significant devolution of immigration federalism in the United States shows
how localities have been particularly critical in combating federal-level anti-immigrant policies
and enforcement. This is evident, for example, with cities and states that have passed sanctuary
laws and ordinances, Los Angeles’ legalization of street vending, California’s expansion of
Medicaid to undocumented youth, San Francisco’s ordinance allowing non-citizens to vote in local
elections, and Los Angeles’ and California’s cash assistance programs for undocumented
immigrants impacted by COVID-19. These policies show how hyper localized mechanisms can
advance social equity for immigrants by providing them rights that have been denied to them by
the federal policies on immigration. In contrast, local factors and policies can also create hostile
places for immigrants, making it more difficult for immigrants to live and integrate. Though my
dissertation is limited by data availability to fully expand on how hyper localized factors shape
pathways to citizenship (e.g., quantitative indicators measuring local context of reception), I
present the tenets and principles of Right to the City and domicile citizenship as it relates to critical
race theory and applied intersectionality to provide a framework for future research that will
expand on the place-based and localized barriers to naturalization.
The first essay, co-authored with Manuel Pastor, takes a dialectical approach in
interrogating how the U.S. immigration and citizenship regime has created racialized barriers to
naturalization. Previous empirical work on naturalization does not explicitly account for likely
undocumented immigrants in their data such that results are vulnerable to statistical noise and bias
due to the inclusion of immigrants who are systematically unable to naturalize due to their lack of
lawful status. We use a series of empirical models and imputation strategies to estimate who in the
dataset (2016 American Community Survey 5-year microdata estimates) is likely undocumented.

9
This allows us to estimate the impact of different individual, place-based, and network
characteristics on naturalization more accurately. Taking advantage of how the ACS microdata
can also link individuals within the same household and family unit, we are able to isolate how the
criminalization of undocumented immigrants in the U.S. has created chilling effects on eligible-
to-naturalize immigrants in mixed-status families—a factor that has been understudied in previous
empirical research on naturalization. Among our conclusions, we find that family networks
substantially shape immigrants’ pathways to citizenship with the presence of naturalized family
members statistically improving the odds of naturalization and the presence of undocumented
family members reducing the odds significantly. In this essay, we also explore other salient
predictors of naturalization, including race/ethnicity, educational attainment, English language
proficiency, and the varied context of reception for immigrants from TPS-designated countries
compared to those from traditionally sending refugee countries. This essay provides empirical
evidence on which mechanisms policymakers and immigrant-serving organizations should focus
on to lower barriers to naturalization and to improve access to citizenship. However, as my third
essay highlights, this is a starting point that future research should further investigate through an
applied intersectionality lens to ensure more accurate analyses of specific immigrant communities.
In the second essay, I examine the intersectional impacts of naturalization on income and
wage earnings, considering how citizenship interacts differently for racially minoritized immigrant
groups and immigrant women. Considering the barriers to naturalization, self-selection is a
significant issue for research that can bias results in the absence of longitudinal data and
randomized treatment. Those who choose to naturalize may have unobserved characteristics that
may influence their income and wage earnings that would have occurred even in the absence of
naturalizing. I address these limitations by following the modeling in the first essay by using a rich

10
set of covariates and applying a series of propensity score methods to build comparable treatment
and control groups (i.e., similar propensities to naturalize). This allows me to measure and isolate
the impact of naturalizing on income and wage earnings. I further extrapolate how the effects are
moderated by race/ethnicity and gender—a nuanced point that has not yet been explored
empirically in previous research on naturalization. I find differential effects across immigrant
groups with immigrants who are Black, Latinx, and/or women experiencing the largest relative
gains in income and wage earnings. In this essay, I provide empirical evidence on how economic
barriers facilitated by citizenship status are particularly racialized and gendered. I argue how
lowering barriers to citizenship is a key element often overlooked in addressing broader racial and
gender inequalities in the U.S.
In the third essay, I take a more nuanced approach in disaggregating pathways to
citizenship for Asian American immigrants. In problematizing the model minority myth, I utilize
this analysis to highlight how the paradoxical treatment of Asian Americans also as perpetual
foreigners has shaped disparate barriers to citizenship. Though Asian Americans are often touted
as model minorities, research has shown that there are concerning intragroup disparities that are
often overlooked by aggregated numbers and overly generalized findings (Poon et al. 2016; Shih,
Chang, and Chen 2019). In aggregate, Asian American immigrants exhibit high rates of
naturalization with the common belief that it is associated with their high socioeconomic status
and high levels of human capital (Johnson, Reyes, Mameesh, and Barbour 1999). However, in
disaggregating naturalization trends, I find that Asian American immigrants face varying costs and
benefits of naturalizing that are tied to group-specific immigration patterns, including the inability
to retain country-of-origin citizenship. These group-specific differences influence how each group
differently values U.S. citizenship and how they rely on different strategies to become citizens, if

11
at all, leading to disparate rates of naturalization. Though some immigrant groups, including
immigrants from Vietnam and Cambodia, have lower educational attainment and higher rates of
limited English proficiency, they have significantly higher rates of naturalization compared to
other Asian American immigrants who are more educated and more likely to have the resources
to successfully naturalize, such as immigrants from China and Japan—contradicting traditional
models in predicting naturalization (e.g., Aptekar 2015; Johnson et al. 1999; Jones-Correa 2001;
Yang 1994). The results suggest that some Asian American immigrants are likely turning to
information sharing and pooled resources from their social networks, specifically from those who
have already naturalized, to supplement their limited human capital. In this essay, I find further
evidence on the importance of social capital in shaping pathways to citizenship, especially for
immigrants with more limited resources and those in mixed-status families. In exploring intragroup
disparities and barriers to naturalization within the Asian American immigrant community, I
highlight the important need to disaggregate data and analyses to develop community-specific
interventions not only for different Asian American immigrant groups but also for other
communities whose diverse lived experiences are often overshadowed by broad trends. Such
nuanced analyses and discussions can shape more effective policies and programs to equitably
improve naturalization and immigrant integration.
Together, these essays help further our understanding of the diverse and often racialized
barriers that confront immigrants in pathways to citizenship. By critically examining these barriers
as artifacts of generations of discriminatory policies, I provide evidence and nuanced discussions
on how multiple mechanisms and barriers to citizenship simultaneously reproduce and perpetuate
social, economic, and political inequalities within and across immigrant groups. With a growing
immigrant population and increasing anti-immigrant sentiment in the U.S., there continues to be a

12
growing need to address the institutionalized barriers shaped by past discriminatory policies that
hinder immigrants’ pathways to citizenship. Recent controversies on deportation and detention,
the social construction and criminalization of illegalities, and anti-immigrant sentiment
discriminately place immigrant communities under immense scrutiny across the political
spectrum. The U.S. needs specific and equitable policies to address past discriminatory practices
and to guarantee immigrants’ rights and access to citizenship. The U.S. Census Bureau predicts
the U.S. population will be one in five foreign born by 2060, outpacing the native-born population
growth (Colby and Ortman 2015). As such, determining how access to naturalization and
citizenship can be equitably improved is particularly significant when considering the impact
immigrants will have on future economic growth, government finance, and the political landscape
(Myers 2015).

13
PLACE
The role of place in immigrant
integration is an important factor in
determining outcomes as different
localities and regions can experience
vastly dissimilar contexts of reception as
the result of immigrant composition,
policies, and local economy
POWER
Whether it is the power to select,
control, or integrate immigrants, the
existing hegemonic relationship
within and among localities, states,
and the federal government is an
ongoing theme in immigration
studies and public policy
RACE
Disparities in integration and access to citizenship is a
product of systematic race-driven inequities perpetuated by
decades of exclusionary policies and discriminatory
institutions
SOCIAL
EQUITY
FRAMEWORK
INTERSECTIONALITY
An intersectionality framework focuses on the
intersections of identities (e.g., race, ethnicity,
sexuality, gender identity, immigration status,
class, regional identity) to understand unique
barriers resulting from multiplicative layers of
disadvantages (Collins 2012; Crenshaw 1990;
Hancock 2007; Romero 2008b).
RIGHT TO THE CITY
& DOMICILE CITIZENSHIP
A Right to the City framework argues for the
equal right to access urban life and spaces
through public services, resources, and platforms
to change the city beyond the constraints of the
state, politics, and capitalism (Harvey, 2003;
Lefebvre, 1968; Lefebvre, 1996). Such inclusion
would provide legitimacy for localities to redefine
citizenship under the jus domicile principle
despite issues of legal status as defined by relics
of the American nation-state (Bauder 2012).
CRITICAL RACE THEORY
Critical race theory centers the core
mechanisms of inequities and disparities
as a product of racialized power
relations and social positions that have
been dictated by historical and existing
policies (Crenshaw et al. 1995; Garcia
1995; Romero 2008a). Through this
paradigm, such distribution of power
and social locating on the basis of race
are understood to have historically led
to the institutionalized marginalization
and subordination of racial minorities.

Figure i: A diagram of themes and related frameworks in studying immigrant integration.

14

Divergent Pathways to Citizenship:
The Disparate Drivers and Barriers to Naturalizing in the United States

Thai V. Le
Ph.D. Candidate, Public Policy and Management
Sol Price School of Public Policy
University of Southern California

Manuel Pastor
University of Southern California

Abstract

Studies on naturalization often focus on the significance of individual and place-
based determinants, however, limited empirical work has been done to explore and
measure the simultaneous impact of networks. In this paper, we examine the
unequal barriers that lead to disparate naturalization outcomes in the U.S.,
contributing a novel analysis on family network effects. Utilizing a series of
multivariate regressions and conditional imputations, we isolate and empirically
measure the impact both naturalized and undocumented family members have on
eligible immigrants’ propensities to naturalize. The results show that having a
naturalized spouse prior to one’s own naturalization is associated with 72.4 percent
greater odds of naturalizing whereas being married to a spouse who is either a
lawful permanent resident, U.S.-born, or undocumented reduces the odds of
naturalizing by 47.8 percent, 13.7 percent, and 85.7 percent, respectively. Similarly,
having a naturalized adult in the family other than a spouse improves the odds of
naturalization by 39 percent, but having an undocumented family member other
than a spouse reduces the odds by 32.6 percent. Whereas eligible immigrants with
naturalized family members are more likely to improve their access to citizenship
through pooled resources and increased information sharing, eligible immigrants
with undocumented family members are more likely to avoid the naturalization
process due to chilling effects shaped by immigrant enforcement and policies that
target close ties with liminal legality or undocumented status. To equitably improve
immigrants’ access to citizenship, these findings suggest that 1) more resources and
services are needed to reach immigrants who are the first in their families to
naturalize; and 2) policies that improve the context of reception for undocumented
immigrants and mixed-status families are needed to help mitigate chilling effects.

15
Introduction
The immigrant community has been an integral part of the United States (U.S.). At approximately
50.7 million in 2019, the U.S. is home to more immigrants than any other country (Migration
Policy Institute 2019). The U.S. Office of Immigration Statistics (OIS) estimates 13.6 million
lawful permanent residents (LPR) reside in the U.S. with 9.1 million eligible to naturalize, nearly
80 percent of whom have been eligible for at least five years (Baker 2020). Given the individual
and collective benefits that come with citizenship, understanding what drives or delays
naturalization can lead to more effective policies and tools that foster greater immigrant
integration.
Studies on naturalization often focus on the significance of individual and place-based
determinants (e.g., Abascal 2017; Aptekar 2015; Johnson et al. 1999; Jones-Correa 2001; Woroby
and Groves 2016; Yang 1994); however, limited empirical work has been done to explore and
measure the simultaneous impact of networks. In this paper, we examine the unequal barriers that
lead to disparate naturalization outcomes in the U.S., contributing a novel analysis on family
network effects. Our methodological approach allows us to isolate and empirically measure the
impact both naturalized and undocumented family members have on immigrants’ propensities to
naturalize. This is a particularly salient topic to study as individuals in mixed-status families
experience conflicting threats and privileges stemming from differential treatment under the U.S.
immigration system (Aranda, Menjívar, and Donato 2014; Fix and Zimmermann 2001; Gomberg-
Muñoz 2017; Vargas and Pirog 2016). We argue further that whereas eligible immigrants with
naturalized family members are more likely to have greater access to citizenship through pooled
resources and increased information sharing, eligible immigrants with undocumented family

16
members are more likely to face chilling effects shaped by immigrant enforcement and policies
that discriminately target close ties with liminal legality or undocumented status.
In this paper, we explore the literature on the multifaceted benefits and determinants of
naturalization with a particular focus on how the U.S. citizenship regime produces unequal
conditions for immigrants of different race/ethnicity and national origin. This framework allows
us to examine how existing naturalization disparities are simultaneously influenced by individual,
place-based, and network attributes that are uneven across immigrant groups. We discuss our
methodology in estimating who in our dataset is eligible to naturalize and the effects of specific
determinants on naturalization. We then present our results, including the significant effects of
family networks which have been largely omitted in previous empirical research on naturalization.
We conclude with a discussion on the policy implications that this research can have by focusing
on multiple mechanisms that could promote more equitable naturalization.

Benefits of Naturalization
In the U.S., naturalization comes with privileges and benefits that can further facilitate immigrants’
social, economic, and political integration. Gaining citizenship provides adult immigrants the right
to participate in the U.S. political system through voting and running for most public office. In
particular, voting is often cited as one of the main reasons why immigrants choose to naturalize so
that they can more directly affect policy change and have more agency in choosing who represents
them (Aptekar 2015; National Academies of Sciences, Engineering, and Medicine 2015).
Naturalizing has long-term socioeconomic benefits seen in increased wages and earnings
(Bratsberg, Ragan, and Nasir 2002; Enchautegui and Giannarelli 2015; Pastor and Scoggins 2012;
Sumption and Flamm 2012). Citizenship reduces barriers that discriminate against noncitizens,

17
including restrictions on certain employment opportunities and scrutiny in accessing public
assistance programs and safety nets. U.S. citizenship is commonly required to work in the majority
of U.S. federal government jobs and private-sector work requiring high security clearance (Ayers
2018; Enchautegui and Giannarelli 2015). In employment where citizenship is not a requirement,
naturalization can still play a factor in promoting more gainful employment as noncitizens are
more likely to face wage and hiring discrimination due to their citizenship status (Brettell 2011;
Morrison 2018). Lewis, Liu, and Edwards (2014), for example, find that in state and local
government where citizenship is not always a requirement but a preference, an immigrant’s odds
of gainful employment substantially improve with citizenship.
Citizenship also improves access to public assistance programs. Though eligible-to-
naturalize immigrants often qualify for welfare benefits that require at least five years of residence
(e.g., Medicaid and Temporary Aid for Needy Families), the combination of public charge rules,
increased anti-immigrant practices, and attempts to legislate citizenship requirements contribute
to chilling effects that deter qualified immigrants who lack citizenship (Bojorquez and Fry-Bowers
2019; Fortuny and Chaudry 2011; Pedraza and Zhu 2015; Watson 2014). Vargas and Pirog (2016)
find such chilling effects to be especially striking for mixed-status families living in places with
higher rates of deportation. They find that mixed-status families, particularly those that are more
likely to be targeted because of their race/ethnicity, are significantly less likely to access WIC
(Women, Infants, and Children), the third largest federally funded food program in the U.S. that
supports pregnant women or women with children under the age of five.
With protection from deportation and greater ease in travelling internationally, citizenship
also provides immigrants with more security (Aptekar 2015; Asad 2020; National Academies of
Sciences, Engineering, and Medicine 2015). It is worth noting that even though naturalized citizens

18
are broadly protected from deportation, their status adjustment does not always alleviate
deportation fears. Asad (2020) finds that though deportation fears among Latino noncitizens are
significantly higher, naturalized Latino immigrants are experiencing significant growth in their
deportation fears that nears parity with their noncitizen counterparts despite their adjusted status.
This high rate links to Latino’s high rates of membership in a mixed-status network where the
possibility of their noncitizen family members or close ties being deported is perceived to be high
(Amuedo-Dorantes and Lopez 2020; Asad 2020).
Naturalization additionally provides greater access to family reunification through
prioritized and expanded family sponsorship to the U.S. Citizens not only have on average a shorter
waiting period to have their petitions to be reviewed and approved, but they can also sponsor a
wider range of relatives, including siblings, parents, and married or single children (Aptekar 2015;
Carr and Tienda 2013). LPRs can only petition to sponsor spouses and unmarried children whereas
undocumented immigrants are not able to sponsor anyone. Despite increased access to sponsorship
with citizenship, however, mixed-status families face different challenges with family
reunification due to how existing immigration laws criminalize undocumented immigrants
(Enchautegui and Menjívar 2015; Abrego et al. 2017; López 2017). In particular, the Illegal
Immigration Reform and Immigrant Responsibility Act of 1996 (IIRIRA) created disproportionate
barriers for low-income mixed-status families by requiring a minimum income requirement and
instating temporary bars from reentry to the U.S. of up to 10 years if found to have lived without
legal authorization in the U.S. (López 2017). The process to adjust status leaves undocumented
spouses vulnerable to deportation from the U.S. and further separation that often leads to strains
on family dynamics and well-being (López 2017). Family reunification thus may not be as great
of an incentive to naturalize for mixed status families who face barriers even as citizens.

19

Factors Impacting Naturalization
Past research on naturalization examines immigrants’ individual characteristics as well as the
places they come from and currently reside in to understand the push and pull factors paving
immigrants’ varying pathways to citizenship. Though limited work has been done to empirically
explore the impact of networks on naturalization, we turn to qualitative and ethnographic studies
that examine network effects on immigrant integration. In this section, we discuss these
determinants as interacting mechanisms that impact immigrants’ decisions and abilities to
naturalize.

Individual Characteristics
Immigrants with greater human capital have greater odds of naturalizing as they are better
equipped to meet U.S. naturalization requirements (Aptekar 2015; Bloemraad 2006; Johnson,
Reyes, Mameesh, Barbour, et al. 1999; Yang 1994). Higher educational attainment, for example,
often provides immigrants the experience, skillset, and knowledge to prepare for the required U.S.
civics, history, and English exams which are similarly formatted to assessments administered in
the U.S. education system (Aptekar 2015; Kunnan 2009). Relatedly, as the naturalization process
is primarily in English for most applicants, immigrants with English proficiency have greater odds
of passing the naturalization tests and are able to more effectively navigate procedural barriers,
including the application and study materials provided by USCIS (Aptekar 2015; Bloemraad 2006;
Flores 2018).
Income affects naturalization in multiple ways with the most direct impact being the ability
to afford the application fees, however, this is becoming less of a barrier considering improved

20
access to fee waivers and reductions for low-income applicants (Yasenov et al. 2019). Beyond
application fees, immigrants can face other financial costs in naturalizing, including retaining
lawyers and services to assist in what can be a complex naturalization process (Aptekar 2015). For
immigrants who may not have the skillset to pass the naturalization tests, income may be needed
to access services and classes that can improve an applicant’s odds of passing the required tests
(Aptekar 2015).
Owning a home correlates with stable employment and financial resources for immigrants,
but it is also an indicator of immigrants’ ties and intentions to stay in the U.S. (McConnell and
Marcelli 2007). This particular association should be taken with caution, however, as barriers to
homeownership such as mortgages are not equally experienced across immigrant groups with
racial/ethnic minorities, undocumented immigrants, and mixed-status families facing
disproportionate discrimination in the housing market (McConnell 2015; Mundra and Oyelere
2017).
Demographic characteristics help to explain naturalization disparities as the process in
gaining citizenship in the U.S. tends to favor immigrants who are wealthier, more educated, and
from English-speaking countries. Latino immigrants—particularly immigrants from Mexico—
consistently exhibit the lowest naturalization rates (Aptekar 2015; Gonzalez-Barrera 2017;
Waldinger 2021). Latino immigrants primarily cite language and financial barriers as individual
reasons for not naturalizing (Gonzalez-Barrera 2017). This is often linked to a higher share of
Latino immigrants’ arriving with limited educational opportunities, less resources, and at an older
age when language acquisition becomes more challenging (Bleakley and Chin 2010). However,
studies have also shown how existing immigration policies and racialized enforcement have
significantly impeded Latino immigrant communities and their ability to integrate (Golash-Boza

21
and Hondagneu-Sotelo 2013; Romero 2008b; Waldinger 2021). The undocumented immigrant
population in the U.S. has been predominantly Latino, leading to enforcement that
disproportionately targets Latino communities. This has created racialized chilling effects that
influence how Latino immigrants access public resources and processes where immigration
officials are involved (Amuedo-Dorantes and Lopez 2020; Aranda et al. 2014; Asad 2020; López
2017; Waldinger 2021). Consequently, despite the incentives of naturalizing, immigrants with
undocumented family members in danger of deportation face higher costs and barriers in pursuing
citizenship.
Similarly, naturalization disparities exist across gender identities. Immigrant women
comprise a larger share of the U.S. LPR population and naturalize at higher rates than immigrant
men (Amuedo-Dorantes and Lopez 2020; Dziadula 2018). Research has shown that differences in
naturalization rates are often associated with the gender gap in educational attainment, English
proficiency, and social networks that can provide additional naturalization information and
resources (National Academies of Sciences, Engineering, and Medicine 2015). More recent work
by Amuedo-Dorantes and Lopez (2020) finds that immigrant women—particularly those in non-
mixed-status households and families—have higher propensities to naturalize as a response to
increasing immigrant enforcement. Such responses are gendered and shaped by migration patterns
and policies, including the disproportionate policing and targeted enforcement of immigrant men
(Golash-Boza and Hondagneu-Sotelo 2013).
LPR status is a requirement to naturalize in the U.S., but an immigrant’s prior status before
adjusting can also impact their choice to pursue citizenship. In their research on previous
undocumented experience and naturalization, Cheong (2020) finds that immigrants who once
entered the U.S. without authorization are more likely to naturalize when becoming eligible than

22
their immigrant counterparts with no history of illegality. The author utilizes the theory of
defensive naturalization to explain this increased propensity among once-undocumented LPRs
who find citizenship “a crucial commodity with which to achieve legal security and formal equality
in a host country that had previously excluded them from the mainstream” (Cheong 2020:25).
Massey and Pren (2012) find similar trends of defensive naturalization when anti-immigrant
measures are proposed or implemented, however, their analysis is situated within the context and
wake of large-scale legalization programs (e.g., the Immigration Reform and Control Act (IRCA)
in 1986) that led to entire families securing their LPR status in the U.S. More than three decades
after IRCA, the number of LPRs and U.S. citizens in a family where at least one member is
undocumented nears 7 million (Migration Policy Institute 2020). With growing anti-immigrant
enforcement and the increasing presence of mixed-status families, defensive naturalization may
not always be a feasible option for eligible immigrants. Mixed-status couples face increasing odds
of separation with some eligible immigrants forgoing naturalizing and family reunification benefits
altogether to avoid any chance of exposing their spouses’ documentation status to immigration
officials (Aranda et al. 2014; López 2017). For mixed-status families that do pursue naturalization
under the U.S. citizenship regime, there are additional legal, financial, emotional costs that further
fragment the family as they go through a scrutinized legalization process that has no guaranteed
outcome despite its increasingly demanding standard for mixed-status families in justifying their
case (Gomberg-Muñoz 2017).

Place-based Attributes
Places immigrants live and come from have policies and conditions that act as external
mechanisms that either facilitate or hinder immigrants’ pathways to citizenship. Poor economic

23
conditions and limited economic opportunities are among the most cited factors leading to
international out-migration flows, but such sustained conditions like unlivable wages, scarce
employment prospects, and inadequate safety nets can dissuade immigrants from returning to their
country of origin as they may see relatively better economic opportunities in the U.S. (Portes and
Rumbaut 2014; Rosenblum and Brick 2011). Similarly, concerns of safety from events like
political violence and persecution have led to significant international migration and deterred
immigrants’ permanent return (Abrego 2014; Menjívar 2000; Portes and Rumbaut 2014). Asylum
seekers and refugees, for example, often flee their country of origin with little intention to return
due to the uncertainty of their survival that awaits them in their homeland compared to the idea of
permanent security and safety in the U.S. (Bloemraad 2018). Though this desire to stay in the U.S.
has led to higher rates of naturalization, many refugees and asylum seekers in the U.S. come with
limited human capital and resources thus facing other challenges in gaining citizenship despite
their intentions to become U.S. citizens (Mossaad et al. 2018).
Allowing dual citizenship can also influence an immigrant’s choice to naturalize. Only a
handful of countries officially allow their citizens to retain their country-of-origin citizenship if
they choose to naturalize in the U.S. Immigrants may be hesitant to forfeit their country-of-origin
citizenship for a multitude of reasons, such as symbolic attachments and intentions for return
migration in some form (Leblang 2017; Mazzolari 2009). Research finds that once this cost of
forgoing citizenship is removed, immigrants in the U.S. from countries that allow dual citizenship
are more likely to seek naturalization (Jones-Correa 2001; Mazzolari 2009). However, the effect
may vary by country of origin as dual citizenship rules and benefits can differ with some nations
revoking voting rights for citizens with multiple citizenships (Sejersen 2008).

24
The context of reception for immigrants in the U.S. also influences individual choice and
ability to naturalize. States and localities that have pro-immigrant policies and attitudes are more
likely to have the resources and political will to foster a more welcoming environment that can
improve immigrant integration and naturalization outcomes (de Graauw and Bloemraad 2017;
Bloemraad 2006; Van Hook, Brown, and Bean 2006; Woroby and Groves 2016). However, places
with pro-immigrant policies and attitudes are also more likely to attract noncitizen immigrants who
often arrive with limited human capital needed to facilitate naturalization, such as low educational
attainment and limited English proficiency (Johnson, Reyes, Mameesh, Barbour, et al. 1999).
Studies have found mixed results when immigrants reside in places with anti-immigrant
policies and attitudes. For example, Latino immigrants in California experienced a significant
increase in their naturalization rate after Proposition 187 passed which attempted a state-run
citizenship screening system that barred undocumented immigrants from accessing public
education, health care, and other social services provided by the state (Cort 2012). Many saw this
proposition as a targeted initiative threatening the Latino community due to the increasing
undocumented Latino population in the state, and in response, immigrant advocates and groups
mobilized resources to increase naturalization among Latino LPRs to protect their access to public
benefits and to gain voting power needed to change the political landscape (Cort 2012). Similarly,
rising immigrant enforcement can also increase naturalization rates among LPRs seeking to gain
citizenship as a measure to secure their stay in the country (Amuedo-Dorantes and Lopez 2020).
Woroby and Groves (2016) argue that anti-immigrant policies that target undocumented
immigrants can increase naturalization rates among authorized immigrants who turn to citizenship
as a way to distinguish themselves from the undocumented. Though limited empirical work has
been done to explore how immigrant enforcement and anti-immigrant policies impact

25
naturalization outcomes for mixed-status families, ethnographic studies including Gomberg-
Muñoz (2017) and López (2017) find LPRs and U.S. citizens avoid accessing public benefits and
processes like naturalization and family reunification in fear of risking the safety of their
undocumented family member.

Network Effects
Naturalization and other forms of immigrant integration are impacted by networks that can
increase an immigrant’s social capital and access to resources (Abascal 2017; Bloemraad 2006).
Networks develop and span at different scales, including within families and within communities.
In line with Menjívar’s (2000) analysis of social networks as a conduit of information and source
of assistance among immigrants, going through the process of applying for citizenship can be
easier if a family member or someone with close ties share relevant resources and information on
how to navigate the naturalization process based on their own experiences (Liang 1994). In other
ways, however, such family networks and social ties can hinder immigrants’ pathways to
citizenship through misinformation and chilling effects from policies targeting close ties with
liminal legality or undocumented status (Aranda et al. 2014; López 2017; Menjívar 2000; Woroby
and Groves 2016).
Within a community, immigrants can rely on other forms of networks for resources and
information, including co-ethnic nationals and organizations they may be affiliated with (Menjívar
2000). There are competing theories on how ethnic enclaves impact naturalization. One side posits
the importance of shared identities and networks in facilitating the flow of information and
resources, and another side argues that homogenous networks create exclusive in-group
relationships that limit interactions with mainstream culture and act as barriers to assimilation

26
(Abascal 2017). Abascal (2017) finds that the former is more likely and that the concentration of
naturalized co-ethnics is positively associated with greater sharing of naturalization information
because such communities have a stronger sense of a shared hyphenated American identity.
Woroby and Groves (2016), however, argue some immigrant networks can be disadvantageous,
particularly if immigrants who lack the knowledge and intentions to naturalize cluster. Immigrant-
serving organizations and religious congregations also serve as important nodes in immigrant
networks as they provide services, resources, and connections with other members of the
community (Bloemraad 2006; Manglos-Weber 2020).

Hypotheses
Based on previous studies on naturalization and immigrant integration in the U.S. reviewed above,
we offer the following hypotheses (note that two of these have an alternative hypothesis based on
competing strands in the existing literature):

H1: All else equal, immigrants with greater human capital and financial resources have
greater propensities to naturalize.

To gain U.S. citizenship, immigrants are expected to go through a relatively costly, time-
and labor-intensive process that, by design, is broadly more accessible to wealthier immigrants
with higher educational attainment and English proficiency (Abascal 2017; Johnson, Reyes,
Mameesh, Barbour, et al. 1999; National Academies of Sciences, Engineering, and Medicine 2015;
Yang 1994).

27
H2: All else equal, immigrants living in more immigrant-friendly states and localities have
greater propensities to naturalize; or

H2-alt: All else equal, immigrants living in less immigrant-friendly states and localities
have greater propensities to naturalize.

Individual characteristics alone do not determine an immigrant’s choice and ability to
naturalize. Research has found place-based attributes and networks also play significant roles in
shaping immigrants’ pathways to citizenship. In particular, immigrants living in places with pro-
immigrant policies and attitudes may feel more welcomed and have better access to resources and
services nurturing their integration, human capital, and networks (de Graauw and Bloemraad 2017;
Bloemraad 2006; Van Hook, Brown, and Bean 2006). On the other hand, eligible immigrants
residing in places with anti-immigrant policies and enforcement may naturalize as a defense
mechanism to secure their rights to stay in the U.S., to preserve their access to public benefits that
have been threatened to be taken away, and/or to prove their allegiance to the U.S. (Amuedo-
Dorantes and Lopez 2020; Cheong 2020; Cort 2012).

H3: All else equal, immigrants with stronger conviction and incentive to stay in the U.S.
as a result of poor social, economic, and political conditions in their country-of-origin have
greater propensities to naturalize.

Immigrants’ intentions to return permanently to their country of origin are influenced by
the opportunities and conditions on their return. While immigrants may seek permanent residency

28
and citizenship in the U.S. if they believe that there are better economic and social opportunities
than in their country of origin, immigrants may also be incentivized to secure their stay as U.S.
citizens if their return could leave them and their families in unsafe situations due to political
persecution and violence (Bloemraad 2006; Abrego 2014; Menjívar 2000; Portes and Rumbaut
2014). In many cases, gaining U.S. citizenship means forfeiting any other citizenship one may
hold. Some may see this exchange as weakening symbolic ties and civic duties to one’s homeland,
but it could also create additional barriers to visit family and important relationships still in the
country of origin (Leblang 2017; Mazzolari 2009). However, as Jones-Correa (2001) and
Mazzolari (2009) have found, this cost of naturalizing may be mitigated if dual citizenship is
allowed.

H4: All else equal, immigrants with stronger ties and networks to those with resources and
information on naturalizing have greater propensities to naturalize.

Naturalization can be a cumbersome and complex process that is difficult to navigate
without the necessary human capital, financial resources, and information. Networks can moderate
naturalization outcomes by supplementing pooled resources and information to overcome barriers
immigrants with limited human capital may face (Abascal 2017; Bloemraad 2006; Liang 1994;
Menjívar 2000). We expect that immigrants with close ties to a naturalized family member who
can share resources and information will have greater odds of naturalizing.

H5: All else equal, immigrants with an undocumented family member have lower
propensities to naturalize; or

29

H5-alt: All else equal, immigrants with an undocumented family member have greater
propensities to naturalize.

Immigrants in mixed-status families face different challenges in their integration compared
to families without a history of illegalities as the U.S. has created systems that criminalize
undocumented immigrants’ existence with spillover effects felt by families and close ties (Abrego
et al. 2017; Asad 2020; López 2017). We expect immigrants who have an undocumented family
member, on average, to be less likely to naturalize due to their fears that any contact with
immigration and federal officials, including during the naturalization process, can put the safety
and security of their family at risk of deportation.
On the other hand, eligible-to-naturalize immigrants in mixed-status families may resort to
naturalization as a mechanism to protect undocumented family members. By securing citizenship
in the U.S., immigrants can improve their access to public benefits and family sponsorship
programs (Amuedo-Dorantes and Lopez 2020; Cheong 2020). This outcome may be especially
plausible in settings where policies and institutions are seen to infringe even on the rights of LPRs,
and where external forces like immigrant-serving organizations are able to provide the needed
resources to mobilize and naturalize (Cort 2012).

Data, Methodology, and Model Specifications
For this analysis, we use the 2016 American Community Survey (ACS) from IPUMS (Integrated
Public Use Microdata Series) which provides microdata level estimates (Ruggles et al. 2020). The
ACS microdata has a comprehensive selection of individual and household characteristics with an

30
adequately large and representative sample size that allow for place-based estimates. This dataset
captures key indicators to help answer our research questions on naturalization, including
information on immigrants’ naturalization status, years in the U.S., sociodemographic
characteristics, and reported measures of human capital like educational attainment and English
proficiency. This micro dataset also allows us to link individuals within the same family and
household unit to determine family network effects.
The limitation of this dataset, however, is that while each observation can be identified as
either citizen or noncitizen, it does not explicitly specify documentation status. In order to address
this shortcoming, we apply a series of logical edits and probability imputations building on work
estimating the undocumented immigrant population (e.g., Capps et al. 2013; Pastor and Scoggins
2016; Warren 2014; Jennifer Van Hook et al. 2015). Previous empirical studies on naturalization
often do not account for the likely undocumented observations in their dataset who, despite
meeting residency and age requirements, are not eligible to naturalize as they currently lack the
required LPR status. After creating our sample of naturalized and eligible-to-naturalize
immigrants, we then utilize a series of logit models to determine the impact of specific individual
characteristics, place-based attributes, and networks on the odds of naturalization.

Estimating Who is Eligible to Naturalize and Who Recently Naturalized
Immigrants in the U.S. become eligible to naturalize when they satisfy the following conditions:
1) be at least 18 years of age at the time of filing the application for naturalization (Form N-400);
2) have resided in the U.S. as a lawful permanent resident for at least five years (or three if married
to a U.S. citizen); 3) be physically present in the U.S. for at least 30 months; 4) be deemed a person
of good moral character by USCIS officers; 5) have the ability to write, speak, and read in English;

31
6) have a basic understanding of U.S. civics and history; 7) demonstrate attachment to the
Constitution and its principles; and 8) able to take the Oath of Allegiance (U.S. Citizenship and
Immigration Services 2019).
Once likely undocumented immigrants are identified and removed from the sample (see
Appendix for details on methodology), the pool of observations is narrowed further to LPRs who
are at least 18 years old and have been in the U.S. for at least five years, or three years if married
to a U.S. citizen. Though there are other requirements to be eligible to naturalize, these are the
conditions that can be clearly identified in our dataset. While the ACS microdata includes
information on English speaking ability, this criterion was not relied on to determine eligibility
because English speaking ability is not a measure used to determine eligibility in the official
estimates of the eligible-to-naturalize population published by OIS (Lee and Baker 2017a).
Additionally, there are many immigrants who have naturalized despite reporting limited English
proficiency. For example, our data suggests that 18 percent of immigrant adults who report
speaking English “not at all” are naturalized citizens, a share that increases to 31 percent for those
who report speaking English “not well.”
In order to validate our estimates of the eligible to naturalize, we compare them to the
estimates available from OIS (Baker 2020). OIS estimates are used for reference as their numbers
are based on government administrative data (essentially, those who have LPR status and meet the
residency tenure thresholds). Given that OIS estimates include children under 18, LPR children
are temporarily identified and included in the dataset as eligible to naturalize if either of their
parents are eligible so that a more consistent comparison can be made. The results of the
comparison are shown in Table 1.1. While our estimates are slightly larger for the eligible-to-
naturalize population in 2016 than OIS’s estimates (8.97 million versus 8.88 million), the

32
distribution of the population by period of entry, country of birth, and state of residence is relatively
similar.

Table 1.1: Comparing OIS and Le & Pastor estimates of the eligible to naturalize

Number Percent Number Percent Number Percent Number Percent
Period of Entry
Before 1990 2,680,000 29% 3,010,000 34% 2,400,000 27% 2,900,000 32%
1990 - 1999 1,940,000 21% 2,190,000 25% 2,270,000 26% 2,440,000 27%
2000 - 2009 2,650,000 29% 3,030,000 34% 3,920,000 44% 3,240,000 36%
2010 - 2014 1,730,000 19% 650,000 7% 290,000 3% 390,000 4%
2015 + … 120,000 1% 0 0% 0 0% 0 0%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
Country of Birth
Mexico 2,490,000 27% 2,540,000 29% 2,710,000 31% 3,060,000 34%
China 490,000 5% 430,000 5% 330,000 4% 330,000 4%
Philippines 370,000 4% 350,000 4% 340,000 4% 280,000 3%
Cuba 350,000 4% 300,000 3% 300,000 3% 330,000 4%
Dominican Republic 340,000 4% 320,000 4% 300,000 3% 250,000 3%
India 310,000 3% 270,000 3% 250,000 3% 390,000 4%
Canada 250,000 3% 260,000 3% 260,000 3% 260,000 3%
El Salvador 220,000 2% 230,000 3% 250,000 3% 310,000 3%
United Kingdom 220,000 2% 230,000 3% 230,000 3% 280,000 3%
Vietnam 220,000 2% 210,000 2% 200,000 2% 130,000 1%
South Korea 200,000 2% 190,000 2% 190,000 2% 180,000 2%
Haiti 160,000 2% 160,000 2% 150,000 2% 120,000 1%
Jamaica 160,000 2% 160,000 2% 160,000 2% 130,000 1%
Colombia 140,000 2% 140,000 2% 140,000 2% 130,000 1%
Guatemala 120,000 1% 120,000 1% 120,000 1% 200,000 2%
Rest of Countries 3,090,000 34% 2,970,000 33% 2,950,000 33% 2,590,000 29%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
State of Residence
California 2,340,000 26% 2,360,000 27% 2,470,000 28% 2,270,000 25%
New York 1,130,000 12% 1,080,000 12% 1,040,000 12% 1,050,000 12%
Texas 990,000 11% 950,000 11% 960,000 11% 900,000 10%
Florida 880,000 10% 830,000 9% 830,000 9% 810,000 9%
New Jersey 380,000 4% 380,000 4% 370,000 4% 340,000 4%
Illinois 380,000 4% 370,000 4% 380,000 4% 360,000 4%
Massachusetts 210,000 2% 210,000 2% 200,000 2% 200,000 2%
Washington 190,000 2% 180,000 2% 180,000 2% 180,000 2%
Arizona 190,000 2% 180,000 2% 190,000 2% 210,000 2%
Virginia 180,000 2% 170,000 2% 160,000 2% 160,000 2%
Rest of States 2,260,000 25% 2,170,000 24% 2,100,000 24% 2,490,000 28%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
Note: Numbers are rounded to the nearest 10,000.
OIS Estimates (2019) Le & Pastor (2012-2016) OIS Estimates (2016) OIS Estimates (2014)

33
Among those who have naturalized, we narrow our analysis to individuals who recently
naturalized. A comparison of those who are eligible to all those who have naturalized could be
misleading since research has shown shifts in income, education, and English language abilities
among immigrants after longer periods of naturalization (Pastor and Scoggins 2012). For example,
a finding of higher income among all naturalized observations would not necessarily be strong
evidence that income is a significant driver for naturalization as such economic gains could have
occurred after gaining citizenship. To account for possible maturation bias and effects of
naturalization that may change an immigrant’s social and human capital, we only include
observations who naturalized within three years of the ACS survey, with the control group being
those who were eligible over that period but did not naturalize. Focusing on the recently naturalized
also ensures that our results and interpretations more accurately reflect the current social,
economic, and political climate around immigration and naturalization.

Modeling Propensity to Naturalize
We utilize binomial logit models to determine the likelihood of naturalization. Logit models help
determine the log-odds of the probability of a binary outcome to occur based on a series of
characteristics and variables. The logit models in this analysis explore the probability of an eligible
LPR to naturalize based on individual characteristics, place-based attributes, and networks.
Incorporating a series of variables and predictors that previous research have found to be
explanatory in an individual’s propensity to naturalize, our models determine the probabilities of
naturalizing with a maximum likelihood estimation of given observations. We control and hold
constant a set of predictors that is linked with naturalization so that we can isolate the effects of
specific variables. Model 1 includes variables measuring individual and network characteristics

34
but uses state and country-of-origin fixed effects in lieu of certain place-based variables to address
possible omitted variable bias related to place-based heterogeneity. Model 2 excludes the state and
country-of-origin fixed effects and incorporates place-based variables previously found to be
associated with naturalizing. We do this to estimate the impact of specific place-based variables
with effects on naturalization that would be absorbed by the state and country-of-origin fixed
effects. The following equation is estimated where Y is the binary outcome of naturalization, s is
the state fixed effect (omitted in model 2), c is the country-of-origin fixed effects (omitted in
model 2), t is the year fixed effects, and Xi represent the individual, place, and network predictors:

𝑌𝑖 =
1
1 + 𝑒 −(  + 𝑿𝒊 + 𝒔 + 𝒄 + 𝒕 + )

We predict the average impact a variable has on an immigrant’s propensity to naturalize through
an odds ratio value by calculating the exponential relationship of the log-odds.
To estimate the effects of individual characteristics on naturalization, we include
demographic variables and human capital measures. Our demographic predictors include gender,
race/ethnicity, age at entry (linear and quadratic forms), years of eligibility (logged), and number
of children. Research on naturalization has found certain demographic characteristics are
associated with higher probability of naturalization, such as being female, whereas other
characteristics like age at entry and years since becoming eligible have non-linear effects (Johnson,
Reyes, Mameesh, Barbour, et al. 1999; Yang 1994). We control for age at entry as research
indicates that immigrants who arrive and attend school in the U.S. at a younger age are able to
assimilate and learn English much more easily than their older counterparts (National Academies
of Sciences, Engineering, and Medicine 2015; Waldinger 2021). To estimate the impact of human

35
capital, we include educational attainment, employment status, occupational prestige, reported
English proficiency, homeownership status, and household income. Immigrants with greater
human capital and resources are more able to afford the financial costs of naturalizing and have
higher odds of passing the English language and U.S. civics and history exams (Aptekar 2015;
Johnson, Reyes, Mameesh, Barbour, et al. 1999; Yang 1994).
Place-based attributes for where immigrants currently reside include the unemployment
rate in the MSA (Metropolitan Statistical Area) and the political leaning of the state in which the
observation resides. We include the MSA unemployment rate for immigrant adults as some adults
may naturalize as a way to increase employment opportunities and earnings (Aptekar 2015;
Johnson, Reyes, Mameesh, Barbour, et al. 1999; Pastor and Scoggins 2012). The results of the
U.S. Presidential Election in 2012 are used as a proxy to measure the state’s general political
leaning during that time. Though there are underlying assumptions using election results as a
measure of state ideology, election results have been consistently used in political research due to
simplicity, availability of data, and representation of constituency behavior (Leogrande and Jeydel
1997). The limitation of this variable is that it does not fully measure the nuances of politics such
as political conservatives who support immigration and have created important political
opportunities for immigrants in the U.S.
1
However it is also true that, in general, liberal-leaning
states express more positive attitudes towards immigration and are more supportive of policies that
promote citizenship (Krogstad 2015).
We include in Model 2 the distance between an immigrant’s country of origin and their
current state of residence, whether or not dual citizenship is allowed, whether their country of

1
For example, see the Bibles, Badges, and Business coalition convened by the National
Immigration Forum at https://immigrationforum.org/landing_page/bibles-badges-business/.

36
origin is a TPS-designated (Temporary Protected Status) country
2
or a historically refugee-sending
country
3
, and the country’s GDP (gross domestic product) per capita. Yang (1994) finds the
probability of naturalizing increases the further the country of origin is from the host country. This
is likely due to challenges of returning home associated with geographical distance. Immigrants
who are allowed dual citizenship are expected to have greater odds of naturalizing as costs
associated with forfeiting country-of-origin are absent (Mazzolari 2009). Temporary Protected
Status (TPS) and refugee status are included to measure political, physical, and social turmoil in
an immigrant’s country of origin which may be indicative of their inability or unwillingness to
permanently return (Mossaad et al. 2018; Portes and Rumbaut 2014). GDP per capita normalized
using purchasing power parity (PPP) is an indicator of the economic opportunities available in the
country of origin. These variables measure general conditions that might deter immigrants from
permanently returning and thus incentivizing their stay in the U.S. as citizens.
To estimate family network effects, we include spouse’s citizenship and immigration status
to disentangle the impact of being married to someone who has gone through the naturalization
process compared to having an undocumented spouse. Similarly, we include variables that control
for other naturalized or undocumented adults in the family. Our dataset provides the years in which
immigrants arrived in the U.S., naturalized, and married their spouse. This information and the
ability to link observations within the same family unit allows us to more accurately code spouses’
and adult family members’ citizenship and immigration status prior to each observation’s

2
We include all countries with a TPS designation during or prior to 2016. More information on
which countries these are can be found at https://www.uscis.gov/humanitarian/temporary-
protected-status.
3
We identify refugee-sending countries from data provided by the Office of Immigration
Statistics at the Department of Homeland Security. More information on which countries these
are can be found at https://www.dhs.gov/immigration-statistics/refugees-asylees.

37
naturalization. Immigrants with close ties with someone who has naturalized would have access
to valuable information whereas immigrants in mixed-status families may experience chilling
effects that either hinder their choice to naturalize (Aranda et al. 2014; Asad 2020) or spur
naturalization as a defense mechanism to improve access to public benefits and family sponsorship
programs (Amuedo-Dorantes and Lopez 2020; Cort 2012). To estimate network effects within
communities, we include the concentration of co-ethnic immigrants in a PUMA (Public Use
Microdata Area) to indirectly measure network resources and information sharing within the local
immigrant community (Abascal 2017; Menjívar 2000; Yang 1994).

Results
Similar to previous studies, we find multiple individual and place-based factors drive
naturalization in the U.S. Our results also suggest family networks are significant in determining
naturalization. More specifically, naturalized family members and spouses significantly and
positively improve odds of naturalization whereas presence of undocumented family members and
spouses have a large negative effect—a novel finding that has not been fully explored in previous
empirical studies. Estimates and significance of each predictor are presented in Table 1.2.

Individual Characteristics
With the exceptions of race/ethnicity and household income, both Model 1 and Model 2 yield
qualitatively similar results for individual characteristics. Our results show immigrant men, those
who enter the U.S. at an older age, and those who have been eligible for naturalization longer are
less likely to naturalize. In terms of race/ethnicity, Model 1 yields significant results for Black and
Asian Pacific Islander American immigrants who show higher odds of naturalizing. The

38
race/ethnicity effects for Latinx immigrants seem to be absorbed by the country-of-origin fixed
effects, signaling the differential experiences with naturalization across Latinx immigrants from
different countries of origin. Model 2 yields significant results for each of the race/ethnicity groups
with Black immigrants having higher odds of naturalization and Latino, Asian Pacific Islander,
and “Other” immigrants having lower odds of naturalizing than their white immigrant
counterparts. By controlling for common determinants of naturalization such as human and social
capital, these significant results suggest other racialized factors that are not directly captured in the
models such as possible structures and functions of racism that reinforce racial hierarchies in
accessing citizenship. This, for example, is evident in the naturalization process where immigrants
of a certain race/ethnicity and religion often experience prolonged delays and disproportionate
scrutiny due to being stereotyped and discriminated against by USCIS (Ahluwalia 2014).
As expected, we find immigrants with more resources and greater human capital more
likely to naturalize. Compared to those with less than a high school degree, immigrants with higher
educational attainment—particularly some college or a college degree—have greater odds of
naturalizing. We also find English proficiency significantly impacts naturalization outcomes with
the least likely to naturalize to be those who do not speak English at all. Compared to those who
do not speak English at all, our results show that those who report any proficiency with English
have much greater odds of naturalizing with those who speak English at least “well” having more
than twice the odds of naturalizing.
Relative to unemployed immigrants, immigrants who are employed have higher odds of
naturalizing whereas those not in the labor force have lower odds. Occupational prestige, as
measured by the Duncan Socioeconomic Index, is associated with slightly higher odds of
naturalizing among employed immigrants. As expected, owning a home prior to naturalization is

39
also associated with greater odds of naturalizing. Though Model 1 yields no significant results for
household income, likely absorbed by the state and country-of-origin fixed effects, we observe a
statistical negative effect on the odds of naturalizing in Model 2. This negative effect is likely
linked to the standardization of fee waivers and reductions that has improved access to
naturalization for low-income immigrants who may find greater economic incentives and
motivation to naturalize than their wealthier counterparts (Yasenov et al. 2019).

Place-based Attributes
In Model 2, we include place-based attributes that were omitted from Model 1 whose
effects would have been absorbed by the state and country-of-origin fixed effects. We confirm
Yang’s (1994) earlier findings that greater geographical distance from one’s country of origin is
associated with greater odds of naturalizing. Similar to Mazzolari (2009), we also find that
immigrants from countries that allow dual citizenship have greater odds of naturalizing. To analyze
the impact of economic conditions, we use GDP per capita adjusted by purchasing power parity
and find that immigrants from wealthier countries with more economic opportunities have lower
odds of naturalizing.
Using TPS and refugee-sending designations as proxies for the social, economic, and
political conditions in immigrants’ country of origin, we find mixed results. Immigrants from a
country that is traditionally refugee-sending have greater odds of naturalizing whereas immigrants
from TPS designated countries have lower odds. This significant increase in odds for immigrants
from refugee-sending countries may be related to several factors, including an increased network
of co-ethnic nationals and family members who have already gone through the naturalization
process and additional federal resources specifically designated for refugees (Mossaad et al. 2018).

40
In contrast, immigrants from TPS-designated countries may have unobserved characteristics that
are associated with lower rates of naturalization including a limited network of naturalized
immigrants due to the liminal legality associated with TPS (Abrego 2014; Menjívar 2000). There
may also be some measurement issues given that our approach to estimating the undocumented is
still limited in distinguishing which individuals in the survey may or may not have TPS status. We
recommend that this issue receives further analysis in the future with particular attention to the
context of reception immigrants from TPS-designated countries experience compared to those
from traditionally refugee-sending countries. Whether an immigrant resides in left-leaning states
and our measure of unemployment in the MSA yields statistically insignificant results.

Network Effects
Of notable significance in our analysis and novel to the empirical literature on naturalization, we
find family networks to significantly influence naturalization. Our methodological approach
allows us to examine family structures and networks by identifying whether family members and
spouses are either naturalized or undocumented. We find that being married does improve the odds
of naturalization, but this effect is driven entirely by immigrants who are married to someone who
has already naturalized. As indicated in Model 1, compared to not being married at all, having a
naturalized spouse prior to one’s own naturalization is associated with 72.4 percent greater odds
of naturalizing whereas having an LPR, U.S.-born, or undocumented spouse reduces the odds of
naturalizing by 47.8 percent, 13.7 percent, and 85.7 percent, respectively. Similarly, having a
naturalized adult in the family other than a spouse improves the odds of naturalization by 39
percent, but having an undocumented family member other than a spouse reduces the odds by 32.6
percent. These findings support our hypotheses on the importance of having close ties to someone

41
Table 1.2: Estimated effects on the odds of naturalization

Odds Ratio SE Odds Ratio SE
Male/Men 0.891
***
0.008 0.892
***
0.008
Age at Entry 0.976
***
0.001 0.976
***
0.001
Age at Entry (Sq.) 1.000
***
0.000 1.000
***
0.000
Years Eligible (log) 0.810
***
0.004 0.783
***
0.004
Number of Children in Household 1.049
***
0.004 1.044
***
0.004
Compared to white
Black 1.245
***
0.052 1.085
***
0.023
Latinx 1.011 0.048 0.877
***
0.015
Asian Pacific Islander American 1.093
*
0.041 0.826
***
0.013
"Other" Race 1.059 0.039 0.969 0.030
Compared to less than high school degree
High School Graduate 1.023
†
0.014 1.070
***
0.014
Some College 1.380
***
0.020 1.478
***
0.021
Bachelor's Degree 1.437
***
0.024 1.557
***
0.025
Greater than Bachelor's 1.180
***
0.023 1.308
***
0.024
Compared to unemployed
Not in Labor Force 0.931
**
0.021 0.926
***
0.020
Employed 1.178
***
0.025 1.162
***
0.024
Occupational Prestige 1.003
***
0.000 1.003
***
0.000
Compared to Speaks English "Not at All"
Speaks English "Not Well" 1.593
***
0.033 1.590
***
0.033
Speaks English "Well" 2.393
***
0.051 2.351
***
0.050
Speaks English "Very Well" 2.548
***
0.056 2.409
***
0.052
Speaks English "Only" 2.052
***
0.052 1.773
***
0.042
Homeowner 1.231
***
0.012 1.195
***
0.011
Household Income (log) 1.005 0.004 0.993
*
0.003
Dual Citizenship Allowed 1.184
***
0.013
Distance from Country of Origin (per 1000 KM) 1.016
***
0.002
Origin Country has had TPS Status 0.725
***
0.014
Origin Country is traditional Refugee-Sending Country 1.423
***
0.020
GDP Per Capita PPP of Country of Origin (log) 0.745
***
0.005
Reside in Left-Leaning State 0.956 0.097
Unemployment Rate in MSA 1.003 0.004 0.995 0.004
Percent of Co-Ethnic Nationals in PUMA 1.003
***
0.001 0.996
***
0.001
Comapred to no spouse
U.S.-born Spouse 0.863
***
0.012 0.822
***
0.012
Naturalized Spouse 1.724
***
0.022 1.816
***
0.023
LPR Spouse 0.522
***
0.008 0.514
***
0.008
Undocumented Spouse 0.143
***
0.004 0.138
***
0.004
Naturalized Adult in Family (not spouse) 1.390
***
0.020 1.477
***
0.021
Undocumented Adult in Family (not spouse) 0.674
***
0.010 0.664
***
0.010
State Fixed Effects
Country-of-Origin Fixed Effects
Year Fixed Effects
R-Squared
N (Unweighted)
Coefficients are in odds ratio; status of spouse represents likely status prior to observation ’s own naturalization.
† p < .1; * p<.05; **p<.01; ***p<.001 (two-tailed).
415,182 415,182
0.124 0.111
Model 1 Model 2
Yes
Yes
Yes Yes

42
with experiences navigating the naturalization process, but also the significant and negative
spillover effects on naturalization when an undocumented spouse or family member is present.
We also find the concentration of co-ethnic nationals in a PUMA slightly improves an
immigrant’s odds of naturalizing. In particular, a 10-percentage point increase in the percent of
co-ethnic nationals improves the odds of naturalizing by 3 percent. Though this effect is relatively
small, it supports earlier findings on the importance of information exchange and networks among
immigrants with shared identities and experiences (Abascal 2017).

Discussion and Conclusion
In examining the individual characteristics, place-based attributes, and network effects associated
with immigrants’ propensities to naturalize, we find barriers to naturalization are multidimensional
and vary across immigrant groups. Despite the many social, economic, and political benefits that
come from gaining U.S. citizenship, the naturalization process is often by design inaccessible to
immigrants with limited human and social capital—often Latinx immigrants (Abascal 2017;
Waldinger 2021). Consistent with previous studies on naturalization, our results show immigrants
with more human capital have greater odds of naturalizing. In particular, immigrants with stronger
English proficiency and higher educational attainment have greater odds of gaining citizenship as
these skills help to navigate the cumbersome naturalization process, including the application, the
English-proctored interviews, and the U.S. civics and history exam (Aptekar 2015). We also find
that place-based attributes influence immigrants’ decision and ability to naturalize. For example,
immigrants coming from countries with precarious conditions may be disincentivized to return
permanently and instead turn to U.S. citizenship as a way to secure their stay in the country and
access to public benefits (Portes and Rumbaut 2014; Rosenblum and Brick 2011).

43
Contributing to the literature, our research finds novel empirical evidence indicating the
significant effects of social capital and networks on naturalization. Our results indicate having a
naturalized spouse or family member positively impacts an immigrant’s propensity to gain
citizenship. In contrast, having an undocumented spouse or family member present leads to
significantly lower odds of naturalizing. As Menjívar (2000) argues, social networks can be
multifaceted and yield contradictory results. Immigrants can better navigate the naturalization
process if they have close ties that can provide pooled resources and valuable information based
on their own experiences gaining citizenship. However, immigrants with close familial ties who
are undocumented may avoid the naturalization process entirely due to concerns stemming from
the persistent criminalization and deportation of undocumented immigrants in the U.S. (Asad
2020). Though previous studies have found increased immigrant enforcement and anti-immigrant
policies to increase naturalization among immigrants (e.g., Amuedo-Dorantes and Lopez 2020;
Cort 2012), immigrants with undocumented family members have greater risks and costs that
hinder what Massey and Pren (2012) call defensive naturalization.
In future work, we hope to disentangle the effects of immigrant-serving organizations on
naturalization. With resources to mobilize immigrants and direct services that foster civic
incorporation and naturalization, Bloemraad (2006) and Bloemraad and Gleeson (2012) find that
immigrant-serving organizations are key actors in improving the context of reception. However,
as immigrants may rely on varying types of organizations and programs for support, this is
something that cannot be feasibly examined within the scope of this paper. Organizational data
may also present potential collinearity and endogeneity concerns related to how immigrant-serving
organizations are often located in proximity to immigrant-clustered areas with greater needs
(Gleeson and Bloemraad 2013).

44
Multiple mechanisms, policy levers, and tools are needed to improve naturalization rates
and immigrant integration. By identifying the factors that drive or thwart naturalization,
policymakers and advocates can push for a more equitable naturalization process or services that
will build immigrants’ human and social capital. As Bloemraad (2006) and Jones-Correa (2001)
find, policies and institutional factors can moderate barriers to naturalization and improve access
to citizenship. Though cities and organizations across the country offer subsidized classes to help
immigrants improve their English proficiency and prepare for their citizenship interview and exam,
this is a limited resource (Aptekar 2015). Increasing free and accessible services can help improve
naturalization outcomes, particularly for low-income immigrants. However, it is important to also
consider how time- and labor-intensive these classes can be, and thus inaccessible to immigrants
who may lack the time and resources (e.g., transportation) to attend these classes even if they are
free or subsidized. To make the naturalization process more accessible, similar to how
standardizing and expanding the fee waiver and reduction program have helped improve
naturalization outcomes for low-income immigrants (Yasenov et al. 2019), policymakers could
also consider expanding language exemptions to reduce barriers to citizenship for immigrants with
limited English proficiency.
Immigrant-serving organizations and advocates can improve naturalization rates by also
strategically prioritizing and reaching immigrant communities and families without a naturalized
member. As we have found, having a naturalized spouse, family member, or close tie can
significantly improve an immigrant’s propensity to naturalize. This can create positive network
and spillover effects through pooled resources and information sharing. In the same regard,
promoting naturalization among immigrant women who we find to have greater odds of
naturalization can create a ripple effect in paving pathways to citizenship for the rest of the family.

45
Though strategies to improve naturalization are often associated with increasing
immigrants’ individual skills and capital, it is imperative to also understand how the U.S.
citizenship regime has created unfavorable conditions that disproportionately impact some
immigrants seeking naturalization and resources that could improve their odds of gaining
citizenship. Even with the human and social capital needed to successfully naturalize, certain
immigrant communities face other costs and risks that their white and more privileged counterparts
do not, including the constant fear of deportation. Such immigrants are in difficult situations
because though naturalization could provide an eventual path to permanent residency for their
undocumented family member, they may also be risking immediate separation. Service providers
and policymakers should consider how to assist families with undocumented members in a way
that ensures them of their security and safety, including advocating for the N-400 form to be
revised so that applicants are not required to disclose family members’ immigration status in a way
that threatens their safety. Legislatively, providing lawful status or pathways to citizenship for
currently undocumented immigrants, similar to IRCA, can mitigate negative network and spillover
effects that hinder naturalization outcomes for eligible-to-naturalize immigrants with
undocumented family members.
Encouraging naturalization has long been a point of unity among those on all sides of the
immigration debate as it provides individual and collective benefits. Immigration politics is
becoming more polarized with many previously shared values being challenged. The backlog of
citizenship applications has nearly doubled since the start of 2016 and wait times for interviews
have stretched well beyond the usual six months, a pattern anathema to the usual consensus that
gaining citizenship is a benefit for new Americans (Pastor and Jayapal 2018). Despite the current
politics, immigrants remain an integral part of economies and societies, and research on what

46
propels and what deters immigrants’ pathways to citizenship can shape policies that contribute to
more equitable efforts at immigrant integration.

47

Segmented Citizenship:
The Racialized and Gendered Economic Gains of Naturalizing in the United States

Thai V. Le
Ph.D. Candidate, Public Policy and Management
Sol Price School of Public Policy
University of Southern California

Abstract

Studies analyzing the effects of naturalization on immigrant lives and other forms
of integration are limited due to the lack of comprehensive longitudinal data
following immigrants on their journeys to citizenship and thereafter. As economic
gains are found to be some of the primary benefits of naturalization, this paper looks
to measure the impact of naturalization on individual income and wage/salary
earnings. Using the 2016 5-year American Community Survey microdata—
adjusted with imputation strategies to net out undocumented immigrants
systematically unable to naturalize—a series of propensity score methods and
regression adjustments are applied to measure the impact of naturalization and to
address self-selection bias previous studies have encountered. Findings show, on
average, naturalization is associated with a 13.24 percent increase in total personal
income and 10.07 percent increase in wage/salary earnings within the first ten years
of gaining citizenship. These relative changes in economic outcomes vary in
magnitude by increment of time since naturalizing, race, and gender. Black
immigrants, Latinx immigrants, and immigrant women see larger relative gains in
income and earnings through naturalization compared to their white and man/male
counterparts. This leads to speculations of how economic barriers facilitated by
citizenship status are particularly racialized and gendered.

48
Introduction
Immigrants in the United States experience integration disparately as measured across social,
economic, and political indicators. Given existing structures of inequality and the discriminate
impact of immigration policies, immigrants continue to face mounting barriers to mobility.
Citizenship is often studied and promoted as a way to progress immigrants’ mobility and
integration, including the right to vote, protection from deportation, increased job opportunities,
and improved access to public benefits. (Kerwin and Warren 2019; National Academies of
Sciences, Engineering, and Medicine 2015). However, though naturalization offers many benefits
and privileges, there are institutionalized barriers that prevent immigrants from fully taking
advantage of their acquired citizenship. This includes racial/ethnic disparities in voting turnout
among naturalized citizens (Jones-Correa 2001; Ong and Nakanishi 1996) and decreased use of
public benefits for naturalized citizens likely in mixed-status families (Watson 2014).
Understanding these disparities and why they exist is important in shaping policies aimed to
improve access to benefits and privileges associated with citizenship, such as voting. In this paper,
to further measure the benefits of naturalization, I examine the economic outcomes of gaining
citizenship by estimating the impact on personal total income and wage/salary earnings. In doing
so, I also extrapolate whether such economic benefits vary across immigrant groups. This allows
a more critical analysis of whether citizenship is a greater barrier to economic mobility for
minoritized immigrants, including women and racial minorities.
A lack of comprehensive longitudinal data that follow immigrants on their journeys to
citizenship and thereafter has consequently led to limited work done on measuring the effects of
naturalization on immigrant lives and integration. An exception is a study by Bratsberg, Ragan,
and Nasir (2002) that followed approximately 330 young immigrant men from 1979 to 1991.

49
While this study finds that naturalization has a positive effect on wage earnings, it is unclear how
generalizable the results are considering that the sample is limited to young immigrant men
primarily from Mexico and Central America. These findings may not be directly applicable to the
current group of diverse immigrants in the United States that are naturalizing during a time when
the labor market is even more stratified and racialized (Portes and Rumbaut 2014). In particular,
the significant shift to highly educated and skilled immigrants from Asia—such as immigrants
from India and China, origin countries that have sent more immigrants to the U.S. than Mexico
since 2013—and working-age immigrant women has led to a more diverse workforce who face
different obstacles and opportunities in the labor market (Batalova et al. 2021).
More recent studies examining the effect of citizenship on immigrants’ earnings have used
various methods on nonexperimental samples that consequently are likely vulnerable to statistical
noise and bias. Pastor and Scoggins (2012) apply an ordinary least squares (OLS) regression model
to cross-sectional data, finding a positive relationship between naturalization and wage earnings
when controlling for different factors, including labor market conditions and the
immigration/citizenship status of an observation’s spouse. They estimate the growth in wages
associated with naturalization to be 8 percent on average though this estimate varies by length of
naturalization. They find that naturalization is associated with a 5.6 percent to 7.2 percent increase
in wages in the first 2 years of naturalization, a 6.8 percent to 9.9 percent growth if naturalized for
3 to 6 years, and an 8.8 percent to 12.4 percent increase if naturalized for 7 to 11 years. Using a
simple OLS regression on cross-sectional data assumes that those who naturalized and those who
did not are on average the same (i.e., “treatment” is as good as random). However, considering the
barriers to naturalization, self-selection is a significant issue that can bias the results. In other
words, those who choose to naturalize may have unobserved characteristics that may influence

50
their personal income and wage/salary increases that would have occurred even in the absence of
naturalizing. Thus, this simple OLS approach is likely to overestimate the effect of naturalization
on economic outcomes.
Enchautegui and Giannarelli (2015) address this underlying concern by applying a
propensity score matching model on a sample of approximately 135,000 observations
representative of 21 focus cities in 2011-2013. They find that naturalization is associated with an
8.9 percent average increase in wages. Again, use of a non-random sample means that the results
may not be applicable to immigrants outside of these large and populous urban cities. Additionally,
with any propensity score method, unobservable heterogeneity is a primary concern especially
with a limited set of predictors. This particular study uses gender, age at arrival, the number of
years in the United States, the number of years of education, whether they have attended college,
whether they are Asian or Latinx, the percent of the foreign-born in the city, and whether there is
an undocumented immigrant in the household as predictors of naturalization. Including other social
and human capital measures like an immigrant’s English-speaking ability and employment status
would improve the predictive power of this model while additional receiving and country-of-origin
variables (e.g., local unemployment rate) and fixed effects would help to control for variance
across place that would impact naturalization outcomes (e.g., dual citizenship).
To address these empirical gaps, this study utilizes a more comprehensive set of predictors
and a series of propensity score methods with regression adjustments for robustness. In estimating
the effect of naturalization on different economic indicators, I utilize a large and geographically
representative sample to improve the external validity of the results for immigrants across the
United States. In addition, to capture the nuanced effects across immigrant groups and to explore
citizenship status disproportionately impacting minoritized immigrants, I examine how race and

51
gender moderate the impact of naturalization on economic integration. Previous empirical studies
on naturalization have yet to study the gendered and racialized effects of naturalization on different
economic indicators through rigorous empirical methods. This research offers a critical
examination of how citizenship status as a barrier interacts differently for racially minoritized
immigrant groups and immigrant women.
Understanding the divergent impacts that naturalization has on different immigrant groups
can help to identify the varying and uneven barriers to integration that marginalized and
disenfranchised immigrant communities face. Naturalization is one of many mechanisms
influencing immigrant integration and so policies should focus on the interactions and
relationships of these mechanisms that contribute to inequitable integration outcomes. Particularly
for citizenship, which many immigrants see as a milestone to their American Dream,
understanding how the benefits of naturalization are uneven and moderated by other factors is
imperative in creating comprehensive policies that address the multiple factors working
simultaneously in perpetuating inequalities within the immigrant community. A more intentional
approach that centers race and gender would deconstruct how citizenship across immigrant groups
is segmented and how it may be valued differently.

Naturalization and Economic Assimilation
Economic assimilation refers to the degree in which immigrants achieve income and wage parity
with U.S.-born counterparts (Gathmann and Keller 2018; Villarreal and Tamborini 2018). In
studying immigrant integration and assimilation, economic indicators are often used to assess
immigrants’ mobility and ability to incorporate themselves into society—particularly within the
labor market. Previous research on economic assimilation commonly focuses on the relationship

52
of income and wage/salary assimilation with socioeconomic factors and the human capital of
immigrants on arrival (Alba 2009; Kim and Sakamoto 2010; White and Glick 2009). Earlier
studies conclude that immigrants are slowly closing the socioeconomic gap with their U.S.-born
counterparts, however, immigrants who come to the United States of America with higher
educational attainment and skills are making the largest strides (Borjas and Katz 2007). They find
that over time, immigrants and their children gain the needed education and skills to help them
assimilate into the labor market though large disparities still exist when compared specifically to
the U.S.-born white group (Park and Myers 2010; Villarreal and Tamborini 2018; Waters and
Jiménez 2005). Compared to U.S.-born co-ethnic groups, immigrants are more likely to reach
parity—signaling larger structural issues contributing to racial and ethnic disparities across U.S.-
born and immigrant groups (Villarreal and Tamborini 2018).
Economic assimilation cannot be entirely studied outside the context of socioeconomic and
human capital indicators as they contribute to an immigrant’s ability to participate in a stratified
labor market. However, more recent work on economic assimilation argues that focusing solely
on socioeconomic factors and human capital ignores larger structural issues that work against
immigrants—primarily racially and ethnically minoritized immigrants (Tesfai and Thomas 2020;
Villarreal and Tamborini 2018). Both the labor market and demographic makeup of immigrants
have changed significantly over the last few decades with increasing demands for high and low-
skilled jobs and less opportunities in middle-range-skilled jobs—jobs that are more likely to
provide immigrants with limited education and human capital a pathway to greater economic
mobility (Portes and Rumbaut 2014). In addition to a more bifurcated labor market, immigrants
face other barriers and differential treatment to employment and economic mobility based on their
race/ethnicity and gender (Blau and Kahn 2017; Villarreal and Tamborini 2018). Experimental

53
and quasi-experimental studies have found mounting evidence of workplace discrimination and
hiring bias with women and racial/ethnic minorities more likely to be underpaid and less likely to
be hired despite comparable qualifications with their male and white counterparts (Carlsson and
Rooth 2008; Lancee 2021; Zschirnt and Ruedin 2016). Though this is true across contexts, wage
and hiring disparities associated with discrimination are particularly stark among workers with
lower education levels, whereas racial skill gaps are the main drivers among workers with higher
educational attainment (Borowczyk-Martins, Bradley, and Tarasonis 2017).
In addition to racial/ethnic discrimination, immigrants face multiple barriers to economic
mobility often due to their foreign qualifications and their lack of citizenship (Carlsson and Rooth
2008). In addressing the latter, naturalization plays a significant role in facilitating economic
assimilation as it reduces certain barriers to public benefits and the labor market. Naturalization
opens economic opportunities that are available only to citizens, including the majority of federal
government jobs and contractors, private-sector jobs that require high security clearance, and
public-school teachers and police officers in some states (Ayers 2018; Enchautegui and Giannarelli
2015; Pastor and Scoggins 2012). In local government where citizenship is not always a
requirement, gaining citizenship can substantially improve an immigrant’s odds of gainful
employment in the public sector (Lewis et al. 2014). Naturalized immigrants also gain increased
access to federal and subfederal public benefits—some restricted to U.S. citizens and others open
to lawful permanent residents (LPRs) but are not fully accessed as a result of chilling effects from
public charge rules and states’ attempts to legislate citizenship requirements for certain public
benefits (Bojorquez and Fry-Bowers 2019; Fortuny and Chaudry 2011; Pedraza and Zhu 2015;
Watson 2014).

54
Understanding Economic Outcomes Through a Critical Lens
In studying immigrant integration and well-being, it is imperative to critically examine how
immigrants fare economically to extrapolate the extent to which immigrants are incorporated into
the host country’s labor market, as well as any associated barriers. Past studies focusing on
different immigrant groups (e.g., race, gender, class, space, and time) find varying conclusions,
but a common theme is that with “rising inequality in the labor market and the increasing returns
to higher education…immigrants and especially their children need rapid growth in educational
attainment to experience rising incomes over time” (National Academies of Sciences, Engineering,
and Medicine 2015:249). However, it is clear that not all immigrants have equal access and
opportunity to such socioeconomic attainment as a result of institutionalized barriers and systemic
discrimination based on immigration and documentation status, race, gender, and class (e.g.,
Abrego 2006; Kasinitz et al. 2009; Provine and Doty 2011; Zhou 2014). Along with racialized and
gendered barriers to education, immigrants face other challenges in obtaining the necessary tools
to assimilate economically including hiring discrimination, spatial mismatch, skill-to-job
mismatch, and disproportionate enforcement (Bottia 2019; Carlsson and Rooth 2008; Liu and
Painter 2012; Romero 2008b; Villarreal and Tamborini 2018).
However, comparing economic indicators alone to measure immigrants’ ability to integrate
presents many issues. This approach often lacks a comprehensive examination of the existing
structures that disadvantage immigrants, including factors that drive racial and gender disparities
among predictors used to measure economic assimilation. Research on immigrant integration—
including naturalization—requires a more critical approach that intentionally explores structures
and existing disparities that work against minoritized groups such as immigrants, people of color,
women, and the intersections of these identities. A critical framework interrogates the impact of

55
these structures by disentangling the effects associated with these marginalized identities, while
controlling for other factors that are often credited to economic assimilation. For example, studies
have found that racially minoritized immigrants are less likely to reach economic parity and
occupational integration to U.S.-born white counterparts than white immigrants with the same
level of human capital (Tesfai and Thomas 2020; Villarreal and Tamborini 2018). By controlling
for common predictors of economic outcomes, these findings show how Black and Latinx
immigrants with the same skills and qualifications as their white counterparts are statistically less
likely to experience the same level of economic mobility due to unobserved differences, such as
racial/ethnic discrimination.
Minoritized immigrants struggle to reach economic parity with their U.S.-born
counterparts—and even more so with the U.S.-born white group—because of labor market barriers
(e.g., disparities in education and work experience, differential treatment, foreign qualifications)
created and exacerbated by existing white and nativist power structures (Carlsson and Rooth
2008). When minoritized immigrants can build their human capital, they are able to see greater
strides towards parity with their U.S.-born counterparts. Villarreal and Tamborini (2018) find that
racially minoritized immigrants experience quicker economic assimilation (i.e., “accelerated
assimilation”) with higher educational attainment and can actually surpass parity with co-ethnic
U.S.-born counterparts but still never reach parity with U.S.-born whites with similar educational
attainment. In fact, the gap between Black immigrants with a college degree and U.S.-born whites
with a college degree is more than 20 percent points greater than the gap between Black immigrants
without a college degree and their U.S.-born white counterparts. This suggests the significance of
education for Black immigrants but also recognizes the institutionalized barriers that Black
Americans, regardless of immigration status and nativity, face in their economic mobility,

56
including occupational segregation and wage disparities worsened at the intersection of race and
citizenship status (Stewart and Dixon 2010; Tesfai and Thomas 2020).
In regards to gender, Schoeni (1998) finds that immigrant women’s economic
assimilation—as measured by labor force participation, unemployment, and earnings—trails
immigrant men in addition to both U.S.-born men and women. They link this gap to the relative
difference in human capital. Donato, Piya, and Jacobs (2014) conclude on similar findings on labor
force participation in that immigrant women are systematically faced with two dimensions of
disadvantages that lead them to be less likely to participate in the labor force than their U.S.-born
counterparts and men. Though earlier waves of immigrant women are more likely to have less
human capital and work-related experiences, immigrant women today are just as educated as
immigrant men and entering occupations that have been traditionally dominated by men at greater
rates than before (Batalova 2020; Feliciano and Rumbaut 2005). Despite strides in human capital,
immigrant women face discrimination and institutionalized barriers because of their gender and
immigration status, making it more difficult to enter an increasingly stratified and competitive
market—though this varies by racial and ethnic group (Portes and Rumbaut 2014). Existing
structures and a labor market that disproportionately benefit men, particularly U.S.-born men,
exacerbate not only disparities in labor participation but also the gender wage gap (Blau and Kahn
2017).

Data
This analysis uses the 2012-2016 5-year American Community Survey (ACS) microdata from
IPUMS (Integrated Public Use Microdata Series) USA (Ruggles et al. 2020) to examine the effects
of naturalization on personal total income and earned income from wages and salaries. The 5-year

57
ACS microdata provides comprehensive individual and household data that is needed to improve
predicting an individual’s propensity to naturalize. In addition, with a large pool of observations
to choose from, this dataset increases the likelihood of identifying and finding control observations
(i.e., eligible-to-naturalize immigrants) that are statistically similar to treated observations (i.e.,
naturalized immigrants) with the exception of being naturalized.
The limitation of the 5-year ACS microdata, however, is that it contains observations that
are likely to be undocumented—immigrants who are systematically unable to naturalize. To
address this issue, I use the 2014 Survey of Income and Program Participation (SIPP) which
contains sociodemographic and socioeconomic data including the individual’s documentation
status upon arrival to the United States. Discussed further in the next section of this paper, applying
computational and statistical models similar to past research (e.g., Capps et al. 2013; Pastor and
Scoggins 2012; Van Hook et al. 2015) to this data assists in estimating observations that are likely
to be undocumented and subsequently netted out from the primary dataset. This improves the
integrity of the sample by focusing on individuals who meet the minimum eligibility to naturalize.
Additionally, with ACS data, I can link individuals within the same household and family unit.
This allows me to determine which eligible-to-naturalize immigrants are likely in mixed-status
families—a strong predictor of naturalization (Le and Pastor n.d.).

Methodology and Model Specifications
To explore the varying economic values of naturalization, I first narrow the data to immigrants
eligible to naturalize and those that have recently naturalized. I then use a logit model to estimate
each observation’s propensity to naturalize which then is applied in a series of propensity score

58
methods (PSM) to isolate the effects of naturalization on personal income and wage/salary
earnings within two years, five years, and ten years of gaining citizenship.

Estimating the Eligible-to-Naturalize and Recently Naturalized
I focus on the recently naturalized rather than all immigrants who have naturalized to reduce
confoundedness and issues of endogeneity associated with longer periods of naturalization. Some
predictors of naturalization may change substantially (e.g., education and reported English-
speaking ability) after naturalization which could potentially lead to biased and inconsistent
results. In determining the recently naturalized, the ACS microdata provides information on how
many years an observation has been naturalized for. I create three subset samples by focusing on
immigrants who naturalized within 2 years, 5 years, and 10 years of taking the ACS survey.
In determining the eligible-to-naturalize sample and to account for undocumented
immigrant’s systematic barriers to citizenship, I rely on the same estimation and imputation
methods in Le and Pastor (n.d.). Figure 2.1 illustrates a flow chart summarizing the computational
process in estimating the eligible-to-naturalize population including how I net out those who are
likely to be undocumented. This allows me to reduce statistical noise created by undocumented
immigrants who are systematically unable to naturalize and to explore the impact of mixed-status
homes as a moderating factor on the effects of naturalization.
To create my baseline eligible-to-naturalize sample, I first identify foreign-born
observations in the ACS microdata who have not yet naturalized. I then remove undocumented
observations from the sample by applying logical and conditional edits with stratified probability
edits. I use logical and conditional edits to identify non-citizens most likely to be documented
based on conditions predictive of lawful status, including having worked in the public sector or

59
occupations requiring documents (e.g., police), received certain public benefits (e.g., social
security), arrived in the U.S. before 1982—likely to have gained legal status through the
Immigration Reform and Control Act of 1986 (Warren 2014)—, military experience, and or
married to a U.S. citizen. To implement stratified probability edits, I determine which variables
are predictive of documentation status by using a logit model on the SIPP dataset. The SIPP dataset
is significant in that it asks respondents a series of sociodemographic and socioeconomic
questions, including their documentation status upon arrival into the United States. Earlier SIPP
surveys ask in a subsequent wave whether the same respondent has adjusted their documentation
status. Those who arrived without LPR status and indicated in the subsequent wave that they did
not adjust status are assumed to be undocumented. Similar to Van Hook et al.’s (2015) and Pastor
and Scoggins’ (2016) predictive model of undocumented immigrants, I include gender, age, years
since arrival, education levels, marital status, whether respondent’s own children reside in the
home, English ability, and controls for broad region of origin to predict the impact of each variable
on the probability of the respondent being undocumented.
Once I determine the impact of each sociodemographic and socioeconomic measure on
predicting who is undocumented in the SIPP dataset, I then apply these probability estimates of
each variable to the ACS microdata to identify each observation’s likelihood of being
undocumented. This then allows me to identify observations most likely to be undocumented based
on both their propensity of being undocumented and country thresholds (i.e., estimated number of
undocumented immigrants by country) from the Office of Immigration Statistics (OIS).
Specifically, I temporarily net out those who are likely to be lawful permanent residents (LPRs)
based on the logical and conditional edits to create a subsample of observations that are potentially
undocumented. I then tag and remove 20 percent of the probability of each strata to be

60
undocumented by selecting observations sequentially from the highest probability of each strata.
Each iteration calculates an additional 20 percent points of each strata’s probability to tag and
remove which of the remaining observations are undocumented. This iteration is repeated until
each country threshold is met.
Once likely undocumented observations are removed from the sample, I then remove
immigrants who are ineligible to naturalize from the sample. This includes those who have not
been in the U.S. for more than five years—three years if married to a U.S. citizen—and those under
18 years old. To validate the estimates, I compare them to OIS estimates (Baker 2019; Lee and
Baker 2017b) across different characteristics and indicators (see Table 2.1).

Table 2.1: Comparing OIS estimates of the eligible to naturalize

Number Percent Number Percent Number Percent Number Percent
Period of Entry
Before 1990 2,680,000 29% 3,010,000 34% 2,400,000 27% 2,900,000 32%
1990 - 1999 1,940,000 21% 2,190,000 25% 2,270,000 26% 2,440,000 27%
2000 - 2009 2,650,000 29% 3,030,000 34% 3,920,000 44% 3,240,000 36%
2010 - 2014 1,730,000 19% 650,000 7% 290,000 3% 390,000 4%
2015 + … 120,000 1% 0 0% 0 0% 0 0%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
Country of Birth
Mexico 2,490,000 27% 2,540,000 29% 2,710,000 31% 3,060,000 34%
China 490,000 5% 430,000 5% 330,000 4% 330,000 4%
Philippines 370,000 4% 350,000 4% 340,000 4% 280,000 3%
Cuba 350,000 4% 300,000 3% 300,000 3% 330,000 4%
Dominican Republic 340,000 4% 320,000 4% 300,000 3% 250,000 3%
India 310,000 3% 270,000 3% 250,000 3% 390,000 4%
Canada 250,000 3% 260,000 3% 260,000 3% 260,000 3%
El Salvador 220,000 2% 230,000 3% 250,000 3% 310,000 3%
United Kingdom 220,000 2% 230,000 3% 230,000 3% 280,000 3%
Vietnam 220,000 2% 210,000 2% 200,000 2% 130,000 1%
South Korea 200,000 2% 190,000 2% 190,000 2% 180,000 2%
Haiti 160,000 2% 160,000 2% 150,000 2% 120,000 1%
Jamaica 160,000 2% 160,000 2% 160,000 2% 130,000 1%
Colombia 140,000 2% 140,000 2% 140,000 2% 130,000 1%
Guatemala 120,000 1% 120,000 1% 120,000 1% 200,000 2%
Rest of Countries 3,090,000 34% 2,970,000 33% 2,950,000 33% 2,590,000 29%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
State of Residence
California 2,340,000 26% 2,360,000 27% 2,470,000 28% 2,270,000 25%
New York 1,130,000 12% 1,080,000 12% 1,040,000 12% 1,050,000 12%
Texas 990,000 11% 950,000 11% 960,000 11% 900,000 10%
Florida 880,000 10% 830,000 9% 830,000 9% 810,000 9%
New Jersey 380,000 4% 380,000 4% 370,000 4% 340,000 4%
Illinois 380,000 4% 370,000 4% 380,000 4% 360,000 4%
Massachusetts 210,000 2% 210,000 2% 200,000 2% 200,000 2%
Washington 190,000 2% 180,000 2% 180,000 2% 180,000 2%
Arizona 190,000 2% 180,000 2% 190,000 2% 210,000 2%
Virginia 180,000 2% 170,000 2% 160,000 2% 160,000 2%
Rest of States 2,260,000 25% 2,170,000 24% 2,100,000 24% 2,490,000 28%
Total 9,130,000 100% 8,880,000 100% 8,880,000 100% 8,970,000 100%
Note: Numbers are rounded to the nearest 10,000.
OIS Estimates (2019) Le & Pastor (2012-2016) OIS Estimates (2016) OIS Estimates (2014)

61
4. COMPARE AND VALIDATE WITH OIS ESTIMATES
In order to validate estimates, compare them to recent aggregated estimates (total
and by country-of-origin) available from the Office of Immigrant Statistics (OIS)
3. REMOVE INELIGIBLE IMMIGRANTS FROM SAMPLE
To apply for naturalization, LPRs must have been in the U.S. for more than 5 years —
or 3 years if married to a U.S. citizen —and must be at least 18 years old
2. REMOVE UNDOCUMENTED OBSERVATIONS FROM SAMPLE
Identify and exclude undocumented immigrants from sample as they are
systematically unable to naturalize
1. IDENTIFY BASELINE SAMPLE
Use 2012-2016 5-year ACS microdata datasets —keeping only observations who
identify as foreign-born (i.e., immigrant)
PHASE 3 –
Determine 20% of the
probability (e.g., 20% of
observations in the 60%
probability strata is 12%) of
each strata (~60) then tag that
percentage of observations in
each strata as undocumented —
selecting observations
sequentially from highest
probability in each strata. Each
subsequent iteration calculates
20% points more of the strata ’s
probability to tag that many
more of the strata ’s remaining
observation as undocumented.
Repeat until each country
threshold is met.
PHASE 1 –
Using SIPP and a logistic
model, determine the
probability estimates of
each variable then apply to
ACS microdata to identify
each observations
probability of being
undocumented.
PHASE 2 –
Net out LPRs based on logical
edits and disaggregated by
country of origin.
2A. LOGICAL AND CONDITIONAL EDITS
2B. STRATIFIED PROBABILITY EDITS
Identifying non-citizens who are documented based on conditions linked to LPR
status including having worked in public sector or occupation requiring documents
(e.g., police), received certain public benefits (e.g., social security), arrived in the
U.S. before 1982, military experience, or married to a U.S. citizen.

Figure 2.1: Statistical and computational process in determining eligible-to-naturalize and recently naturalized sample.

62
Estimating Propensity to Naturalize
To estimate an individual’s propensity to naturalize, I utilize a binomial logit model where the
outcome of interest (Yi) is whether the observation has successfully naturalized. This model
utilizes maximum likelihood estimation on a set of predictors and controls to determine each
variable’s impact on naturalization outcomes. Based on these estimates, I assign each observation
a propensity score that predicts their probability of naturalizing given their observed
characteristics. The following equation is estimated where Y is the binary outcome of
naturalization, s is the state fixed effect, c is the country-of-origin fixed effects, t is the year fixed
effects, and the vector of individual and place-based control variables is Xi:
𝑌𝑖 =
1
1 + 𝑒 −(  + 𝑿𝒊 + 𝒔 + 𝒄 + 𝒕 + )

To control for individual determinants of naturalization, I include a variety of demographic
characteristics, socioeconomic predictors, and human capital measures. Demographic
characteristics include gender, race/ethnicity, quadratic specifications for age arrived in the United
States, and years eligible to naturalize. Immigrant women have been found to be more likely to
naturalize as they, on average, have greater access to human and social capital to help facilitate
their pathways to citizenship (National Academies of Sciences, Engineering, and Medicine 2015).
Additionally, prior scholars also argue that immigrant women, compared to immigrant men, are
more motivated to naturalize to improve their social position (Amuedo-Dorantes and Lopez 2020;
Yang 1994, 2002). Race/ethnicity is also indicative of immigrants’ propensities to naturalize as
racial disparities in resources and access to the naturalization process has been shaped by
immigration policies that have disproportionately targeted certain racial/ethnic groups, such as the
targeted enforcement of Latinx and Asian immigrant communities (Aranda et al. 2014; Ong and
Nakanishi 1996; Watson 2014). Age at arrival is often used as one of several proxies for an

63
immigrant’s ability to assimilate into mainstream culture like adopting cultural values and
improving English language proficiency (Waldinger 2021).
In estimating immigrants’ propensities to naturalize, I include various measures of
socioeconomic status and human capital, including educational attainment, household income, and
employment status. Previous studies have found that those with more resources have greater access
to naturalization as they are better equipped to navigate the complicated naturalization process
(Aptekar 2015; Barkan and Khokhlov 1980; Johnson, Reyes, Mameesh, and Barbour 1999; Yang
1994). Among the more common predictors of naturalization, those with greater English
proficiency have greater odds of passing the English-proctored interviews and exams, and also
have greater ease in navigating many of the procedural barriers like the application (Aptekar 2015).
Place-based variables include the local unemployment rate for immigrants and the
percentage of co-ethnic nationals in the PUMA (Public Use Microdata Area). I control for the local
unemployment rate among immigrants because some adults may naturalize to increase
employment opportunities and earnings (Johnson, Reyes, Mameesh, and Barbour 1999; Yang
1994). I include the percent of co-ethnic nationals within a PUMA (public use microdata area) to
account for potential ethnic enclaves where resource and information-sharing among immigrants
with similar racial and ethnic backgrounds are more likely to occur (Abascal 2017; National
Academies of Sciences, Engineering, and Medicine 2015; Woroby and Groves 2016). I include
state and country-of-origin fixed effects to control for other place-based characteristics, including
whether countries allow dual citizenship and the social, economic, and political conditions that
may disincentive immigrants’ permanent return.
Networks within families and within communities are also significant predictors of
naturalization as they act as conduits of information and avenues to pooled resources (Menjívar

64
2000). In terms of family networks, I control for whether observations in the data are likely in a
mixed-status family to account for potential chilling effects created by increased anti-immigrant
sentiment and immigration enforcement (Amuedo-Dorantes and Lopez 2020; Aranda et al. 2014;
Asad 2020; Le and Pastor n.d.). I also include whether observations have a naturalized family
member as such social networks have been found to significantly improve one’s odds of
naturalizing since naturalized family members can share their experiences and invaluable
information about the process (Liang 1994; Le and Pastor n.d.). I extrapolate family networks by
including measures that estimate spouse’s immigration and citizenship status prior to one’s own
naturalization and whether a naturalized citizen or undocumented immigrant other than a spouse
is present in the family unit.

Estimating the Economic Impact of Naturalization
Propensity score methods minimize statistical noise from self-selection bias by creating
comparable treatment and control groups based on a comprehensive set of covariates that
determine an observation’s propensity to naturalize (Austin 2011; Rosenbaum and Rubin 1985).
In other words, I construct an observationally similar control group (i.e., eligible to naturalize) to
compare to the treated group (i.e., naturalized) based on observed characteristics that predict
naturalization. I utilize both propensity score matching and propensity score weighting to assess
the impact of naturalization on logged personal total income and logged wage/salary earnings.
I rely on the nearest neighbor approach for propensity score matching. This takes each
treated observation and matches it with the most similar control observation(s). Ties are allowed
with a maximum of two control matches to treatment. This then takes the average of difference in

65
outcomes (Y) between those who naturalized and those who are eligible-to-naturalize and
matched:
𝐴𝑇𝐸𝑇 ̂
=
1
𝑛 𝑇 ∑(𝑌 𝑇𝑖
− 𝑌 ̅
𝐶𝑖
)
𝑖𝜖𝑇
ATET is the average treatment effect on the treated. T (treated) denotes the naturalized
observations and C (control) denotes the matched eligible-to-naturalize observations. With
propensity score weighting, I calculate the average difference in outcomes between the naturalized
and eligible-to-naturalize groups with weights for each observation based on their propensity
scores (P). Each naturalized observation is given a weight of 1 whereas eligible-to-naturalize
observations receive a weight of:
𝑤 𝑖 =
𝑃 (𝑋 𝑖 )
1 − 𝑃 (𝑋 𝑖 )

This process gives more weight to eligible-to-naturalize observations that look more
similar to the naturalized observations (i.e., higher propensity to naturalize). This is done to create
a weighted distribution of the eligible-to-naturalize group’s propensity scores so that it mirrors the
distribution of the naturalized group. To calculate the average treatment effect (ATE), in contrast
with ATET, I use the inverse probability weights. For the naturalized observations:
𝑤 𝑖 =
1
𝑃 (𝑋 𝑖 )

For eligible-to-naturalize observations:
𝑤 𝑖 =
1
1 − 𝑃 (𝑋 𝑖 )

66
For additional robustness and to minimize concerns of selection on observables (i.e.,
treatment is uncorrelated with omitted factors), I apply regression adjustments to both the
propensity score matching and propensity score weighting models using the same set of covariates
to predict naturalization and also the number of hours worked per week. I include the number of
hours worked per week to control for possible gains from simply working more hours even at low
hourly wages. Rather than applying the propensity score weights to calculating the differences in
group means, I use the weights in a regression which helps to remove correlation between the
predictors (Xi) and outcomes—adjusting for direct effects between the controls and the economic
indicators (Austin 2011).
With both propensity score matching and propensity score weighting, I restrict the sample
to the range where the propensity scores for both the naturalized and eligible-to-naturalize groups
overlap (i.e., common support). This is done to account for potential outliers and so that naturalized
observations will not be matched with vastly dissimilar eligible-to-naturalize observations. I apply
Crump et al. (2009) rule and trim the common support to observations with propensity scores
between 0.1 and 0.9.
I interact race and gender with the treatment variable (i.e., naturalization) to estimate
moderating effects on the varying economic indicators. More specifically, I include these
interaction terms (e.g., race/ethnicityi*naturalizationi, genderi*naturalizationi) in the propensity
score weighting model with regression adjustments to estimate how the effects of naturalization
on total personal income and wage earnings differ across race/ethnicity and gender.
Results
Overall findings show that naturalization is statistically and positively associated with total
personal own income and wage earnings. Though I present both the average treatment effect (ATE)

67
and the average treatment effect on the treated (ATET), I rely on the latter as naturalization is a
non-random “treatment” where adult immigrants self-select to become immigrants. In this section,
I first explore the characteristics of the eligible-to-naturalize and the recently naturalized groups.
I then present a brief overview of the logit results used to estimate each observation’s propensity
to naturalize before presenting my main findings on the impact of naturalization and the
moderating effects of race and gender.

Characteristics of the Eligible-to-Naturalize and the Recently Naturalized
Compared to the naturalized, the eligible-to-naturalize are slightly more likely to be men (47.11
percent) and slightly older at arrival (26.94). Across all subgroups that have naturalized (i.e.,
naturalized within 2 years, 5 years, and 10 years) and those that are eligible to naturalize, white
immigrants make up about a fifth. At 50 percent, Latinx immigrants comprise the majority of those
who are still eligible to naturalize, whereas they make up slightly more than a third across the
naturalized groups. Asian Pacific Islander (21.23 percent), Black (6.11 percent), and other/mixed
race (1.6 percent) immigrants make up smaller portions of the eligible-to-naturalize than the
naturalized (30.25 percent to 32.03 percent, 10.29 percent to 11.59 percent, and 2.21 percent to
2.27 percent, respectively). The eligible-to-naturalize group is more likely to be married to other
LPRs (19.21 percent), whereas those who are naturalized are most likely to have been married to
other naturalized citizens prior to their own naturalization (32.79 percent to 35.8 percent).
Regarding other family dynamics, a larger share of the eligible-to-naturalize is likely to have an

68
Table 2.2: Summary statistics of the eligible-to-naturalize and the recently naturalized

Summary Statistics

Naturalized

Eligible to
Naturalize 2 years 5 years 10 years
Male/Man 44.82% 45.02% 44.78% 47.11%
Age at Entry 26.10 26.16 26.15 26.94
Years Eligible (log) 11.89 11.70 11.41 16.85
White 20.87% 20.65% 21.09% 20.58%
Black 11.59% 11.05% 10.29% 6.11%
Latinx 35.02% 34.98% 34.39% 50.47%
Asian Pacific Islander 30.25% 31.06% 32.03% 21.23%
Other or Mixed Race 2.27% 2.26% 2.21% 1.60%
Married to U.S.-born Citizen 13.58% 12.98% 12.22% 15.69%
Married to Naturalized Citizen 32.79% 34.29% 35.80% 17.06%
Married to LPR 10.91% 10.11% 8.76% 19.21%
Married to Undocumented Immigrant 1.67% 1.57% 1.32% 10.18%
Other Naturalized Family Member 14.39% 13.03% 11.81% 10.78%
Other Undocumented Family Member 8.41% 7.81% 6.93% 11.36%
Family Size in Household 3.52 3.52 3.51 3.46
Less than a High school Degree 18.84% 19.29% 19.59% 34.22%
High School Degree or Equivalent 21.00% 20.41% 20.34% 24.27%
Some College 24.76% 23.87% 23.26% 17.74%
Bachelor’s Degree 20.88% 21.45% 21.46% 12.97%
Higher than a bachelor’s degree 14.52% 14.98% 15.35% 10.80%
Employed 70.39% 70.81% 70.49% 59.32%
Unemployed 4.60% 4.34% 4.03% 4.44%
Not in Labor Force 25.02% 24.85% 25.47% 36.24%
SEI 34.92 35.47 35.61 26.52
Speaks English Not at All 4.87% 4.97% 4.82% 13.62%
Speaks English Not Well 14.45% 14.44% 14.85% 21.43%
Speaks English Well 23.79% 23.69% 23.87% 19.62%
Speaks English Very Well 41.37% 41.54% 40.85% 27.57%
Speaks English Only 15.52% 15.37% 15.61% 17.76%
Homeowner 59.87% 62.37% 65.73% 56.61%
Co-ethnic Nationals in PUMA 4.89% 4.96% 5.02% 7.01%
Unemployment Rate in MSA 7.64% 7.64% 7.69% 7.73%
Observation 80,430 168,024 309,987 347,211

69
undocumented spouse (10.18%) and an undocumented family member other than a spouse (11.36
percent)—more than those who are naturalized (1.32 percent to 1.67 percent and 6.93 percent to
8.41 percent, respectively).
Those who have already naturalized have, on average, more human capital. In terms of
education, individuals who are eligible-to-naturalize are less likely to be educated. Approximately
41.51 percent of the eligible-to-naturalize have an education higher than a high school degree. This
contrasts with the 60.08 percent to 60.30 percent of the naturalized group. Naturalized immigrants
are also more likely to report higher English-speaking proficiency (80.33 percent to 80.68 percent)
than the eligible-to-naturalize group (64.95 percent)—a particularly important skill to pass the
English-proctored citizenship exam and interview. Table 2.2 provides more details on these
summary statistics.

Propensity to Naturalize
To predict each observation’s propensity to naturalize, I use a logit model for each subsample that
contains all eligible-to-naturalize immigrants and immigrants who naturalized within the last two,
five, and ten years of answering the 2012-2016 5-year ACS survey. These results are presented in
Table 2.3 as odds ratios.
Demographically, immigrant men and those who arrive in the U.S. at a later age have
statistically lower odds of naturalizing. On average, all else equal, Black and Asian Pacific Islander
immigrants have statistically higher odds of naturalizing compared to white immigrants. This is
likely because Black and Asian Pacific Islander immigrants may find greater value in naturalizing
than their white counterparts to prove their “American-ness” and to become “model citizens” as a
form of self-protection (Ong 2010; Pierre 2004; Yang 1994) . The coefficient for Latinx is
statistically insignificant when including the country-of-origin fixed effects and measures for

70
mixed-status families. Being in a mixed-status family (i.e., likely having an undocumented family
member in the same household) statistically reduces an immigrant’s odds of naturalizing.
Immigrants who are married to someone who is undocumented have more than 85 percent lower
odds of naturalizing across the three subsamples. If an undocumented immigrant, other than a
spouse, is present in the family unit, it reduces the odds of naturalizing by at least 37.5 percent
across the three subsamples. In contrast, if an immigrant is married to a naturalized citizen, their
odds of naturalizing increases by at least 55.6 percent across the subsamples.
Immigrants with greater human capital have greater odds of naturalizing. Higher education
is statistically associated with higher odds of naturalization with up to 44.5 percent greater odds
for those with a bachelor’s degree. The exception—when including immigrants who naturalized
within the last five and ten years—is having a high school degree or equivalent (e.g., General
Education Degree), which lower odds of naturalizing by 3.6 percent and 6.1 percent, respectively,
compared to those without a high school degree. Those who are unemployed or not in the labor
force have up to 22.2 percent lower odds of naturalizing across the subsamples when compared to
those who are employed.
Among the predictors, English-speaking proficiency has a relatively larger impact on
naturalization. Compared to speaking English “not at all,” speaking English “not well” is
associated with 60 percent higher odds of naturalization and speaking English “well” is associated
with approximately 140 percent higher odds when examining immigrants who naturalized within
the last two years. This trend is evident across the subsamples indicating that any level of English
proficiency can significantly improve an immigrant’s odds of naturalization.

71
Table 2.3: Estimated effects on the odds of naturalization across subsamples

(1)
Naturalized w/in
2 years
(2)
Naturalized w/in
5 years
(3)
Naturalized w/in
10 years

OR SE OR SE OR SE

Male/Man 0.887
***
(0.008) 0.910
***
(0.006) 0.929
***
(0.006)
Number of Children 1.040
***
(0.003) 1.040
***
(0.002) 1.039
***
(0.002)
Age at Arrival 0.977
***
(0.001) 0.977
***
(0.001) 0.977
***
(0.001)
Age at Arrival (squared) 1.000
***
(0.000) 1.000
***
(0.000) 1.000
***
(0.000)
Years Eligible (log) 0.816
***
(0.004) 0.769
***
(0.003) 0.709
***
(0.003)

Compared to White
Black 1.245
***
(0.052) 1.209
***
(0.040) 1.112
***
(0.032)
Latinx 1.010 (0.048) 1.061 (0.040) 1.012 (0.032)
Asian/Pacific Islander 1.089
*
(0.041) 1.096
**
(0.033) 1.031 (0.027)
Other or Mixed Race 1.055 (0.039) 1.033 (0.031) 0.980 (0.025)

Compared to Not Married
Married to U.S. born 0.856
***
(0.012) 0.766
***
(0.009) 0.667
***
(0.006)
Married to Naturalized 1.699
***
(0.022) 1.643
***
(0.017) 1.556
***
(0.013)
Married to LPR 0.518
***
(0.008) 0.469
***
(0.005) 0.396
***
(0.004)
Married to undocumented 0.142
***
(0.004) 0.123
***
(0.003) 0.096
***
(0.002)

Naturalized Family Member (not spouse) 1.304
***
(0.019) 1.111
***
(0.013) 0.924
***
(0.010)
Undocumented Family Member (no spouse) 0.635
***
(0.010) 0.565
***
(0.007) 0.481
***
(0.005)

Compared to Less than High School
HS degree or equivalent 1.023
†
(0.014) 0.964
***
(0.010) 0.939
***
(0.008)
Some college 1.381
***
(0.020) 1.282
***
(0.015) 1.215
***
(0.012)
Bachelor’s degree 1.445
***
(0.024) 1.367
***
(0.018) 1.286
***
(0.014)
Advanced degree 1.190
***
(0.023) 1.101
***
(0.016) 1.057
***
(0.013)

Compared to Speaks English “Not at All”
Not well 1.600
***
(0.033) 1.546
***
(0.024) 1.608
***
(0.021)
Well 2.402
***
(0.051) 2.298
***
(0.037) 2.337
***
(0.031)
Very well 2.553
***
(0.056) 2.459
***
(0.041) 2.452
***
(0.034)
English only 2.063
***
(0.052) 2.027
***
(0.039) 2.118
***
(0.034)

Compared to Employed
Unemployed 0.843
***
(0.017) 0.803
***
(0.013) 0.778
***
(0.011)
Not in labor force 0.784
***
(0.009) 0.813
***
(0.008) 0.897
***
(0.007)

Duncan Socioeconomic Index 1.003
***
(0.000) 1.004
***
(0.000) 1.004
***
(0.000)
Homeowner 1.217
***
(0.012) 1.375
***
(0.010) 1.630
***
(0.011)
Co-Ethnic Nationals in PUMA (%) 1.002
***
(0.001) 1.003
***
(0.001) 1.004
***
(0.000)
Unemployment Rate in MSA (%) 1.004 (0.004) 0.998 (0.003) 0.991
***
(0.003)

Country-of-Origin/State/Year FE Yes Yes Yes
Observations 415182 501200 633587
Coefficients are in odds ratio; Standard errors in parentheses.
†
p < .1,
*
p < .05,
**
p < .01,
***
p < .001 (two-tailed tests).

72
Effect of Naturalization on Personal Total Income and Personal Wage/Salary Earnings
As shown earlier, the recently naturalized group is observationally dissimilar to the eligible-to-
naturalize group demographically and by measures of human capital. However, with the large size
of this dataset, I am able to find substantial overlap of observations that share similar propensities
to naturalize between the two groups. Figure 2.2 visualizes this overlap across the different
subsamples with more overlap as the sample size increases by including naturalized immigrants
who have been citizens for longer. This overlap allows for more conducive matching and
weighting between the treated (i.e., naturalized) and control (i.e., eligible to naturalize) groups.
As mentioned in the methodology section, I use Crump et al.’s (2009) rule where I trim the
sample to only include observations with propensity scores between 0.1 and 0.9 to avoid possible
matching treatment observations with the most dissimilar control observations. I use graphs
(Figure 2.3) to illustrate how the weighting scheme changes the weighted distribution of the
propensity scores for the eligible-to-naturalize group to mirror the distribution of the naturalized
group. This is done by giving more weight to eligible-to-naturalize observations that look more
similar to the naturalized observations. As illustrated, each subsample reflects more
observationally similar treatment and control groups after weighting. This process validates the
treatment-control balance of propensity scores in my constructed estimation sample.
To verify the treatment-control balance of individual covariates between the two groups, I
present Table 2.4 (sample with personal total income reported) and Table 2.5 (sample with
personal wage/salary earnings reported). These tables show the summary statistics for the
predictors after matching and weighting. As expected, after matching and weighting, both groups
look more observationally similar.

73
Figure 2.2: Overlap of observations by propensity to naturalize.

2a: Naturalized within the last 2 years

2b: Naturalized within the last 5 years

2c: Naturalized within the last 10 years

2a: Naturalized within the last 2 years

2b: Naturalized within the last 5 years

2c: Naturalized within the last 10 years

2a: Naturalized within the last 2 years

2b: Naturalized within the last 5 years

2c: Naturalized within the last 10 years

74
Figure 2.3: Distribution of propensity scores across subsamples, unweighted and weighted.

75

Treated Control Treated Control Treated Control Treated Control Treated Control Treated Control
Male 0.48 0.48 0.49 0.48 0.48 0.48 0.48 0.49 0.49 0.49 0.48 0.49
Age at Entry 26.29 26.20 26.26 26.19 26.16 26.08 26.29 25.66 26.26 25.62 26.16 25.56
Years Eligible (log) 2.21 2.21 2.18 2.17 2.15 2.12 2.21 2.23 2.18 2.20 2.15 2.17
White 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21 0.21
Black 0.12 0.12 0.11 0.12 0.11 0.11 0.12 0.12 0.11 0.12 0.11 0.11
Latinx 0.35 0.35 0.35 0.35 0.34 0.35 0.35 0.35 0.35 0.35 0.34 0.35
Asian Pacific Islander 0.29 0.29 0.30 0.30 0.32 0.31 0.29 0.29 0.30 0.30 0.32 0.30
Other or Mixed Race 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Not Married 0.35 0.34 0.33 0.33 0.32 0.32 0.35 0.35 0.33 0.33 0.32 0.32
Married to Non-Citizen 0.18 0.19 0.18 0.19 0.17 0.18 0.18 0.18 0.18 0.18 0.17 0.17
Married to Citizen 0.47 0.47 0.48 0.49 0.51 0.51 0.47 0.47 0.48 0.49 0.51 0.51
Family Size in Household 3.49 3.48 3.49 3.47 3.48 3.43 3.49 3.31 3.49 3.33 3.48 3.40
Less than a High school Degree 0.18 0.18 0.19 0.19 0.19 0.20 0.18 0.18 0.19 0.19 0.19 0.19
High School Degree or Equivalent 0.21 0.21 0.20 0.20 0.20 0.20 0.21 0.21 0.20 0.20 0.20 0.20
Some College 0.24 0.24 0.23 0.24 0.23 0.23 0.24 0.25 0.23 0.24 0.23 0.23
Bachelors Degree 0.21 0.22 0.22 0.21 0.22 0.22 0.21 0.21 0.22 0.22 0.22 0.21
Higher than a Bachelors Degree 0.16 0.15 0.16 0.15 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
Employed 0.82 0.82 0.81 0.81 0.80 0.79 0.82 0.83 0.81 0.84 0.80 0.84
Unemployed 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
Not in Labor Force 0.15 0.15 0.15 0.16 0.17 0.18 0.15 0.13 0.15 0.13 0.17 0.14
SEI 38.57 38.73 38.88 38.67 38.78 38.52 38.57 39.46 38.88 39.89 38.78 39.94
Speaks English Not at All 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.04 0.05 0.04 0.05 0.04
Speaks English Not Well 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14
Speaks English Well 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
Speaks English Very Well 0.42 0.42 0.42 0.42 0.42 0.41 0.42 0.43 0.42 0.43 0.42 0.42
Speaks English Only 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.16
Homeowner 0.60 0.59 0.62 0.62 0.66 0.64 0.60 0.59 0.62 0.61 0.66 0.64
Mixed Status Family 0.10 0.10 0.10 0.10 0.08 0.08 0.10 0.10 0.10 0.09 0.08 0.08
Percent Immigrant in PUMA 26.27 26.31 26.25 26.31 26.40 26.47 26.27 26.29 26.25 26.32 26.40 26.52
Unemployment Rate in MSA 7.63 7.63 7.63 7.62 7.63 7.63 7.63 7.64 7.62 7.63 7.63 7.60
2 years 5 years 10 years
Propensity Score Matching Propensity Score Weighting
2 years 5 years 10 years
Table 2.4: Balancing table for propensity score analysis on total personal income.

76

Treated Control Treated Control Treated Control Treated Control Treated Control Treated Control
Male 0.49 0.49 0.50 0.49 0.50 0.49 0.49 0.50 0.50 0.50 0.50 0.50
Age at Entry 24.33 24.25 24.22 24.11 23.90 23.81 24.33 24.35 24.22 24.39 23.90 24.37
Years Eligible (log) 2.16 2.17 2.12 2.12 2.08 2.06 2.16 2.14 2.12 2.11 2.08 2.08
White 0.21 0.21 0.20 0.20 0.21 0.20 0.21 0.20 0.20 0.20 0.21 0.20
Black 0.13 0.13 0.12 0.13 0.11 0.12 0.13 0.12 0.12 0.12 0.11 0.11
Latinx 0.34 0.34 0.34 0.34 0.33 0.34 0.34 0.35 0.34 0.35 0.33 0.34
Asian Pacific Islander 0.30 0.30 0.31 0.31 0.32 0.31 0.30 0.30 0.31 0.31 0.32 0.32
Other or Mixed Race 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02
Not Married 0.34 0.34 0.32 0.32 0.30 0.30 0.34 0.33 0.32 0.32 0.30 0.30
Married to Non-Citizen 0.19 0.19 0.19 0.19 0.18 0.18 0.19 0.19 0.19 0.19 0.18 0.18
Married to Citizen 0.47 0.47 0.49 0.49 0.52 0.52 0.47 0.48 0.49 0.50 0.52 0.52
Family Size in Household 3.53 3.51 3.54 3.50 3.55 3.50 3.53 3.35 3.54 3.37 3.55 3.43
Less than a High school Degree 0.14 0.14 0.14 0.15 0.14 0.15 0.14 0.15 0.14 0.15 0.14 0.16
High School Degree or Equivalent 0.20 0.20 0.19 0.19 0.19 0.20 0.20 0.20 0.19 0.20 0.19 0.20
Some College 0.25 0.26 0.25 0.25 0.24 0.25 0.25 0.25 0.25 0.24 0.24 0.24
Bachelors Degree 0.23 0.23 0.24 0.24 0.24 0.24 0.23 0.23 0.24 0.23 0.24 0.23
Higher than a Bachelors Degree 0.17 0.17 0.18 0.17 0.18 0.17 0.17 0.17 0.18 0.18 0.18 0.18
Employed 0.94 0.93 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94
Unemployed 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
Not in Labor Force 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03
SEI 43.18 43.26 43.93 43.63 44.55 43.93 43.18 43.92 43.93 44.25 44.55 44.33
Speaks English Not at All 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.02 0.03 0.02 0.03
Speaks English Not Well 0.12 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.11 0.12 0.11 0.13
Speaks English Well 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24
Speaks English Very Well 0.46 0.46 0.47 0.46 0.46 0.46 0.46 0.46 0.47 0.46 0.46 0.45
Speaks English Only 0.16 0.16 0.16 0.16 0.16 0.16 0.16 0.15 0.16 0.15 0.16 0.16
Homeowner 0.60 0.61 0.63 0.63 0.67 0.66 0.60 0.59 0.63 0.61 0.67 0.64
Mixed Status Family 0.11 0.11 0.10 0.10 0.09 0.09 0.11 0.11 0.10 0.10 0.09 0.09
Percent Immigrant in PUMA 25.72 25.74 25.68 25.72 25.80 25.96 25.72 26.10 25.68 26.13 25.80 26.36
Unemployment Rate in MSA 7.58 7.58 7.57 7.58 7.58 7.59 7.58 7.60 7.57 7.59 7.58 7.59
Propensity Score Matching Propensity Score Weighting
2 years 5 years 10 years 2 years 5 years 10 years
Table 2.5: Balancing table for propensity score analysis on personal wage/salary earnings.

77

Table 2.6 shows the average treatment effect (ATE) and average treatment effect on the treated
(ATET) across the different propensity score methods for those who naturalized within two years,
five years, and ten years. As propensity score methods with regression adjustments provide doubly
robust estimates and because naturalization is non-random, I primarily focus on the results
presented in column 4 and 8 in Table 2.6.
As expected, naturalization is statistically and positively associated with higher total
personal income and wage/salary earnings. Among those who naturalized within the last two years,
estimates from the propensity score matching model with regression adjustments show that
immigrants experience an approximate 1.91 percent increase in their total personal income and a
2.69 percent increase in their wage/salary earnings associated with gaining citizenship. Within five
years of naturalizing, immigrants experience as much as a 7.07 percent growth in their personal
total income and a 6.01 percent increase in their wage/salary earnings. This growth continues to
increase over time with naturalized immigrants experiencing up to a 13.26 percent increase in their
total personal income and a 10.03 percent increase in their wage/salary earning within the first ten
years of naturalizing.
Turning to the estimates from the propensity score weighting model with regression
adjustments, results are relatively similar which adds further confidence that the model
specifications are internally valid. Naturalization is associated with a 1.94 percent increase in total
personal income and a 2.29 percent growth in wage/salary earnings within the first two years of
gaining citizenship. Within five years, naturalization increases total personal income and
wage/salary earnings by 6.94 percent and 5.82 percent, respectively. Total personal income and

78
wage/salary earnings continue to grow up to 13.24 percent and 10.07 percent, respectively, within
the first ten years of naturalizing.
Table 2.6: Results from propensity score methods estimating impact of naturalization on total
personal income (log) and personal wage/salary earnings (log).

Matching
Matching with
Regression
Adjustments
Weighting
Weighting with
Regression
Adjustments

(1)
ATE
(2)
ATET
(3)
ATE
(4)
ATET
(5)
ATE
(6)
ATET
(7)
ATE
(8)
ATET
Within 2 years

Personal Income (log)
0.0434 0.0298 0.0187 0.0191 0.0344 0.0285 0.0272 0.0194
Wage Earning (log) 0.0282 0.0309 0.0299 0.0269 0.0226 0.0293 0.0244 0.0229
Within 5 years

Personal Income (log) 0.0761 0.0876 0.0707 0.0702 0.0739 0.0872 0.0719 0.0694
Wage Earning (log) 0.0639 0.0839 0.0601 0.0609 0.0567 0.0801 0.0524 0.0582
Within 10 years

Personal Income (log) 0.1356 0.1599 0.1295 0.1326 0.1353 0.1643 0.1267 0.1324
Wage Earning (log) 0.1089 0.1444 0.0812 0.1003 0.1069 0.1445 0.0887 0.1007

Note: All results are statistically significant at the .001 level (two-tailed tests).

The Moderating Effects of Race and Gender
To explore how the impact of naturalization on economic outcomes differ by race/ethnicity and
gender, I include interaction terms in my weighted regression models. I find that race/ethnicity and
gender both have moderating effects on naturalization but is sensitive to increments of time. I
present the interaction results in Table 2.7.
Across racial/ethnic groups, Black immigrants show to have the largest relative increase in
their total personal income and wage/salary earnings associated with naturalization. With the
exception of wage/salary earnings within the first two years of naturalizing, Black immigrants
experience at least a 3 percentage point increase growth greater than their white immigrant
counterparts across the two economic indicators and time increments—netting a 5.4 percent and
5.3 percent naturalization effect on total personal income and wage/salary earnings, respectively,

79
within the first two years of gaining citizenship; 9.8 percent and 8.9 percent within the first five
years; and 14.9 percent and 12.9 percent within the first ten years. However, despite these
relatively advantageous economic growth from naturalization, Black immigrants continue to see
overall lower economic outcomes compared to their white counterparts. As indicated by the
coefficients for the Black variable in the ten-year sample, Black immigrants on average have total
personal incomes 13.7 percent lower than white immigrants when not naturalized. This is relatively
consistent also when looking at the five-year (-14 percent) and two-year (-12.3 percent) samples.
Looking specifically at wage/salary earnings, Black immigrants also significantly fall behind white
immigrants as they make 7.7 percent, 9.4 percent, and 8.1 percent less in the two-year, five-year,
and ten-year samples, respectively. So though Black immigrants benefit relatively the most
economically from naturalization, they still experience much lower incomes and wage/salary
earnings than their naturalized white immigrant counterpart.
Latinx immigrants see a relative naturalization advantage over white immigrants only in
their total personal income with 2.6 percentage points, 3.4 percentage points, and 2.7 percentage
points greater gains—netting a 4.6 percent, 8.7 percent, and 13.4 percent naturalization effect—in
the two-year, five-year, and ten-year samples, respectively. However, like Black immigrants,
Latinx immigrants experience overall lower personal income and wage/salary earnings than their
white immigrant counterparts. Latinx immigrants who do not naturalize on average have 10.8
percent, 13.7 percent, and 13.7 percent less total personal income than their white immigrant
counterparts in the two-year, five-year, and ten-year samples, respectively. Latinx immigrants who
do not naturalize make 7.6 percent, 8 percent, and 7.3 percent less in their wage/salary earnings
than white immigrant who are not naturalized. Though Latinx immigrants benefit more from
naturalization in their relative total personal income gains than white immigrants, naturalized

80
Latinx immigrants on average still have less total personal income than their eligible-to-naturalize
white immigrant counterparts even when controlling for other factors like education.
Relative to naturalized white immigrants, estimates show that Asian and Pacific Islander
immigrants in the two years following naturalization experience 3.7 percentage points and 3.8
percentage points lower benefits in their total personal income and wage/salary earnings,
respectively. This shows a net negative effect of 1.7 percent on total personal income and .7 percent
on wage/salary earnings within the first two years of naturalizing. This relative disadvantage
continues with 2.1 percentage points and 3 percentage points lower naturalization effects within
the first five years and 2 percentage points and 2.2 percentage points in the first ten years. However,
the net effects of naturalization on these economic indicators turn positive beginning sometime
between two and five years after naturalizing. Specifically, Asian and Pacific Islander immigrants
see a net 3.2 percent increase on total personal income and a 2.9 percent increase on wage/salary
earnings within the first five years of naturalizing; and an 8.7 percent and 7.6 percent naturalization
effect within the first ten years. This needs to be further extrapolated and explored especially
because of the significant heterogeneity in ethnicity, unobserved skills, and human capital within
this group (Gradín 2013; Lueck 2018; Paik et al. 2014). This initial downward trend could
potentially indicate a citizenship disadvantage specifically for Asian and Pacific Islander
immigrants since human capital and other demographic measures have been controlled for in this
model. Alternatively, the model specification may be sensitive to the contrasting migration
patterns from Asia. While a significant share of immigrants from Asia are obtaining high-skill and
high-paying jobs (e.g., jobs in analytics and the tech industry), there is also an increasing number
of low-skilled Asian immigrants arriving as refugees or through family reunification (Basu 2016;
Batalova and Fix 2017; Covarrubias and Liou 2014). Further analysis needs to be done.

81
Immigrant women begin to experience a relative naturalization advantage on total personal
income and wage/salary earnings five to ten years after gaining citizenship. Within ten years,
immigrant women gain 2.7 percentage points and 1.4 percentage points greater growth than men
on their total personal income and wage/salary earnings, respectively, from naturalizing—netting
a 13.4 percent naturalization effect on total personal income and a 11.2 percent naturalization effect
on wage/salary earnings. However, as indicated by the significant and negative coefficients on the
woman/female variable, immigrant women still make substantially less than their naturalized and
eligible-to-naturalize male counterparts. This is not surprising considering the systemic gender
discrimination women face in the job industry.
Table 2.7: Estimates of the mediating impact of race and gender on the benefits of naturalization
on total personal income (log) and personal wage/salary earnings (log).
2-year Sample 5-year Sample 10-year Sample
(1) (2) (3) (4) (5) (6)
Total
Personal
Income (log)
Wage/Salary
earnings
(log)
Total
Personal
Income (log)
Wage/Salary
earnings
(log)
Total
Personal
income (log)
Wage/Salary
earnings
(log)

Naturalized 0.020
*
0.031
**
0.053
***
0.059
***
0.107
***
0.098
***

(0.011) (0.011) (0.009) (0.009) (0.008) (0.008)
x Black 0.034
*
(0.016)
0.022

(0.017)
0.045
***
(0.014)
0.030
*
(0.014)
0.042
***
(0.013)
0.031
*
(0.013)
x Latinx 0.026
*
(0.013)
-0.006
(0.013)
0.034
***
(0.010)
-0.002
(0.010)
0.027
**
(0.009)
-0.007
(0.009)
x Asian/PI -0.037
*
(0.013)
-0.038
**
(0.013)
-0.021
*

(0.011)
-0.030
*
(0.011)
-0.020
*

(0.010)
-0.022
*

(0.010)
x Other/Mixed 0.003
(0.034)
0.018
(0.034)
-0.007
(0.028)
0.006
(0.028)
-0.005
(0.025)
0.006
(0.025)
x Female -0.003
(0.009)
0.002
(0.009)
0.009
(0.007)
0.008
(0.007)
0.027
***
(0.006)
0.014
*
(0.006)
Black -0.123
***
-0.077
***
-0.140
***
-0.094
***
-0.137
***
-0.081
***

(0.022) (0.022) (0.019) (0.019) (0.017) (0.018)
Latinx -0.108
***
-0.076
**
-0.137
***
-0.080
***
-0.137
***
-0.073
***

(0.027) (0.027) (0.022) (0.020) (0.019) (0.018)
Asian/Pacific Islander -0.101
***
-0.057
**
-0.102
***
-0.057
**
-0.101
***
-0.050
**

(0.022) (0.021) (0.018) (0.018) (0.017) (0.016)
Other or Mixed Race -0.116
***
-0.113
***
-0.122
***
-0.113
***
-0.119
***
-0.100
***

(0.025) (0.025) (0.024) (0.024) (0.023) (0.023)
Female -0.268
***
-0.228
***
-0.287
***
-0.242
***
-0.303
***
-0.252
***

(0.006) (0.006) (0.005) (0.005) (0.005) (0.005)
Observations 236,432 192,257 373,212 292,000 515,551 395,499
Note: Standard errors in parentheses; comparison group for race is White; controlled covariates in these models are
not included in this table.
*
p < .05,
**
p < .01,
***
p < .001 (two-tailed tests).

82

Discussion and Conclusion
In examining the economic impact of naturalization, the results show a statistical and positive
impact across the different subsamples with larger increases in personal total income and
wage/salary earnings with more time since naturalizing. This increase associated with
naturalization is likely due to several factors, including higher-paying job opportunities and
increased access to public benefits. While lawful permanent residents are eligible to utilize some
public programs, chilling effects and public charge rules prevent a significant number of
immigrants from using these public benefits (Bojorquez and Fry-Bowers 2019; Fortuny and
Chaudry 2011; Pedraza and Zhu 2015; Watson 2014). Beyond the benefits of voting, security from
deportation, expediting the visa and sponsorship process for immediate family members,
naturalization provides economic benefits for immigrants. This positive effect can have many
implications for the well-being of immigrants and the potential opportunities for their children as
they become more economically integrated, but the growth in spending power can also have a
large impact on localities and the country as naturalized immigrants can contribute more to taxes
and local economies through spending (Enchautegui and Giannarelli 2015; Sumption and Flamm
2012).
In exploring how gender and race/ethnicity interact with gaining citizenship, the results
indicate that there are moderating effects on naturalization in that certain racial/ethnic groups and
women experience relatively higher economic gains from naturalization. The segmented effects of
naturalization on different minoritized groups signal the importance of naturalization as a tool for
immigrants who are women, Black, and/or Latinx and their economic mobility. For Black and
Latinx immigrants, naturalization helps to close the income gap with their white immigrant
counterparts as early as within the first two years of naturalizing. Naturalization also helps to

83
minimize the wage/salary gap between Black and white immigrants beginning sometime between
two and five years after gaining citizenship. For Black immigrants who already suffer from
significant income and wage disparities due to institutionalized racism and discrimination,
naturalizing helps to mitigate barriers to mobility associated with citizenship.
Despite not seeing a relative naturalization advantage over their white immigrant
counterparts on wage/salary earnings, Latinx immigrants see a relatively higher increase in their
total personal income from naturalizing, and this is likely due to their improved access to public
benefits and social safety net programs (e.g., SNAP, Medicaid, cash assistance) (Enchautegui and
Giannarelli 2015; Pino 2020). As the Latinx immigrant community is disproportionately targeted
by local and federal agencies, lacking U.S. citizenship can often mean increased fears of
deportation especially if living with an undocumented family member (Asad 2020; Golash-Boza
and Hondagneu-Sotelo 2013; Menjívar et al. 2018; Romero 2008b). This often results in a chilling
effect where immigrants even with legal status avoid all public officials and programs to protect
themselves and their family from deportation and possibly becoming a public charge. In addition
to the public benefits restricted to U.S. citizens, naturalization provides a sense of security for
many immigrants to access public assistance that they have been entitled to even before gaining
citizenship.
Compared to immigrant men, immigrant women begin to realize a relative naturalization
advantage on their total personal income and wage/salary earning sometime between five and ten
years after becoming citizens. Though there is still a significant income and wage gap between
immigrant women and men, naturalization serves to reduce this disparity. Like for racially
minoritized immigrants, gaining citizenship for immigrant women could help reduce the

84
intersectional barriers and glass ceilings they face when navigating the labor market, including
greater opportunities in the public sector (Donato et al. 2014; Zhou and Lee 2013).
Further analysis needs to be done to understand the nuances within racial groups including
how refugee status of some ethnic groups facilitates different naturalization effects on economic
outcomes compared to other ethnic groups of the same race. Also, further work applying an
intersectional lens will help disentangle the varying and multiplicative impact of race and gender
on naturalization and economic outcomes. For example, how does naturalization impact the
economic outcome of racially minoritized immigrant women who face multiple layers of
disadvantages because of their race, gender, and immigration status? Prior studies looking at the
experiences and inequalities reproduced at this intersection have found that Black and Latinx
immigrant men are more likely to be segregated into low-wage jobs compared to their women
counterparts due to how the labor market has been racialized and gendered (Gradín 2013).
Additionally, working-class Latinx immigrant men are more likely to be targeted for deportation,
thus making it more difficult to find work compared to other immigrants of a different class,
race/ethnicity, and gender (Golash-Boza and Hondagneu-Sotelo 2013).
Particularly for Latinx immigrants who have the lowest naturalization rates (Aptekar 2015;
Waldinger 2021), this research provides evidence that should motivate more targeted strategies
and policies that would effectively improve naturalization rates as a means to increase immigrants’
economic mobility. This includes reducing financial and language barriers to naturalization by
expanding fee waivers (Hainmueller et al. 2018), language exemptions, and trainings. In addition,
this research further validates how studying and measuring immigrant integration cannot be devoid
of critical analyses that extrapolate the moderating effects of race/ethnicity and gender. Such an
intersectional approach will only advance our understanding of immigrant communities and

85
contribute to more equitable and effective policies that will improve immigrant integration and
well-being.

86

The Model Minority and Perpetual Foreigner Paradox:
A Disaggregated Analysis of Naturalization Among Asian American Immigrants

Thai V. Le
Ph.D. Candidate, Public Policy and Management
Sol Price School of Public Policy
University of Southern California

Abstract

There is increasing interest and importance in understanding how Asian American
immigrants are integrating as they continue to grow and become a significant share
of the electorate in local, state, and federal political arenas. Despite increased
participation in nonvoting and nonelectoral political acts, Asian American
noncitizens continue to face citizenship barriers that exclude them from the
opportunity to fully participate in the U.S. political system, including voting and
running for political office. Research on Asian American immigrants and their
political incorporation into the U.S. is often done in a way that overlooks within-
group heterogeneity. Like studies on Asian American political participation, further
work needs to be done to disaggregate the nuanced intragroup differences with
Asian American immigrants’ pathways to citizenship. In examining Asian
American immigrants’ paradoxical treatment as model minorities and perpetual
foreigners, I explore how disparities in individual, place-based, and network
characteristics have diversely shaped their pathways to citizenship. By
disaggregating the traditional models of predicting naturalization by subgroups, I
find heterogenous effects that provide further evidence supporting the need to have
more nuanced analyses of the Asian American immigrant experience. Notable
empirical findings include how immigrant groups with limited human capital turn
to their social capital and networks to supplement and improve their access to
naturalization. These findings can help shape policies to be more effective and
equitable by addressing group-specific needs in facilitating integration and
representation of an increasingly diverse group of immigrants.

87
Introduction
Asian Americans represent the fastest-growing racial/ethnic group in the United States; driven
primarily by Asian American immigrants who are expected to be the largest immigrant group in
the country by 2055 (Budiman & Ruiz 2021a; 2021b). This historic shift in the demographic
makeup of the U.S signals an increasing need to understand how Asian American immigrants are
integrating socially, economically, and politically. Asian American immigrants have been and are
expected to continue to be a significant force of the electorate in local, state, and federal political
arenas; however, their full political participation often hinges on their ability to naturalize
(Masuoka, Ramanathan, & Junn 2019; Ong & Nakanishi 1996). Despite increased participation in
nonvoting and nonelectoral political acts, Asian American noncitizens continue to face citizenship
barriers that exclude them from the opportunity to fully participate in the U.S. political system,
including voting and running for political office (Masuoka et al. 2019; Wong et al. 2011). The
consequences to uneven political integration include a lack of representation by elected officials.
A recent study on the diversity of elected officials finds Asian Americans to be the most
underrepresented racial/ethnic group in the U.S. by a factor of negative 85 percent compared to
white Americans who were found to be overrepresented by 46 percent (Reflective Democracy
Campaign 2021). One significant mechanism that can further facilitate a more democratic and
represented society is improving access to naturalization (Ong and Nakanishi 1996). In my
analysis, I apply a series of multivariate logit models to explore the differential barriers Asian
American immigrants face in naturalizing, focusing particularly on how individual, place-based,
and network attributes may impact Asian American immigrant groups differently. I take this
approach in disaggregating the Asian American immigrant experience to identify potential group-
specific mechanisms and policies that can equitably improve access to naturalization.

88
In recent decades, immigrants from Asia have been among the most likely racial/ethnic
group to naturalize in the U.S. with rates higher than their immigrant counterparts from Africa,
Europe, Oceania, South America, Central America, and Mexico (Teke 2020; Yang 2002). With
the increasingly large inflow of immigrants from Asia in recent years, it is not surprising that Asian
American immigrants made up nearly 40 percent of all immigrants who naturalized in 2019—the
largest share of any race/ethnicity (Teke 2020). Spending a median of seven years as lawful
permanent residents (LPR) prior to naturalization compared to eight years for all immigrants,
immigrants from Asia are also more likely to gain citizenship sooner (Portes and Rumbaut 2014;
Teke 2020). When looking at these statistics alone, it may seem reasonable to conclude that Asian
American immigrants are integrating much quicker and in greater numbers than most other groups
if, like many other studies, naturalization is used as a proxy (e.g., Kerwin and Warren 2019;
National Academies of Sciences, Engineering, and Medicine 2015). However, disaggregating
these numbers by ethnic groups and countries of origin show significant intragroup disparities in
naturalization rates that suggest not all Asian American immigrants have equal access and abilities
to naturalize (Masuoka, Ramanathan, & Junn 2019; Yang 1994; 2002). For example, though
immigrants from Vietnam (76 percent) and Cambodia (81 percent) have naturalization rates well
above the average for all immigrants (52 percent), immigrants from countries like Japan (34
percent) and Nepal (36 percent) are naturalizing at much lower rates (Hanna and Batalova 2021).
Relying on aggregated statistics of Asian American immigrants naturalizing not only perpetuate a
detrimental model minority myth of a group that is experiencing the greatest income inequality
(Kochhar and Cilluffo 2018) but also overlooks the differential barriers to citizenship that Asian
American immigrant groups face, including ones that are tied to group-specific migration patterns
and immigration experiences (Yang 2002).

89
An analysis of the nuanced and distinct barriers to naturalization that diverse Asian
American immigrant communities face is pertinent to equitably promote naturalization and
facilitate a more integrated and politically represented Asian American immigrant community.
Yang’s (2002) study is perhaps the only other comprehensive research on naturalization that
disaggregates the Asian American immigrant experience, however, their work focuses on cohorts
of Asian American immigrants who arrived in the U.S. between 1965 and 1984. Though this work
was pivotal during its time in highlighting the distinct differences of six major Asian American
immigrant group, it is likely outdated because (1) the naturalization process has since changed and
(2) the sociodemographic makeup of Asian American immigrants who arrived in the U.S. within
the last few decades is qualitatively different than those prior to 1990 by several parameters,
including educational attainment, socioeconomic status, and English literacy. The portrait of
immigrants arriving in the U.S. has substantially changed due to significant shifts in immigrations
patterns and policies (Portes and Rumbaut 2014). Asian American immigrants who arrived in the
U.S. since the turn of the century are on average more educated, wealthy, and likely to have high-
wage and high-skill jobs (Basu 2017). For example, Yang’s (2002) 1990 sample consisted of
Asian American immigrants who 36 percent had at least a bachelor’s degree; in contrast, 54 percent
of Asian American immigrants in 2019 had at least a bachelor’s degree, a byproduct of the
Immigration Act of 1990 which stipulated preferences for highly skilled and highly educated
immigrant workers (Hanna and Batalova 2021).
The naturalization process in the U.S. has become one of the costliest in the world with
fees increasing from $35 to $650 since 1985—not including the additional $85 for biometrics
services (Capps & Echeverria-Estrada 2020). Though fee waivers have been available for decades,
they were difficult to access for many low-income applicants until 2010 when the United States

90
Citizenship and Immigration Service (USCIS) standardized the process—a feat that has proven to
directly increase the naturalization rate by 1.5 percentage points (Yasenov et al. 2019).
Additionally, both the history and civics test and English-language assessment were significantly
revamped in 2009 during a time of increased anti-immigrant sentiment and “in response to
criticism that the old test was administered inconsistently, did not serve to measure meaningful
understanding of American history and government, and did not sufficiently encourage patriotism”
(Aptekar 2015, p.31). Despite these changes in the naturalization process and increased processing
time, the number of immigrants gaining citizenship annually has grown nearly five folds since
1984 (Teke 2020; U.S. Department of Homeland Security 2018).
Beyond capturing a more accurate analysis of Asian American immigrants naturalizing in
today’s context, this paper also supplements Yang’s (2002) earlier work by (1) netting out likely
undocumented Asian American immigrants who are systematically unable to naturalize due to
their current immigration status and (2) measuring family network effects that have been found to
significantly impact naturalization results, including the impact of having an undocumented family
member (Le and Pastor, n.d.). It is estimated that Asian American immigrants represent 14 percent
(approximately 1.5 million) of the undocumented population in the U.S.—a significant share of
whom are in mixed-status families, a population that has been understudied in many aspects
(Hanna and Batalova 2021; Kim and Yellow Horse 2018). This point needs further analysis to
understand how Asian American immigrants, like Latinx immigrants (Amuedo-Dorantes and
Lopez 2020; Aranda et al. 2014), may experience chilling effects from U.S. policies that target and
criminalize undocumented immigrants.
In this paper, I examine group-specific mechanisms and barriers to naturalization across
Asian American immigrant groups by first exploring the historical policies and migration patterns

91
that have led to existing disparities within the Asian American immigrant community. I discuss
past research to identify predictors of naturalization and existing limitations in understanding
Asian American immigrants’ heterogenous experiences. Building from this literature review, I
integrate critical race theory, intersectionality, and the concept of racial triangulation into a
theoretical framework to disentangle how the model minority myth and perpetual foreigner
stereotype have manifested in immigration policies and context of reception in ways that have
shaped uneven pathways to citizenship for Asian American immigrants. I then discuss my data
and methodological approach in estimating the effects of specific determinants on naturalization
across Asian American immigrant groups. I conclude with an in-depth discussion of the results,
policy implications, and need for future research to disaggregate the Asian American experience.

An Overview of Historical Policies Shaping Asian American Immigration
Asian American immigrants have faced a long history of discrimination in the U.S stemming from
xenophobic ideals that were only overlooked when the need to extract low-skilled workers outgrew
domestic concerns of the “Yellow Peril” (Lee 2007). Such nativist ideals, however, festered with
the exploitation of low-skilled workers from Asia, further perpetuating anti-Asian racism and
exclusionary policies that were not lifted until the mid-20
th
century (Duleep et al. 2021; Lee 2007).
Up until 1952 when the Immigration and Nationality Act (McCarran-Walter Act) was passed,
Asian American immigrants were largely denied citizenship as a result of laws and policies that
enforced racial restrictions on the rights to naturalize—creating a population of naturalized citizens
who were mostly white (e.g., Naturalization Act of 1790; Naturalization Act of 1870; Cable Act
of 1922; Ozawa v. U.S. case; 1923 Thind v. U.S. case; National Origins Quota of 1924; 1933
Roldan v. Los Angeles County case; Luce-Celler Act of 1946). Immigration from Asia

92
significantly changed after 1965 with the Immigration and Nationality Act (Hart-Celler Act)
which, among many other things, ended the national origins quota system that disproportionately
impacted immigrants from Asia who by law were broadly barred from migrating to the U.S. (e.g.,
Page Act of 1875; Chinese Exclusion Act of 1882; Geary Act of 1892; Gentleman’s Agreement
of 1907; Asiatic Barred Zone Act of 1917; 1922 Immigration Act of 1924; National Origins Quota
of 1924; Tyding-McDuffle Act of 1934; Magnuson Act of 1943; Luce-Celler Act of 1946). Prior
to 1965, Asian American immigrants were primarily made up of Chinese, Japanese and Asian
Indian Americans who typically arrived with limited educational attainment and less valued
skills—often manual labor—compared to natives (Yang 1999). The Immigration and Nationality
Act of 1965 stipulated greater family reunification and skills-based migration that led to a post-
1965 Asian American immigrant population that was not only more diverse in culture, languages,
and histories but often more educated and more skilled with some exceptions, such as significant
shares of refugees who arrived in the U.S. with limited educational opportunities and capital
(Hanna and Batalova 2021; Yang 1999). However, as global economic development continues to
advance in select regions, a non-negligible share of immigrants in recent waves from traditionally
sending refugee countries like Vietnam have tended to be wealthier, more educated, and have what
the U.S. defines as higher-valued skills than their refugee predecessors, while also embodying
different cultural and political ideals (Le & Su 2018).
Immigration from Southeast Asia significantly increased the Asian American population
after 1965 as global events created large waves of refugees from countries like Vietnam and
Cambodia. With the 1975 Indochina Migration and the 1980 Refugee Resettlement Act, the U.S.
admitted and helped resettle more than 1.1 million refugees from Southeast Asia over the span of
three decades, many of whom arrived with limited English proficiency, low educational

93
attainment, and virtually no financial resources (Southeast Asia Resource Action Center 2020).
Though the number of refugees from Southeast Asia decreased significantly since the mid-1990s,
the rise in Southeast Asian American immigrants continued to grow as a result of family
reunification and employment-based immigration. In 2017 alone, 95 percent of immigrants from
Southeast Asia arrived in the U.S. with green cards through immediate family members or family-
based preferences—an important point as more recent Southeast Asian American immigrants are
able to rely on their extensive network to integrate into the mainstream culture (Duleep et al. 2021;
Southeast Asia Resource Action Center 2020).
Immigration from South Asia also significantly grew since 1965 with a dominant share
from India, making Indian American immigrants the largest Asian foreign-born population in the
U.S. and the second largest overall immigrant group after immigrants from Mexico (Hanna and
Batalova 2020). The first wave of immigrants from India primarily comprised of agricultural and
railroad laborers. However, like other immigrants from Asia, immigration from India and other
South Asian countries was broadly barred prior to 1965 (e.g., Asiatic Barred Zone Act of 1917;
Emergency Quota Act of 1921; Immigration Act of 1924). Though the Luce-Celler Act of 1946
lifted the restriction for immigrants from India, the quota was limited to 100 immigrants a year. It
was not until the Immigration and Nationality Act of 1965 did immigration from India begin to
grow exponentially. Post-1965, the number of Indian American immigrants not only increased
significantly, but their characteristics also shifted towards a more highly skilled and educated
populace due to a series of educational exchange programs, employment-based immigration, and
temporary visas specifically for highly skilled and educated immigrants (Hanna and Batalova
2020). Many of these immigrants also were able to bring their family which contributed to how

94
the Indian American immigrant population grew over 1,200 percent from 1980 to 2019 (Hanna
and Batalova 2020).
With the exponential growth of South and Southeast Asian American immigrants after
1965, the Asian American immigrant population that was once primarily made up of Chinese and
Japanese immigrants is now more diverse and qualitatively different than previous Asian
American immigrant cohorts across multiple parameters, which I discuss in the next section. In
2019, Southeast Asian American immigrants (4.4 million) comprised the largest share of the Asian
American immigrant community followed by South Central Asian American immigrants (4.3
million), East Asian American immigrants (4.2 million), and West Asian American immigrants
(1.2 million) (Hanna and Batalova 2021).
4
The rates of immigrants migrating to the U.S. from
Western Asia has seen a much slower growth and has been shaped by other policies and global
events, including the extensive war in the Middle East and stigmas associated with the U.S. war
on terrorism. As such, immigrants from Western Asian countries often have different experiences
that are so nuanced that it cannot be adequately explored within the scope of this paper. I further
discuss this issue in the data section. The ethnic and cultural makeup of Asian American
immigrants have significantly changed over the decades with shifting immigration policies and
global events. A more nuanced and disaggregated analysis recognizing such heterogeneity across
and within Asian American immigrant groups would produce more relevant and valid research on
this growing and diversifying population.

4
Southeast Asian American immigrants include immigrants from Burma/Myanmar, Cambodia, Laos, the
Philippines, Thailand and Vietnam; South Central Asian American immigrants include immigrants from Bangladesh,
India, Iran, Nepal, and Pakistan; East Asian American immigrant include immigrants from China, Japan, Korea, and
Taiwan; and Western Asian American immigrants include immigrants from Iraq, Israel, Lebanon, Syria, and Turkey.

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The Drivers of Naturalization and Intragroup Disparities
There are several factors that shape immigrants’ pathways to citizenship. Broadly, they can be
grouped as either (1) individual characteristics, (2) place-based attributes, or (3) networks. Though
individual characteristics and place-based attributes have been extensively studied in previous
naturalization research (e.g., Aptekar 2015; Johnson et al. 1999; Jones-Correa 2001; Yang 1994),
there is less empirical work examining network effects. Even more limited empirical work is
available to understand how each of these factors may impact immigrant communities differently,
including the effects of being in a mixed-status family. Thus, it is constructive to also discuss how
intragroup disparities and differences within the Asian American immigrant community may lead
to different expectations and assumptions of which drivers of naturalization are more significant
than others.

Individual Characteristics
Previous studies find that human capital, particularly measured by English proficiency level and
educational attainment, are often among the most significant predictors of naturalization (Aptekar
2015; Johnson, Reyes, Mameesh, and Barbour 1999; Le and Pastor n.d.; Yang 1994). This is as
expected due to the design of the naturalization process which requires most applicants to pass a
civics and history exam administered in English. Additionally, some level of English proficiency
is required to effectively navigate around procedural barriers, such as the actual application forms
and study materials provided by USCIS (Aptekar 2015). Compared to other immigrant groups,
Asian American immigrants tend to have higher educational attainment, however, substantial
differences exist across immigrant cohorts by ethnicity and immigrant wave (Covarrubias and Liou
2014; Duleep et al. 2021; Yang 1999). Yang’s (1999) earlier work finds that Asian American

96
immigrants post-1965 are generally more educated than their pre-1965 predecessors. However,
they also argue that the Asian American immigrant population cannot be perceived as monolithic
due to educational attainment differences across subgroups. In their study of the largest Asian
American immigrant subgroups in 1990, they find that Indian American immigrants were the most
educated and had the highest rate of advanced degrees at 32.5 percent, whereas Vietnamese
American immigrants were among the least educated with 39.4 percent with less than a high school
degree (Yang 1999). Such educational disparities within the Asian American immigrant
community persist. Recent estimates show Indian American and Taiwanese American immigrants
having significantly higher levels of educational attainment with 79 and 73 percent estimated to
have a college degree, respectively, whereas only 19 percent of Cambodian American immigrants
and 15 percent of Lao American immigrants are college educated (Hanna and Batalova 2021).
Intragroup disparities for English proficiency are almost as stark. A Migration Policy
Report finds that 42 percent of Asian American immigrants in 2019 have limited English
proficiency—reporting that they speak English less than “very well” on the 2019 American
Community Survey—compared to 46 percent of all immigrants in the U.S. (Hanna and Batalova
2021). Among Asian American immigrants, Burmese/Myanmar American (71 percent),
Vietnamese American (65 percent), Lao American (62 percent), and Cambodian American (59
percent) immigrants report substantially higher rates of limited English proficiency, whereas
Indian American (22 percent) and Filipino American (28 percent) immigrants have among the
lowest rates of limited English proficiency—a product of how the English language has been
integrated into each country’s economy and education curriculum (Hanna and Batalova 2021;
Madrunio, Martin, and Plata 2016).

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Financial resources are also an important factor in determining access to naturalization.
Previous research has cited the financial cost of naturalizing, including the cost of the application
and legal services, as one of the primary barriers in immigrants’ pathways to citizenship (Aptekar
2015). Households headed by Asian American immigrants are on average wealthier with a median
household income of $88,000 in 2019 compared to a median of $64,000 for all immigrant groups
and $66,000 for all U.S.-born groups (Hanna and Batalova 2021). By another measure, Asian
American immigrants (11 percent) are also less likely to live in poverty than those born in the U.S.
(12 percent) and other immigrants (14 percent) (Hanna and Batalova 2021). Disaggregating these
statistics, however, there are large intragroup disparities. Households headed by Indian American
($130,000), Taiwanese American ($105,000), or Filipino American ($99,000) immigrants have a
median household income significantly higher than households headed by other Asian American
immigrant groups, including households headed by Burmese/Myanmar American ($52,800), Thai
American ($68,000), Nepalese American ($66,000), and Vietnamese American ($71,000)
immigrants; similarly, Filipino American (5 percent) and Indian American (6 percent) immigrants
have much lower rates of poverty compared to other Asian American immigrant group, such as
Vietnamese American (14 percent) and Chinese American (18 percent) immigrants.
5

Previous work on naturalization argues that gender helps to predict naturalization as
immigrant women in recent decades tend to have greater educational attainment, English
proficiency, and social networks that facilitate access to naturalization (National Academies of
Sciences, Engineering, and Medicine 2015). However, these assumptions for all immigrants do
not necessarily apply to Asian American immigrants. Lueck (2018) finds that among Asian
American immigrants, men have nearly 50 percent greater probability of socioeconomic success

5
Author used 2019 ACS 1-year estimates to calculate median household income by country of origin.

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as a factor of English language proficiency, social networks, and other predictors of social mobility
in the U.S. Beyond the gender disparities in human capital, women also have different motivators
for naturalizing including how immigrant women have greater propensities to naturalize in
response to increasing immigrant enforcement and as a defensive strategy to protect their families
(Amuedo-Dorantes and Lopez 2020). Yang (2002) argues Asian American immigrant women
have greater incentives to naturalize “because of their vulnerable position and stronger motivations
to gain independence and freedom” (381). They also find gender to have differential effects across
Asian American immigrant groups, arguing that immigrant women from some ethnic groups—
such as Chinese, Japanese, Korean, and Vietnamese—have “lower status… [that are] strongly
influenced by Confucianism” and thus have greater motivation to obtain citizenship to mitigate
barriers to mobility as women, racial minorities, and immigrants (Yang 2002:398). This
assumption, however, may need to be updated to fit today’s context as recent studies would argue
that Asian American immigrant women are facing institutionalized sexism, racism, and
imperialism linked also to existing structures and ideals perpetuated by systems within the U.S.
(Chun, Lipsitz, and Shin 2013; Mukkamala and Suyemoto 2018).

Place-Based Attributes
Place-level characteristics, such as policies and local conditions, act as external forces that broadly
shape immigrants’ pathways to citizenship. Within the naturalization and migration literature, poor
economic conditions and limited economic opportunities are often cited as factors that instigate
migration and, in many situations, disincentive immigrants’ permanent return to their countries of
origin (Portes and Rumbaut 2014; Rosenblum and Brick 2011). Such country-of-origin conditions
that can persuade immigrants to seek ways to stay in the U.S. (e.g., naturalization) include

99
unlivable wages, limited and unsustainable employment opportunities, and a lack of safety nets
(National Academies of Sciences, Engineering, and Medicine 2015; Portes and Rumbaut 2014;
Rosenblum and Brick 2011). Similarly, safety concerns in other countries are common reasons
why immigrants migrate to the U.S. and may decide to stay permanently and seek U.S citizenship
(Abrego 2014; Menjívar 2000; Portes and Rumbaut 2014). Many refugees and asylees flee from
their countries of origin with little to no intention to return because of the uncertainty of their safety
and survival (Bloemraad 2018). Though the number of refugees and asylees from Asia peaked
during the two decades following the Vietnam War, 9 percent of Asian American immigrants who
adjusted to LPR status in 2019 were either refugees or asylees, whereas 40 percent received LPR
status through immediate relatives of U.S. citizens, 21 percent through employment-based
preferences, 24 percent through family-sponsored preferences, and 4 percent through the Diversity
Visa lottery (Hanna and Batalova 2021). It varies significantly, however, when disaggregating
these trends by country. For example, the majority from South Korea (57 percent) received their
green card through employment-based preferences, whereas the vast majority from Cambodia (70
percent), Laos (83 percent), and Thailand (67 percent) gained LPR status through immediate
relatives with U.S. citizenship (Hanna and Batalova 2021).
Related, the context of reception for immigrants weighs heavily on immigrants’ abilities
and decisions to naturalize. Pro-immigrant policies and attitudes can significantly bolster
immigrants’ human capital and improve their odds of successfully naturalizing, and they can also
create a more welcoming environment that encourages integration and naturalization through more
immigrant-friendly rather than assimilationist approaches (de Graauw and Bloemraad 2017;
Bloemraad 2006; Van Hook, Brown, and Bean 2006; Woroby and Groves 2016). However,
Johnson and colleagues (1999) argue that pro-immigrant states and localities are also more likely

100
to be home to immigrants with limited human capital and thus may be a less reliable parameter in
predicting naturalization. Hummel (2016) conversely finds no effect and argues that “local
immigration policy may have had no relationship to the decision to live and/or relocate to a
community for the foreign-born population in general” (Hummel 2016:1227). In contrast,
immigrants residing in places with more anti-immigrant policies and attitudes could experience
conflicting scenarios: (1) resort to defensive naturalization as a way to secure one’s place in the
U.S. and access to public benefits (Amuedo-Dorantes and Lopez 2020; Cort 2012; Ong 2010), or
(2) avoid the naturalization process entirely due to chilling effects from increased threats of
deportation and enforcement (Aranda et al. 2014; Gomberg-Muñoz 2017; López 2017). The Asian
American immigrant diaspora in the U.S. has traditionally skewed toward relatively more
immigrant-friendly regions, particularly California where approximately 30 percent of Asian
American immigrants resided in 2019 (Hanna and Batalova 2021). Examining the diaspora at a
more local level, the top metropolitan areas where Asian American immigrants reside include Los
Angeles (1,695,000), New York (1,680,000), San Francisco (824,000), and Chicago (497,000)—
areas known to be more liberal and relatively welcoming of immigrants in policies and attitudes
(Hanna and Batalova 2021; New American Economy 2019).
Additionally, an immigrant’s choice to naturalize is also influenced by whether their
country of origin allows dual citizenship. Relatively few countries officially allow their citizens to
retain their country-of-origin citizenship if they choose to naturalize in the U.S. Immigrants may
be unwilling to forfeit their country-of-origin citizenship for various reasons, including symbolic
attachments (Leblang 2017; Mazzolari 2009). Once the cost of forgoing citizenship is removed
and dual citizenship is allowed, immigrants are more likely to naturalize (Jones-Correa 2001;
Mazzolari 2009). Many Asian countries, such as China, Japan, and Burma/Myanmar restrict dual

101
citizenship and require their nationals to forfeit their citizenship if they choose to naturalize in
another country. However, in some countries where dual citizenship is allowed, there are
restrictions that may moderate the effect of dual citizenship on naturalization, such as nations
revoking voting rights and rights to run for public office, including South Korea and Sri Lanka
(Sejersen 2008).

Network Effects
Menjívar (2000) argues the importance of social networks among immigrants as a source of
information and resources. In understanding the factors that shape pathways to citizenship and
immigrant integration, networks are significant in that they often improve immigrants’ social
capital (Abascal 2017; Bloemraad 2006). The naturalization process can be difficult and
challenging for someone who lacks information on how to effectively navigate procedural barriers,
such as the application (Aptekar 2015). Immigrants with close ties or family members who have
gone through the naturalization process have access to invaluable information and experiences
(Gomberg-Muñoz 2017; Liang 1994). Asian American immigrants, on average, are more likely
than other immigrant groups to obtain LPR status through an immediate family member who is a
U.S. citizen, however, as mentioned earlier, there are intragroup disparities. There are also large
intragroup differences when examining naturalization rates. For example, Cambodian American
(81 percent), Lao American (80 percent), and Vietnamese American (76 percent) immigrants have
significantly higher rates of naturalization than other Asian American immigrant groups, including
Japanese American immigrants (34 percent) (Hanna and Batalova 2021). These points allude to
the assumption that Southeast Asian American immigrants are more likely to have networks of
immediate family members and close ties who have naturalized compared to other groups like

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Japanese American immigrants. This gap in immigration pathways and naturalization rates thus
can lead to an information gap that disadvantages immigrant groups without a network of close
ties who have experiences navigating the naturalization process.
Networks can also have a negative impact on immigrants’ access to naturalization. In
particular, misinformation about the process and chilling effects from policies that target close ties
with liminal legality or undocumented status can impede immigrants’ pathways to citizenship
(Aranda et al. 2014; López 2017; Menjívar 2000; Woroby and Groves 2016). Increased
enforcement of immigrant communities consequently creates fear among immigrants, particularly
undocumented immigrants and mixed-status families (Aranda et al. 2014; Asad 2020; Buenavista
2018). Immigrants in mixed-status families face unique challenges as they are in a position of
privilege as lawful residents but also disadvantaged as they struggle to access resources and
services in fear of putting their undocumented close ties in danger of deportation (Aranda et al.
2014; Watson 2014). Despite an immigrant’s own lawful status, how undocumented immigrants
have been criminalized in the U.S. has had spillover effects felt by families and close ties. Despite
the decline in undocumented immigrants since the recession, the number of undocumented Asian
American immigrants has risen. Approximately 14 percent of the undocumented population in the
U.S. are Asian American, but such rates vary when disaggregated by country of origin. Indian
American immigrants make the largest share of the undocumented population among Asian
American immigrants and the fourth largest overall (Migration Policy Institute 2020). Among the
approximately 1.5 million undocumented Asian American immigrants, it is estimated that 32
percent are from India, 26 percent from China and Hong Kong, 16 percent from the Philippines, 8
percent from Korea, and 19 percent from the remaining Asian countries (Capps et al. 2020). With

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some Asian American immigrants more likely to be living in mixed-status families, there is a likely
gap in the potential chilling effects felt by different groups.
Immigrants can also tap into networks that span beyond the family unit. In communities,
immigrants build social networks with co-ethnic nationals to not only share information but also
to pool resources, including funds (Menjívar 2000). In regards to naturalization, there are
competing theories about the manner in which ethnic enclaves can impact pathways to citizenship:
(1) shared identities and networks can help facilitate the flow of information and resources, and
(2) homogenous networks can develop exclusive in-group relationships that hinder assimilation to
mainstream culture (Abascal 2017). Abascal (2017) argues that the former is more common and
that the increased presence of naturalized co-ethnics who relate by culture and language can
significantly improve information sharing among communities that have a stronger sense of a
shared hyphenated American identity. Woroby and Groves (2016), however, find that some
immigrant networks can be disadvantageous if immigrants without the experience and intention to
naturalize cluster, potentially leading to echo chambers of misinformation. Among Asian
American immigrants, because of sheer numbers, some are more likely to have access to a network
of co-ethnic nationals, including Chinese American and Vietnamese American immigrants who
cluster in specific regions of the country (e.g., San Gabriel Valley and Orange County, California)
(Võ 2008; Zhou, Tseng, and Kim 2008). Such clustering is argued to be advantageous for
immigrants who may find more ease and comfort seeking information, services, and help from co-
ethnics who share similar cultural experiences and speak the same language (Abascal 2017).

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A Framework in Understanding the Asian American Immigrant Experience
In developing my analysis, I rely on a framework that integrates critical race theory,
intersectionality, and the concept of racial triangulation to disentangle how the model minority
myth and perpetual foreigner stereotype have jointly manifested in immigration policies and
contexts of reception—consequently shaping uneven pathways to citizenship for Asian American
immigrants. I utilize critical race theory to problematize existing disparities among Asian
American immigrants as a byproduct of racist (immigration) policies that serve to reinforce
structural inequities of power and privilege manifesting across generations in several forms,
including resource deprivation, detrimental stereotypes, and race-driven violence (Chang 1993).
Intersectionality provides a framework to further examine how race/ethnicity intersects with other
marginalized identities (e.g., immigration status) to reproduce multiplicative disadvantages and
lesser social positions of power in a matrix of domination (Collins 2000; Crenshaw 1990; Shih et
al. 2019). In particular, intersectionality provides key elements in developing an analytical strategy
that unravels the diverse lived experiences of Asian American immigrants beyond just race to
understand how pathways to citizenship and within-group differences are reciprocally constructing
phenomena shaped by social norms and dictated by intersecting identities, including ethnicity and
nationality (Collins 2015).
In synthesizing both critical race theory and intersectionality into a framework relevant to
studying naturalization among Asian American immigrants, I also draw from Claire Jean Kim’s
(1999) racial triangulation theory where Kim examines the racial position of Asian Americans in
relation to their white and Black counterparts. Challenging the Black-white dichotomy of the
traditional racial hierarchy framework, Kim argues that a group’s position in the field of racial
positions is multidimensional and is determined in relation to other groups’ positions and

105
identities. One dimension measures valorization, whereas the other dimension determines civic
ostracism. Kim posits that the white majority valorizes Asian Americans above their Black
counterparts due to their socioeconomic achievements, but also argues that the white majority
civically ostracizes Asian Americans—relegating Asian Americans as unassimilable. Such
triangulation alludes to how Asian Americans have been simultaneously treated as model
minorities but also perpetual foreigners, a mechanism rooted in white supremacy to maintain
inequitable power structures that pit racially minoritized groups against each other.
To understand the disparities in naturalization among Asian American immigrants, the
model minority myth first needs to be disentangled to underscore how the Asian American
experience and pathways to citizenship is not a monolith. The Asian American community, a
majority of whom are immigrants, has often been labeled and depicted as “model minorities” able
to achieve social and economic success by “pulling themselves up by their own bootstraps” despite
hardship and oppression (Shih et al. 2019). This term has been coined to describe the achievements
and successes of the Asian American community compared to other racial minorities—often used
as a political and hegemonic tool to contest race-driven and equity-oriented policies, including
affirmative action (Ng, Lee, and Pak 2007; Shih et al. 2019). The model minority identity
inaccurately highlights how Asian Americans have excelled and achieved high socioeconomic
status, including higher rates of educational attainment, higher household income, and lower rates
of unemployment and poverty (Ahmad & Weller, 2014; Lee, 2015). The inaccuracy in this claim
lies in its ignorance of the stark disparities within the Asian American community across multiple
measures, such as educational attainment and poverty rates. For example, though Asian Americans
are seen as the most educated group in the U.S. with 50 percent having a Bachelor’s degree, only
14 percent of Lao Americans and 17 percent of Cambodian Americans have a bachelor’s degree,

106
significantly lower than the national rate of 30 percent (Southeast Asia Resource Action Center
2020). Such disparities in these socioeconomic measures are tied to migration patterns where many
East Asian American immigrants (e.g., Chinese, Taiwanese, Indian) who arrived in the U.S. post-
1965 were highly educated and skilled, whereas most Southeast Asian American immigrants post-
1965 were overwhelmingly refugees with more limited education and skills (Yang 1999). The
model minority myth has perpetuated stereotypes about the Asian American community that
overshadow the diverse challenges and barriers Asian Americans face. The needs of Asian
American minorities and underrepresented groups, like Southeast Asian Americans, are
consequently disregarded, including in policy arenas. Arguing against the legitimacy of the model
minority identity requires a dialectical analysis of how certain Asian American groups experience
structural and historical inequities shaped by systemic differences in their access to opportunities
and resources. Regarding naturalization, reducing the Asian American immigrant experience to
aggregated statistics ignores intragroup disparities and group-specific barriers in accessing
citizenship. Traditional models in predicting naturalization show educational attainment and
English proficiency to be positively associated with naturalization (Aptekar 2015; Johnson, Reyes,
Mameesh, and Barbour 1999; Yang 1994). However, why then are some groups more limited in
their educational attainment and English proficiency achieving higher rates of naturalization, such
as Lao and Cambodian American immigrants, than their more educated and English proficient
counterparts, including Chinese American immigrants? Diving deeper into how these groups are
different may lead to more insight on what is driving naturalization for some while thwarting
pathways to citizenship for others.
In problematizing the model minority myth and to motivate a more nuanced discussion and
analysis of how Asian American immigrants are experiencing uneven pathways to citizenship, it

107
is important to also understand how Asian Americans have been treated as perpetual foreigners in
the U.S. As Kim (1999) discusses, Asian Americans have long been perceived as perpetual
foreigners and civically ostracized despite their nativity and citizenship status. Many scholars and
historians have argued that such xenophobic attitudes towards Asian Americans have been molded
over decades and reproduced in discriminatory immigration policies, fear-mongering propaganda
of an Asian invasion and “Yellow Peril”, anti-Asian attacks, and segregation from “white flight”
(Kim 1999; Ng et al. 2007; Ong and Nakanishi 1996; Shih et al. 2019). From restrictive policies
that barred Asian Americans from gaining citizenship until 1952 and the internment of Japanese
Americans to the significant underrepresentation of Asian Americans in U.S. politics, American
Orientalism is apparent throughout U.S. history, reifying the perpetual foreigner stereotype that
continues to other Asian Americans (Reflective Democracy Campaign 2021; Volpp 2001).
Research finds that such othering leads to lower psychological well-being and lower sense of
belonging to American culture (Huynh, Devos, and Smalarz 2011). This discrimination may
weaken attachment and lead Asian American immigrants to feel perpetually excluded and less
motivated to navigate the cumbersome process of becoming a citizen in a country that regardless
of their citizenship will be treated as a foreigner. In other cases, Asian American immigrants may
resort to naturalization as a way to combat the perpetual foreigner stereotype and to prove their
“American-ness” especially in times of heightened anti-immigrant activities and sentiment (Ong
2010; Volpp 2001).

Data, Methodology, and Model Specifications
In assembling a sample to examine the mechanisms that drive naturalization among Asian
American immigrants, I exclusively include observations who (1) identify as Asian, (2) report a

108
birthplace from an Asian country with at least 1,000 unweighted observations within the ACS
microdata for sampling power, and (3) are either eligible to naturalize or have naturalized within
the last 2 years of the ACS survey. In extrapolating the contextual factors that impact naturalization
for Asian American immigrants, points one (1) and two (2) are particularly salient as immigrants
integrate and are treated differently depending on their own racial/ethnic identity and how others
view and treat them (Feagin and Elias 2013; Omi and Winant 2018). The term Western Asia itself
is controversial as a vast majority of immigrants from the Western Asian geographical area are
more likely to report being white rather than Asian (Hanna and Batalova 2021; Parvini and Simani
2019). Regarding the second point, immigrants who identify as Asian but may be born in a non-
Asian country may face different challenges and privileges depending on their county of origin’s
relationship with the U.S., resources, and cultural values. For example, a Vietnamese American
immigrant born in Canada would have different privileges than a Vietnamese American immigrant
born in Vietnam, including access to more comprehensive safety nets, English education, and less
incentive to stay permanently as citizens (Anderson and Lee 2005; Dorais 2001). Similarly, I
consciously do not group Pacific Islanders with Asian American immigrants as complex U.S.
relationships and jurisdictions in the pacific requires a deeper analysis that is beyond the scope of
this paper.

The Eligible-to-Naturalize and Recently Naturalized Asian American Immigrants
I draw my sample from the 2016 5-year American Community Survey (ACS) dataset from the
Integrated Public Use Microdata Series USA (IPUMS-USA) (Ruggles et al. 2020). This dataset
provides micro-level data on individual and household characteristics with a large and
representative sample to determine place-based attributes, such as percent of co-ethnic nationals

109
in an area. This dataset provides key measures needed to predict naturalization, including
naturalization status, years in the U.S., socioeconomic and sociodemographic characteristics,
reported educational attainment, reported English proficiency, age at arrival, and birthplace.
Another advantage to this dataset is its ability to link individuals within the same family and
household unit to determine family network effects, like whether an observation is married to a
naturalized citizen.
This dataset is limited, however, as it does not explicitly identify immigrants who are
potentially undocumented. To address this, I rely on a series of logical edits and probability
imputations that build on prior work estimating the undocumented immigrant population (e.g.,
Capps et al. 2013; Pastor and Scoggins 2016; Warren 2014; Jennifer Van Hook et al. 2015).
Previous empirical studies on naturalization, including Yang’s (2002) previous work on
naturalization among Asian American immigrants, often do not account for the likely
undocumented observations in their dataset. This can create statistical noise in the analysis as
undocumented immigrants, despite meeting all other requirements, are not eligible to naturalize as
they lack the required legal status.
After netting out the likely undocumented immigrant observation (see Appendix for details
on imputation strategy), I narrow the pool of observations further to individuals with lawful
permanent resident status who are at least 18 years old and have been in the U.S. for at least five
years, or three years if married to a U.S. citizen. These are the conditions of eligibility that can be
clearly identified in the dataset. I then limit the sample of who naturalized to those who gained
citizenship within two years of completing the ACS survey. Comparing those who are eligible to
all those who have naturalized in the dataset would be inaccurate since socioeconomic status and
human capital (e.g., income, education, and English language proficiency) among immigrants are

110
likely to shift after longer periods of naturalization (Pastor and Scoggins 2012). As such, a finding
of higher income or English language proficiency among all naturalized observations would lead
to questionable evidence that they are significant drivers for naturalization because such gains
could have occurred after gaining citizenship. This allows me to account for possible maturation
bias and naturalization effects that may change an immigrant’s social and human capital. Focusing
on the recently naturalized also provides more accurate interpretations of findings to reflect the
current social, economic, and political climate around immigration and naturalization. After
creating a sample of recently naturalized and eligible-to-naturalize immigrants, I then narrow the
sample further to only include observations who identify as Asian and report a birthplace from an
Asian country.

Estimating the Differential Drivers and Barriers to Naturalizing
With my sample of eligible-to-naturalize and recently naturalized Asian American immigrants, I
apply a series of binomial logit models to determine the impact of specific individual
characteristics, place-based attributes, and networks on the odds of naturalization for each Asian
American immigrant subgroup meeting the sampling threshold: Chinese, Hong Kong, Taiwanese,
Japanese, Korean, Burmese, Cambodian, Indonesian, Lao, Thai, Vietnamese, Bangladeshi, Indian,
Nepalese. A binomial logit model takes a multivariate approach to determine the probability of a
binary outcome (e.g., naturalization) to occur while controlling for a series of parameters (e.g.,
individual, place-based, and network characteristics) to isolate the effects of specific variables with
a maximum likelihood estimation of given observations. Calculating the exponential relationship
of the log-odds, the models predict the average impact a variable has on an individual’s propensity
to naturalize through an odds ratio value. The following equation is estimated where Y is the binary

111
outcome of naturalization, s is the state fixed effect, t is the year fixed effects, and Xi represent
the individual, place-based, and network predictors:

𝒀𝒊 =
𝟏 𝟏 + 𝒆 −(  + 𝑿𝒊 + 𝒔 + 𝒕 + )

I include demographic variables and human capital measures as individual characteristics
that shape pathways to citizenship. Demographic parameters include gender, age at entry (linear
and quadratic forms), years of eligibility (logged), and number of children. Certain demographic
characteristics are associated with higher probability of naturalization, such as being a woman,
whereas other characteristics like age at entry and years since becoming eligible have non-linear
effects (Johnson, Reyes, Mameesh, Barbour, et al. 1999; Yang 1994). Age at entry is controlled
for as immigrants who arrive and attend school in the U.S. at a younger age are able to integrate
and learn English more easily than immigrants who arrive at a later age (National Academies of
Sciences, Engineering, and Medicine 2015; Waldinger 2021). I also control for educational
attainment, employment status, occupational prestige, reported English proficiency,
homeownership status, and household income. Immigrants with greater human capital and
resources are more likely able to afford the costs of naturalizing and are more likely to meet the
English language and U.S. civics and history requirements (Aptekar 2015; Johnson, Reyes,
Mameesh, Barbour, et al. 1999; Yang 1994).
I include the metropolitan unemployment rate for immigrant adults as some adults may
naturalize as a way to increase employment opportunities and earnings (Aptekar 2015; Johnson,
Reyes, Mameesh, Barbour, et al. 1999; Pastor and Scoggins 2012). Since I apply separate models
by country of origin, I do not include the typical place-based parameters that measure country of

112
origin characteristics, including whether it is a TPS-designated or historically refugee-sending
country, the country’s gross domestic product per capita, and whether the country allows dual
citizenship. However, to estimate network effects within communities, I include the concentration
of co-ethnic immigrants in a PUMA (Public Use Microdata Area) to indirectly measure network
resources and information sharing within the local immigrant community (Abascal 2017; Menjívar
2000; Yang 1994). In a separate set of logit models without the state fixed effects, as this would
absorb the effect, the results of the U.S. Presidential Election in 2012 is used as a proxy to measure
the state’s general political leaning during that time (i.e., whether they live in a Democratic-leaning
state or not). Using election results to measure state ideology has been consistently used in political
research due to its simplicity, availability of data, and representation of constituency behavior
(Leogrande and Jeydel 1997). In general, liberal-leaning states express more positive attitudes
towards immigration and are more supportive of policies that promote citizenship (Krogstad 2015).
However, states with more anti-immigrant sentiment may also see an influx of immigrants resort
to defensive naturalization (Cort 2012; Ong 2010).
In estimating family network effects, I include spouse’s citizenship and immigration status
to examine the effects of being married to someone with experiences going through the
naturalization process compared to having an undocumented spouse. Similarly, I control for other
naturalized or undocumented members in the family. ACS provides information on when
immigrants arrived in the U.S., naturalized, and married their spouse. With the ability to connect
observations within the same family unit, this information allows me to more accurately code
spouses’ and family members’ citizenship and immigration status prior to each observation’s
naturalization. Immigrants who have close ties to someone who has naturalized would have access
to invaluable information, whereas immigrants in mixed-status families are likely to experience

113
chilling effects that either disincentive them to naturalize (Aranda et al. 2014; Asad 2020) or
motivate naturalization as a defense mechanism (Amuedo-Dorantes and Lopez 2020; Cort 2012).

Results
In extrapolating the effects of different individual, place-based, and network characteristics on
naturalization, my findings show several mechanisms to be significant predictors for some Asian
American immigrant groups while not for others. Tables 3.1 to 3.3 show these results in detail.

Individual Characteristics
In general, Asian American immigrant men have lower odds of naturalizing compared to women.
This is consistent with immigrant men from China, Hong Kong, Laos, Thailand, and India,
whereas immigrant men from Japan show 35 percent greater odds of naturalizing than their women
counterpart, all else equal. Gender does not show to have a statistical difference for other groups,
such as immigrants from Taiwan, Korea, Cambodia, the Philippines, and Vietnam. Examining age
at entry, Asian American immigrants generally show decreasing odds of naturalizing associated
with older age, but then the odds of naturalizing begin increasing with age at entry for adults who
arrive in the U.S. around 50 years old—likely due to several factors including the language
exemptions given to older immigrants during the naturalization process. This trend is consistent
across Asian American immigrants except for immigrants from Cambodia who see the opposite
effect where age at entry increases the odds of naturalizing until approximately 23 years old then
the odds of naturalizing decline again. Age at entry has no statistical impact for immigrants from
Burma/Myanmar, Indonesia, Thailand, Bangladesh, and Nepal. Consistent with previous research,
Asian American immigrants become less likely to naturalize the longer they wait since becoming

114
eligible. However, this is not a statistically significant predictor for immigrants from Taiwan,
Japan, Korea, Burma, Indonesia, Laos, and Nepal.
For Asian American immigrants in general, human capital is positively associated with
naturalization. However, the results vary across groups. Asian American immigrants with some
college education and a bachelor’s degree have statistically greater odds of naturalizing compared
to their counterparts with less than a high school degree. Disaggregating this effect, however, the
findings show contrasting stories across Asian American immigrant groups. Though immigrants
from Taiwan, Korea, Cambodia, Laos, the Philippines, Indonesia, Thailand, Vietnam, and India
generally show greater odds of naturalizing associated with greater educational attainment,
immigrants from China have lower odds of naturalizing if they have a bachelor’s or advanced
degree, likely a result of the high number of immigrants from China on student visas. For other
groups, educational attainment has no statistical effect on naturalization. In aggregate, for Asian
American immigrants, any level of English proficiency improves the odds of naturalization—
consistent with findings for immigrants in general. There are mixed results when disaggregating
this effect. For immigrants from Japan, Cambodia, Indonesia, Thailand, and Nepal, English
proficiency is not statistically associated with naturalization. Curiously, Lao American immigrants
with greater English proficiency show lower odds of naturalizing—this needs to be further
explored but could potentially be influenced by self-reporting bias on the ACS.
Another measure worth highlighting is the diverse effects income has on naturalization. In
aggregate, household income has a negative association with naturalization for Asian American
immigrants—consistent with findings for immigrants in general and as expected due to the
increasing use of standardized fee waivers when applying for naturalization among low-income
immigrants (Yasenov et al. 2019). Disaggregating the results, this seems to be driven primarily by

115
immigrants from India, whereas all other groups show no statistical relationship. The exceptions
are immigrants from the Philippines who have approximately 5 percent greater odds of naturalizing
for each percentage increase in their household income.

Place-based Attributes
In examining the place-based attributes of where immigrants currently reside, a few parameters
stand out regarding their heterogenous effects across Asian American immigrant groups. Though
the local unemployment rate for immigrants is not statistically significant for immigrants in
general, I find a statistically positive relationship when only looking at Asian American
immigrants. In other words, as the local unemployment rate for immigrants increase, the odds of
an Asian American immigrant naturalizing increases. Again, this finding is driven primarily by
one group, immigrants from Thailand, while other Asian American immigrant groups show no
effect.
The results also show conflicting effects for whether immigrants reside in a Democratic-
leaning state or not. For immigrants in general, living in a Democratic-leaning state is associated
with greater odds of naturalization, whereas there is an opposite effect for Asian American
immigrants in aggregate. However, by disaggregating the analysis, I find this trend is primarily
true for immigrants from Taiwan and the Philippines who, on average, have lower odds of
naturalizing by living in Democratic-leaning states. This needs to be further explored, but could
be due to several reasons, including how they may be clustering in more liberal cities within
conservative states. In contrast, immigrants from Laos, all else equal, have more than twice the
odds of naturalizing if they live in a Democratic-leaning state. All other Asian American immigrant
groups show no statistical relationship. Examining the share of co-ethnic nationals in the local

116
geographical area (i.e., Public Use Microdata Areas), Asian American immigrants have greater
odds of naturalizing if they are in physical proximity to other immigrants of the same ethnic
origin—contrasting with the negative association seen for immigrants in general. Disaggregated,
this effect is only statistically significant for immigrants from Thailand, India, and Nepal.
Results show that the effects of country-of-origin attributes on Asian American
immigrants’ odds of naturalization are mostly consistent with findings for immigrants in general.
As expected, Asian American immigrants who are allowed to have dual citizenship with the U.S.
and their country of origin are statistically more likely to naturalize with approximately 23 percent
greater odds. Regarding social, political, and economic conditions, Asian American immigrants
from TPS-designated countries have about 29 percent lower odds of naturalizing, all else equal,
while immigrants from traditionally-refugee sending countries have 15 percent greater odds of
naturalizing. Country-of-origin GDP per capita, normalized using purchasing power parity, is
statistically and negatively associated with Asian American immigrants’ odds of naturalizing,
indicating that immigrants from wealthier countries are less likely to naturalize. More specifically,
a ten percent increase in country-of-origin GDP is associated with approximately 24 percent lower
odds of naturalizing, all else equal.

Family Network Effects
Relatively novel to empirical studies on naturalization, I find family networks to have statistically
significant effects on naturalization with varying effects across different Asian American
immigrant groups. For Asian American immigrants in aggregate, having a naturalized spouse prior
to one’s own naturalization is associated with 98 percent greater odds of naturalization compared
to having no spouse, whereas having a U.S.-born or LPR spouse reduces the odds of naturalizing

117
by 12 percent and 64 percent, respectively. When disaggregated, this effect is consistent across
Asian American immigrant groups except for immigrants from Hong Kong, Indonesia, Laos, and
Vietnam who do not see any statistical difference from having no spouse at all. However, for these
groups, there is a negative effect on naturalization if married to a lawful permanent resident
compared to no spouse at all. The effects of a naturalized family member aside from a spouse is
also evident. All else equal, having a naturalized member in the family other than a spouse
increases an Asian American immigrant’s odds of naturalizing by 50 percent. However, when
disaggregated, this statistically significant effect is only evident for immigrants from Taiwan,
Korea, the Philippines, Thailand, Vietnam, and India.
As expected, having an undocumented family member is statistically and negatively
associated with naturalization. For Asian American immigrants, all else equal, being married to an
undocumented immigrant reduces the odds of naturalization by 90 percent compared to having no
spouse. This is consistent across Asian American immigrant groups except for Cambodian
American immigrants who show no statistical difference. Beyond the spouse, having an
undocumented family member also has negative implications for immigrants seeking
naturalization. Asian American immigrants with an undocumented family member other than their
spouse, all else equal, have 26 percent lower odds of naturalizing. This effect is stronger for some
groups, including immigrants from Taiwan (48 percent lower odds), Korea (42 percent lower
odds), Burma/Myanmar (47 percent lower odds), the Philippines (40 percent lower odds), Laos
(42 percent lower odds), Thailand (57 percent lower odds), Vietnam (33 percent lower odds), and
Nepal (59 percent lower odds). There are no statistical effects for other groups.

118
Table 3.1: Estimated effects on the odds of naturalization across immigrant groups

All Asian Chinese Hong Kong Taiwanese Japanese
Man/male 0.892
***
0.965
*
0.829
***
0.742
*
0.871 1.347
*

(0.008) (0.016) (0.035) (0.100) (0.092) (0.200)
Age at entry 0.976
***
0.963
***
0.944
***
0.954
**
0.955
***
0.946
***

(0.001) (0.002) (0.005) (0.017) (0.013) (0.015)
Years since eligible (log) 0.783
***
0.784
***
0.880
***
0.724
***
1.028 1.026

(0.004) (0.007) (0.020) (0.057) (0.059) (0.087)
Compared to no spouse
U.S.-born spouse 0.822
***
0.882
***
0.813
*
0.872 0.948 0.687
*

(0.012) (0.027) (0.078) (0.214) (0.176) (0.124)
Naturalized citizen spouse 1.816
***
1.975
***
1.155
*
1.392 1.741
***
1.963
***

(0.023) (0.048) (0.072) (0.282) (0.258) (0.398)
LPR spouse 0.514
***
0.361
***
0.481
***
0.441
***
0.327
***
0.366
***

(0.008) (0.010) (0.030) (0.108) (0.059) (0.093)
Undocumented spouse 0.138
***
0.100
***
0.145
***
0.101
**
0.081
***
0.126
*

(0.004) (0.006) (0.016) (0.079) (0.043) (0.129)
Other naturalized family 1.477
***
1.501
***
1.139
†
1.303 1.657
**
1.752
†

(0.021) (0.039) (0.077) (0.276) (0.280) (0.546)
Other undocumented family 0.664
***
0.739
***
0.878
*
1.001 0.522
*
1.253

(0.010) (0.021) (0.058) (0.279) (0.163) (0.581)
Number of children 1.044
***
1.059
***
1.139
***
1.082 1.088 0.883

(0.004) (0.009) (0.028) (0.088) (0.067) (0.071)
Compared to less than high school
High school degree 1.070
***
1.014 0.885
†
0.795 1.626
†
0.812

(0.014) (0.030) (0.059) (0.219) (0.480) (0.250)
Some college 1.478
***
1.283
***
1.085 0.782 1.716
†
0.719

(0.021) (0.039) (0.082) (0.222) (0.500) (0.222)
Bachelor’s degree 1.557
***
1.323
***
0.816
**
0.816 2.156
**
0.652

(0.025) (0.041) (0.063) (0.228) (0.613) (0.204)
Advanced degree 1.308
***
1.062
†
0.511
***
0.684 2.014
*
0.674

(0.024) (0.037) (0.045) (0.209) (0.587) (0.231)
Compared to unemployed
Not in the labor force 0.926
***
0.843
***
0.786
*
0.599 0.684 0.519

(0.020) (0.037) (0.089) (0.197) (0.186) (0.216)
Employed 1.162
***
1.131
**
1.123 0.712 1.109 0.620

(0.024) (0.047) (0.121) (0.221) (0.292) (0.246)
Compared to speaks Eng. “not at all”
Speaks Eng. “not well” 1.590
***
1.632
***
1.922
***
1.567 1.833
†
1.994

(0.033) (0.069) (0.143) (0.663) (0.627) (2.139)
Speaks Eng. “well” 2.351
***
2.053
***
2.543
***
2.353
*
2.700
**
4.722

(0.050) (0.090) (0.221) (1.008) (0.942) (4.995)
Speaks Eng. “very well” 2.409
***
1.989
***
2.672
***
2.055 2.996
**
4.890

(0.052) (0.090) (0.251) (0.907) (1.070) (5.176)
Speaks Eng. only 1.773
***
1.779
***
2.024
***
1.725 3.183
**
6.111
†

(0.042) (0.088) (0.230) (0.800) (1.241) (6.492)
Homeowner 1.195
***
1.257
***
1.129
*
1.252 1.326
*
1.234

(0.011) (0.022) (0.054) (0.199) (0.166) (0.195)
Household Income (log) 0.993
*
0.980
***
1.014 1.040 1.028 1.046

(0.003) (0.006) (0.014) (0.049) (0.030) (0.055)
Co-ethnic national in PUMA 0.996
***
1.009
***
1.004 0.950 0.998 0.995

(0.001) (0.002) (0.004) (0.091) (0.027) (0.068)
Unemployment rate in metro 0.995 1.024
***
0.991 1.041 0.997 1.006

(0.004) (0.007) (0.022) (0.077) (0.044) (0.066)
Lives in Democratic-leaning state 1.126
***
0.951
*
1.002 0.731 0.618
**
0.849

(0.013) (0.021) (0.070) (0.163) (0.097) (0.171)
State/Year FE Yes Yes Yes Yes Yes Yes
Observations 415182 98225 16863 1359 2636 5712
Coefficients are in odds ratio; standard errors in parentheses; all controls are not listed for legibility of table; status of spouse represents likely status prior to
observation’s own naturalization; coefficient for “Lives in Democratic-leaning state” configured in separate set of models without state fixed effects.
†
p < .1,
*
p < .05,
**
p < .01,
***
p < .001

119
Table 3.2: Estimated effects on the odds of naturalization across immigrant groups

Korean Burmese Cambodian Filipino Indonesian Lao
Man/male 1.037 1.132 1.112 1.004 0.716
†
0.725
*

(0.065) (0.205) (0.165) (0.039) (0.129) (0.101)
Age at entry 0.930
***
1.016 1.045
*
0.958
***
1.014 1.034
*

(0.006) (0.029) (0.020) (0.005) (0.025) (0.017)
Years since eligible (log) 0.955 1.219
†
0.756
***
0.745
***
1.077 0.922

(0.036) (0.128) (0.058) (0.015) (0.113) (0.083)
Compared to no spouse
U.S.-born spouse 0.749
*
0.530 1.584 1.114
†
0.629
†
0.636

(0.085) (0.293) (0.478) (0.068) (0.170) (0.196)
Naturalized citizen spouse 1.361
***
2.845
***
1.987
***
1.898
***
1.428 0.973

(0.122) (0.753) (0.371) (0.105) (0.349) (0.176)
LPR spouse 0.389
***
0.583
*
0.401
**
0.394
***
0.226
***
0.246
***

(0.041) (0.153) (0.112) (0.027) (0.068) (0.065)
Undocumented spouse 0.113
***
0.165
***
0.276
†
0.112
***
0.157
***
0.217
*

(0.023) (0.083) (0.185) (0.017) (0.076) (0.138)
Other naturalized family 1.371
**
1.105 1.308 1.305
***
1.586 0.872

(0.144) (0.309) (0.247) (0.071) (0.498) (0.164)
Other undocumented family 0.580
***
0.531
*
0.769 0.598
***
0.894 0.582
*

(0.068) (0.153) (0.201) (0.035) (0.290) (0.147)
Number of children 1.053 1.199
*
0.935 1.051
**
1.113 1.223
***

(0.037) (0.095) (0.055) (0.019) (0.100) (0.053)
Compared to less than high school
High school degree 1.194 0.965 1.020 1.256
**
3.151
*
0.840

(0.162) (0.242) (0.190) (0.110) (1.665) (0.156)
Some college 1.492
**
1.562 1.493
†
1.577
***
3.270
*
1.221

(0.206) (0.433) (0.326) (0.134) (1.719) (0.272)
Bachelor’s degree 1.881
***
0.903 2.160
*
1.829
***
3.797
*
2.029
*

(0.252) (0.258) (0.651) (0.156) (2.004) (0.592)
Advanced degree 1.689
***
0.782 0.733 1.543
***
3.624
*
1.669

(0.249) (0.328) (0.376) (0.162) (2.017) (0.953)
Compared to unemployed
Not in the labor force 0.814 1.094 1.133 1.059 0.841 1.116

(0.138) (0.509) (0.391) (0.108) (0.401) (0.355)
Employed 1.090 1.035 1.043 1.366
**
1.322 1.104

(0.179) (0.454) (0.340) (0.131) (0.591) (0.339)
Compared to speaks Eng. “not at all”
Speaks Eng. “not well” 1.531
*
7.626
***
1.193 1.506 1.856 0.655
*

(0.261) (3.918) (0.299) (0.389) (2.186) (0.137)
Speaks Eng. “well” 1.729
**
16.418
***
1.557 2.101
**
2.139 0.590
*

(0.308) (8.774) (0.442) (0.534) (2.515) (0.149)
Speaks Eng. “very well” 2.039
***
22.275
***
1.218 2.305
**
1.650 0.401
**

(0.374) (12.702) (0.382) (0.586) (1.956) (0.116)
Speaks Eng. only 2.327
***
7.217
**
0.632 1.744
*
2.185 0.614

(0.458) (4.581) (0.250) (0.455) (2.622) (0.194)
Homeowner 1.319
***
2.025
***
1.317
†
1.132
**
0.791 1.080

(0.086) (0.382) (0.208) (0.045) (0.151) (0.159)
Household Income (log) 1.007 0.980 1.070 1.052
*
1.086 1.022

(0.017) (0.062) (0.064) (0.021) (0.088) (0.056)
Co-ethnic national in PUMA 0.997 0.983 1.051 0.999 1.207 0.993

(0.008) (0.142) (0.059) (0.007) (0.401) (0.070)
Unemployment rate in metro 0.974 0.967 1.051 1.018 1.042 1.000

(0.033) (0.090) (0.047) (0.015) (0.073) (0.042)
Lives in Democratic-leaning state 0.881 1.086 1.154 0.883
*
0.833 2.100
***

(0.071) (0.249) (0.251) (0.052) (0.203) (0.440)
State/Year FE Yes Yes Yes Yes Yes Yes
Observations 8545 1023 1216 16480 1137 1397
Coefficients are in odds ratio; Standard errors in parentheses; all controls are not listed for legibility of table; status of spouse represents likely status prior to
observation’s own naturalization; coefficient for “Lives in Democratic-leaning state” configured in separate set of models without state fixed effects.
†
p < .1,
*
p < .05,
**
p < .01,
***
p < .001

120
Table 3.3: Estimated effects on the odds of naturalization across immigrant groups

Thai Vietnamese Bangladeshi Indian Nepalese
Man/male 0.692
**
0.976 1.283
†
0.914
*
1.189

(0.087) (0.054) (0.167) (0.039) (0.208)
Age at entry 1.014 0.977
***
0.991 0.946
***
0.995

(0.014) (0.007) (0.019) (0.006) (0.038)
Years since eligible (log) 0.735
***
0.692
***
0.570
***
0.883
***
0.993

(0.045) (0.019) (0.034) (0.021) (0.102)
Compared to no spouse
U.S.-born spouse 0.741
*
0.771
†
0.424
*
1.195
†
0.593

(0.113) (0.104) (0.175) (0.112) (0.325)
Naturalized citizen spouse 1.420
*
1.144
†
1.837
***
3.515
***
2.213
**

(0.239) (0.090) (0.332) (0.239) (0.614)
LPR spouse 0.395
***
0.616
***
0.523
**
0.241
***
0.952

(0.090) (0.058) (0.103) (0.017) (0.248)
Undocumented spouse 0.197
**
0.178
***
0.183
***
0.048
***
0.092
***

(0.108) (0.032) (0.052) (0.007) (0.052)
Other naturalized family 1.433
*
1.181
*
1.115 2.241
***
0.971

(0.231) (0.089) (0.209) (0.177) (0.303)
Other undocumented family 0.436
***
0.666
***
0.708
†
0.860
†
0.409
**

(0.107) (0.057) (0.133) (0.074) (0.119)
Number of children 1.049 1.010 0.965 1.128
***
1.094

(0.046) (0.026) (0.057) (0.027) (0.117)
Compared to less than high school
High school degree 0.940 1.069 0.854 0.958 1.371

(0.176) (0.076) (0.163) (0.095) (0.448)
Some college 1.479
*
1.710
***
1.026 1.424
***
1.703

(0.272) (0.139) (0.199) (0.140) (0.566)
Bachelor’s degree 1.547
*
1.460
***
1.088 1.307
**
1.526

(0.285) (0.150) (0.207) (0.119) (0.496)
Advanced degree 1.358 1.356
*
0.803 0.951 0.603

(0.311) (0.192) (0.176) (0.090) (0.225)
Compared to unemployed
Not in the labor force 0.945 1.087 0.714 0.576
***
0.704

(0.281) (0.159) (0.207) (0.066) (0.298)
Employed 1.542 1.233 0.779 0.960 1.316

(0.442) (0.174) (0.215) (0.102) (0.502)
Compared to speaks Eng. “not at all”
Speaks Eng. “not well” 1.118 2.017
***
3.300
***
3.007
***
2.170

(0.401) (0.182) (1.069) (0.465) (2.539)
Speaks Eng. “well” 1.409 2.633
***
4.803
***
4.119
***
4.516

(0.503) (0.276) (1.589) (0.641) (5.196)
Speaks Eng. “very well” 1.368 2.345
***
3.867
***
4.474
***
3.307

(0.500) (0.274) (1.337) (0.703) (3.823)
Speaks Eng. only 1.561 1.573
**
2.086
†
4.121
***
2.919

(0.597) (0.231) (0.867) (0.685) (3.574)
Homeowner 1.252
†
1.025 1.068 1.563
***
1.545
*

(0.154) (0.061) (0.135) (0.076) (0.301)
Household Income (log) 1.048 0.963 0.968 0.940
**
0.944

(0.051) (0.023) (0.044) (0.018) (0.075)
Co-ethnic national in PUMA 1.349
*
1.008 1.033 1.012
*
1.559
*

(0.185) (0.005) (0.052) (0.006) (0.324)
Unemployment rate in metro 1.111
**
1.026 1.075 1.036
†
0.970

(0.045) (0.026) (0.085) (0.019) (0.118)
Lives in Democratic-leaning state 0.961 1.066 1.099 1.011 0.973

(0.149) (0.076) (0.072) (0.208) (0.249)
State/Year FE Yes Yes Yes Yes Yes
Observations 2097 7381 1678 18882 1003
Coefficients are in odds ratio; standard errors in parentheses; all controls are not listed for legibility of table; status of spouse represents likely status prior to
observation’s own naturalization; coefficient for “Lives in Democratic-leaning state” configured in separate set of models without state fixed effects.
†
p < .1,
*
p < .05,
**
p < .01,
***
p < .001

121
Discussion and Conclusion
Research on Asian American immigrants and their integration into the U.S. too often overlooks
within-group heterogeneity. Similar to the critiques of studies on Asian American political
participation (e.g., Masuoka, Ramanathan, and Junn 2019; Ong and Nakanishi 1996), further work
needs to be done to disaggregate the nuanced intragroup differences with Asian American
immigrants’ pathways to citizenship. By disaggregating the traditional models of predicting
naturalization for Asian American immigrants, I find heterogenous effects that provide further
evidence supporting the need to have more nuanced analyses and discussions of the Asian
American immigrant experience. These findings can help shape policies to be more effective and
equitable in addressing group-specific needs in facilitating integration and better representation.
Examining Asian Americans as a monolith can provide inaccurate and erroneous discussions on
how to better improve access to naturalization. In interrogating Asian American immigrants’
paradoxical treatment as model minorities and perpetual foreigners, I examine how disparities in
individual, place-based, and network characteristics have diversely shaped their pathways to
citizenship.
An aggregated analysis of Asian American immigrants show that educational attainment
and English proficiency are both positively associated with naturalization—consistent with
previous analyses of immigrants in general. However, such an assumption across all Asian
American immigrant groups would be inaccurate and leave a significant question unanswered:
why are highly educated groups who also report higher rates of English proficiency exhibiting
lower rates of naturalization (e.g., Chinese American and Japanese American immigrants),
whereas Asian American immigrant groups with limited educational attainment and lower English
proficiency are naturalizing at greater rates (e.g., Cambodian American and Vietnamese American

122
immigrants)? As the results show, while some groups experience socioeconomic successes that
play into the model minority image, they face other challenges gaining citizenship. For Chinese
American immigrants, having a bachelor’s or advanced degree reduces the odds of naturalizing
significantly. This is likely because highly skilled and highly educated immigrants from China
may find more value in retaining their Chinese citizenship so that they have the option to return to
China where their education and skills would provide them more attractive opportunities in
China’s growing economy (Liu 2012). Similarly, the strict prohibition of dual citizenship in hand
with Japan’s already high level of economic development and standard of living are significant
mechanisms shaping pathways to citizenship for Japanese American immigrants who exhibit
significantly lower rates of naturalization (Chiswick and Miller 2009; Yang 2002). Despite the
human capital needed to naturalize, many Asian American immigrants face state-sanctioned
barriers to citizenship that are motivated by nationalist beliefs that plural citizenship threatens state
interests, state sovereignty, and state power (Spiro 2010). The debate on dual and plural citizenship
continues to be relevant as it has implications on lowering barriers to naturalization and expanding
human and political rights transnationally for immigrants who straddle multiple identities rooted
in their belonging to places beyond legal boundaries (Spiro 2010).
Whereas immigrants from China and Japan may grapple with additional social and political
costs from naturalizing in the form of forfeiting their country-of-origin citizenship, other Asian
American immigrants from traditionally sending refugee sending countries like Burma, Cambodia,
and Vietnam may not face this same barrier as they may have little to no intention to return due to
less desirable conditions in their countries of origin (Bloemraad 2006). As such, immigrants from
these countries are exhibiting higher rates of naturalization despite relatively lower human capital.
However, an even more disaggregated look into these Asian American immigrant groups would

123
reveal further heterogeneity on those who arrived with refugee status compared to their
counterparts, such as those who obtained LPR status through an immediate family member.
Vietnamese American immigrants, for example, who arrive as refugees have more limited
educational attainment and English proficiency than Vietnamese American immigrants who
obtained green cards through employment preferences (Mossaad et al. 2018). Similarly, refugees
have less social capital and networks than their counterparts who arrived through family-sponsored
preferences or as immediate relatives of U.S. citizens (Mossaad et al. 2018). So, for many refugees
who seek to naturalize, human and social capital are important mechanisms that can equitably
improve access to citizenship.
Social capital helps to facilitate naturalization as networks provide valuable information
and resources to help navigate the naturalization process. In examining family networks, the results
indicate a significant increase in the odds of naturalizing if an immigrant has close ties to someone
who has already naturalized, including a spouse or other family member. Asian American
immigrant groups who have higher rates of naturalization and are more likely to have come to the
U.S. through an immediate family member thus are more likely to have greater social capital and
access to someone who has already naturalized. Immigrants from Cambodia, Laos, and Vietnam
then may have higher rates of naturalization by supplementing their limited human capital with
their social capital. This logic also applies to immigrants from China, Japan, and Korea who have
lower rates of naturalization compared to other Asian American immigrant groups. Recent
immigrants from these countries are more likely to have come through employment pathways than
through family-sponsored preferences or with green cards as immediate relatives of U.S. citizens
(Hooper and Batalova 2015; Yang 2002); so it is likely that they may lack the family network and
support of naturalized citizens to help facilitate their naturalization (Liu 2012).

124
As much as family networks can help improve access to naturalization, they can also
present challenges. The results allude to how Asian Americans continue to be othered and treated
as perpetual foreigners in the U.S. through state-sanctioned and racialized criminalization of
undocumented immigrants—creating chilling effects on mixed-status families. Asian American
immigrants comprised about 12 percent of the U.S. undocumented population in 2012, a number
that has increased to 14 percent in 2019 and is expected to continue to grow (Buenavista 2014;
Hanna and Batalova 2021). Though a relatively smaller share than the Latinx immigrant
community, Asian Americans represent more than 20 percent of undocumented immigrants who
return or are deported each year (Simanski 2014). The increasing enforcement and policing of
immigrant communities of color, including Asian American communities, have created a chilling
effect that force Asian American undocumented immigrants and their families to live incognito
lives as their fear of disclosure and deportation looms (Buenavista 2018; Kwon 2012). In
examining the differential effects across Asian American groups, it is mostly consistent in that
having an undocumented spouse or family member significantly reduces the odds of
naturalization; except for Cambodian American immigrants who show no statistical effect. This is
interesting considering the increasing deportation of Cambodian American immigrants (Southeast
Asia Resource Action Center 2020); however, this may signal that a more prevalent share of
Cambodian American immigrants are resorting to naturalization as a way to help undocumented
family members adjust status and to secure access to public benefits and government assistance
(Ong 2010).
As discussed, there are several factors that shape immigrants’ access to naturalization, and
they vary across groups as immigrants rely on different resources to address a diverse set of barriers
to citizenship. The model minority myth has created challenges to more nuanced discussions and

125
analyses of the Asian American immigrant community. By taking a more intentional approach in
disaggregating the Asian American immigrant experience, I investigate how Asian American
immigrants’ pathways to citizenship have been tied to their paradoxical treatment as model
minorities and perpetual foreigners. Research like this can provide more comprehensive analyses
to shape policies to be more specific, effective, and equitable rather than relying on a generalized
“one-size-fits-all” approach inflexible to the vastly heterogenous needs of the growing community.
In aggregate, the Asian American immigrant community may seem like model minorities and fully
integrated with higher socioeconomic status and naturalization rate, but a closer look reveals that
Asian American immigrants are facing vastly different barriers to their social, economic, and
political integration that need greater attention.

126
Conclusion and Policy Implications
In this dissertation, I lay the groundwork for future research on immigrant integration by
examining the factors that shape pathways to citizenship and how improving access to
naturalization can help mitigate inequalities experienced along racial/ethnic and gender lines.
From empirical studies to more normative work on the topic, I also discuss what has already been
done in understanding disparities among immigrants including gaps in the literature I believe my
research begins to address. Immigrants’ pathways to citizenship are complex and require nuanced
models and discussions to extrapolate immigrants’ diverse lived experiences. It is also important
to understand that immigrants are not monolithic and that their experiences as immigrants
integrating in the U.S. intersect with other identities and factors, including race/ethnicity, their
social networks, context of reception, and past and present policies. The three essays examine and
highlight the need for policies to be more specific and equitable in improving pathways to
citizenship to address growing social, economic, and political disparities experienced within and
across immigrant groups.
Throughout the three essays, I show and argue that immigrants differ in terms of their
propensities to naturalize due to varying reasons, including different levels of educational
attainment, English language proficiency, and spillover effects from networks. Multiple
mechanisms, policy levers, and tools are thus needed to improve access to naturalization
effectively and equitably. Identifying the factors that drive or thwart naturalization allows
policymakers and advocates to push for a more equitable naturalization process. For example,
though subsidized classes are available across the country to help immigrants improve their
English proficiency and prepare for their citizenship interview and exam, this is a limited resource
(Aptekar 2015). Increasing free and accessible services can substantially help improve

127
naturalization outcomes, particularly for low-income immigrants with limited English proficiency.
It is important, however, to also consider the time and labor costs of these classes, thus inaccessible
to immigrants who may lack the time and resources (e.g., transportation) to attend these classes
even if they are free or subsidized. To improve the accessibility of the naturalization process,
similar to how standardizing and expanding the fee waiver and reduction program has helped
improve naturalization outcomes for low-income immigrants (Yasenov et al. 2019), policymakers
should also consider expanding language exemptions to reduce barriers to citizenship for
immigrants with limited English proficiency.
Additionally, policymakers and immigrant-serving organizations can help improve
naturalization rates by also strategically developing policies and programs that reach immigrant
communities and families without a naturalized member. Having a naturalized spouse, family
member, or close tie can significantly improve an immigrant’s propensity to naturalize. This can
create positive network and spillover effects through pooled resources and information sharing.
Though policy and programmatic strategies to improve naturalization are often associated
with improving immigrants’ individual skills and capital, there is also a need to understand how
unfavorable conditions and anti-immigrant policies have disproportionately impacted some
immigrants seeking naturalization and resources that could improve their odds of gaining
citizenship. Even with the human and social capital needed to successfully naturalize, racially
marginalized immigrant communities face other costs and risks that their white and more
privileged counterparts do not, including the constant fear of deportation. Such immigrants are in
difficult situations because though naturalization could provide an eventual path to permanent
residency for their undocumented family member, they may also be risking immediate separation.
It is important for localities to improve the context of reception for immigrants. Service providers

128
and policymakers should consider how to reach and assist families with undocumented members
in a way that ensures them of their security and safety, including advocating for a revised N-400
form where applicants are not required to disclose family members’ immigration status in a way
that threatens their safety. Legislatively, providing lawful status or pathways to citizenship for
currently undocumented immigrants, similar to IRCA, can also mitigate negative network and
spillover effects that impede immigrants’ access to naturalization, especially for immigrants in
mixed-status families.
Furthermore, I find that many barriers to naturalization and immigrant integration are
complex, multifaceted, and racialized. As policymakers and immigrant-serving organizations
formulate and implement strategies to improve access to naturalization, it is important to have
nuanced discussions on group-specific barriers to citizenship. This requires deliberate
conversations and participatory co-design directly with immigrant communities of interest (e.g.,
Japanese American immigrants, Salvadorian American immigrants, mixed-status families) to
identify key barriers to naturalization and the necessary policies or programs to address them. This
research is a starting point in analyzing barriers to naturalization, but more needs to be done to
further disaggregate immigrants’ diverse pathways to citizenship, including more intentional
analyses on specific immigrant communities and their intersectional identities.
Though I primarily rely on quantitative methods in this dissertation to investigate barriers
to naturalization and integration, I position these essays as groundwork for future mixed-methods
research that will also incorporate surveys, ethnographic interviews, and spatial analyses to further
examine possible micro, mezzo, and macro mechanisms that will lower barriers to immigrant
integration, including the role of immigrant-serving community organizations and more inclusive
forms of local and state citizenship. This approach will allow me to explore generalizable trends

129
and patterns from big data while also contextualizing the results with in-depth case studies and
qualitative findings. A mixed-methods approach in studying immigrant integration and citizenship
is particularly important to tease out other factors shaping pathways to citizenship that are difficult
to measure, like how individuals may value U.S. citizenship differently.
In writing this dissertation, I apply a social equity framework that is shaped primarily by
critical race theory and applied intersectionality, with some elements from the Right to the City
framework and domicile citizenship (i.e., jus domicile). A social equity framework is especially
helpful in explicitly exploring how race/ethnicity, place, and power intersect with issues of
immigrant integration through the lens of public policy. Such a framework is needed to deconstruct
and link existing disparities to past discriminatory policies driving barriers to immigrant
integration. Aptly identifying these institutionalized inequities and their causal mechanisms will
help formulate more effective and just policies. I plan to further develop this framework in future
research on immigrant integration to highlight the complexities of immigration issues and the
importance of equity-oriented policies in improving the livelihood of immigrant communities.

130
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Appendix

Estimating the Undocumented Population
In determining who is likely to be undocumented in the dataset, conditional and logical edits are
applied where each noncitizen, non-Cuban foreign-born observation in the ACS microdata sample
are assigned an initial documented status based on observable characteristics. We exclude Cubans
because until 2017, any Cuban arriving in the U.S. regardless of whether they held a visa was
automatically allowed to pursue legal residency a year after arrival. ACS microdata provides data
on characteristics that signal the observation to be an LPR rather than undocumented including
working in the public sector; having an occupation that requires documents (e.g., police officer);
having received certain public benefits such as social security; or having received food stamps as
a household head or spouse in a household without a child who could have been the lawful
beneficiary of said public assistance (Pastor and Scoggins 2016). Additional parameters that are
included to signal LPR status are observations who immigrated as adults and are currently enrolled
in higher education as they are likely to be student visa holders; observations who arrived before
1982 as they are likely to have gained legal status through the Immigration Reform and Control
Act of 1986; and observations who reported to have received certain government-supported health
services, including Medicare, Indian Health Services, or Veterans Affair Care (Warren 2014).
Through this logical editing process, it can be assumed that observations exhibiting these
characteristics are documented, but it cannot be assumed that any observation not exhibiting these
characteristics are undocumented. We rely on probability edits to impute who in the remainder of
our dataset is likely to be undocumented.
Similar to Capps et al.’s (2013) approach, we develop a statistical model that determines
the probability of being undocumented based on data from the 2014 Survey of Income and

146
Program Participation (SIPP). In the 2014 SIPP, respondents answered a series of
sociodemographic and socioeconomic questions including their LPR status upon arrival. We
utilize a method based on work using the previous 2008 SIPP wave 2 data to identify those most
likely to have arrived without LPR status as likely status adjusters (i.e., eventually achieving LPR
status) with the remainder assumed to be undocumented. In previous work estimating the
undocumented population (e.g., Pastor and Scoggins 2016), the 2008 SIPP (wave 2) was used
because a subset of respondents was directly asked their LPR status on arrival and if they
eventually achieved LPR status. By answering no to both questions, the respondent is assumed to
be undocumented. In the 2014 SIPP, respondents are asked status upon arrival but not asked if
they ever adjusted status. However, the advantage of the 2014 SIPP is that the data is more
consistent with the 2012-2016 5-year ACS microdata. To use the 2014 SIPP, we estimate whether
adjustment had occurred through a conditional editing process that determine which adults are
likely to have adjusted status using key characteristics
6
and estimates consistent with the 2008
SIPP (wave 2).
Based on the estimated characteristics of undocumented immigrants and similar to Van
Hook et al.’s (2015) approach, we use a logistic model where the outcome is undocumented status
and the predictors are a series of sociodemographic and socioeconomic variables including gender,
age, years since arrival, education levels, marital status, whether the respondent’s own children
reside in the home, English ability, and dummy variables for broad region of origin. This model

6
Significant characteristics of status adjusters include arrival with non-permanent status but had
subsequently served in the military; was not a student, but had time in country and completed a
Ph.D.; was in public employment or held a job that likely required documentation; received
Medicare or was receiving Medicaid but had not recently given birth (since there is an exception
for undocumented mothers); was married to a U.S. citizen for ten years or more; or had arrived
before the IRCA cut-off and so was likely to have adjusted status.

147
predicts the impact of each variable on the probability of the respondent being undocumented.
These coefficients, along with sample weights, are then applied to the remaining observations in
the ACS microdata to determine their probability of being undocumented.
In addition to the conditions and probabilities set to each observation, like in Pastor and
Scoggin’s (2016) work, country controls are applied to adjust and standardize the number of
undocumented immigrants by country of origin across different reputable estimates including
those from the Center for Migration Studies (Warren and Warren 2013), the Office of Immigrant
Statistics (Baker and Rytina 2014), and the Migration Policy Institute (Capps et al. 2013). These
standardized estimates for each sending country act as comparable markers for the estimates in
this analysis.
After temporarily netting out LPRs through the logical edits, our probability estimates are
used to determine whether each adult observation in the dataset are either likely documented or
undocumented. In a manner similar to the multiple imputation strategy used by Bachmeier et al.
(2014) and Batalova et al. (2014), we proportionally sample each of the resulting 60 probability
strata (i.e., groups of individuals who share the same probability of being undocumented, rounded
to the nearest whole percentage point) in multiple iterations until each country control (i.e.,
threshold) is met. This is repeated twenty times for each of the sixty strata to obtain a sample that
mimics the probability distribution that we see in the 2014 SIPP sample. To understand the process,
assume that there are only three strata with probability ratios of .6, .3, and .1. In the first round, we
sample from each stratum, taking .6 of those in the first strata, .3 of those in the second strata, and
.1 of those in the third strata. We then go through subsequent rounds until we hit each country
estimated threshold. Each subsequent iteration tags more of the strata’s remaining observation as
undocumented, repeating this until each country control is met. For each country, 5 percent of the

148
observations with the lowest probability of being undocumented are assigned to the last iteration
so the minimum probability in all other iterations may not be the lowest in the sample for that
country. This is done so that observations with the lowest probability of being undocumented are
only tagged if the country controls are not met by the last iteration.

Abstract (if available)

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Asset Metadata

Creator Le, Thai Van (author)

Core Title Diverging pathways to citizenship and immigrant integration in the U.S.

School School of Policy, Planning and Development

Degree Doctor of Philosophy

Degree Program Public Policy and Management

Degree Conferral Date 2021-12

Publication Date 11/17/2021

Defense Date 06/22/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Citizenship,immigrant integration,immigration,Naturalization,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Musso, Juliet (committee chair), Pastor, Manuel (committee member), Schweitzer, Lisa (committee member)

Creator Email thaivanle@gmail.com,thaivle@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC17138482

Unique identifier UC17138482

Legacy Identifier etd-LeThaiVan-10234

Document Type Dissertation

Format application/pdf (imt)

Rights Le, Thai Van

Type texts

Source 20211118-wayne-usctheses-batch-898-nissen (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

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