Does Citizenship Deliver? Persistent Gaps in Employment and Earnings after Naturalization

Does Citizenship Deliver? 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Persistent Gaps in Employment and Earnings after Naturalization Rakkshet Singhaal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6978281/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines whether naturalization improves employment and income outcomes for immigrants in the United States. Drawing on 22 years of panel data from the National Longitudinal Survey of Youth 1997 (NLSY97), this analysis tracks 313 naturalized immigrants to assess changes in employment status, labor force participation, annual income, and year-over-year income growth. Contrary to prevailing assumptions, findings indicate that naturalization does not produce consistent or positive economic returns. On average, naturalization is associated with a 9.7 percentage point decline in employment rates and an 11.8 percent decrease in annual income. Income growth also drops by 4.2 percentage points post-naturalization. Disaggregated analyses reveal that women experience an 18 percent income drop and a 10.2 percentage point reduction in employment, while non-White immigrants see a 10.5 percentage point drop in employment and a 6.8 percentage point decline in proportion of weeks worked. Dynamic year-by-year models further demonstrate that these adverse effects persist or even worsen over time. These findings challenge the assumption that citizenship alone reliably facilitates economic mobility, highlighting significant limitations of naturalization as a standalone strategy for immigrant integration. Naturalization Citizenship and economic integration Immigrant earnings Longitudinal immigrant data Employment post-naturalization Economic mobility Citizenship premium Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Citizenship acquisition through naturalization stands at the heart of immigrant integration in the United States, symbolizing full legal inclusion and unlocking a range of social, political, and economic rights. As of 2024, approximately 31.6 million immigrants in the United States were either naturalized citizens or eligible to become citizens, with around 7.4 million eligible immigrants yet to naturalize, representing 33.6% of the noncitizen population (American Immigration Council, 2024 ). Historically, citizenship acquisition in the United States has been influenced by racial and socio-economic barriers, starting from the Naturalization Act of 1790, which limited citizenship eligibility to “free white persons” and established a legacy of exclusion based on race and ethnicity. Subsequent policies, including the Chinese Exclusion Act and the Immigration Act of 1924, reinforced these racial and ethnic exclusions. While explicit racial criteria have since been abolished by legislative reforms, notably the Immigration and Nationality Act of 1965, contemporary institutional barriers like high naturalization fees, language proficiency requirements, and bureaucratic complexity continue to disproportionately affect immigrants from disadvantaged racial and socio-economic backgrounds, perpetuating historical inequalities (Dhillon, 2023 ; Bloemraad, 2002 ; Ngai, 2014 ). Scholarly research provides substantial evidence supporting the notion that citizenship can positively affect labor market integration, employment stability, and wage growth. Studies highlight a “citizenship premium,” indicating that naturalized citizens earn higher wages and enjoy greater employment security than non-citizens, even after accounting for education, work experience, and English proficiency (Bratsberg, Ragan & Nasir, 2002 ; Sumption & Flamm, 2012 ). Pastor and Scoggins ( 2016 ) documented immediate wage increases of 5–7% following naturalization, expanding to over 10% after a decade, with substantial broader economic impacts projected from increased consumer spending and enhanced tax revenues. Similarly, Sumption and Flamm ( 2012 ) estimated a citizenship premium of approximately 5–8%, attributing this to greater labor market access and reduced employment discrimination. Enchautegui and Giannarelli ( 2015 ) reported an average individual earnings increase of approximately 8.9% for immigrants upon naturalization. International research further corroborates these findings, underscoring consistent economic advantages linked to naturalization across diverse contexts, including Germany, Canada, and several EU countries, which have documented enhanced labor market participation and wage growth following naturalization (Steinhardt, 2012 ; Stadlmair, 2018 ; OECD, 2011 ). Despite widespread acknowledgment of these general economic benefits, current literature has notable limitations. Most research utilizes cross-sectional data or short-term observations, often masking critical variations across demographic and socio-economic subgroups. The assumption that naturalization universally improves economic standing overlooks potential persistent or even exacerbated inequalities, especially for women, racial minorities, and low-income immigrants. For instance, Enchautegui and Giannarelli ( 2015 ) highlighted significant variability in economic benefits by immigrant subgroup and location, emphasizing greater relative gains for younger or lower-income immigrants. Additionally, research on dual citizenship policies underscores varying effects on immigrants’ economic trajectories depending on country of origin and personal circumstances, further highlighting the complexity of citizenship’s economic impact (Mazzolari, 2009 ). Thus, understanding how economic outcomes evolve over the long-term post-naturalization and how these effects vary by intersectional identities remains crucial and insufficiently explored (Gathmann, 2020 ; Steinhardt, 2012 ). This paper addresses these limitations by employing longitudinal data from the National Longitudinal Survey of Youth 1997 (NLSY97), examining within-person changes before and after citizenship acquisition over two decades. This approach uniquely captures nuanced shifts in employment status, income levels, and earnings trajectories, allowing for disaggregated analysis by gender, race, and other critical demographic variables. By tracking individual labor market outcomes over time, the analysis is able to distinguish the effects of naturalization from confounding factors, providing a more accurate and dynamic portrait of how citizenship reshapes economic experiences. The findings challenge prevailing assumptions by revealing persistent gaps and, in some cases, negative effects associated with naturalization. Contrary to expectations, naturalization is associated with significant declines in employment rates (approximately 9.7 percentage points), lower annual earnings (an average decrease of about 11.8%), and decreased income growth (a reduction of 4.2 percentage points in the probability of income improvement), effects most pronounced among women and non-white immigrants. For example, women experience an employment rate reduction of about 10.2 percentage points and a notable income decrease of approximately 18%. Non-white respondents similarly face substantial reductions, including an employment rate decline of about 10.5 percentage points and a decrease in the proportion of weeks worked by 6.8 percentage points. These disparities indicate that naturalization does not offer a uniform pathway to economic mobility; rather, its economic benefits are mediated by structural inequalities embedded in labor market institutions and intersecting forms of marginalization. The longitudinal design further reveals that these negative outcomes persist over time, suggesting that naturalization does not catalyze the upward economic trajectory frequently promised in integration narratives. Instead, it may be operating within a system where existing disparities are simply carried forward under a new legal status. This study significantly contributes to existing literature by providing detailed empirical insights into the differential economic impacts of naturalization. By exploring intersectional dynamics, it reveals the limits of citizenship as a tool for economic integration and underscores the critical need for targeted policies addressing the underlying structural inequalities faced by marginalized immigrant populations. Specifically, the findings suggest that policy efforts should not only promote access to naturalization but also address post-citizenship barriers in employment and wage advancement—particularly for women and racial minorities. The analysis thus informs both scholarly discourse and practical policy-making aimed at achieving genuine immigrant economic integration and equality in labor market outcomes. The paper proceeds as follows: next section presents a comprehensive literature review covering the historical, institutional, and economic dimensions of naturalization. Subsequent sections describe the data and methodology employed, followed by detailed results examining both overall effects and subgroup variations in labor market outcomes. The paper then discusses implications of these findings for policy and research, concluding with suggestions for future study directions. Literature Review Naturalization in United States Naturalization, the process by which foreign-born individuals become citizens, has been a core mechanism through which immigrants are formally integrated into U.S. society. The motivations, processes, legal frameworks, and barriers associated with naturalization have evolved significantly, reflecting both historical contexts and ongoing policy debates. Historically, U.S. naturalization laws have been deeply entangled with race and ethnicity. The Naturalization Act of 1790 set early precedents by limiting citizenship eligibility to “free white persons,” embedding racial hierarchies within citizenship criteria. Subsequent legal milestones, including the Chinese Exclusion Act and the Asiatic Barred Zone, reinforced racial barriers, particularly targeting Asian immigrants and explicitly linking citizenship eligibility to racial identity (Dhillon, 2023 ). Such exclusions underscored citizenship as both a legal and symbolic reinforcement of racial capital, privileging white individuals and systematically excluding others, which reshaped racial boundaries and influenced U.S. legal definitions of alienage and belonging (Dhillon, 2023 ). In the decades following, significant legal reforms gradually dismantled explicit racial criteria from U.S. immigration and naturalization laws. Landmark legislation, such as the Immigration and Nationality Act of 1965 (also known as the Hart-Celler Act), eliminated national origins quotas, marking a pivotal shift towards a more inclusive immigration policy. This act substantially diversified the immigrant population by removing previous racial and ethnic restrictions and prioritizing family reunification and skilled labor (Lee, 2015 ). Despite these legal advancements, racial disparities and barriers persist in contemporary immigration processes, reflecting historical legacies. For example, contemporary debates and policies around border control, refugee admissions, and pathways to citizenship often implicitly reflect ongoing racial and ethnic tensions (Ngai, 2014 ). Current naturalization processes continue to be shaped by historical contexts and institutional barriers, including high application fees and language proficiency requirements, which disproportionately impact immigrants from disadvantaged socio-economic and racial backgrounds, perpetuating historical inequities within citizenship access (Bloemraad, 2002 ; Hainmueller et al., 2018 ). These structural barriers, however, exist alongside powerful personal and practical incentives that continue to drive immigrants toward naturalization. Motivations for immigrants to pursue U.S. citizenship are multifaceted, integrating individual aspirations, socio-economic benefits, and political or personal security. Immigrants often perceive naturalization as a gateway to better employment opportunities, social stability, political participation, and civic integration (Bloemraad & Sheares, 2017 ). For many, the decision to naturalize stems from a desire to fully participate in the political and civic life of their adopted country, gaining not only legal recognition but also a sense of equality with native-born citizens (Bloemraad & Sheares, 2017 ). Access to federal jobs and other employment opportunities that require citizenship is a powerful incentive, particularly in sectors such as civil service, law enforcement, and defense, where stable, well-paying jobs may be out of reach for non-citizens (Hainmueller et al., 2018 ). Naturalization also enables immigrants to petition for immediate family members to immigrate to the U.S., facilitating family reunification and enhancing household security (Yang, 1994 ). Equally important are the protections that citizenship affords. Naturalized individuals are protected from deportation, even if they face legal challenges, offering a degree of permanence and peace of mind not available to lawful permanent residents (Bloemraad & Sheares, 2017 ). This legal security allows immigrants to invest more confidently in their futures, whether by purchasing homes, pursuing long-term careers, or furthering their education (Sumption & Flamm, 2012 ). Moreover, naturalization enhances civic integration by enabling full participation in democratic processes through voting and running for office, allowing immigrants to advocate for their communities and influence policies that affect their lives (Bloemraad & Sheares, 2017 ). This civic empowerment fosters a stronger commitment to civic duties and enhances feelings of social responsibility (Pastor & Scoggins, 2016 ). The process of preparing for naturalization—learning U.S. history, government, and language—also contributes to civic and cultural assimilation, equipping immigrants with tools for more effective participation in American society (National Academies of Sciences, Engineering, and Medicine, 2015 ). It also highlights that increased citizenship rates enhance national cohesion and economic productivity by more fully integrating immigrants into American societal structures. Finally, philosophical perspectives emphasize the egalitarian and symbolic importance of citizenship. Sharp ( 2023 ) argues that naturalization serves as a crucial mechanism for combating social hierarchies, promoting equality, and fostering social solidarity, and contends that citizenship goes beyond mere legal status, embodying a public commitment to social equality and democratic participation. In this way, the motivations to naturalize are not solely about practical gains but are deeply embedded in the pursuit of dignity, inclusion, and long-term stability (Bloemraad & Sheares, 2017 ). However, naturalization rates in the U.S. remain notably lower compared to other immigrant-receiving countries like Canada, Australia, and the United Kingdom. Bloemraad ( 2002 ) highlights institutional barriers as pivotal factors behind this gap. Unlike Canada’s active government-sponsored integration programs, the U.S. exhibits a less supportive institutional framework, with fewer public resources dedicated to immigrant outreach, linguistic integration, and citizenship promotion, leading to a significant “naturalization gap” between the two North American neighbors (Bloemraad, 2002 ). Empirical research has underscored significant barriers faced by low-income immigrants in the naturalization process. Financial costs, particularly the high application fee (currently $ 725), significantly deter eligible immigrants from applying, demonstrating the financial dimensions of citizenship barriers. A randomized controlled trial within the NaturalizeNY program indicated that providing fee vouchers substantially increased naturalization application rates among low-income immigrants, clearly identifying economic barriers as major deterrents to citizenship (Hainmueller et al., 2018 ; Shashkevich, 2018 ). Non-financial obstacles also significantly deter naturalization, particularly among the poorest groups, who face challenges like language barriers, limited awareness of eligibility and processes, fear, and a lack of accessible assistance. This reveals the complexity of citizenship barriers that extend beyond financial concerns (Immigration Policy Lab, 2018 ). Yang’s foundational research (1994) articulates that immigrants’ decisions to naturalize are deeply influenced by both individual-level factors—such as education, English proficiency, and socio-economic integration—and broader contextual influences, including political conditions in countries of origin and reception. Yang emphasizes that political refugees and immigrants escaping repressive regimes are especially inclined toward citizenship acquisition, underscoring the intersection of individual experiences with larger geopolitical contexts (Yang, 1994 ). The literature collectively underscores that naturalization in the U.S. is a complex interplay of historical legacies, individual motivations, and institutional frameworks. Scholars have thoroughly examined the socio-cultural and political dimensions of naturalization, revealing how citizenship shapes identity, belonging, and civic engagement. Equally vital is an exploration of naturalization’s role in facilitating immigrants’ economic integration—particularly through its influence on wage growth, employment stability, and access to higher-quality job opportunities across diverse populations. Naturalization Impact on Employment and Income Having established the multifaceted nature of naturalization—shaped by historical exclusion, institutional structures, and personal motivations—it is equally important to examine how the acquisition of citizenship translates into tangible economic outcomes. Naturalization not only marks a legal and symbolic transition but also plays a critical role in shaping immigrants’ economic trajectories, particularly through enhanced labor market integration, employment stability, and long-term income growth Empirical research consistently demonstrates positive impacts of citizenship on labor market outcomes. Bratsberg, Ragan, and Nasir ( 2002 ) provided compelling evidence, highlighting accelerated wage growth among naturalized immigrants compared to non-citizens, primarily due to increased access to higher-paying positions in the public sector or jobs requiring security clearance, alongside reduced employer discrimination (Bratsberg et al., 2002 ). Sumption and Flamm ( 2012 ) quantified the “citizenship premium,” finding that naturalized immigrants earn approximately 5–8% more than their non-citizen counterparts, even after controlling for education, language proficiency, and work experience. This wage premium reflects the compounded advantages that come with legal security, increased eligibility for a wider range of jobs—including those in the public sector or requiring security clearance—and the positive signaling effect that citizenship confers in the labor market. Employers may interpret citizenship as a sign of long-term commitment, cultural assimilation, and reliability, which can enhance job prospects and upward mobility. Additionally, the protections afforded by citizenship may empower individuals to take greater risks in pursuing promotions, changing employers, or investing in further education or training, thereby accelerating their economic advancement over time (Sumption & Flamm, 2012 ). Pastor and Scoggins ( 2016 ) identified a substantial wage premium associated with naturalization, noting immediate increases of 5–7% within two years, expanding to over 10% after ten years. Their research projected significant broader economic impacts, suggesting that widespread naturalization could potentially generate between $ 20–45 billion in economic growth over a decade through increased consumer spending and enhanced tax revenues (Pastor & Scoggins, 2016 ). Further reinforcing these findings, Enchautegui and Giannarelli ( 2015 ) reported an earnings premium ranging from 8–11%, utilizing rigorous econometric methods like propensity score matching. Their study emphasized substantial economic mobility benefits for younger or lower-income immigrants, significantly contributing to local economies via increased consumer expenditure and tax contributions (Enchautegui & Giannarelli, 2015 ). Mazzolari ( 2009 ) provided additional nuance by examining the implications of dual citizenship policies. Her findings indicated that dual citizenship notably boosted naturalization rates and economic outcomes, as immigrants no longer faced the deterrent of renouncing their original citizenship, thus enabling greater economic integration (Mazzolari, 2009 ). Additionally, Gathmann ( 2020 ) synthesized global research confirming substantial benefits of naturalization, particularly for first-generation immigrants. Their analysis underscored significant wage growth, employment stability, and upward mobility, especially pronounced for immigrants from developing nations and women. This improvement is often driven by increased investment in host-country-specific skills, enhancing employability and earnings (Gathmann, 2020 ). International evidence from Germany, presented by Steinhardt ( 2012 ), similarly underscores immediate positive wage impacts and accelerated wage growth post-naturalization, reinforcing the global applicability of these findings. In the German context, naturalization leads to particularly strong gains for immigrants from non-EU countries who face higher structural barriers. Citizenship signals a more permanent legal status to employers, boosting their willingness to hire or promote naturalized individuals. It also encourages immigrants to make long-term investments in country-specific capital—such as professional training, language acquisition, and social networks—further enhancing economic integration over time (Steinhardt, 2012 ). These effects are not limited to Germany. A comparative study by Stadlmair ( 2018 ) across nine EU countries—including France, the Netherlands, and the United Kingdom—finds that naturalization yields measurable labor market benefits, especially for immigrants from lower-income regions. Naturalized citizens were more likely to experience upward occupational mobility and income growth, attributable not only to reduced discrimination but also to access to public sector jobs and increased social legitimacy. However, many European countries impose economic criteria—such as proof of stable employment or income levels—as prerequisites for naturalization, which can restrict access for more vulnerable immigrants. Still, those who succeed in naturalizing tend to see significant long-term economic improvements. The OECD ( 2011 ) further supports these conclusions, reporting that in countries like Canada, Australia, and the Nordic states, naturalized immigrants outperform non-citizens with similar backgrounds in terms of employment rates and earnings. These gains persist even after controlling for education and other covariates, suggesting that the act of naturalization itself—through legal security, labor market signaling, and civic inclusion—carries independent economic value. Collectively, these international findings affirm that the economic advantages of citizenship are not unique to the United States but are observable across varied institutional and policy environments. Collectively, existing studies leave little doubt that acquiring U.S. citizenship is associated, on average, with higher earnings, faster wage growth, and more stable employment. Yet most of this evidence reports mean effects for broad immigrant populations, relying largely on cross-sectional data or short panels that mask differences across intersections of race, gender, class of admission, and sector of employment. Far less is known about how the economic dividends of naturalization unfold over time, whether they are concentrated in particular industries, and which sub-populations benefit—or fail to benefit—the most. The present study tackles these unanswered questions by exploiting two decades of longitudinal NLSY97 data and a within-person analytic design that traces labor-market trajectories before and after naturalization. By disaggregating results by gender, race, and sector—and by charting year-by-year effects—this paper provides a more precise and nuanced portrait of citizenship’s economic payoff, generating evidence that can guide finely targeted integration and workforce policies. Methodology Data Sample This study draws on data from the NLSY97, a nationally representative panel consisting of approximately 9,000 individuals born between 1980 and 1984. The NLSY97 began with annual interviews in 1997 and shifted to a biennial schedule after 2011, continuing to follow respondents into their mid-forties. It provides extensive, time-varying information on employment, income, educational attainment, family structure, geographic mobility, and legal status, including U.S. citizenship. Of particular relevance to this analysis, the survey allows researchers to track the exact timing of naturalization alongside detailed labor market trajectories. The analytical sample includes all foreign-born respondents in the NLSY97 who reported becoming naturalized citizens during the survey period. This results in a focus group of 313 individuals who transitioned from non-citizen to citizen status over the course of 22 years. Rather than comparing naturalized individuals to non-citizens, the study uses within-person variation to examine how economic outcomes evolve in the post-naturalization period. This design allows for a focus on changes over time for the same individuals, minimizing bias due to unobserved, time-invariant individual characteristics. The goal is to estimate the average and subgroup-specific effects of naturalization on labor market performance, accounting for both observable covariates and broader time trends. Variables Dependent variables : The analysis focuses on four key outcome variables that reflect employment quantity and earnings dynamics. Employment status is defined as a binary indicator of whether an individual was employed for the majority of the year in a given survey period. Proportion of weeks employed measures the intensity of labor force attachment, calculated as the number of weeks an individual worked at least 20 hours in a given year divided by 52, resulting in a normalized value ranging from 0 to 1. Log income reflects the logged value of total annual earnings and is used to evaluate changes in income on a proportional scale. Year-over-year income growth is captured as a binary variable, indicating whether the respondent earned more income than in the previous year. Together, these variables provide a comprehensive picture of both employment stability and wage progression following naturalization. Independent variables : The key explanatory variable is a binary indicator for post-naturalization status, equal to 1 for all years after an individual has become a U.S. citizen and 0 for all years prior. This variable serves as the treatment in a quasi-difference-in-differences framework, allowing the analysis to estimate how outcomes shift after the acquisition of citizenship. In addition to this binary treatment variable, analysis is conducted on a variable for years since naturalization, capturing the number of years that have elapsed since an individual became a citizen. This specification enables the analysis to go beyond average treatment effects and examine how the impact of naturalization evolves over time, identifying whether economic benefits emerge immediately, accumulate gradually, or diminish in the long run. Covariates : A rich set of time-varying covariates is included to account for alternative explanations and to isolate the effect of naturalization from other factors known to influence labor market outcomes. Age is included to capture life-cycle effects, as labor force participation and earnings typically vary with experience and seniority. Sex accounts for well-documented gender disparities in employment opportunities, wage levels, and occupational sorting. Race is controlled for given persistent racial and ethnic inequalities in the U.S. labor market, which can shape both access to employment and returns to education or skills. Household size serves as a proxy for economic need and caregiving responsibilities, both of which may influence labor force attachment. Urban versus rural residence is included to reflect geographic variation in labor market structure, job availability, and access to services, all of which may affect employment outcomes. Educational attainment is controlled as a key dimension of human capital, directly affecting employability and earning potential. English fluency, both in reading and speaking, is a critical measure of linguistic integration and communication ability, which strongly influence access to higher-skilled jobs and career advancement. Years since naturalization is incorporated to track the temporal dimension of citizenship effects, acknowledging that any economic benefits associated with naturalization may take time to manifest. Finally, survey year fixed effects control for broader macroeconomic shifts, such as recessions, policy changes, or labor market shocks, that may impact all individuals regardless of their personal characteristics. These controls are theoretically grounded in models of human capital, segmented labor markets, and structural inequality, and empirically justified by decades of research identifying them as key determinants of employment and income dynamics among immigrant populations. Estimation Model In order to estimate the effects of naturalization, the analysis employs Ordinary Least Squares (OLS) regression. Given the longitudinal structure of the data, standard errors are clustered at the individual level to adjust for repeated observations and serial correlation. The core model is specified as follows: $$\:{Y}_{it}={\beta\:}_{0}+{\beta\:}_{1}CitizenshipStatu{s}_{it}+\:{X}_{it}\:+{\delta\:}_{t}+{ϵ}_{it}$$ In this equation, \(\:{Y}_{it}\) represents the labor market outcome of interest for individual i in year t , including employment status, proportion of weeks employed, log income, or year-over-year income growth. The variable \(\:CitizenshipStatu{s}_{it}\) is a binary treatment indicator equal to 1 if individual i has naturalized by year t , and 0 otherwise. This variable captures the average effect of acquiring U.S. citizenship on economic outcomes, holding other factors constant. The vector \(\:{X}_{it}\) includes a rich set of time-varying covariates that are theoretically and empirically linked to labor market outcomes. These include age, sex, race, household size, urban or rural residence, educational attainment beyond high school, English reading and speaking fluency, and survey year fixed effects ( \(\:{\delta\:}_{t}\) ), which controls macroeconomic shocks and temporal variation. The error term \(\:{ϵ}_{it}\) captures unobserved, time-specific individual shocks. In addition to the binary treatment indicator, an extended specification incorporates a variable for years since naturalization, denoted as \(\:Yea{rsSinceNaturalization}_{it}\) . This variable allows for dynamic treatment effects by estimating separate coefficients for each year after naturalization, rather than imposing a constant average effect. Including \(\:Yea{rsSinceNaturalization}_{it}\) enables the analysis to assess whether the impact of citizenship status is immediate, delayed, accumulative, or possibly declines over time. This is particularly important in testing assumptions about the long-term benefits of naturalization and whether economic integration improves gradually after legal status changes. Results Descriptive Statistics Table 1 reports summary statistics from the final wave of the NLSY97 panel (2022) for the sample of 313 naturalized immigrants who are the focus of this analysis. These descriptive measures highlight several characteristics that are relevant to understanding the potential labor market implications of naturalization. The average age of respondents is just over 40 years, consistent with their mid-career status and the fact that many were naturalized as adults. The gender distribution is evenly split between men and women, allowing for balanced gender comparisons in subgroup analyses. Approximately 98% of the sample resides in urban areas, suggesting strong geographic concentration in metropolitan labor markets. Racially, 65% of the sample identifies as non-white, reflecting the diversity of recent U.S. immigration patterns. Educational attainment is moderate, with 44% reporting education beyond high school—a factor that may influence earnings potential and access to higher-quality employment. Just under three-quarters of respondents report fluent English reading skills (74%) and slightly fewer report fluency in speaking English (68%), which are important indicators of linguistic integration. On average, respondents live in relatively large households (mean household size of 6.3) and report having 1.22 biological children, highlighting potential caregiving responsibilities and economic pressures. A majority (59%) are currently married, which may shape household income dynamics and labor supply decisions. The data reveal a sample of naturalized immigrants who are demographically diverse, linguistically proficient to varying degrees, and integrated into urban labor markets—but who also face potential structural constraints related to education, racial inequality, and family responsibilities. These characteristics offer important context for interpreting the patterns observed in the subsequent regression analyses. Table 1 Descriptive Statistics from Panel Survey Data (Final Wave: 2022) (N = 313) Variables Mean Stan. Dev. Minimum Maximum Age 40.04 1.45 38 42 Sex 0.5 0.5 0 1 Residence 0.98 0.14 0 1 Race 0.65 0.48 0 1 Education Beyond High School 0.44 0.5 0 1 Currently Married 0.59 0.49 0 1 Number of Biological Child 1.22 1.3 0 6 Household Size 6.3 2.08 0 19 Fluently Reads English 0.74 0.44 0 1 Fluently Speaks English 0.68 0.47 0 1 Father’s Education Beyond High School 0.5 0.5 0 1 Mother’s Education Beyond High School 0.43 0.5 0 1 Descriptive statistics are based on the final wave (2022) of the NLSY97 for 313 individuals who became naturalized U.S. citizens. All variables are measured at the individual level. “Sex” is a binary variable coded as 1 = Female, 0 = Male. “Residence” is coded as 1 = Urban, 0 = Rural. “Race” is coded as 1 = Non-White, 0 = White. “Education Beyond High School” is coded as 1 = Yes (any education beyond a high school diploma), 0 = No. “Currently Married” is coded as 1 = Yes, 0 = No. “Fluently Reads English” and “Fluently Speaks English” are both binary variables coded as 1 = Yes, 0 = No, based on self-reported English proficiency. “Father’s Education Beyond High School” and “Mother’s Education Beyond High School” are each coded as 1 = Yes (parent had postsecondary education), 0 = No. “Household Size” counts all cohabiting members, including the respondent. These variables offer contextual insight into human capital, family structure, and linguistic integration, which are relevant to labor market outcomes. Post-Naturalization Effects Table 2 presents the baseline OLS estimates examining the relationship between naturalization and four core labor market outcomes: employment status, proportion of weeks employed, logged income, and year-over-year income growth. These models control for a comprehensive set of covariates, including age, sex, race, education, household size, English fluency, urban residence, years since naturalization, and survey year fixed effects. All standard errors are clustered at the individual level to account for repeated observations in the panel structure. Table 2 Effect of Naturalization on Employment and Income Variables Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) (1) (2) (3) (4) Post-Naturalization -0.097 *** -0.057 ** -0.118 * -0.042 * (0.034) (0.028) (0.061) (0.022) Age Controlled Controlled Controlled Controlled Sex Controlled Controlled Controlled Controlled Race Controlled Controlled Controlled Controlled Household Size Controlled Controlled Controlled Controlled Residence Controlled Controlled Controlled Controlled Education Controlled Controlled Controlled Controlled English Fluency Controlled Controlled Controlled Controlled Years since Naturalization Controlled Controlled Controlled Controlled Survey Year Controlled Controlled Controlled Controlled R-squared 0.284 0.374 0.110 0.069 Root MSE 0.424 0.362 0.971 0.482 Observations 8764 8764 8451 8764 This table reports coefficients from OLS regression models estimating the average impact of U.S. naturalization on four economic outcomes: (1) Employment Status – binary variable for whether the respondent was employed during the survey year; (2) Proportion of Weeks Employed – the number of weeks worked divided by 52; (3) Log Income – natural logarithm of total annual earnings to normalize income distribution; and (4) Year-over-Year Income Growth – binary indicator of whether income increased compared to the previous year. “Post-Naturalization” is a treatment variable coded 1 for all years after citizenship acquisition. All models control for age, gender, race, household size, urban residence, education, English fluency (reading and speaking), years since naturalization, and survey year fixed effects. Standard errors are clustered at the individual level to account for repeated measures. * p < 0.10, ** p < 0.05, *** p < 0.01 Contrary to the widely held expectation that naturalization facilitates economic mobility, the estimates reveal a consistent pattern of negative and statistically significant associations between citizenship acquisition and labor market outcomes. Specifically, naturalization is associated with a 9.7 percentage point decline in the probability of being employed, significant at the 1% level. A similar pattern is observed for employment intensity, where naturalization is linked to a 5.7 percentage point reduction in the proportion of weeks worked during the year (p < 0.05). These results suggest that naturalization does not, on average, lead to improvements in labor force attachment—and may, in fact, coincide with employment decline. The income-based measures further reinforce this pattern. The log income model shows that naturalization is associated with an 11.8% decrease in annual earnings, a result that is marginally significant at the 10% level. Year-over-year income growth also appears to be negatively affected, with a 4.2 percentage point reduction in the probability of income improvement relative to the prior year. While these magnitudes are modest, the consistency of the negative direction across all outcome measures challenges the assumption that citizenship acquisition reliably enhances economic prospects. It is important to note that these results persist even after adjusting for an extensive set of individual-level covariates, suggesting that the observed declines are not attributable solely to shifts in age, education, or household context. The findings raise important concerns about the prevailing policy narrative that equates legal integration with labor market advancement. Heterogeneity Results While the baseline models reveal average negative effects of naturalization on employment and income, these effects may not be uniformly experienced across all groups. To assess the distribution of outcomes, Tables 3 and 4 disaggregate the effects by gender and race, respectively. These subgroup analyses provide critical insight into whether naturalization functions as an equalizing force or whether its economic returns vary systematically across social lines. Table 3 displays regression results separately for male and female respondents. For women, the effects of naturalization are consistently more negative and statistically significant across all four outcome measures. Naturalization is associated with a 10.2 percentage point drop in employment probability (p < 0.05), a 6.2 point reduction in weeks worked, a significant 18% decrease in logged income, and a 7.5 percentage point decline in year-over-year income growth (p < 0.05). These findings suggest that for women, acquiring citizenship may not only fail to deliver labor market benefits—it may coincide with material setbacks. In contrast, the effects for men are generally smaller and statistically weaker. While the signs of the coefficients for men are also negative, only the employment status effect (− 9.7 percentage points, p < 0.10) reaches marginal significance. Table 3 Effect of Naturalization on Employment and Income by Gender Male Female Variables Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) (1) (2) (3) (4) (5) (6) (7) (8) Post-Naturalization -0.097 * -0.056 -0.077 -0.006 -0.102 ** -0.062 * -0.180 ** -0.075 ** (0.052) (0.042) (0.088) (0.031) (0.046) (0.037) (0.088) (0.030) Age Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Race Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Household Size Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Residence Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Education Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled English Fluency Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Years since Naturalization Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Survey Year Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled R-squared 0.328 0.412 0.110 0.075 0.267 0.364 0.128 0.076 Root MSE 0.412 0.350 0.972 0.482 0.429 0.366 0.968 0.481 Observations 4340 4340 4162 4340 4424 4424 4289 4424 This table reports regression results stratified by gender to examine differential post-naturalization effects on labor market outcomes. Columns (1)–(4) reflect outcomes for male respondents; columns (5)–(8) reflect outcomes for females. Definitions of outcome variables and covariates are consistent with Table 2 . The analysis tests whether naturalization yields gender-specific effects across employment intensity, income levels, and upward earnings mobility. Controls include individual demographics, human capital characteristics, and fixed effects by survey year. Standard errors in parentheses are clustered at the individual level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table 4 turns to racial heterogeneity, comparing effects for white and non-white respondents. For non-white naturalized citizens, the results reveal statistically significant declines in three out of four outcomes: a 10.5 percentage point drop in employment, a 6.8 point decline in weeks worked, and a 5.1 point reduction in income growth. Although the effect on logged income (− 5.9%) is negative, it does not reach statistical significance. For white respondents, none of the outcomes show statistically meaningful changes, except for the coefficient for logged income (− 20.3%) is large and marginally significant (p < 0.10), warranting further scrutiny. Overall, these patterns suggest that non-white naturalized citizens experience more immediate and consistent labor market setbacks following naturalization, while white citizens face weaker or more variable effects. These findings support the notion that the formal acquisition of citizenship does not erase racial stratification in economic outcomes and may, in some cases, leave underlying inequities unaddressed or even exacerbated. Table 4 Effect of Naturalization on Employment and Income by Race White Non-White Variables Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) (1) (2) (3) (4) (5) (6) (7) (8) Post-Naturalization -0.073 -0.032 -0.203 * -0.020 -0.105 ** -0.068 * -0.059 -0.051 * (0.056) (0.047) (0.106) (0.039) (0.044) (0.035) (0.074) (0.027) Age Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Sex Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Household Size Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Residence Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Education Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled English Fluency Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Years since Naturalization Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Survey Year Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled R-squared 0.289 0.393 0.134 0.075 0.288 0.372 0.109 0.072 Root MSE 0.424 0.356 0.950 0.482 0.423 0.363 0.979 0.482 Observations 3067 3067 2958 3067 5697 5697 5493 5697 This table estimates separate models for White (columns 1–4) and non-White (columns 5–8) respondents. Non-White includes individuals identifying as Black, Hispanic, Asian, or other racial minorities. Models examine whether naturalization impacts vary by race across employment, labor force attachment, earnings, and income growth. All models include identical covariates and specifications as Table 2 to ensure comparability across groups. Standard errors in parentheses are clustered at the individual level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table 5 focuses on intersectional effects by including interaction terms between post-naturalization status and key demographic identities. The interaction between Post-Naturalization × Female is negative and statistically significant across all four outcomes, indicating that women experience unique disadvantages after acquiring citizenship. Specifically, the interaction term is associated with a 7.1 percentage point decline in employment probability (p < 0.10), a 7.8 point reduction in proportion of weeks worked (p < 0.05), a 12.4% decrease in logged income (p < 0.10), and a 4.4 percentage point drop in the likelihood of year-over-year income growth (p < 0.10). These consistent negative effects suggest that naturalization may exacerbate rather than alleviate gender disparities in the labor market. The interaction between Post-Naturalization × Non-White is similarly negative for labor force attachment and income growth. Non-white naturalized citizens experience a 5.8 point reduction in weeks employed (p < 0.10) and a 5.9 point decline in year-over-year income growth (p < 0.05). These results highlight that the labor market penalties following naturalization are most pronounced at the intersection of gender and race, underscoring the limits of legal status change as a tool for economic inclusion among structurally marginalized groups. Table 5 Intersection of Gender and Race on Employment and Income Gender Race Variables Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) (1) (2) (3) (4) (5) (6) (7) (8) Post- Naturalization 0.272 *** 0.337 *** -0.068 0.019 -0.105 ** -0.048 -0.090 0.032 (0.029) (0.028) (0.065) (0.021) (0.041) (0.035) (0.070) (0.024) Female -0.016 -0.005 0.068 0.022 (0.023) (0.022) (0.050) (0.019) Non-White -0.002 0.016 -0.001 0.039 * (0.022) (0.021) (0.050) (0.021) Post- Naturalization × Female -0.071 * -0.078 ** -0.124 * -0.044 * (0.037) (0.038) (0.072) (0.025) Post- Naturalization × Non-White -0.033 -0.058 * -0.070 -0.059 ** (0.035) (0.035) (0.076) (0.027) Age Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Race Controlled Controlled Controlled Controlled Sex Controlled Controlled Controlled Controlled Household Size Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Residence Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Education Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled English Fluency Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Years since Naturalization Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled Survey Year Controlled Controlled Controlled Controlled Controlled Controlled Controlled Controlled R-squared 0.190 0.228 0.096 0.008 0.259 0.333 0.095 0.008 Root MSE 0.449 0.404 0.976 0.495 0.430 0.376 0.976 0.495 Observations 8764 8764 8451 8764 8764 8764 8451 8764 This table introduces interaction terms between naturalization status and demographic indicators for gender and race. Columns (1)–(4) estimate models with “Post-Naturalization × Female,” and columns (5)–(8) include “Post-Naturalization × Non-White.” The goal is to assess whether post-citizenship outcomes vary not just by gender or race independently, but at their intersection. Main effects and interactions are interpreted relative to the omitted category. Outcome definitions and covariates are consistent with Table 2 . Standard errors in parentheses are clustered at the individual level. * p < 0.10, ** p < 0.05, *** p < 0.01 Lastly, Table 6 introduces a triple interaction term to assess whether the negative effects of naturalization are particularly acute for non-white women—a group at the intersection of multiple forms of marginalization. The results reveal that the interaction between Post-Naturalization × Female × Non-White is significantly negative for three of the four outcomes. Specifically, non-white women experience an 11.5 percentage point decline in employment probability (p < 0.01), a 10.9 point reduction in the proportion of weeks worked (p < 0.01), and a 23.8% drop in logged income (p < 0.05) following naturalization. These effects are large in magnitude and statistically robust, underscoring that naturalization coincides with especially steep economic setbacks for this subgroup. In contrast, none of the other two-way interaction terms display this level of consistent significance across outcomes. These findings highlight that the labor market penalties associated with naturalization are not only gendered and racialized but are most severe at their intersection, emphasizing the importance of intersectional analysis in evaluating the true distribution of citizenship’s economic returns. Table 6 Triple Interaction Effect on Employment and Income Variables Employment Status (Binary) Proportion of Weeks Employed Log Income Year-over-Year Income Growth (Binary) (1) (2) (3) (4) Post-Naturalization 0.207 *** 0.292 *** -0.176 ** 0.016 (0.027) (0.024) (0.072) (0.030) Female -0.039 -0.039 -0.128 * 0.017 (0.028) (0.025) (0.075) (0.031) Non-White -0.019 -0.010 -0.023 0.047 * (0.025) (0.023) (0.069) (0.028) Female × Non-White 0.036 0.053 * 0.080 -0.020 (0.034) (0.031) (0.093) (0.038) Post- Naturalization × Female 0.015 0.004 0.164 * -0.026 (0.034) (0.031) (0.092) (0.038) Post- Naturalization × Non-White 0.043 0.019 -0.001 -0.030 (0.030) (0.027) (0.081) (0.033) Post- Naturalization × Female × Non-White -0.115 *** -0.109 *** -0.238 ** -0.016 (0.042) (0.038) (0.114) (0.047) Age Controlled Controlled Controlled Controlled Household Size Controlled Controlled Controlled Controlled Residence Controlled Controlled Controlled Controlled Education Controlled Controlled Controlled Controlled English Fluency Controlled Controlled Controlled Controlled Years since Naturalization Controlled Controlled Controlled Controlled Survey Year Controlled Controlled Controlled Controlled R-squared 0.199 0.199 0.199 0.199 Root MSE 0.446 0.446 0.446 0.446 Observations 8764 8764 8451 8764 This table includes a three-way interaction term (“Post-Naturalization × Female × Non-White”) to isolate the unique labor market effects of naturalization for non-White women—a group experiencing intersecting disadvantages. The dependent variables are the same four labor market outcomes as in Table 2 . All lower-order terms and demographic controls (age, household size, urban residence, education, English fluency, years since naturalization, and survey year) are included. Standard errors in parentheses are clustered at the individual level. * p < 0.10, ** p < 0.05, *** p < 0.01 In essence, the subgroup analyses by gender, race, and their intersection reveal that the labor market consequences of naturalization are deeply uneven. While some individuals may experience neutral or modest outcomes, women and non-white immigrants consistently face diminished economic returns after acquiring citizenship. Most notably, non-white women experience the steepest post-naturalization penalties across employment, labor force attachment, and income levels, suggesting that legal status change does little to counteract the compounding effects of structural disadvantage. These findings underscore that naturalization alone does not level the economic playing field and must be understood within the broader context of systemic inequality and labor market segmentation. The promise of citizenship remains fundamentally constrained by intersecting barriers that legal inclusion alone cannot dismantle. Year-to-Year Effects of Naturalization Lastly, to better understand how the impact of naturalization unfolds over time, the analysis incorporates dynamic year-by-year estimates using a categorical variable for years since naturalization. Figures 1 through 4 plot the estimated coefficients for each post-naturalization year, allowing for a detailed examination of whether labor market outcomes improve, decline, or remain stagnant over time. Each figure is divided into three panels: Panel A shows the overall effect for the full sample, Panel B disaggregates by gender, and Panel C by race. These visualizations, supported by full regression outputs in Tables A1 , A2 , and A3 (Appendix A), allow for assessment of temporal and demographic variation in the post-citizenship economic trajectory. Figure 1 presents the estimated effect of naturalization on employment status, measured as a binary indicator of whether an individual was employed during a given year. The results in Panel A show no clear trend toward improved employment over the 22-year post-naturalization period. The coefficients remain largely negative, indicating that naturalization is not followed by increases in employment rates. In Panel B, gender disaggregation reveals that female respondents experience a sustained decline in employment starting around year 4, with no signs of recovery. Male respondents, in contrast, exhibit more variability across years, including occasional years of weak improvement, though none reach consistent statistical significance. Panel C, disaggregated by race, indicates that non-white respondents experience a persistent negative effect across all 22 years. White respondents show more fluctuation, with smaller effect sizes and wider confidence intervals, but no sustained improvement. These patterns suggest that the acquisition of citizenship does not provide a delayed or compounding employment benefit for the sample as a whole or for major subgroups. Figure 2 explores the effect of naturalization on the proportion of weeks employed, which captures labor force intensity. The overall trend in Panel A is largely flat or slightly negative, indicating that on average, naturalization does not lead to stronger or more consistent labor force attachment over time. In Panel B, gender-specific trends show that women’s employment intensity declines sharply beginning in the first year after naturalization and remains below baseline levels for the entire two-decade span. In contrast, men’s trajectories are more stable and exhibit less pronounced downward drift. Panel C illustrates similar racial patterns: non-white respondents see a steady deterioration in weeks employed, while white respondents maintain a relatively stable profile. These findings reinforce the idea that naturalization is not associated with long-term labor market integration and, for some groups, may coincide with gradual exclusion from steady work. Figure 3 shows the year-by-year effect of naturalization on logged income, which reflects changes in annual earnings on a proportional scale. In Panel A, the overall effect becomes increasingly negative after the first few years post-naturalization. The income gap appears to widen over time rather than close. In Panel B, the decline in income is particularly pronounced for female respondents, whose coefficients become increasingly negative and statistically significant around year 5. By year 10 and beyond, estimated income losses exceed 40–70% compared to their pre-naturalization levels. Male respondents, on the other hand, exhibit more moderate and statistically unstable patterns, with coefficients oscillating near zero. Panel C reveals that non-white individuals experience persistent and compounding income declines, especially after year 5. In contrast, white individuals show more moderate losses, with some years approaching baseline levels. This figure provides some of the strongest evidence that naturalization is not translating into economic mobility and may even mark the beginning of long-term income stagnation or decline for many. Figure 4 reports results for year-over-year income growth, a dynamic measure of upward mobility. In Panel A, the full sample shows no consistent positive trend following naturalization, and many years display negative coefficients. In Panel B, the income growth trajectory for women remains flat or negative across nearly all post-naturalization years, while men display more variation but do not exhibit a sustained pattern of upward momentum. In Panel C, non-white respondents show consistently lower probabilities of income growth, with the most severe stagnation occurring between years 7 and 20. White respondents again demonstrate more variation but fail to show significant improvement over time. These results suggest that even if naturalization offers legal security, it does not necessarily facilitate wage progression or consistent earnings improvement—particularly for groups already marginalized in the labor market. This illustrates that naturalization does not appear to yield delayed or cumulative economic benefits for most individuals in this sample. Rather than reversing initial post-naturalization setbacks, labor market outcomes either remain unchanged or deteriorate further over time. Women and non-white immigrants experience the steepest and most persistent declines, while men and white respondents see greater variability but few signs of sustained economic gain. Discussion and Conclusion This study explores whether acquiring citizenship through naturalization leads to improved labor market outcomes for immigrants in the United States, using longitudinal data from the National Longitudinal Survey of Youth 1997 (NLSY97). By examining within-person changes over two decades, this research provides nuanced insights into the economic trajectories of 313 naturalized immigrants, specifically analyzing employment rates, income levels, labor force participation, and income growth. Contrary to widely held assumptions, the findings demonstrate that naturalization is generally not associated with enhanced employment or earnings, with results showing a 9.7 percentage point decline in employment and an 11.8% decrease in annual income post-naturalization. The data reveal persistent or deepening negative impacts post-naturalization, especially for women and non-white immigrants. These outcomes challenge prevailing narratives that portray naturalization as a universally beneficial economic integration strategy. This paper contributes significantly to existing literature by addressing important methodological and substantive gaps. Prior studies have predominantly employed cross-sectional data or short-term panels, often overlooking critical demographic distinctions. By leveraging extensive longitudinal data, this analysis captures temporal dynamics, providing clear evidence of how economic outcomes evolve over the long term post-naturalization. Furthermore, disaggregating results by gender and race reveals that naturalization does not uniformly benefit all immigrant groups. This intersectional approach highlights systemic inequalities within labor market institutions and underscores the complexities surrounding citizenship’s economic impact. Thus, the study advances scholarly understanding by emphasizing that naturalization alone does not guarantee economic advancement, particularly for structurally marginalized populations. Moreover, the nuanced findings concerning the differential effects across demographic groups deepen existing discussions around citizenship and inequality. The consistent negative outcomes identified for women and non-white immigrants indicate that legal status changes alone may reinforce existing labor market inequalities rather than alleviate them. These insights align with theoretical frameworks emphasizing structural barriers and intersectional inequalities, providing empirical support for policies aimed at more comprehensive immigrant integration. Nevertheless, the study faces several limitations that warrant careful consideration. Although the longitudinal design of the NLSY97 dataset allows for robust within-person analysis, the relatively small sample size of 313 naturalized individuals may limit the generalizability of the findings across the broader and more diverse immigrant population. Moreover, the cohort studied—individuals born between 1980 and 1984—represents a specific generational slice, meaning that their labor market trajectories and naturalization experiences may not fully reflect those of younger or newly arriving immigrant groups facing different economic and policy environments. Additionally, while the models control for a wide range of demographic and socioeconomic variables, the absence of regional economic trends, and subjective factors such as motivation or social capital may obscure important mechanisms behind the observed outcomes. These limitations suggest caution in interpreting causal claims and highlight the need for further research that can validate and build upon these findings using larger and more diverse datasets, multi-cohort comparisons, and mixed-methods approaches. Future studies should also investigate sector-specific patterns, regional labor market conditions, and immigrants’ lived experiences post-naturalization to better understand the structural constraints and opportunities that shape economic integration. Cross-national comparisons would further help identify whether these negative patterns are uniquely American or part of a broader global trend shaped by similar institutional dynamics. Policy implications arising from this study are significant. Given the limited and, at times, negative economic outcomes associated with naturalization, policymakers should reconsider relying solely on citizenship acquisition as a primary integration strategy. Instead, more comprehensive approaches addressing structural barriers, including labor market discrimination, occupational segregation, and targeted support for marginalized immigrant populations, are crucial. Policymakers might enhance naturalization benefits by coupling legal status changes with active labor market policies, such as employment counseling, vocational training, language programs, and stronger anti-discrimination measures. In essence, this study reveals that naturalization does not universally yield positive economic returns for immigrants, highlighting substantial and persistent disparities for women and non-white individuals. By illustrating the nuanced and often negative impacts of citizenship acquisition on labor market outcomes, the research underscores the urgent need for integration strategies that move beyond legal status changes. Future policies must address deeper systemic inequalities to realize the full potential of naturalization as a tool for genuine economic and social integration. Declarations Author Contribution R.S. conceived the study, conducted the data analysis, interpreted the results, and wrote the manuscript. R.S. reviewed and approved the final version of the manuscript. Data Availability This study uses publicly available data from the National Longitudinal Survey of Youth 1997 (NLSY97), provided by the U.S. Bureau of Labor Statistics. The dataset can be accessed at: https://www.bls.gov/nls/nlsy97.htm. Competing Interest: The author declares that there are no financial or non-financial competing interests related to the content of this manuscript. References American Immigration Council (2024). Naturalization in the United States: Key facts . https://www.americanimmigrationcouncil.org/fact-sheet/naturalization-united-states/ Bloemraad, I. (2002). The North American Naturalization Gap: An Institutional Approach to Citizenship Acquisition in the United States and Canada. International Migration Review , 36 (1), 193–228. https://doi.org/10.1111/j.1747-7379.2002.tb00077.x Bloemraad, I., & Sheares, A. (2017). Understanding membership in a world of global migration:(How) does citizenship matter? International Migration Review , 51 (4), 823–867. https://doi.org/10.1111/imre.12354 Bratsberg, B., RaganJr, J. F., & Nasir, Z. M. (2002). The effect of naturalization on wage growth: A panel study of young male immigrants. Journal of labor economics , 20 (3), 568–597. https://www.journals.uchicago.edu/doi/abs/10.1086/339616 Bureau of Labor Statistics, U.S. Department of Labor. National Longitudinal Survey of Youth 1997 cohort, 1997–2021 (rounds 1–20). Produced and distributed by the Center for Human Resource Research (CHRR), The Ohio State University. Columbus, OH (2024). https://www.bls.gov/nls/nlsy97.htm Dhillon, H. The Making of Modern US Citizenship and Alienage: The History of Asian Immigration, Racial Capital, and, & Law, U. S. (2023). Law and History Review , 41 (1), 1–42. https://doi.org/10.1017/S0738248023000019 Enchautegui, M. E., & Giannarelli, L. (2015). The economic impact of naturalization on immigrants and cities. Urban Institute . https://www.urban.org/sites/default/files/publication/76241/2000549-The-Economic-Impact-of-Naturalization-on-Immigrants-and-Cities.pdf?ieNocache=156 Gathmann, C. M. (2020). Naturalization and citizenship: Who benefits? IZA World of Labor . https://doi.org/10.15185/izawol.125.v2 Hainmueller, J., Lawrence, D., Gest, J., Hotard, M., Koslowski, R., & Laitin, D. D. (2018). A randomized controlled design reveals barriers to citizenship for low-income immigrants. Proceedings of the National Academy of Sciences , 115 (5), 939–944. https://doi.org/10.1073/pnas.1714254115 Immigration Policy Lab (2018). Lifting barriers to citizenship . 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Impossible subjects: Illegal aliens and the making of modern America . Princeton University Press. https://www.degruyterbrill.com/document/doi/10.1515/9781400850235/html OECD. (2011). Naturalisation: A passport for the better integration of immigrants? OECD Publishing . https://doi.org/10.1787/9789264099104-en Pastor, M., & Scoggins, J. (2016). Estimating the eligible-to-naturalize population. University of Southern California (USC), Los Angeles, CA . https://dornsife.usc.edu/eri/research/map-eligible-to-naturalize-puma-2023/ Scoggins, J. (2012). Citizen gain: the economic benefits of naturalization for immigrants and the economy . Center for the Study of Immigrant Integration. https://dornsife.usc.edu/eri/publications/citizen-gain/ Sharp, D. (2023). Immigration, naturalization, and the purpose of citizenship. Pacific Philosophical Quarterly , 104 (2), 408–441. https://doi.org/10.1111/papq.12428 Shashkevich, A. (2018). Low-income immigrants face barriers to U.S. citizenship . Stanford Report. https://news.stanford.edu/stories/2018/01/low-income-immigrants-face-barriers-u-s-citizenship Stadlmair, J. (2018). Earning citizenship. Economic criteria for naturalisation in nine EU countries. Journal of Contemporary European Studies , 26 (1), 42–63. https://doi.org/10.1080/14782804.2018.1437025 Steinhardt, M. F. (2012). Does citizenship matter? The economic impact of naturalizations in Germany. Labour Economics , 19 (6), 813–823. https://doi.org/10.1016/j.labeco.2012.09.001 Sumption, M., & Flamm, S. (2012). The economic value of citizenship for immigrants in the United States . Migration Policy Institute. https://www.issuelab.org/resources/29844/29844.pdf U.S. Congress (1790). An act to establish an uniform Rule of Naturalization (1 Stat. 103). https://www.ourdocuments.gov/doc.php?flash=false&doc=47 U.S. Congress (1882). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6978281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477088523,"identity":"84ce1828-d83a-4b01-bb6f-d28f394ea328","order_by":0,"name":"Rakkshet Singhaal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACNmYGxgYGBn4JBhBtARaTIEaLhOQMsBYJErQY3CBWi3n7sWTDnzts6oxvNx/7OKNCQs7gAPPB2zx4tMicSTuczHsmTcLszrHkmRvOSBgbHGBLtsanRYIhvfkwY9thCbMbOcaMD9skEjcc4DGTxquF/3nzwZ9t/yWMZ+R/Znz4T6J+wwH+b/i1SKQdTuBtOyBhIJHDzLixQSLB4AAPGwEtz5KNeduSJWfcOWbMOOOYhOHMw2zGlnPwOizNWPJnmx0//+zmx4w9NTbyfMebH954g0cLJlA4TJJyEJBvIFnLKBgFo2AUDHMAAC38S8pcuDGZAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Berkeley","correspondingAuthor":true,"prefix":"","firstName":"Rakkshet","middleName":"","lastName":"Singhaal","suffix":""}],"badges":[],"createdAt":"2025-06-26 00:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6978281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6978281/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85542803,"identity":"ecaf5832-5828-4fb1-8e16-1df592fc472e","added_by":"auto","created_at":"2025-06-27 07:12:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219692,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated Effect of Naturalization on Employment Status Over Time\u003c/p\u003e\n\u003cp\u003eThis figure displays year-by-year regression estimates of the effect of naturalization on employment status, defined as a binary indicator (1 = Employed during survey year, 0 = Not employed). Estimates are plotted for the full sample (Panel A), by gender (Panel B), and by race (Panel C). Each point reflects the difference in employment probability for naturalized individuals relative to their pre-naturalization baseline. Negative values indicate lower likelihood of employment after acquiring citizenship. Models control for age, household size, education, English fluency, urban residence, and survey year fixed effects. Standard errors are clustered at the individual level.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/b653de9281e37294c9616029.png"},{"id":85542807,"identity":"7c2f7128-fe7b-4f83-8337-f7fa55a84ab6","added_by":"auto","created_at":"2025-06-27 07:12:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209137,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of Naturalization on Employment Intensity (Proportion of Weeks Employed)\u003c/p\u003e\n\u003cp\u003eThis figure presents dynamic effects of naturalization on labor force intensity, measured as the proportion of weeks worked in a given year (range: 0 to 1). Panel A shows the full sample, Panel B separates male and female respondents, and Panel C distinguishes between White and non-White respondents. Each estimate reflects the change in employment intensity compared to the pre-citizenship period. Values below zero indicate a decline in weeks worked following naturalization. All models include demographic, socioeconomic, and temporal controls, with individual-level clustered standard errors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/ae23d258a83b9c433457dde1.png"},{"id":85542806,"identity":"ef915cc7-d382-41d7-885b-5e7aedaaa716","added_by":"auto","created_at":"2025-06-27 07:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":210870,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated Impact of Naturalization on Logged Income Levels\u003c/p\u003e\n\u003cp\u003eThis figure shows the effect of naturalization on logged annual income (natural logarithm of total yearly earnings), which standardizes income data and allows proportional comparisons. Panels A–C disaggregate the results by overall sample, gender, and race, respectively. Negative coefficients indicate reduced earnings relative to the pre-naturalization period. Models account for individual demographics, human capital (education, English fluency), geographic location, and survey year. Log income helps account for skewness and reflects percentage-based income changes. Standard errors are clustered at the individual level.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/d58782999ac44750b2b765bb.png"},{"id":85542942,"identity":"e94bc924-baca-4b2b-a514-0cf7d9835db2","added_by":"auto","created_at":"2025-06-27 07:20:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228798,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Naturalization on Year-over-Year Income Growth\u003c/p\u003e\n\u003cp\u003eThis figure plots the impact of naturalization on year-over-year income growth, defined as a binary variable equal to 1 if income increased relative to the prior year, and 0 otherwise. Panels A, B, and C present effects for the overall sample, by gender, and by race, respectively. Values below zero indicate a reduced probability of income improvement after naturalization. Models include full covariate controls (e.g., age, education, English fluency, household size, urban residence) and year fixed effects. Standard errors are clustered at the individual level to account for repeated observations over time.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/54d97f78758093081aea5acf.png"},{"id":86700717,"identity":"da4d1155-ed8c-4214-91b2-4af9aca10a17","added_by":"auto","created_at":"2025-07-14 16:12:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2128182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/8a6148fb-8a9f-4b60-a6fa-0b59e6e3340a.pdf"},{"id":85542804,"identity":"60b2bf52-bc14-423d-a38b-e31499914449","added_by":"auto","created_at":"2025-06-27 07:12:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47693,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6978281/v1/d0fb0c95a867e66f61d3267e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does Citizenship Deliver? Persistent Gaps in Employment and Earnings after Naturalization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCitizenship acquisition through naturalization stands at the heart of immigrant integration in the United States, symbolizing full legal inclusion and unlocking a range of social, political, and economic rights. As of 2024, approximately 31.6\u0026nbsp;million immigrants in the United States were either naturalized citizens or eligible to become citizens, with around 7.4\u0026nbsp;million eligible immigrants yet to naturalize, representing 33.6% of the noncitizen population (American Immigration Council, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Historically, citizenship acquisition in the United States has been influenced by racial and socio-economic barriers, starting from the Naturalization Act of 1790, which limited citizenship eligibility to \u0026ldquo;free white persons\u0026rdquo; and established a legacy of exclusion based on race and ethnicity. Subsequent policies, including the Chinese Exclusion Act and the Immigration Act of 1924, reinforced these racial and ethnic exclusions. While explicit racial criteria have since been abolished by legislative reforms, notably the Immigration and Nationality Act of 1965, contemporary institutional barriers like high naturalization fees, language proficiency requirements, and bureaucratic complexity continue to disproportionately affect immigrants from disadvantaged racial and socio-economic backgrounds, perpetuating historical inequalities (Dhillon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bloemraad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ngai, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eScholarly research provides substantial evidence supporting the notion that citizenship can positively affect labor market integration, employment stability, and wage growth. Studies highlight a \u0026ldquo;citizenship premium,\u0026rdquo; indicating that naturalized citizens earn higher wages and enjoy greater employment security than non-citizens, even after accounting for education, work experience, and English proficiency (Bratsberg, Ragan \u0026amp; Nasir, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sumption \u0026amp; Flamm, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Pastor and Scoggins (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) documented immediate wage increases of 5\u0026ndash;7% following naturalization, expanding to over 10% after a decade, with substantial broader economic impacts projected from increased consumer spending and enhanced tax revenues. Similarly, Sumption and Flamm (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) estimated a citizenship premium of approximately 5\u0026ndash;8%, attributing this to greater labor market access and reduced employment discrimination. Enchautegui and Giannarelli (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported an average individual earnings increase of approximately 8.9% for immigrants upon naturalization. International research further corroborates these findings, underscoring consistent economic advantages linked to naturalization across diverse contexts, including Germany, Canada, and several EU countries, which have documented enhanced labor market participation and wage growth following naturalization (Steinhardt, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stadlmair, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; OECD, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite widespread acknowledgment of these general economic benefits, current literature has notable limitations. Most research utilizes cross-sectional data or short-term observations, often masking critical variations across demographic and socio-economic subgroups. The assumption that naturalization universally improves economic standing overlooks potential persistent or even exacerbated inequalities, especially for women, racial minorities, and low-income immigrants. For instance, Enchautegui and Giannarelli (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) highlighted significant variability in economic benefits by immigrant subgroup and location, emphasizing greater relative gains for younger or lower-income immigrants. Additionally, research on dual citizenship policies underscores varying effects on immigrants\u0026rsquo; economic trajectories depending on country of origin and personal circumstances, further highlighting the complexity of citizenship\u0026rsquo;s economic impact (Mazzolari, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, understanding how economic outcomes evolve over the long-term post-naturalization and how these effects vary by intersectional identities remains crucial and insufficiently explored (Gathmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Steinhardt, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper addresses these limitations by employing longitudinal data from the National Longitudinal Survey of Youth 1997 (NLSY97), examining within-person changes before and after citizenship acquisition over two decades. This approach uniquely captures nuanced shifts in employment status, income levels, and earnings trajectories, allowing for disaggregated analysis by gender, race, and other critical demographic variables. By tracking individual labor market outcomes over time, the analysis is able to distinguish the effects of naturalization from confounding factors, providing a more accurate and dynamic portrait of how citizenship reshapes economic experiences.\u003c/p\u003e \u003cp\u003eThe findings challenge prevailing assumptions by revealing persistent gaps and, in some cases, negative effects associated with naturalization. Contrary to expectations, naturalization is associated with significant declines in employment rates (approximately 9.7 percentage points), lower annual earnings (an average decrease of about 11.8%), and decreased income growth (a reduction of 4.2 percentage points in the probability of income improvement), effects most pronounced among women and non-white immigrants. For example, women experience an employment rate reduction of about 10.2 percentage points and a notable income decrease of approximately 18%. Non-white respondents similarly face substantial reductions, including an employment rate decline of about 10.5 percentage points and a decrease in the proportion of weeks worked by 6.8 percentage points. These disparities indicate that naturalization does not offer a uniform pathway to economic mobility; rather, its economic benefits are mediated by structural inequalities embedded in labor market institutions and intersecting forms of marginalization. The longitudinal design further reveals that these negative outcomes persist over time, suggesting that naturalization does not catalyze the upward economic trajectory frequently promised in integration narratives. Instead, it may be operating within a system where existing disparities are simply carried forward under a new legal status.\u003c/p\u003e \u003cp\u003eThis study significantly contributes to existing literature by providing detailed empirical insights into the differential economic impacts of naturalization. By exploring intersectional dynamics, it reveals the limits of citizenship as a tool for economic integration and underscores the critical need for targeted policies addressing the underlying structural inequalities faced by marginalized immigrant populations. Specifically, the findings suggest that policy efforts should not only promote access to naturalization but also address post-citizenship barriers in employment and wage advancement\u0026mdash;particularly for women and racial minorities. The analysis thus informs both scholarly discourse and practical policy-making aimed at achieving genuine immigrant economic integration and equality in labor market outcomes.\u003c/p\u003e \u003cp\u003eThe paper proceeds as follows: next section presents a comprehensive literature review covering the historical, institutional, and economic dimensions of naturalization. Subsequent sections describe the data and methodology employed, followed by detailed results examining both overall effects and subgroup variations in labor market outcomes. The paper then discusses implications of these findings for policy and research, concluding with suggestions for future study directions.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNaturalization in United States\u003c/h2\u003e \u003cp\u003eNaturalization, the process by which foreign-born individuals become citizens, has been a core mechanism through which immigrants are formally integrated into U.S. society. The motivations, processes, legal frameworks, and barriers associated with naturalization have evolved significantly, reflecting both historical contexts and ongoing policy debates.\u003c/p\u003e \u003cp\u003eHistorically, U.S. naturalization laws have been deeply entangled with race and ethnicity. The Naturalization Act of 1790 set early precedents by limiting citizenship eligibility to \u0026ldquo;free white persons,\u0026rdquo; embedding racial hierarchies within citizenship criteria. Subsequent legal milestones, including the Chinese Exclusion Act and the Asiatic Barred Zone, reinforced racial barriers, particularly targeting Asian immigrants and explicitly linking citizenship eligibility to racial identity (Dhillon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such exclusions underscored citizenship as both a legal and symbolic reinforcement of racial capital, privileging white individuals and systematically excluding others, which reshaped racial boundaries and influenced U.S. legal definitions of alienage and belonging (Dhillon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the decades following, significant legal reforms gradually dismantled explicit racial criteria from U.S. immigration and naturalization laws. Landmark legislation, such as the Immigration and Nationality Act of 1965 (also known as the Hart-Celler Act), eliminated national origins quotas, marking a pivotal shift towards a more inclusive immigration policy. This act substantially diversified the immigrant population by removing previous racial and ethnic restrictions and prioritizing family reunification and skilled labor (Lee, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Despite these legal advancements, racial disparities and barriers persist in contemporary immigration processes, reflecting historical legacies. For example, contemporary debates and policies around border control, refugee admissions, and pathways to citizenship often implicitly reflect ongoing racial and ethnic tensions (Ngai, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Current naturalization processes continue to be shaped by historical contexts and institutional barriers, including high application fees and language proficiency requirements, which disproportionately impact immigrants from disadvantaged socio-economic and racial backgrounds, perpetuating historical inequities within citizenship access (Bloemraad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hainmueller et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These structural barriers, however, exist alongside powerful personal and practical incentives that continue to drive immigrants toward naturalization.\u003c/p\u003e \u003cp\u003eMotivations for immigrants to pursue U.S. citizenship are multifaceted, integrating individual aspirations, socio-economic benefits, and political or personal security. Immigrants often perceive naturalization as a gateway to better employment opportunities, social stability, political participation, and civic integration (Bloemraad \u0026amp; Sheares, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For many, the decision to naturalize stems from a desire to fully participate in the political and civic life of their adopted country, gaining not only legal recognition but also a sense of equality with native-born citizens (Bloemraad \u0026amp; Sheares, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Access to federal jobs and other employment opportunities that require citizenship is a powerful incentive, particularly in sectors such as civil service, law enforcement, and defense, where stable, well-paying jobs may be out of reach for non-citizens (Hainmueller et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Naturalization also enables immigrants to petition for immediate family members to immigrate to the U.S., facilitating family reunification and enhancing household security (Yang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEqually important are the protections that citizenship affords. Naturalized individuals are protected from deportation, even if they face legal challenges, offering a degree of permanence and peace of mind not available to lawful permanent residents (Bloemraad \u0026amp; Sheares, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This legal security allows immigrants to invest more confidently in their futures, whether by purchasing homes, pursuing long-term careers, or furthering their education (Sumption \u0026amp; Flamm, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, naturalization enhances civic integration by enabling full participation in democratic processes through voting and running for office, allowing immigrants to advocate for their communities and influence policies that affect their lives (Bloemraad \u0026amp; Sheares, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This civic empowerment fosters a stronger commitment to civic duties and enhances feelings of social responsibility (Pastor \u0026amp; Scoggins, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The process of preparing for naturalization\u0026mdash;learning U.S. history, government, and language\u0026mdash;also contributes to civic and cultural assimilation, equipping immigrants with tools for more effective participation in American society (National Academies of Sciences, Engineering, and Medicine, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It also highlights that increased citizenship rates enhance national cohesion and economic productivity by more fully integrating immigrants into American societal structures.\u003c/p\u003e \u003cp\u003eFinally, philosophical perspectives emphasize the egalitarian and symbolic importance of citizenship. Sharp (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argues that naturalization serves as a crucial mechanism for combating social hierarchies, promoting equality, and fostering social solidarity, and contends that citizenship goes beyond mere legal status, embodying a public commitment to social equality and democratic participation. In this way, the motivations to naturalize are not solely about practical gains but are deeply embedded in the pursuit of dignity, inclusion, and long-term stability (Bloemraad \u0026amp; Sheares, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, naturalization rates in the U.S. remain notably lower compared to other immigrant-receiving countries like Canada, Australia, and the United Kingdom. Bloemraad (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) highlights institutional barriers as pivotal factors behind this gap. Unlike Canada\u0026rsquo;s active government-sponsored integration programs, the U.S. exhibits a less supportive institutional framework, with fewer public resources dedicated to immigrant outreach, linguistic integration, and citizenship promotion, leading to a significant \u0026ldquo;naturalization gap\u0026rdquo; between the two North American neighbors (Bloemraad, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Empirical research has underscored significant barriers faced by low-income immigrants in the naturalization process. Financial costs, particularly the high application fee (currently \u003cspan\u003e$\u003c/span\u003e725), significantly deter eligible immigrants from applying, demonstrating the financial dimensions of citizenship barriers. A randomized controlled trial within the NaturalizeNY program indicated that providing fee vouchers substantially increased naturalization application rates among low-income immigrants, clearly identifying economic barriers as major deterrents to citizenship (Hainmueller et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ; Shashkevich, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ). Non-financial obstacles also significantly deter naturalization, particularly among the poorest groups, who face challenges like language barriers, limited awareness of eligibility and processes, fear, and a lack of accessible assistance. This reveals the complexity of citizenship barriers that extend beyond financial concerns (Immigration Policy Lab, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ). \u003c/p\u003e \u003cp\u003eYang\u0026rsquo;s foundational research (1994) articulates that immigrants\u0026rsquo; decisions to naturalize are deeply influenced by both individual-level factors\u0026mdash;such as education, English proficiency, and socio-economic integration\u0026mdash;and broader contextual influences, including political conditions in countries of origin and reception. Yang emphasizes that political refugees and immigrants escaping repressive regimes are especially inclined toward citizenship acquisition, underscoring the intersection of individual experiences with larger geopolitical contexts (Yang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe literature collectively underscores that naturalization in the U.S. is a complex interplay of historical legacies, individual motivations, and institutional frameworks. Scholars have thoroughly examined the socio-cultural and political dimensions of naturalization, revealing how citizenship shapes identity, belonging, and civic engagement. Equally vital is an exploration of naturalization\u0026rsquo;s role in facilitating immigrants\u0026rsquo; economic integration\u0026mdash;particularly through its influence on wage growth, employment stability, and access to higher-quality job opportunities across diverse populations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNaturalization Impact on Employment and Income\u003c/h3\u003e\n\u003cp\u003eHaving established the multifaceted nature of naturalization\u0026mdash;shaped by historical exclusion, institutional structures, and personal motivations\u0026mdash;it is equally important to examine how the acquisition of citizenship translates into tangible economic outcomes. Naturalization not only marks a legal and symbolic transition but also plays a critical role in shaping immigrants\u0026rsquo; economic trajectories, particularly through enhanced labor market integration, employment stability, and long-term income growth\u003c/p\u003e \u003cp\u003eEmpirical research consistently demonstrates positive impacts of citizenship on labor market outcomes. Bratsberg, Ragan, and Nasir (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) provided compelling evidence, highlighting accelerated wage growth among naturalized immigrants compared to non-citizens, primarily due to increased access to higher-paying positions in the public sector or jobs requiring security clearance, alongside reduced employer discrimination (Bratsberg et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSumption and Flamm (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) quantified the \u0026ldquo;citizenship premium,\u0026rdquo; finding that naturalized immigrants earn approximately 5\u0026ndash;8% more than their non-citizen counterparts, even after controlling for education, language proficiency, and work experience. This wage premium reflects the compounded advantages that come with legal security, increased eligibility for a wider range of jobs\u0026mdash;including those in the public sector or requiring security clearance\u0026mdash;and the positive signaling effect that citizenship confers in the labor market. Employers may interpret citizenship as a sign of long-term commitment, cultural assimilation, and reliability, which can enhance job prospects and upward mobility. Additionally, the protections afforded by citizenship may empower individuals to take greater risks in pursuing promotions, changing employers, or investing in further education or training, thereby accelerating their economic advancement over time (Sumption \u0026amp; Flamm, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Pastor and Scoggins ( \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e ) identified a substantial wage premium associated with naturalization, noting immediate increases of 5\u0026ndash;7% within two years, expanding to over 10% after ten years. Their research projected significant broader economic impacts, suggesting that widespread naturalization could potentially generate between \u003cspan\u003e$\u003c/span\u003e20\u0026ndash;45\u0026nbsp;billion in economic growth over a decade through increased consumer spending and enhanced tax revenues (Pastor \u0026amp; Scoggins, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e ). Further reinforcing these findings, Enchautegui and Giannarelli ( \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e ) reported an earnings premium ranging from 8\u0026ndash;11%, utilizing rigorous econometric methods like propensity score matching. Their study emphasized substantial economic mobility benefits for younger or lower-income immigrants, significantly contributing to local economies via increased consumer expenditure and tax contributions (Enchautegui \u0026amp; Giannarelli, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e ). \u003c/p\u003e \u003cp\u003eMazzolari (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) provided additional nuance by examining the implications of dual citizenship policies. Her findings indicated that dual citizenship notably boosted naturalization rates and economic outcomes, as immigrants no longer faced the deterrent of renouncing their original citizenship, thus enabling greater economic integration (Mazzolari, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, Gathmann (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) synthesized global research confirming substantial benefits of naturalization, particularly for first-generation immigrants. Their analysis underscored significant wage growth, employment stability, and upward mobility, especially pronounced for immigrants from developing nations and women. This improvement is often driven by increased investment in host-country-specific skills, enhancing employability and earnings (Gathmann, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInternational evidence from Germany, presented by Steinhardt (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), similarly underscores immediate positive wage impacts and accelerated wage growth post-naturalization, reinforcing the global applicability of these findings. In the German context, naturalization leads to particularly strong gains for immigrants from non-EU countries who face higher structural barriers. Citizenship signals a more permanent legal status to employers, boosting their willingness to hire or promote naturalized individuals. It also encourages immigrants to make long-term investments in country-specific capital\u0026mdash;such as professional training, language acquisition, and social networks\u0026mdash;further enhancing economic integration over time (Steinhardt, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese effects are not limited to Germany. A comparative study by Stadlmair (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) across nine EU countries\u0026mdash;including France, the Netherlands, and the United Kingdom\u0026mdash;finds that naturalization yields measurable labor market benefits, especially for immigrants from lower-income regions. Naturalized citizens were more likely to experience upward occupational mobility and income growth, attributable not only to reduced discrimination but also to access to public sector jobs and increased social legitimacy. However, many European countries impose economic criteria\u0026mdash;such as proof of stable employment or income levels\u0026mdash;as prerequisites for naturalization, which can restrict access for more vulnerable immigrants. Still, those who succeed in naturalizing tend to see significant long-term economic improvements.\u003c/p\u003e \u003cp\u003eThe OECD (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) further supports these conclusions, reporting that in countries like Canada, Australia, and the Nordic states, naturalized immigrants outperform non-citizens with similar backgrounds in terms of employment rates and earnings. These gains persist even after controlling for education and other covariates, suggesting that the act of naturalization itself\u0026mdash;through legal security, labor market signaling, and civic inclusion\u0026mdash;carries independent economic value. Collectively, these international findings affirm that the economic advantages of citizenship are not unique to the United States but are observable across varied institutional and policy environments.\u003c/p\u003e \u003cp\u003eCollectively, existing studies leave little doubt that acquiring U.S. citizenship is associated, on average, with higher earnings, faster wage growth, and more stable employment. Yet most of this evidence reports mean effects for broad immigrant populations, relying largely on cross-sectional data or short panels that mask differences across intersections of race, gender, class of admission, and sector of employment. Far less is known about how the economic dividends of naturalization unfold over time, whether they are concentrated in particular industries, and which sub-populations benefit\u0026mdash;or fail to benefit\u0026mdash;the most. The present study tackles these unanswered questions by exploiting two decades of longitudinal NLSY97 data and a within-person analytic design that traces labor-market trajectories before and after naturalization. By disaggregating results by gender, race, and sector\u0026mdash;and by charting year-by-year effects\u0026mdash;this paper provides a more precise and nuanced portrait of citizenship\u0026rsquo;s economic payoff, generating evidence that can guide finely targeted integration and workforce policies.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Sample\u003c/h2\u003e \u003cp\u003eThis study draws on data from the NLSY97, a nationally representative panel consisting of approximately 9,000 individuals born between 1980 and 1984. The NLSY97 began with annual interviews in 1997 and shifted to a biennial schedule after 2011, continuing to follow respondents into their mid-forties. It provides extensive, time-varying information on employment, income, educational attainment, family structure, geographic mobility, and legal status, including U.S. citizenship. Of particular relevance to this analysis, the survey allows researchers to track the exact timing of naturalization alongside detailed labor market trajectories. The analytical sample includes all foreign-born respondents in the NLSY97 who reported becoming naturalized citizens during the survey period. This results in a focus group of 313 individuals who transitioned from non-citizen to citizen status over the course of 22 years. Rather than comparing naturalized individuals to non-citizens, the study uses within-person variation to examine how economic outcomes evolve in the post-naturalization period. This design allows for a focus on changes over time for the same individuals, minimizing bias due to unobserved, time-invariant individual characteristics. The goal is to estimate the average and subgroup-specific effects of naturalization on labor market performance, accounting for both observable covariates and broader time trends.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDependent variables\u003c/span\u003e: The analysis focuses on four key outcome variables that reflect employment quantity and earnings dynamics. Employment status is defined as a binary indicator of whether an individual was employed for the majority of the year in a given survey period. Proportion of weeks employed measures the intensity of labor force attachment, calculated as the number of weeks an individual worked at least 20 hours in a given year divided by 52, resulting in a normalized value ranging from 0 to 1. Log income reflects the logged value of total annual earnings and is used to evaluate changes in income on a proportional scale. Year-over-year income growth is captured as a binary variable, indicating whether the respondent earned more income than in the previous year. Together, these variables provide a comprehensive picture of both employment stability and wage progression following naturalization.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIndependent variables\u003c/span\u003e: The key explanatory variable is a binary indicator for post-naturalization status, equal to 1 for all years after an individual has become a U.S. citizen and 0 for all years prior. This variable serves as the treatment in a quasi-difference-in-differences framework, allowing the analysis to estimate how outcomes shift after the acquisition of citizenship. In addition to this binary treatment variable, analysis is conducted on a variable for years since naturalization, capturing the number of years that have elapsed since an individual became a citizen. This specification enables the analysis to go beyond average treatment effects and examine how the impact of naturalization evolves over time, identifying whether economic benefits emerge immediately, accumulate gradually, or diminish in the long run.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCovariates\u003c/span\u003e: A rich set of time-varying covariates is included to account for alternative explanations and to isolate the effect of naturalization from other factors known to influence labor market outcomes. Age is included to capture life-cycle effects, as labor force participation and earnings typically vary with experience and seniority. Sex accounts for well-documented gender disparities in employment opportunities, wage levels, and occupational sorting. Race is controlled for given persistent racial and ethnic inequalities in the U.S. labor market, which can shape both access to employment and returns to education or skills. Household size serves as a proxy for economic need and caregiving responsibilities, both of which may influence labor force attachment. Urban versus rural residence is included to reflect geographic variation in labor market structure, job availability, and access to services, all of which may affect employment outcomes. Educational attainment is controlled as a key dimension of human capital, directly affecting employability and earning potential. English fluency, both in reading and speaking, is a critical measure of linguistic integration and communication ability, which strongly influence access to higher-skilled jobs and career advancement. Years since naturalization is incorporated to track the temporal dimension of citizenship effects, acknowledging that any economic benefits associated with naturalization may take time to manifest. Finally, survey year fixed effects control for broader macroeconomic shifts, such as recessions, policy changes, or labor market shocks, that may impact all individuals regardless of their personal characteristics. These controls are theoretically grounded in models of human capital, segmented labor markets, and structural inequality, and empirically justified by decades of research identifying them as key determinants of employment and income dynamics among immigrant populations.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEstimation Model\u003c/h2\u003e \u003cp\u003eIn order to estimate the effects of naturalization, the analysis employs Ordinary Least Squares (OLS) regression. Given the longitudinal structure of the data, standard errors are clustered at the individual level to adjust for repeated observations and serial correlation. The core model is specified as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}CitizenshipStatu{s}_{it}+\\:{X}_{it}\\:+{\\delta\\:}_{t}+{ϵ}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e represents the labor market outcome of interest for individual \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e, including employment status, proportion of weeks employed, log income, or year-over-year income growth. The variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CitizenshipStatu{s}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a binary treatment indicator equal to 1 if individual \u003cem\u003ei\u003c/em\u003e has naturalized by year \u003cem\u003et\u003c/em\u003e, and 0 otherwise. This variable captures the average effect of acquiring U.S. citizenship on economic outcomes, holding other factors constant. The vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e includes a rich set of time-varying covariates that are theoretically and empirically linked to labor market outcomes. These include age, sex, race, household size, urban or rural residence, educational attainment beyond high school, English reading and speaking fluency, and survey year fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e), which controls macroeconomic shocks and temporal variation. The error term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e captures unobserved, time-specific individual shocks. In addition to the binary treatment indicator, an extended specification incorporates a variable for years since naturalization, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Yea{rsSinceNaturalization}_{it}\\)\u003c/span\u003e\u003c/span\u003e. This variable allows for dynamic treatment effects by estimating separate coefficients for each year after naturalization, rather than imposing a constant average effect. Including \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Yea{rsSinceNaturalization}_{it}\\)\u003c/span\u003e\u003c/span\u003e enables the analysis to assess whether the impact of citizenship status is immediate, delayed, accumulative, or possibly declines over time. This is particularly important in testing assumptions about the long-term benefits of naturalization and whether economic integration improves gradually after legal status changes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reports summary statistics from the final wave of the NLSY97 panel (2022) for the sample of 313 naturalized immigrants who are the focus of this analysis. These descriptive measures highlight several characteristics that are relevant to understanding the potential labor market implications of naturalization. The average age of respondents is just over 40 years, consistent with their mid-career status and the fact that many were naturalized as adults. The gender distribution is evenly split between men and women, allowing for balanced gender comparisons in subgroup analyses. Approximately 98% of the sample resides in urban areas, suggesting strong geographic concentration in metropolitan labor markets. Racially, 65% of the sample identifies as non-white, reflecting the diversity of recent U.S. immigration patterns. Educational attainment is moderate, with 44% reporting education beyond high school\u0026mdash;a factor that may influence earnings potential and access to higher-quality employment. Just under three-quarters of respondents report fluent English reading skills (74%) and slightly fewer report fluency in speaking English (68%), which are important indicators of linguistic integration. On average, respondents live in relatively large households (mean household size of 6.3) and report having 1.22 biological children, highlighting potential caregiving responsibilities and economic pressures. A majority (59%) are currently married, which may shape household income dynamics and labor supply decisions. The data reveal a sample of naturalized immigrants who are demographically diverse, linguistically proficient to varying degrees, and integrated into urban labor markets\u0026mdash;but who also face potential structural constraints related to education, racial inequality, and family responsibilities. These characteristics offer important context for interpreting the patterns observed in the subsequent regression analyses.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics from Panel Survey Data (Final Wave: 2022) (N\u0026thinsp;=\u0026thinsp;313)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStan. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation Beyond High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrently Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Biological Child\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluently Reads English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluently Speaks English\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFather\u0026rsquo;s Education Beyond High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMother\u0026rsquo;s Education Beyond High School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDescriptive statistics are based on the final wave (2022) of the NLSY97 for 313 individuals who became naturalized U.S. citizens. All variables are measured at the individual level. \u0026ldquo;Sex\u0026rdquo; is a binary variable coded as 1\u0026thinsp;=\u0026thinsp;Female, 0\u0026thinsp;=\u0026thinsp;Male. \u0026ldquo;Residence\u0026rdquo; is coded as 1\u0026thinsp;=\u0026thinsp;Urban, 0\u0026thinsp;=\u0026thinsp;Rural. \u0026ldquo;Race\u0026rdquo; is coded as 1\u0026thinsp;=\u0026thinsp;Non-White, 0\u0026thinsp;=\u0026thinsp;White. \u0026ldquo;Education Beyond High School\u0026rdquo; is coded as 1\u0026thinsp;=\u0026thinsp;Yes (any education beyond a high school diploma), 0\u0026thinsp;=\u0026thinsp;No. \u0026ldquo;Currently Married\u0026rdquo; is coded as 1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No. \u0026ldquo;Fluently Reads English\u0026rdquo; and \u0026ldquo;Fluently Speaks English\u0026rdquo; are both binary variables coded as 1\u0026thinsp;=\u0026thinsp;Yes, 0\u0026thinsp;=\u0026thinsp;No, based on self-reported English proficiency. \u0026ldquo;Father\u0026rsquo;s Education Beyond High School\u0026rdquo; and \u0026ldquo;Mother\u0026rsquo;s Education Beyond High School\u0026rdquo; are each coded as 1\u0026thinsp;=\u0026thinsp;Yes (parent had postsecondary education), 0\u0026thinsp;=\u0026thinsp;No. \u0026ldquo;Household Size\u0026rdquo; counts all cohabiting members, including the respondent. These variables offer contextual insight into human capital, family structure, and linguistic integration, which are relevant to labor market outcomes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePost-Naturalization Effects\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the baseline OLS estimates examining the relationship between naturalization and four core labor market outcomes: employment status, proportion of weeks employed, logged income, and year-over-year income growth. These models control for a comprehensive set of covariates, including age, sex, race, education, household size, English fluency, urban residence, years since naturalization, and survey year fixed effects. All standard errors are clustered at the individual level to account for repeated observations in the panel structure.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of Naturalization on Employment and Income\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.118\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table reports coefficients from OLS regression models estimating the average impact of U.S. naturalization on four economic outcomes: (1) Employment Status \u0026ndash; binary variable for whether the respondent was employed during the survey year; (2) Proportion of Weeks Employed \u0026ndash; the number of weeks worked divided by 52; (3) Log Income \u0026ndash; natural logarithm of total annual earnings to normalize income distribution; and (4) Year-over-Year Income Growth \u0026ndash; binary indicator of whether income increased compared to the previous year. \u0026ldquo;Post-Naturalization\u0026rdquo; is a treatment variable coded 1 for all years after citizenship acquisition. All models control for age, gender, race, household size, urban residence, education, English fluency (reading and speaking), years since naturalization, and survey year fixed effects. Standard errors are clustered at the individual level to account for repeated measures.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003cp\u003eContrary to the widely held expectation that naturalization facilitates economic mobility, the estimates reveal a consistent pattern of negative and statistically significant associations between citizenship acquisition and labor market outcomes. Specifically, naturalization is associated with a 9.7 percentage point decline in the probability of being employed, significant at the 1% level. A similar pattern is observed for employment intensity, where naturalization is linked to a 5.7 percentage point reduction in the proportion of weeks worked during the year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggest that naturalization does not, on average, lead to improvements in labor force attachment\u0026mdash;and may, in fact, coincide with employment decline.\u003c/p\u003e\n \u003cp\u003eThe income-based measures further reinforce this pattern. The log income model shows that naturalization is associated with an 11.8% decrease in annual earnings, a result that is marginally significant at the 10% level. Year-over-year income growth also appears to be negatively affected, with a 4.2 percentage point reduction in the probability of income improvement relative to the prior year. While these magnitudes are modest, the consistency of the negative direction across all outcome measures challenges the assumption that citizenship acquisition reliably enhances economic prospects. It is important to note that these results persist even after adjusting for an extensive set of individual-level covariates, suggesting that the observed declines are not attributable solely to shifts in age, education, or household context. The findings raise important concerns about the prevailing policy narrative that equates legal integration with labor market advancement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eHeterogeneity Results\u003c/h2\u003e\n \u003cp\u003eWhile the baseline models reveal average negative effects of naturalization on employment and income, these effects may not be uniformly experienced across all groups. To assess the distribution of outcomes, Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e disaggregate the effects by gender and race, respectively. These subgroup analyses provide critical insight into whether naturalization functions as an equalizing force or whether its economic returns vary systematically across social lines.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e displays regression results separately for male and female respondents. For women, the effects of naturalization are consistently more negative and statistically significant across all four outcome measures. Naturalization is associated with a 10.2 percentage point drop in employment probability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a 6.2 point reduction in weeks worked, a significant 18% decrease in logged income, and a 7.5 percentage point decline in year-over-year income growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that for women, acquiring citizenship may not only fail to deliver labor market benefits\u0026mdash;it may coincide with material setbacks. In contrast, the effects for men are generally smaller and statistically weaker. While the signs of the coefficients for men are also negative, only the employment status effect (\u0026minus;\u0026thinsp;9.7 percentage points, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) reaches marginal significance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of Naturalization on Employment and Income by Gender\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost-Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.062\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.180\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table reports regression results stratified by gender to examine differential post-naturalization effects on labor market outcomes. Columns (1)\u0026ndash;(4) reflect outcomes for male respondents; columns (5)\u0026ndash;(8) reflect outcomes for females. Definitions of outcome variables and covariates are consistent with Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis tests whether naturalization yields gender-specific effects across employment intensity, income levels, and upward earnings mobility. Controls include individual demographics, human capital characteristics, and fixed effects by survey year. Standard errors in parentheses are clustered at the individual level.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e turns to racial heterogeneity, comparing effects for white and non-white respondents. For non-white naturalized citizens, the results reveal statistically significant declines in three out of four outcomes: a 10.5 percentage point drop in employment, a 6.8 point decline in weeks worked, and a 5.1 point reduction in income growth. Although the effect on logged income (\u0026minus;\u0026thinsp;5.9%) is negative, it does not reach statistical significance. For white respondents, none of the outcomes show statistically meaningful changes, except for the coefficient for logged income (\u0026minus;\u0026thinsp;20.3%) is large and marginally significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), warranting further scrutiny. Overall, these patterns suggest that non-white naturalized citizens experience more immediate and consistent labor market setbacks following naturalization, while white citizens face weaker or more variable effects. These findings support the notion that the formal acquisition of citizenship does not erase racial stratification in economic outcomes and may, in some cases, leave underlying inequities unaddressed or even exacerbated.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of Naturalization on Employment and Income by Race\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNon-White\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost-Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.203\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.068\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.051\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eThis table estimates separate models for White (columns 1\u0026ndash;4) and non-White (columns 5\u0026ndash;8) respondents. Non-White includes individuals identifying as Black, Hispanic, Asian, or other racial minorities. Models examine whether naturalization impacts vary by race across employment, labor force attachment, earnings, and income growth. All models include identical covariates and specifications as Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e to ensure comparability across groups. Standard errors in parentheses are clustered at the individual level. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e focuses on intersectional effects by including interaction terms between post-naturalization status and key demographic identities. The interaction between Post-Naturalization \u0026times; Female is negative and statistically significant across all four outcomes, indicating that women experience unique disadvantages after acquiring citizenship. Specifically, the interaction term is associated with a 7.1 percentage point decline in employment probability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), a 7.8 point reduction in proportion of weeks worked (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a 12.4% decrease in logged income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), and a 4.4 percentage point drop in the likelihood of year-over-year income growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). These consistent negative effects suggest that naturalization may exacerbate rather than alleviate gender disparities in the labor market. The interaction between Post-Naturalization \u0026times; Non-White is similarly negative for labor force attachment and income growth. Non-white naturalized citizens experience a 5.8 point reduction in weeks employed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) and a 5.9 point decline in year-over-year income growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results highlight that the labor market penalties following naturalization are most pronounced at the intersection of gender and race, underscoring the limits of legal status change as a tool for economic inclusion among structurally marginalized groups.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIntersection of Gender and Race on Employment and Income\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.065)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization \u0026times; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.071\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.078\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.124\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.044\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization \u0026times; Non-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.058\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eThis table introduces interaction terms between naturalization status and demographic indicators for gender and race. Columns (1)\u0026ndash;(4) estimate models with \u0026ldquo;Post-Naturalization \u0026times; Female,\u0026rdquo; and columns (5)\u0026ndash;(8) include \u0026ldquo;Post-Naturalization \u0026times; Non-White.\u0026rdquo; The goal is to assess whether post-citizenship outcomes vary not just by gender or race independently, but at their intersection. Main effects and interactions are interpreted relative to the omitted category. Outcome definitions and covariates are consistent with Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Standard errors in parentheses are clustered at the individual level. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eLastly, Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e introduces a triple interaction term to assess whether the negative effects of naturalization are particularly acute for non-white women\u0026mdash;a group at the intersection of multiple forms of marginalization. The results reveal that the interaction between Post-Naturalization \u0026times; Female \u0026times; Non-White is significantly negative for three of the four outcomes. Specifically, non-white women experience an 11.5 percentage point decline in employment probability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), a 10.9 point reduction in the proportion of weeks worked (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and a 23.8% drop in logged income (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) following naturalization. These effects are large in magnitude and statistically robust, underscoring that naturalization coincides with especially steep economic setbacks for this subgroup. In contrast, none of the other two-way interaction terms display this level of consistent significance across outcomes. These findings highlight that the labor market penalties associated with naturalization are not only gendered and racialized but are most severe at their intersection, emphasizing the importance of intersectional analysis in evaluating the true distribution of citizenship\u0026rsquo;s economic returns.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTriple Interaction Effect on Employment and Income\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment Status (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProportion of Weeks Employed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog Income\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear-over-Year Income Growth (Binary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.176\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.069)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale \u0026times; Non-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization \u0026times; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization \u0026times; Non-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePost- Naturalization \u0026times; Female \u0026times; Non-White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.238\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnglish Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears since Naturalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot MSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table includes a three-way interaction term (\u0026ldquo;Post-Naturalization \u0026times; Female \u0026times; Non-White\u0026rdquo;) to isolate the unique labor market effects of naturalization for non-White women\u0026mdash;a group experiencing intersecting disadvantages. The dependent variables are the same four labor market outcomes as in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. All lower-order terms and demographic controls (age, household size, urban residence, education, English fluency, years since naturalization, and survey year) are included. Standard errors in parentheses are clustered at the individual level. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn essence, the subgroup analyses by gender, race, and their intersection reveal that the labor market consequences of naturalization are deeply uneven. While some individuals may experience neutral or modest outcomes, women and non-white immigrants consistently face diminished economic returns after acquiring citizenship. Most notably, non-white women experience the steepest post-naturalization penalties across employment, labor force attachment, and income levels, suggesting that legal status change does little to counteract the compounding effects of structural disadvantage. These findings underscore that naturalization alone does not level the economic playing field and must be understood within the broader context of systemic inequality and labor market segmentation. The promise of citizenship remains fundamentally constrained by intersecting barriers that legal inclusion alone cannot dismantle.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eYear-to-Year Effects of Naturalization\u003c/h2\u003e\n \u003cp\u003eLastly, to better understand how the impact of naturalization unfolds over time, the analysis incorporates dynamic year-by-year estimates using a categorical variable for years since naturalization. Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e through \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e plot the estimated coefficients for each post-naturalization year, allowing for a detailed examination of whether labor market outcomes improve, decline, or remain stagnant over time. Each figure is divided into three panels: Panel A shows the overall effect for the full sample, Panel B disaggregates by gender, and Panel C by race. These visualizations, supported by full regression outputs in Tables \u003cspan class=\"InternalRef\"\u003eA1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eA2\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003eA3\u003c/span\u003e (Appendix A), allow for assessment of temporal and demographic variation in the post-citizenship economic trajectory.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the estimated effect of naturalization on employment status, measured as a binary indicator of whether an individual was employed during a given year. The results in Panel A show no clear trend toward improved employment over the 22-year post-naturalization period. The coefficients remain largely negative, indicating that naturalization is not followed by increases in employment rates. In Panel B, gender disaggregation reveals that female respondents experience a sustained decline in employment starting around year 4, with no signs of recovery. Male respondents, in contrast, exhibit more variability across years, including occasional years of weak improvement, though none reach consistent statistical significance. Panel C, disaggregated by race, indicates that non-white respondents experience a persistent negative effect across all 22 years. White respondents show more fluctuation, with smaller effect sizes and wider confidence intervals, but no sustained improvement. These patterns suggest that the acquisition of citizenship does not provide a delayed or compounding employment benefit for the sample as a whole or for major subgroups.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e explores the effect of naturalization on the proportion of weeks employed, which captures labor force intensity. The overall trend in Panel A is largely flat or slightly negative, indicating that on average, naturalization does not lead to stronger or more consistent labor force attachment over time. In Panel B, gender-specific trends show that women\u0026rsquo;s employment intensity declines sharply beginning in the first year after naturalization and remains below baseline levels for the entire two-decade span. In contrast, men\u0026rsquo;s trajectories are more stable and exhibit less pronounced downward drift. Panel C illustrates similar racial patterns: non-white respondents see a steady deterioration in weeks employed, while white respondents maintain a relatively stable profile. These findings reinforce the idea that naturalization is not associated with long-term labor market integration and, for some groups, may coincide with gradual exclusion from steady work.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the year-by-year effect of naturalization on logged income, which reflects changes in annual earnings on a proportional scale. In Panel A, the overall effect becomes increasingly negative after the first few years post-naturalization. The income gap appears to widen over time rather than close. In Panel B, the decline in income is particularly pronounced for female respondents, whose coefficients become increasingly negative and statistically significant around year 5. By year 10 and beyond, estimated income losses exceed 40\u0026ndash;70% compared to their pre-naturalization levels. Male respondents, on the other hand, exhibit more moderate and statistically unstable patterns, with coefficients oscillating near zero. Panel C reveals that non-white individuals experience persistent and compounding income declines, especially after year 5. In contrast, white individuals show more moderate losses, with some years approaching baseline levels. This figure provides some of the strongest evidence that naturalization is not translating into economic mobility and may even mark the beginning of long-term income stagnation or decline for many.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reports results for year-over-year income growth, a dynamic measure of upward mobility. In Panel A, the full sample shows no consistent positive trend following naturalization, and many years display negative coefficients. In Panel B, the income growth trajectory for women remains flat or negative across nearly all post-naturalization years, while men display more variation but do not exhibit a sustained pattern of upward momentum. In Panel C, non-white respondents show consistently lower probabilities of income growth, with the most severe stagnation occurring between years 7 and 20. White respondents again demonstrate more variation but fail to show significant improvement over time. These results suggest that even if naturalization offers legal security, it does not necessarily facilitate wage progression or consistent earnings improvement\u0026mdash;particularly for groups already marginalized in the labor market.\u003c/p\u003e\n \u003cp\u003eThis illustrates that naturalization does not appear to yield delayed or cumulative economic benefits for most individuals in this sample. Rather than reversing initial post-naturalization setbacks, labor market outcomes either remain unchanged or deteriorate further over time. Women and non-white immigrants experience the steepest and most persistent declines, while men and white respondents see greater variability but few signs of sustained economic gain.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eThis study explores whether acquiring citizenship through naturalization leads to improved labor market outcomes for immigrants in the United States, using longitudinal data from the National Longitudinal Survey of Youth 1997 (NLSY97). By examining within-person changes over two decades, this research provides nuanced insights into the economic trajectories of 313 naturalized immigrants, specifically analyzing employment rates, income levels, labor force participation, and income growth. Contrary to widely held assumptions, the findings demonstrate that naturalization is generally not associated with enhanced employment or earnings, with results showing a 9.7 percentage point decline in employment and an 11.8% decrease in annual income post-naturalization. The data reveal persistent or deepening negative impacts post-naturalization, especially for women and non-white immigrants. These outcomes challenge prevailing narratives that portray naturalization as a universally beneficial economic integration strategy.\u003c/p\u003e\n\u003cp\u003eThis paper contributes significantly to existing literature by addressing important methodological and substantive gaps. Prior studies have predominantly employed cross-sectional data or short-term panels, often overlooking critical demographic distinctions. By leveraging extensive longitudinal data, this analysis captures temporal dynamics, providing clear evidence of how economic outcomes evolve over the long term post-naturalization. Furthermore, disaggregating results by gender and race reveals that naturalization does not uniformly benefit all immigrant groups. This intersectional approach highlights systemic inequalities within labor market institutions and underscores the complexities surrounding citizenship\u0026rsquo;s economic impact. Thus, the study advances scholarly understanding by emphasizing that naturalization alone does not guarantee economic advancement, particularly for structurally marginalized populations. Moreover, the nuanced findings concerning the differential effects across demographic groups deepen existing discussions around citizenship and inequality. The consistent negative outcomes identified for women and non-white immigrants indicate that legal status changes alone may reinforce existing labor market inequalities rather than alleviate them. These insights align with theoretical frameworks emphasizing structural barriers and intersectional inequalities, providing empirical support for policies aimed at more comprehensive immigrant integration.\u003c/p\u003e\n\u003cp\u003eNevertheless, the study faces several limitations that warrant careful consideration. Although the longitudinal design of the NLSY97 dataset allows for robust within-person analysis, the relatively small sample size of 313 naturalized individuals may limit the generalizability of the findings across the broader and more diverse immigrant population. Moreover, the cohort studied\u0026mdash;individuals born between 1980 and 1984\u0026mdash;represents a specific generational slice, meaning that their labor market trajectories and naturalization experiences may not fully reflect those of younger or newly arriving immigrant groups facing different economic and policy environments. Additionally, while the models control for a wide range of demographic and socioeconomic variables, the absence of regional economic trends, and subjective factors such as motivation or social capital may obscure important mechanisms behind the observed outcomes. These limitations suggest caution in interpreting causal claims and highlight the need for further research that can validate and build upon these findings using larger and more diverse datasets, multi-cohort comparisons, and mixed-methods approaches. Future studies should also investigate sector-specific patterns, regional labor market conditions, and immigrants\u0026rsquo; lived experiences post-naturalization to better understand the structural constraints and opportunities that shape economic integration. Cross-national comparisons would further help identify whether these negative patterns are uniquely American or part of a broader global trend shaped by similar institutional dynamics.\u003c/p\u003e\n\u003cp\u003ePolicy implications arising from this study are significant. Given the limited and, at times, negative economic outcomes associated with naturalization, policymakers should reconsider relying solely on citizenship acquisition as a primary integration strategy. Instead, more comprehensive approaches addressing structural barriers, including labor market discrimination, occupational segregation, and targeted support for marginalized immigrant populations, are crucial. Policymakers might enhance naturalization benefits by coupling legal status changes with active labor market policies, such as employment counseling, vocational training, language programs, and stronger anti-discrimination measures.\u003c/p\u003e\n\u003cp\u003eIn essence, this study reveals that naturalization does not universally yield positive economic returns for immigrants, highlighting substantial and persistent disparities for women and non-white individuals. By illustrating the nuanced and often negative impacts of citizenship acquisition on labor market outcomes, the research underscores the urgent need for integration strategies that move beyond legal status changes. Future policies must address deeper systemic inequalities to realize the full potential of naturalization as a tool for genuine economic and social integration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.S. conceived the study, conducted the data analysis, interpreted the results, and wrote the manuscript. R.S. reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study uses publicly available data from the National Longitudinal Survey of Youth 1997 (NLSY97), provided by the U.S. Bureau of Labor Statistics. The dataset can be accessed at: https://www.bls.gov/nls/nlsy97.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u0026nbsp;\u003c/strong\u003eThe author declares that there are no financial or non-financial competing interests related to the content of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Immigration Council (2024). \u003cem\u003eNaturalization in the United States: Key facts\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.americanimmigrationcouncil.org/fact-sheet/naturalization-united-states/\u003c/span\u003e\u003cspan address=\"https://www.americanimmigrationcouncil.org/fact-sheet/naturalization-united-states/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBloemraad, I. (2002). 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Migration Policy Institute. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.issuelab.org/resources/29844/29844.pdf\u003c/span\u003e\u003cspan address=\"https://www.issuelab.org/resources/29844/29844.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Congress (1790). \u003cem\u003eAn act to establish an uniform Rule of Naturalization\u003c/em\u003e (1 Stat. 103). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ourdocuments.gov/doc.php?flash=false\u0026amp;doc=47\u003c/span\u003e\u003cspan address=\"https://www.ourdocuments.gov/doc.php?flash=false\u0026amp;doc=47\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Congress (1882). \u003cem\u003eAn act to execute certain treaty stipulations relating to Chinese\u003c/em\u003e (22 Stat. 58). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.archives.gov/milestone-documents/chinese-exclusion-act\u003c/span\u003e\u003cspan address=\"https://www.archives.gov/milestone-documents/chinese-exclusion-act\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Congress (1924). \u003cem\u003eAn act to limit the immigration of aliens into the United States, and for other purposes\u003c/em\u003e (43 Stat. 153). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.govinfo.gov/content/pkg/STATUTE-79/pdf/STATUTE-79-Pg911.pdf\u003c/span\u003e\u003cspan address=\"https://www.govinfo.gov/content/pkg/STATUTE-79/pdf/STATUTE-79-Pg911.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolgin, P. E. (2013). \u003cem\u003eTop 5 reasons why citizenship matters\u003c/em\u003e. Center for American Progress. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.americanprogress.org/article/top-5-reasons-why-citizenship-matters/\u003c/span\u003e\u003cspan address=\"https://www.americanprogress.org/article/top-5-reasons-why-citizenship-matters/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, P. Q. (1994). Explaining immigrant naturalization. \u003cem\u003eInternational migration review\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(3), 449\u0026ndash;477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/019791839402800302\u003c/span\u003e\u003cspan address=\"10.1177/019791839402800302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Naturalization, Citizenship and economic integration, Immigrant earnings, Longitudinal immigrant data, Employment post-naturalization, Economic mobility, Citizenship premium","lastPublishedDoi":"10.21203/rs.3.rs-6978281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6978281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines whether naturalization improves employment and income outcomes for immigrants in the United States. Drawing on 22 years of panel data from the National Longitudinal Survey of Youth 1997 (NLSY97), this analysis tracks 313 naturalized immigrants to assess changes in employment status, labor force participation, annual income, and year-over-year income growth. Contrary to prevailing assumptions, findings indicate that naturalization does not produce consistent or positive economic returns. On average, naturalization is associated with a 9.7 percentage point decline in employment rates and an 11.8 percent decrease in annual income. Income growth also drops by 4.2 percentage points post-naturalization. Disaggregated analyses reveal that women experience an 18 percent income drop and a 10.2 percentage point reduction in employment, while non-White immigrants see a 10.5 percentage point drop in employment and a 6.8 percentage point decline in proportion of weeks worked. Dynamic year-by-year models further demonstrate that these adverse effects persist or even worsen over time. These findings challenge the assumption that citizenship alone reliably facilitates economic mobility, highlighting significant limitations of naturalization as a standalone strategy for immigrant integration.\u003c/p\u003e","manuscriptTitle":"Does Citizenship Deliver? Persistent Gaps in Employment and Earnings after Naturalization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 07:12:54","doi":"10.21203/rs.3.rs-6978281/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de44c4e8-4353-427f-ad05-dcc0db3975aa","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:12:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-27 07:12:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6978281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6978281","identity":"rs-6978281","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}