Wealth Inequality and Integration Challenges Discussion

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FUTURIST/INNOVATION SECTION* D. Futurist/Innovation 1. State area/s of article could be improved. Explain. 2. Provide futuristic/innovative idea/s to improve article. 3. Use the designated journal article and textbook chapter citation/s to support your position/s. Popul Res Policy Rev (2016) 35:147–175 DOI 10.1007/s11113-016-9385-1 Wealth Inequality Among Immigrants: Consistent Racial/Ethnic Inequality in the United States Matthew A. Painter II1 • Zhenchao Qian2 Received: 2 February 2015 / Accepted: 24 February 2016 / Published online: 3 March 2016  Springer Science+Business Media Dordrecht 2016 Abstract Wealth is a strong indicator of immigrant integration in U.S. society. Drawing on new assimilation theory, we highlight the importance of racial/ethnic group boundaries and propose different paths of wealth integration among U.S. immigrants. Using data from the Survey of Income and Program Participation and quantile regression, we show that race/ethnicity shapes immigrant wealth inequality across the entire distribution of net worth, along with immigrants’ U.S. experience, such as immigrant status, U.S. education, English language proficiency, and time spent in the United States. Our results document consistent racial/ethnic inequality among immigrants, also evidenced among the U.S. born, revealing that even when accounting for key aspects of U.S. experience, wealth inequality with whites for Latino and black immigrants is strong. Keywords Race/ethnicity  Immigrants  U.S. experience  Wealth inequality Introduction Immigrants move to the United States for a variety of reasons, including the pursuit of opportunities to improve their financial well-being (e.g., Portes and Rumbaut 2006; Smith and Edmonston 1997). A growing body of literature has focused on & Matthew A. Painter II mpainter@uwyo.edu Zhenchao Qian zhenchao_qian@brown.edu 1 Department of Sociology, University of Wyoming, 411 Ross Hall, 1000 E. University Avenue, Laramie, WY 82071, USA 2 Department of Sociology, Brown University, 206 Maxcy Hall, Providence, RI 02912, USA 123 148 M. A. Painter II, Z. Qian wealth as an indicator of financial well-being in an effort to understand how immigrants integrate into U.S. society (e.g., Akresh 2011; Hao 2004, 2007; Painter 2013, 2014; Painter and Qian Forthcoming). This focus is important because wealth signifies a unique set of resources that reflect financial attitudes and behaviors as well as priorities, goals, and values (Hao 2007). Together, the various investments within a financial portfolio represent a pool of resources that can be used to meet short- and long-term needs and provide a number of additional financial advantages (e.g., return on investment, investment collateral, transferability) (Keister 2000b, 2005). How much wealth immigrants possess provides valuable insight into their financial well-being and into how well they are integrating into U.S. society. If immigrants possessed characteristics that mirrored those of the U.S. population, we would expect immigration to have little influence on wealth inequality in the United States because the wealth of immigrants would resemble that of the native born (Hao 2007). Immigrants, however, are mostly racial/ethnic minorities, with some characteristics that facilitate integration into U.S. society while others serve as barriers. The central contribution of this paper is to explore how racial/ethnic realities in the United States provide differential opportunities and constraints for immigrants of different racial/ethnic groups. We move beyond a single summary measure of wealth inequality and use quantile regression techniques to offer a more complete picture of how immigrants’ race/ethnicity affects wealth inequality across the distribution of net worth. This is important because a disproportionate share of immigrants are concentrated near the bottom of the U.S. socioeconomic ladder (e.g., Lichter et al. 2005; Smith and Edmonston 1997). Quantile regression broadens the understanding of wealth inequality by focusing on racial/ethnic wealth disparities at various points of the wealth distribution. To understand how race/ethnicity affects immigrants’ integration into U.S. society, we draw on new assimilation theory to provide insight into contemporary immigrant patterns of incorporation (Alba and Nee 2005). In this reformulation of classical assimilation theory, race/ethnicity is considered a social boundary embedded in a variety of social, economic, and cultural differences not only at the individual level but also in social institutions. While social, financial, and human capital are important for immigrants’ integration into U.S. society, their race/ ethnicity remains an important predictor of wealth. We expect immigrant racial/ ethnic wealth inequality to resemble the patterns of their native-born counterparts because racial/ethnic realities in the United States provide differential opportunities and constraints to both immigrants and natives of different racial/ethnic groups. Our second contribution helps understand how immigrants’ U.S. experiences, including immigrant status, U.S. education, English language proficiency, and time spent in the United States, affects their integration into U.S. society. Specifically, we explore how immigrant status, such as naturalization or legal permanent residency, affects immigrants’ financial well-being. We then turn to two indicators of immigrants’ human capital and assess how acquiring U.S. education and greater English language proficiency ease immigrants’ transition into U.S. society. Last, we investigate the time immigrants have spent in the United States. The longer immigrants live in the United States, the more familiar they become with U.S. customs, financial institutions, and savings/investment opportunities, and thus have 123 Wealth Inequality Among Immigrants… 149 more wealth (Zhang 2003). We expect that immigrants’ characteristics will affect wealth differently depending upon where immigrants lie, all else being equal, along the wealth distribution. We examine wealth inequality by using data from the 2001 and 2004 panels of the Survey of Income and Program Participation (SIPP), national representative data of the non-institutionalized U.S. population. They are well-suited for this study because they contain detailed migration and financial information. Following Hao (2004, 2007), we expand the concept of immigrant financial well-being to include wealth. SIPP collects rich information on assets and debts held at the time of the survey, which include immigrants’ pre-migration financial resources and wealth accumulated in the United States. This means that we are unable to exclude immigrant wealth holdings held abroad at the time of arrival. Fortunately, we are able to control for pre-immigration characteristics, including foreign educational attainment and legal permanent resident (LPR) status at arrival, which are strong proxies of wealth at the time of arrival. In sum, we make a unique contribution in this study and underscore the consistency of racial/ethnic inequality among both immigrants and the native born by examining broad racial/ethnic differences at multiple points of the wealth distribution. Conceptual Framework Assimilation, Immigrant Integration, and Racial/Ethnic Realities Assimilation theory has long been used to understand immigrant experiences in the United States. It captures the process where the distinctiveness of immigrants’ country of origin gradually diminishes over time as later generations and those who lived in the United States for a long time adopt cultural patterns of the majority population (Gordon 1964). This theory explains successfully the experiences of European immigrants and their descendants in the United States at the turn of the twentieth century. At the time of arrival, the first generation European immigrants were highly diverse from an ethnic, cultural, and/or economic standpoint (Hirschman 2005). Yet, over time, ethnic distinctions faded, European immigrants and their descents achieved socioeconomic parity with earlier-arriving European immigrants, and they are all now grouped—and generally group themselves—into a white racial category (Alba 1990; Perlmann and Waldinger 1997). Classical assimilation theory has been criticized, however, in a number of ways, including its inability to address the continued salience of race/ethnicity among contemporary immigrants (for a summary of the criticism, see Alba and Nee 2005, pp. 3–5). The persistent and highly institutionalized racial/ethnic inequality in the United States suggests that immigrants of various racial/ethnic minority backgrounds are unlikely to follow the path of the descendants of European immigrants who arrived to the United States at the turn of the twentieth century. In their reformulation of assimilation theory, Alba and Nee (2005) argue that social boundaries based on race/ethnicity are so deeply rooted and rigid that attitudes and behaviors are formed based on individuals’ positions in a racial/ethnic hierarchy. 123 150 M. A. Painter II, Z. Qian This suggests that racial/ethnic minority immigrants may not integrate well into U.S. society because they are subject to similar—if not more severe—prejudices and discriminations compared to their native-born counterparts, regardless of their socioeconomic position at the time of arrival. In the United States, historically rampant prejudice and discrimination against racial/ethnic minorities, especially African Americans, has created tremendous wealth gaps among racial/ethnic groups (Oliver and Shapiro 2006). Racial/ethnic group boundaries serve as strong barriers to integration because of the highly institutionalized racial/ethnic inequality in the United States (Omi and Winant 1994). Even without contemporary discrimination and prejudice, historical and cumulative racial/ethnic inequalities are unlikely to disappear. Yet, contemporary discrimination and prejudice persist, although they have become more covert and elusive. Racial/ethnic minorities continue to face obstacles in education, employment, housing, and credit and consumer markets (e.g., Logan 2011; Pager and Shepherd 2008; White and Glick 2011). Such structural barriers create and reinforce racial/ethnic boundaries, which will affect racial/ethnic minority immigrants as well. Immigrants’ integration thus depends not only on their social, financial, and human capital, but also on opportunities and constraints available to them given their position within existing racial/ethnic structures. In the end, immigrants’ integration hinges on how well their native-born racial/ethnic counterparts fare in U.S. society. In fact, racial/ethnic minority immigrants may face worse challenges than their native-born counterparts, which influence their job opportunities, social networks, and, ultimately, asset attainment (Hao 2007; Portes and Rumbaut 2006; Waldinger 1996; Waters 1999). Studies on wealth (Hao 2004, 2007; Painter 2013; Painter and Qian Forthcoming) as well as education, earnings, and residential and intermarriage patterns among immigrants (e.g., Alba et al. 1999; Borjas 1994; Kao and Thompson 2003; Qian and Lichter 2007) underscore that racial/ethnic minority immigrants are often doubly disadvantaged, lagging behind their native-born counterparts and white immigrants, and to a large extent, native-born whites. Yet, Alba and Nee’s conceptualization of new assimilation theory views racial/ ethnic boundaries as more flexible over time because such boundaries can be crossed, blurred, and/or shifted. Depending on their racial/ethnic position, some immigrants may integrate more easily into U.S. society than others. For example, Alba and Nee (2005, p. 132) note that the perception of racial distinctiveness between whites and both Asian and lighter-skinned Latino immigrants has already changed, which suggests that their group boundaries may serve as less of an impediment for their integration into U.S. society. Meanwhile, Alba and Nee (2006, p. 133) call the black–white divide the ‘‘most intractable racial boundary,’’ which continues to limit the incorporation of black immigrants (see also Portes and Zhou 1993). Indeed, Waters (1999) shows that West Indian immigrants strive to maintain their ethnic identity as a way of distinguishing themselves from black Americans and to help facilitate upward mobility. In the end, however, ‘‘race as a master status… overwhelms the identities of the immigrants and their children, and they are seen as black Americans’’ (Waters 1999, p. 8). In sum, the salience of race/ 123 Wealth Inequality Among Immigrants… 151 ethnicity—particularly in terms of the black–white divide—shapes immigrants’ integration patterns and influences wealth inequality in the United States. Race/Ethnicity and Wealth Since the racial/ethnic status of both immigrants and the native born is strongly related to wealth, it is essential to briefly review this literature in order to shed light on how race/ethnicity affects wealth for both the native born and immigrants. Broadly, our study builds on a growing body of research that examines immigrant wealth inequality. Some of this work has examined specific immigrant characteristics, including country of origin (Akresh 2011; Cobb-Clark and Hildebrand 2006c; Hao 2004) or place of education (Painter 2013). Other work looked at racial/ethnic inequality for immigrants with positive wealth (Hao 2007) or among LPRs (Painter and Qian Forthcoming). Below, we focus our review on research that examines racial/ethnic differences in overall wealth or net worth. Asians A growing body of research examines Asian wealth inequality. Among the native born, there is mixed evidence with Asian Americans having more (Hao 2004; Painter 2013), less (Campbell and Kaufman 2006; Hao 2007), or equivalent (Painter 2013) wealth as native-born whites, though educational attainment and ethnic diversity may explain these discrepancies. By race/ethnicity, with the exception of Japanese immigrants, Asian immigrants have less wealth than white immigrants (Hao 2004; Painter and Qian Forthcoming). Blacks Most of the literature on racial/ethnic wealth inequality has focused on the black– white divide. Research overwhelmingly demonstrates that native-born blacks have less wealth than native-born whites (e.g., Blau and Graham 1990; Conley 1999; Hao 2004, 2007; Keister 2000a, 2004; Killewald 2013; Oliver and Shapiro 2006; Smith 1995). This pattern holds for immigrants as well (Hao 2004; Painter 2013; Painter and Qian Forthcoming). Latinos The wealth literature has increasingly focused on Latino financial well-being. For net worth, native-born Latinos are less wealthy than native-born whites (Campbell and Kaufman 2006; Cobb-Clark and Hildebrand 2006a, b; Hao 2004, 2007; Painter 2013; Smith 1995). A similar pattern is evident when comparing Latino immigrants to whites, whether they are fellow immigrants or native born (Hao 2004, 2007; Painter 2013; Painter and Qian Forthcoming). Among Latinos, there is some mixed evidence with some research reporting that there is no nativity effect (Painter 2013), while other research finds that Latino immigrants have a lower level of wealth than native-born Latinos (Hao 2007). 123 152 M. A. Painter II, Z. Qian Immigrants’ U.S. Experience Immigrants arrive to the United States with different levels of educational attainment, socioeconomic status, and U.S.-based social networks. Human, financial, and social capital at the time of arrival determine their initial starting position, shape their U.S. experiences, and influence their socioeconomic achievement and wealth attainment (Nee and Sanders 2001). In this section, we discuss four dimensions of U.S. experience: immigrant status, U.S. education, English language proficiency, and time spent in the United States. Each of these factors reflects immigrant integration in U.S. society and affects their integration and wealth. We first describe each dimension and then develop a link to immigrants’ financial wellbeing. For immigrant status, we distinguish between legal permanent residency and naturalization. Immigrants can live in the United States permanently by applying for LPR status in two main ways: adjustment or new arrival. Adjusted immigrants have often lived in the United States for a number of years with a non-immigrant status before they apply for LPR status. New arrival immigrants apply for LPR status in their home country; however, some may actually live in the United States before returning to their home country to file and receive their LPR paperwork. LPR immigrants have the same rights and responsibilities as citizens except that they have no voting rights and lack access to jobs that require U.S. citizenship (Massey and Bartley 2005). Immigrants are eligible to naturalize after living 5 years in the United States and spouses of U.S. citizens, military personnel, and minor children of naturalized citizens are eligible for naturalization sooner (U.S. Citizenship and Immigration Services 2013).1 With naturalization, immigrants gain the right to vote, the ability to sponsor adult relatives for migration, full Social Security benefits, access to a U.S. passport, and eligibility for educational programs, certain employment opportunities, and jury duty responsibilities (Jasso and Rosenzweig 1990; Massey and Bartley 2005; Yang 1994). Immigrants do encounter costs associated with naturalization, which may encourage immigrants to maintain their status as LPRs. For example, unless dual citizenship is permitted, immigrants may lose citizenship in their country of origin, which may result in the forfeiture of access to public benefits (e.g., retirement funds, public health care), restricted travel, and/or constrained home country employment prospects (Van Hook et al. 2006; Yang 1994). Additionally, naturalization is a complex, time-consuming, and expensive process that requires financial resources, the ability to navigate bureaucracy, and satisfactory English language and civics proficiency (Alvarez 1987; Gilbertson and Singer 2003; Van Hook et al. 2006; see also North 1987). We expect naturalized citizens to have similar levels of wealth as the native born. In part, this is due to lowered educational and occupational barriers as naturalization is associated with better jobs, greater wage growth, and higher earnings (Bratsberg et al. 2002; Chiswick and Miller 1 Other requirements for naturalization include immigrants’ physical presence in the United States for set time periods (for some paths to naturalization), good moral character, English and civics knowledge, and attachment to the Constitution (U.S. Citizenship and Immigration Services 2013). 123 Wealth Inequality Among Immigrants… 153 2002). Similarly, LPR immigrants are expected to have lower levels of wealth than citizens (whether native born or naturalized). This is also due to their more tenuous tie to U.S. society. Research consistently finds that foreign education, relative to U.S. education, is associated with worse financial well-being in the United States, either in terms of earnings (Aly and Ragan 2010; Bratsberg and Ragan 2002; Kaushal 2010; Kim and Sakamoto 2010; Schoeni 1997; Tao 2010, 2011; Tong 2010; Zeng and Xie 2004) or wealth (Hao 2007; Painter 2013).2 Foreign education is devalued in the United States for a number of reasons, including a (perceived or actual) lower quality of education in source countries (Bratsberg and Ragan 2002; Friedberg 2000; Schoeni 1997; Zeng and Xie 2004), difficulty in transferring certain majors and/or degrees (Basran and Zong 1998; Bratsberg and Ragan 2002; Friedberg 2000; Grant and Nadin 2007), and/or discrimination by U.S. employers or a lack of familiarity with how to assess the quality and/or level of education (Butcher 1994; Chiswick 1978; Greeley 1976).3 Lower earnings reduce immigrants’ ability to save, invest, and attain wealth. Obtaining U.S. education increases immigrants’ educational attainment, helps immigrants overcome the barriers associated with foreign education, and contributes to higher wealth (Hao 2007; Painter 2013). U.S. education can produce improved financial well-being in several ways. For one, it serves to upgrade and/or authenticate education received in the country of origin, which helps immigrants transfer their source-country specific skills to the U.S. labor market (Bratsberg and Ragan 2002). Additionally, beyond the credential itself, colleges and universities provide valuable job search resources, including access to recruitment networks, internships, and job fairs. Further, a U.S. education improves immigrants’ English language proficiency, increases their contact with U.S. culture, and encourages interactions with U.S. institutions, in particular financial establishments (Chiswick 1978; Hao 2007). This creates opportunities for immigrants to acquire U.S.-specific skills and information (Friedberg 2000; Kaushal 2010). English language proficiency helps improve wealth for several reasons. First, greater command of English indirectly affects wealth through better job access with potentially higher income (e.g., Chiswick and Miller 2002; Hall and Farkas 2008; Tainer 1988). Second, English language ability directly affects wealth through participation in formal U.S. financial institutions, where greater command of the language allows for more familiarity with the customs of these institutions, easier communication with financial personnel (e.g., bank employees, financial advisors, investment brokers), and more comfort within financial settings (Paulson et al. 2 We are aware of several exceptions. Stewart and Hyclak (1984) find no difference in earnings for immigrants’ pre- and post-migration schooling. There is evidence that higher education is rewarded among Arab immigrants (Aly and Ragan 2010). Nurses educated abroad earn higher wages in the United States than U.S.-educated nurses, which reflects the number of nurses with experience working in hospitals or in English-speaking countries (Huang 2011). 3 There is likely important variation by source country in the devaluation of foreign education as immigrants from countries that commit more resources to education and/or have comparable educational systems to the United States likely experience a better transition of their skills and educational credentials (Bratsberg and Ragan 2002; Schoeni 1997). 123 154 M. A. Painter II, Z. Qian 2006). It aids in immigrants’ acquisition of investment knowledge and strategies (Hao 2007). Our expectation, therefore, is that greater English language proficiency will result in higher financial well-being, which is supported by findings that wealth increases with immigrants’ English proficiency (Painter 2013). Last, the length of time immigrants have resided in the United States is an important factor for integration and correlated with the factors discussed above. Greater lengths of stay provide opportunities for immigrants to gain proficiency in English, learn local customs and develop knowledge of economic, social, and political institutions, and adopt new ideas and practices (e.g., Alba and Nee 2005; Bass and Casper 2001; Glick 2000; Gordon 1964). More time means more experience with housing markets, which increases the likelihood of buying a home (Glick 2000). Similarly, immigrants’ familiarity with financial institutions would be essential for improved financial well-being. More time in the United States lets immigrants build social networks, which may provide immigrants with a number of resources to help improve their financial well-being, including help searching for and obtaining financial information and knowledge about particular types of accounts and/or investments (Chang 2005). Indeed, research on immigrant wealth consistently finds that length of time in the United States increases financial wellbeing among immigrants (Akresh 2011; Hao 2004; Painter 2013; Zhang 2003). Data and Methods Data This study uses data from the 2001 and 2004 panels of the SIPP, a continuous series of national multistage-stratified panels of the U.S. civilian non-institutionalized population that interviews all household members 15 years old and over. Respondents are interviewed every four months over the duration of the panel (3 years for the 2001 panel; 2.5 years for the 2004 panel) with interviews designed around a core set of questions with rotating topical modules. SIPP data are especially valuable for immigrant studies because the large sample size yields a relatively substantial sample of immigrants and, in particular, racial/ethnic minorities. SIPP is also ideal to analyze wealth because it collects extensive financial information (Cobb-Clark and Hildebrand 2006a, b, c; Hao 2004, 2007; Painter 2013).4 From the larger SIPP data files, we created a cross-sectional dataset by using select waves from each panel. We did this by combining the core files with the Wave 2 (Migration History) and Wave 3 (Assets and Liabilities) topical modules for both the 2001 and 2004 panels. We also used information from a third module in the 2001 panel because English language proficiency questions are located in Wave 8 (Adult Well-Being).5 4 The quality of the asset and debt data in SIPP have been examined elsewhere (Czajka et al. 2003; Hao 2007) with Hao (2007) providing a detailed explanation of the advantages and disadvantages of the various SIPP wealth measures. She notes that the SIPP wealth data compare favorably to the Survey of Consumer Sciences, which is considered the benchmark data to study U.S. wealth. 5 These questions are included in the Wave 2 topical file in the 2004 data. 123 Wealth Inequality Among Immigrants… 155 The analytic sample included native born and immigrant adults living in the United States. We excluded Native Americans6 and respondents from U.S. territories7 due to small sample sizes. With these restrictions, the analytic sample size was 70,947 and included 2098 non-Latino Asians, 9243 non-Latino blacks, 5861 Latinos, and 53,745 non-Latino whites. In addition, we subset the data by nativity, giving us a subsample of immigrants (N = 7319) and native born (N = 59,910).8 SIPP used a sequential hot deck procedure to impute missing data. This procedure matched a respondent with missing information to a donor respondent according to multiple categories including sex, race, age, and marital status. The missing information for the respondent was then replaced with the donor’s valid data. This resulted in no missing data within waves; therefore, the only source of missing data in SIPP arose when respondents entered or exited a panel between waves (U.S. Census Bureau 2001, pp. 13-15–13-17). Merging multiple waves within a panel thus introduced missing data. For respondents who exited the SIPP sample, but remained in the population represented by the sample, one strategy SIPP recommended was multiple imputation (U.S. Census Bureau 2001, pp. 13-20–13-21). We imputed missing data using SAS Proc MI to create five datasets. Analyses were conducted with SAS Proc Quantreg and final results were returned using SAS Proc MIAnalyze. Measures Net Worth SIPP contains detailed information on asset and debt holdings in the United States. Net worth is measured as the US$2004 value of assets less debts. Assets include the value of financial investments, such as checking and savings accounts, bonds, stocks, and Individual Retirement Accounts (IRAs). Also included are the value of non-financial holdings, such as homes, automobiles, real estate, and other valuable possessions. The value of these assets is weighed against total debts, such as those from credit cards, hospital bills, mortgages, and property liens. Explanatory Variables We use five sets of explanatory variables. First, race/ethnicity is classified as nonLatino white (reference), non-Latino Asian, non-Latino black, and Latino.9 Second, we include five dichotomous variables for immigrant status: native born (reference), naturalized citizen, LPR status at arrival, adjustment to LPR status, and a residual category.10 For the immigrants-only subsample, naturalized citizens are the 6 Native Americans included American Indians, Aleutians, and Eskimos. 7 U.S. Territories included American Samoa, Guam, Puerto Rico, and the Virgin Islands. 8 The nativity subsamples do not total the amount of the full analytical sample. This is due to respondents entering or exiting the SIPP sample between waves. See the missing data discussion for details. 9 For the rest of the paper, we shorten the label for racial/ethnic groups by dropping ‘‘non-Latino.’’ 10 The residual category includes students, certain refugees/asylees, and undocumented immigrants, among others. 123 156 M. A. Painter II, Z. Qian reference category. Third, U.S. education is measured with a dichotomous variable that indicates immigrants’ (and the native born’s) place of education or where they completed their last degree (1 = completed last degree in the United States). For English language proficiency, we identify whether respondents are native English speakers (reference) or speak English ‘‘not at all,’’ ‘‘not well,’’ ‘‘well,’’ or ‘‘very well.’’ Last, we include a measure of immigrant’s length of residence in the United States (age at survey less age at arrival). Notably, the U.S. experience variables are omitted from the analyses of the native-born subsample. Control Variables We use several controls from the life cycle. Continuous variables include age and its square, household size, and income (logged). We account for gender with a dichotomous variable (1 = female). Educational attainment is measured as no high school (reference), high school, some college, college degree, and advanced degree. Marital status is captured with dichotomous variables: married (reference category), never married, separated, divorced, or widowed. For place of residence, we include a variable for urban/rural residency (rural is the reference category) and a set of four dichotomous variables that capture the U.S. Census regions: Northeast (reference category), Midwest, South, and West. Last, we also control for respondents’ participation in a particular panel with a dichotomous variable (1 = 2004 panel). Analytical Approach To model net worth, we use quantile regression analysis. In recent years, the empirical quantile regression literature ‘‘makes a persuasive case for the value of going beyond models for the conditional mean’’ (Koenker and Hallock 2001, p. 151). Wealth variables have many outliers, especially in the higher tail of the distribution, and these outliers affect the results of OLS regression. Quantile regression provides more robust estimates to outliers than the mean. Median regression, for example, estimates the 50th percentile and minimizes the sum of absolute residuals. This minimization equates the number of positive and negative residuals and assures the same number of observations above and below the median (Koenker and Bassett 1978). Further, quantile regression is robust to normality assumption violations as it puts more emphasis on the distribution in close proximity around a particular quantile—like the median—rather than areas of the distribution that are further away from the quantile (Hao and Naiman 2007, pp. 41–42). In this way, median regression provides insight into the financial wellbeing of immigrants and the native born in the middle of the wealth distribution, rather than estimates of the ‘‘average’’ respondent which are affected by influential observations. Further, as the median is a measure of central tendency, median regression provides an appropriate comparison to other studies that analyze wealth with conventional regression techniques (see Hao and Naiman 2007, p. 56). Another advantage of quantile regression is that it provides a more complete assessment of the effects of covariates across the conditional distribution of net worth (at given quantiles). Therefore, we explore other types of quantile regression 123 Wealth Inequality Among Immigrants… 157 by setting the threshold, s, to deciles in order to explore the relationship between our covariates and wealth at different points on the conditional distributions. This analysis provides important insights about racial/ethnic inequality at different points of the conditional distribution of net worth. Figure 1 provides insight into the advantages of using quantile regression to analyze wealth inequality.11 This figure presents wealth values for the full sample and each racial/ethnic group at select percentiles, including the median.12 Here, we chose deciles to mirror our modeling strategy. As Fig. 1 demonstrates, among the least wealthy, there is relatively little wealth inequality; however, gaps among racial/ethnic groups emerge and then widen across the wealth distribution. This pattern supports a quantile regression approach as the presence and amount of racial/ethnic wealth inequality differs depending upon the particular quantity of net worth. A single summary measure of wealth, like the mean, would miss such distinct patterns. Substantively, Fig. 1 shows that race/ethnicity matters greatly among more wealthy individuals, but its effect is muted among the less wealthy. This calls for a close examination of how and why race/ ethnicity results in different patterns of wealth inequality across the wealth distribution. We use a variable-nested modeling approach to explore how immigrants’ U.S. experience affects immigrant wealth inequality. We introduce four models to examine net worth (Table 2). Model 1 introduces the race/ethnic variables. Model 2 adds the measures of immigrant status. Model 3 includes the rest of the U.S. experience variables. Model 4 is the full model, with controls. To explore the relationship between immigrants’ U.S. experience and wealth inequality over the conditional wealth distribution, Table 3 presents quantile regression results by conditional decile. Tables 4 and 5 present quantile regression results by conditional decile for the immigrant and native-born subsamples.13 Here, we focus on the overall pattern of racial/ethnic wealth inequality and do not directly compare the coefficients from the unique conditional wealth distributions. In addition, we test for equality of coefficients within the same model (Clogg et al. 1995; see also Paternoster et al. 1998). Together, these analyses demonstrate the consistency of racial/ethnic wealth inequality and show that racial/ethnic minority immigrants, like their same-race/co-ethnic nativeborn counterparts, experience barriers to acquiring wealth in the United States. Results Descriptive Results Table 1 presents descriptive statistics. Median wealth for the full sample is $66,915. Whites have the highest median wealth ($92,917), followed by Asians ($70,275). Blacks and Latinos have similar median wealth with $8425 and $9080, respectively. 11 Appendix Table 6 contains the values used to create Fig. 1. 12 In additional analyses not shown here, we used the same approach to compare the pattern displayed in Fig. 1 to the wealth distributions of immigrants and the native born. The patterns by nativity status were similar to that presented in Fig. 1. 13 To conserve space, Tables 3, 4, 5 present coefficients and significance levels for the explanatory variables. Full results are available from the authors upon request. 123 158 M. A. Painter II, Z. Qian Fig. 1 Net worth for full sample and racial/ethnic groups, by select percentiles Table 1 Means and standard deviations, SIPP 2001 and 2004, N = 70,947 Total Asian Black Latino White $66,915 $70,275 $8425 $9080 $92,917 Asian 0.03 – – – – Black 0.13 – – – – Latino 0.08 – – – – White 0.76 – – – – Outcome variable Net wortha— median value Explanatory variables Race/ethnicity Immigrant status Native born 0.89 0.21 0.93 0.49 0.95 Naturalized citizen 0.05 0.46 0.04 0.18 0.03 LPR at arrival 0.03 0.17 0.02 0.16 0.01 Adjusted to LPR status 0.01 0.06 0.01 0.07 0.00 Other immigrant status 0.02 0.10 0.01 0.10 0.00 0.40 0.46 0.38 0.40 0.90 0.36 0.95 0.37 0.96 0.32 0.25 0.56 0.20 0.48 0.05 0.31 0.03 0.26 0.02 0.20 0.35 0.22 0.14 0.20 0.02 0.18 0.01 0.11 0.01 Place of education—U.S. degree Immigrants only 0.39 English language proficiency Native speaker Immigrants only Very well Immigrants only Well 123 Wealth Inequality Among Immigrants… 159 Table 1 continued Total Immigrants only Not well Immigrants only Not at all Immigrants only U.S. duration Asian Black Latino White 0.15 0.20 0.11 0.14 0.13 0.02 0.10 0.01 0.17 0.01 0.22 0.16 0.08 0.33 0.13 0.01 0.04 0.00 0.09 0.00 0.11 0.04 0.03 0.19 0.05 2.03 (7.66) 14.11 (12.61) 1.03 (4.99) 9.05 (12.81) 1.10 (6.30) 13.79 (10.49) 10.71 (8.25) 12.71 (9.77) 14.94 (9.99) 14.22 (12.47) No high school degree 0.13 0.10 0.19 0.38 0.10 High school degree 0.28 0.17 0.31 0.27 0.29 Some college 0.33 0.22 0.35 0.25 0.34 College degree 0.16 0.30 0.09 0.06 0.18 Advanced degree 0.09 0.21 0.05 0.03 0.10 Immigrants only Control variables Education Household characteristics Age 49.34 (16.93) 44.66 (14.90) 48.08 (16.26) 41.86 (14.71) 50.55 (17.09) Female 0.52 0.42 0.64 0.48 0.51 Household size 2.58 (1.49) 3.06 (1.64) 2.62 (1.57) 3.53 (1.86) 2.46 (1.38) Incomea $44,180 ($57,910) $64,114 ($73,128) $29,329 ($37,793) $37,888 ($42,477) $46,641 ($60,928) Marital status Married 0.52 0.66 0.32 0.58 0.55 Never married 0.17 0.19 0.31 0.20 0.15 Separated 0.03 0.02 0.07 0.06 0.02 Divorced 0.16 0.08 0.17 0.11 0.16 Widowed 0.11 0.05 0.13 0.05 0.12 Northeast 0.17 0.22 0.15 0.11 0.18 Midwest 0.25 0.13 0.18 0.10 0.29 South 0.37 0.20 0.59 0.36 0.34 West 0.20 0.46 0.07 0.43 0.19 Residency Urban 2004 panel N 0.76 0.93 0.85 0.87 0.73 0.53 0.49 0.53 0.46 0.54 70,947 2098 9243 5861 53,745 Some columns may not total 1.0 due to rounding. Standard deviation in parentheses a US$2004 123 160 M. A. Painter II, Z. Qian Table 2 Median regression estimates for net worth (in thousands), SIPP 2001 and 2004, N = 70,947 Model 1 Model 2 Model 3 Model 4 Explanatory variables Race/ethnicity (ref = white) Asian -22.482 (6.513)* -28.343 (5.846)* -20.374 (6.445)** -17.041 (5.173)** Black -86.063 (1.057)*** -83.849 (0.964)*** -82.847 (1.201)*** -39.411 (1.194)***a,b Latino -85.289 (1.208)*** -77.560 (1.388)*** -70.118 (1.772)*** -18.415 (2.084)*** Immigrant status (ref = native born) Naturalized citizen – 33.993 (3.835)*** 1.813 (5.307) -21.353 (5.414)*** LPR at arrival – -11.602 (1.420)*** -24.303 (2.588)*** -33.863 (3.822)*** Adjusted to LPR status – -8.708 (2.487)*** -23.969 (3.682)*** -33.720 (5.935)*** Other immigrant status – -16.270 (1.071)*** -26.308 (3.121)*** -34.303 (5.147)*** – 2.502 (2.321) 17.132 (3.221)*** Place of education (ref = foreign degree) U.S. degree – English language proficiency (ref = native speaker) Very well – – -6.186 (2.570)* -1.843 (2.516) Well – – -10.486 (2.101)*** -11.756 (4.579)* Not well – – -14.011 (1.938)*** -22.567 (3.175)*** Not at all – – -19.306 (2.616)*** -30.295 (4.195)*** U.S. duration – – 1.185 (0.180)*** 0.537 (0.142)*** Control variables Education (ref = no high school) High school – – – 24.859 (1.773)*** Some college – – – 38.038 (1.871)*** College degree – – – 93.949 (3.224)*** Advanced degree – – – 151.905 (5.020)*** Age – – – 4.844 (0.225)*** Age, squared – – – -0.017 (0.002)*** Female (ref = male) – – – -4.271 (1.142)*** Household size – – – 3.630 (0.459)*** Income (logged) – – – 2.094 (0.161)*** Household characteristics 123 Wealth Inequality Among Immigrants… 161 Table 2 continued Model 1 Model 2 Model 3 Model 4 Marital status (ref = married) Never married – – – -39.416 (1.492)*** Separated – – – -57.736 (2.122)*** Divorced – – – -67.203 (1.465)*** Widowed – – – -65.385 (2.195)*** Residence (ref = northeast) Midwest – – – -8.135 (1.755)*** South – – – -15.473 (1.630)*** – – – 3.171 (2.081) Urban (ref = rural) West – – – 10.932 (1.220)*** 2004 panel (ref = 2001 panel) – – – 7.345 (1.123)*** Intercept 94.661*** 93.830*** 93.171*** -133.669*** a Significantly different (p \ 0.05, two-tailed) from ‘‘Asian’’ coefficient b Significantly different (p \ 0.05, two-tailed) from ‘‘Latino’’ coefficient * p \ 0.05; ** p \ 0.01; *** p \ 0.001, two-tailed Almost 90 % of the sample are native born and an additional 5 % are naturalized citizens. For the remaining categories, 3 % received their LPR status at arrival while 1 % adjusted to LPR status later. By race/ethnicity, the vast majority of black and white respondents are native born and relatively few are LPRs. In contrast to these two groups, the majority of Asians are either naturalized (46 %) or LPR (23 %). Almost half of Latinos are native born, with similar proportions of naturalized citizens and immigrants with LPR status at arrival (18 and 16 %, respectively). Last, for the remainder of the U.S. experience variables, a substantial proportion of immigrants complete their education in the United States (39 %). By race/ ethnicity, there is relatively similarity in the attainment of U.S. education with black immigrants having a slightly higher frequency of completion. For English language proficiency, half of the immigrants in the sample are either native English speakers (32 %) or speak the language ‘‘very well’’ (20 %). In contrast, approximately onethird of the immigrants speak English ‘‘not well’’ or ‘‘not well at all.’’ More than half of black immigrants speak English as a native language, but only 20 % of Latino immigrants and 25 % of Asian immigrants are native speakers. Latinos are least proficient in English. For U.S duration, the average duration is almost 14 years and Asian immigrants have been in the United States for the least amount of time while Latinos and whites have the longest duration of residence. 123 123 0.601 Latino -5.442* -6.322 -17.451 LPR at arrival Adjusted to LPR status Other immigrant status U.S. degree -3.014 -7.506 -12.494*** -14.691*** 0.187* Well Not well Not at all U.S. duration 0.268*** -13.753*** -10.066*** -7.792*** -3.234* 0.442*** -23.076*** -15.574*** -10.431*** -4.476** 7.137** -23.496*** -20.310*** -19.803*** 0.540*** -26.849*** -18.088*** -12.607*** -3.615 11.448*** -28.470*** -25.985*** -25.748*** -20.890*** -12.863*** -30.554***a,b -17.146*** 40th 0.537*** -30.295*** -22.567*** -11.756*** -1.843 17.132*** -34.303*** -33.720*** -33.863*** -21.353*** -18.415*** -39.411***a,b -17.041*** 50th Significantly different (p \ 0.05, two-tailed) from ‘‘Latino’’ coefficient b * p \ 0.05; ** p \ 0.01; *** p \ 0.001, two-tailed Significantly different (p \ 0.05, two-tailed) from ‘‘Asian’’ coefficient a Models include the control variables described in the text and displayed in Model 4, Table 2 -3.540 Very well English language proficiency (ref = native speaker) 3.851 -15.647*** -11.359*** -12.134*** -18.993*** -5.895*** -2.457* -11.591*** -8.245** -21.901***a,b -5.240** 30th -12.675***a,b 20th Place of education (ref = foreign degree) -5.648 Naturalized citizen Immigrant status (ref = native born) 0.785 -7.996*** Asian Black Race/ethnicity (ref = white) 10th Percentiles 0.481* -37.558*** -23.882*** -15.324** -0.841 17.303*** -31.871*** -36.404*** -35.228*** -15.366* -25.508*** -48.291***a,b -25.584*** 60th 0.459 -40.503*** -24.515*** -11.290 -0.366 23.794*** -32.524*** -44.187*** -38.706*** -15.053 -41.208*** -60.281***a,b -31.695*** 70th 0.796 -35.686* -30.976** -5.715 -3.425 37.782*** -41.397*** -58.381* -49.942*** -32.611 -59.897*** -75.356***a,b -32.810* 80th Table 3 Coefficients and significance levels from quantile regression estimates for net worth (in thousands), SIPP 2001 and 2004, N = 70,947 1.500 -51.400* -36.804 -9.333 7.846 56.569* -51.958* -80.927* -57.774** -68.509 -92.393*** -106.621*** -44.930 90th 162 M. A. Painter II, Z. Qian Wealth Inequality Among Immigrants… 163 Median Net Worth (in thousands) $140 $120 $100 $80 $60 $40 $20 $0 Total Nave-born Asian Naturalized cizen Black LPR at arrival Lano LPR via adjustment White Other immigrant Fig. 2 Median net worth by race/ethnicity and immigrant status Median Wealth by Race/Ethnicity and U.S. Experience To provide insight into two dimensions of immigrants’ U.S. experience, we graph wealth by race/ethnicity and immigrant status. Several patterns stand out in Fig. 2. First, there is a clear contrast between the wealth of Asians and whites and that of blacks and Latinos. The wealth inequality between Asians/whites and blacks/ Latinos is larger for native-born and naturalized citizens while the gap is smallest among immigrants with LPR status at arrival and those with a non-LPR status. Second, with the exception of Asians, there is a wealth divide between citizens— either native born or naturalized—and immigrants. As shown in Fig. 2, naturalized citizens have the highest median wealth within each racial/ethnic group, followed by the native born for every group except for Asians. Last, there are differences in wealth by immigrant status within racial/ethnic groups. Among immigrants, adjusted LPRs have a higher median wealth value, followed by LPRs at arrival, and then by other immigrants. Median Quantile Regression Results Table 2 contains four variable-nested models estimated with median regression. Together, these models document the endurance of racial/ethnic wealth inequality, after accounting for important dimensions of the U.S. experience. Model 1 introduces the race/ethnicity variables. In comparison to whites, all racial/ethnic minorities are associated with less wealth. The smallest wealth inequality is between Asians and whites with Asians having $22,482 less wealth. The wealth gap between both blacks and Latinos and whites is much larger. The model predicts that these groups have more than $85,000 less wealth than whites. 123 164 M. A. Painter II, Z. Qian Model 2 adds the immigrant status variables.14 With the introduction of these variables, the racial/ethnic coefficients change relatively little. For the immigrant status variables, naturalized citizens are advantaged relative to the native born with a wealth premium of $33,993. In contrast, immigrants have less wealth. In comparison to the native born, wealth inequality is greatest for immigrants with a non-LPR status ($16,270), followed by LPRs at arrival ($11,602) and adjusted LPRs ($8708). Model 3 introduces the remainder of the U.S. experience variables and their addition only slightly changes the racial/ethnic and immigrant status results from the previous models. The most notable change is that the coefficient for naturalized citizens is no longer significant, which signals wealth parity between this group and the native born. Immigrants who are not native English language speakers experience wealth disadvantage ranging from $6186 for those who speak English ‘‘very well’’ to $19,306 for those who speak English ‘‘not at all.’’ Time spent in the United States leads to greater wealth: each additional year produces a $1185 increase in wealth. Model 4 is the full model with controls. With the addition of the control variables, the coefficients for the race/ethnic and immigrant status variables are reduced (substantially so for blacks and Latinos), but remain significant. Beginning with race/ethnicity, t-tests for the equality of coefficients suggest a three-tiered racial/ethnic hierarchy. Whites have the most wealth and both Asians and Latinos occupy the second tier. These groups have more than $17,000 less wealth than whites. Blacks are at the bottom of the racial/ethnic wealth hierarchy and have $39,411 less wealth. For the immigrant status variables, the wealth gap between the native-born and naturalized citizens is the smallest ($21,353) while immigrants are clustered closely together, independent of their LPR or non-LPR status. A U.S. education is advantageous relative to a foreign education with a wealth premium of $17,132. Speaking English with proficiency below ‘‘very well’’ is related to less wealth, ranging from $11,756 less wealth for immigrants who speak English ‘‘well’’ to $30,295 for immigrants who speak English ‘‘not at all.’’ Last, for U.S. duration, the model predicts $537 more wealth for each additional year of residency. Together, the results are largely in line with expectations. The wealth gaps between whites and both Asians and blacks support the racial wealth inequality expectations from the literature; however, that the wealth inequality between Latinos and whites is similar to the Asian/white contrast is not expected. For the other U.S. experience variables, the results generally reflect our expectations: U.S. education and duration lead to greater wealth while immigrants and those with lesser English language proficiency have lower wealth. 14 In supplemental analyses, we explored interactions between the race/ethnicity and immigrant status variables. The results suggested that there was little variation by immigrant status within racial/ethnic groups. 123 Wealth Inequality Among Immigrants… 165 Quantile Regression Results by Conditional Decile We now explore how race/ethnicity and immigrants’ U.S. experience affects wealth inequality across the entire conditional wealth distribution. Model 4 is re-estimated with threshold, s, set to deciles. Racial/ethnic wealth inequality is consistent at most points of the conditional wealth distribution. The three-tiered hierarchy identified from median regression (see Table 2) holds for most of the conditional wealth distribution. It is only at the 10th and 90th percentiles that a different pattern emerges. At the top of the conditional wealth distribution, Asians and whites and then blacks and Latinos have equivalent wealth. At the bottom of the conditional wealth distribution, a black/nonblack wealth inequality is present. For immigrants, wealth inequality between the native-born and naturalized citizens is evident across most of the conditional wealth distribution. Among immigrants, there appears to be little distinction between LPR and non-LPR immigrants at the bottom half of the conditional wealth distribution. Above the median, there is more variation by LPR status. The remaining explanatory variables largely reflect the pattern identified at the median. U.S. education has a wealth premium, an advantage that grows across the conditional wealth distribution. Those who speak English ‘‘very well’’ are generally associated with a level of wealth equivalent to native English speakers. English proficiency below this level results in lower wealth. Last, the models predict that time spent in the United States generates wealth over most of the wealth distribution; however, above the 70th percentile there is no relationship between U.S. duration and wealth. Quantile Regression Results by Conditional Decile—Immigrant Subsample Table 4 displays results from the same models as Table 3 for the immigrant subsample.15 Overall, the results show that race/ethnicity plays an important role for immigrant wealth inequality. In contrast to the full sample (see Table 3), Asian immigrants have an equivalent level of wealth as white immigrants across most of the conditional wealth distribution. Black and Latino immigrants consistently have less wealth than white immigrants with blacks having the least wealth. T-tests for the equality of coefficients indicate that black and Latino immigrants have equivalent levels of wealth above the 70th percentile of the conditional wealth distribution. For the other explanatory variables, immigrants are associated with less wealth than naturalized citizens for most of the conditional wealth distribution. Similarly, lower levels of English proficiency are consistently associated with less wealth. Longer durations in the United States produce positive wealth returns. Counter to expectations, the association between U.S. education and wealth is inconsistent. 15 The one difference is that the reference group for immigrant status is the native born in Table 3 and naturalized citizens in Table 4. 123 123 -10.338 2.692 Black Latino -1.627 -10.247*** -0.260 20th -6.094 Other immigrant status -7.003* -2.816** 1.217 0.854*** -9.039*** -6.830*** -4.544 a,b 1.535*** -11.625*** -8.990*** -5.236 0.212 -6.300* -13.034*** -11.388*** -11.422*** -15.945*** -27.950*** -7.116* 40th Significantly different (p \ 0.05, two-tailed) from ‘‘Latino’’ coefficient b * p \ 0.05; ** p \ 0.01; *** p \ 0.001, two-tailed Significantly different (p \ 0.05, two-tailed) from ‘‘Asian’’ coefficient a b 2.072*** -13.603** -13.479*** -7.863 0.024 -5.447 -23.015*** -22.346*** -22.409*** -25.346*** -38.666*** -8.301 50th Models include the control variables described in the text and displayed in Model 4, Table 2 0.351*** -7.414** 0.375** -11.836** U.S. duration Not at all -5.069** -8.475* -4.025* -0.178 Not well 0.219 -4.938 Well Very well -3.288 -6.337** -4.740*** -4.457** -6.666*** -5.022* -3.054** b -15.121*** -2.354 30th -2.570 English language proficiency (ref = native speaker) U.S. degree Place of education (ref = foreign degree) -2.974 -2.334 LPR at arrival Adjusted to LPR status Immigrant status (ref = naturalized citizen) 4.580 Asian Race/ethnicity (ref = white) 10th Percentiles b 2.805*** -12.987* -14.914* -6.678 2.321 -5.797 -33.785*** -31.154*** -32.782*** -42.106*** -55.369*** -13.126 60th 3.755*** -18.600 -18.556* -8.226 4.108 -4.768 -38.466*** -39.556*** -41.749*** -69.093*** -77.278*** -24.243 70th a 5.114*** -21.765 -22.577* -4.857 6.140 0.092 -41.426*** -42.148** -50.271*** -111.105*** -125.129*** -59.032**b 80th 7.873*** -22.194 -25.915 10.957 -10.137 21.821 -29.560 -20.231 -46.146 -176.082*** -198.096***a -79.969*b 90th Table 4 Coefficients and significance levels from quantile regression estimates for net worth (in thousands), SIPP 2001 and 2004, immigrant-only subsample, N = 7319 166 M. A. Painter II, Z. Qian Wealth Inequality Among Immigrants… 167 Overall, the racial/ethnic hierarchy in financial well-being observed among immigrants is most similar to the pattern observed with the larger sample for blacks and Latinos and is less consistent with Asians. While the exact ordering of the racial/ethnic groups is slightly different, it is clear that race/ethnicity plays an important role for wealth inequality among immigrants as among the native born. Quantile Regression Results by Conditional Decile—Native-Born Subsample Table 5 presents results for the native-born sample. Here, the pattern of racial/ethnic inequality is quite similar to that presented for immigrants in Table 4. Native-born Asians have equivalent wealth as native-born whites across the entire conditional wealth distribution. T-tests for equality of coefficients indicate that blacks and Latinos have distinct levels of wealth from the 20th to the 70th percentiles, with the racial/ethnic gap with whites greater for blacks. Overall, these results document the consistency of racial/ethnic inequality across the conditional wealth distribution with the racial/ethnic inequality patterns observed among the native born resembling those of immigrants. Discussion and Conclusion Immigrants move to the United States, at least in part, to pursue a higher standard of living and improve their financial well-being (Portes and Rumbaut 2006). Social scientists have long been interested in immigrants’ economic integration into U.S. society, but much of the previous research focuses on immigrants’ low income and poverty (e.g., Lichter et al. 2005; Smith and Edmonston 1997). A relatively new aspect of scholarly interest in immigrant financial well-being is wealth or net worth. Given that economic mobility is often the primary goal for immigrants in the United States, wealth—or the lack thereof—is a strong indication of immigrant integration in U.S. society (Farley 1996; Kritz and Gurak 2001). We examined racial/ethnic wealth inequality across the full conditional distribution of net worth. This approach allowed us to assess the effect of race/ ethnicity on wealth, given other characteristics, among those with differing amounts of financial resources. To understand the implications of race/ethnicity for immigrant integration, we used new assimilation theory to acknowledge that race/ ethnicity is a social boundary, which hinders minority immigrants’ ability to integrate into U.S. society no matter where they fall on the conditional wealth continuum. Immigrants’ incorporation across the conditional wealth distribution depends not only on larger social structures and institutional constraints, but also on social, economic, and cultural differences that are associated with race/ethnicity at the individual level (Alba and Nee 2005; see also Omi and Winant 1994). In this way, we posited that immigrants’ integration into U.S. society would reflect existing racial/ethnic inequalities across the entire distribution of net worth. In order to compare other studies that analyze the conditional mean using conventional regression techniques, we began to explore immigrant and native-born 123 123 -4.181* Latino -9.307*** -13.826***a -2.079 20th -14.584*** -21.991***a -2.865 30th -22.120*** -30.439***a -7.130 40th -28.065*** -38.809***a -5.217 50th Significantly different (p \ 0.05, two-tailed) from ‘‘Latino’’ coefficient * p \ 0.05; ** p \ 0.01; *** p \ 0.001, two-tailed a Models include the control variables described in the text and displayed in Model 4, Table 2 -0.785 -7.035*** Asian Black Race/ethnicity (ref = white) 10th Percentiles -34.074*** -47.075***a 1.016 60th -47.307*** -56.781***a 1.803 70th -62.679*** -68.222*** 28.815 80th -86.787*** -91.185*** 44.292 90th Table 5 Coefficients and significance levels from quantile regression estimates for net worth (in thousands), SIPP 2001 and 2004, native-born only subsample, N = 59,910 168 M. A. Painter II, Z. Qian Wealth Inequality Among Immigrants… 169 racial/ethnic wealth inequality in the middle of the wealth distribution. We found racial/ethnic inequality that corresponded to three tiers: whites, Asians/Latinos, and blacks. These results align with a large body of research (e.g., Blau and Graham 1990; Conley 1999; Hao 2004, 2007; Keister 2000a, 2004; Killewald 2013; Oliver and Shapiro 2006; Smith 1995). Most of this research focuses on the black–white divide and relatively few studies compare whites and either Asians or Latinos (e.g., Campbell and Kaufman 2006; Hao 2004, 2007; Painter 2013; Painter and Qian Forthcoming). We then demonstrated that racial/ethnic inequality is persistent over the whole spectrum of the wealth distribution. For both natives and immigrants, we found that blacks consistently had the least amount of wealth, and Asians and Latinos were at the middle and had similar levels of wealth across most of the conditional wealth distribution. The only variation from this pattern was at the bottom of the conditional wealth distribution for Asians and Latinos and at the top of the conditional distribution for Asians, indicating the absence of racial/ethnic disadvantage for these groups at these particular locations in the wealth distribution, conditional on the other characteristics controlled in the model. Overall, racial/ ethnic wealth differentiation is consistent across the conditional wealth distribution. We then analyzed immigrants and the native born separately. We focused on the overall pattern of racial/ethnic inequality across the two conditional wealth distributions. A clear racial/ethnic hierarchy was again present with blacks having the least wealth of the racial/ethnic groups across the conditional wealth distribution. A key difference was the equivalence in wealth between Asian and white natives and the near equivalence between Asian and white immigrants. Along with the findings based on both natives and immigrants, it is clear that Asian and white immigrants were behind their native-born counterparts in wealth and there is a three-tiered hierarchy in wealth for immigrant and native-born subsamples—Asians and whites on top, Latinos in the middle, and blacks at the bottom. Together, these results demonstrate the robustness of racial/ethnic wealth inequality, even when accounting for important dimensions of the U.S. experience and other factors that influence wealth. Clearly, racial/ethnic inequality affects immigrants as well as the native born, highlighting the importance of racial/ethnic realities in the United States, independent of other factors like nativity status, time in the United States, and educational and linguistic resources. As far as we are aware, this is the first study to examine the consistency of racial/ethnic inequality across the conditional wealth distribution. This approach moves beyond the mean and explores how the relationships among their concepts and variables of interest (do not) change across the full distribution of their outcome variable. Our second contribution focused on how other dimensions of immigrants’ U.S. experiences help improve their financial well-being and integrate into U.S. society. We included four indicators of U.S. experience: immigrant status, place of education, English language proficiency, and time spent in the United States. Overall, these factors affect wealth across the conditional wealth distribution and explain some of the racial/ethnic wealth inequality. Yet, racial/ethnic differences in net worth persist across the wealth continuum. For the specific factors, naturalized immigrants in the United States, at least in theory, should have access to the same resources, privileges, and rights as native- 123 170 M. A. Painter II, Z. Qian born citizens; however, we found that naturalized citizens had less wealth at most points on the conditional wealth distribution and had wealth more similar to those of other types of immigrants. While we cannot explore inheritances or remittances with SIPP data, this difference could reflect naturalized immigrants’ (and their families’) relative lack of wealth inherited across generations and even the financial environments of their home countries prior to their migration. After all, many of them had similar experiences as other immigrants because many did not naturalize until they were adults. For the other dimensions of immigrants’ U.S. experiences, when compared to native speakers, lower levels of English language proficiency were associated with lower levels of financial well-being (Chatterjee and Kim 2011; Fontes 2011; Kim et al. 2012; Painter 2013; but see Osili and Paulson 2008). English language proficiency clearly helps immigrants improve their financial wellbeing with better access to good jobs and U.S. financial institutions. (e.g., Chiswick and Miller 2002; Hall and Farkas 2008; Tainer 1988). Last, U.S. education and time spent in the United States were consistently related to improved financial wellbeing, though U.S. education probably was more tied to educational attainment and did not affect wealth for most of the conditional wealth distribution among immigrants. These findings mostly reflect prior research and illustrate the value of gaining U.S.-specific resources (e.g., Akresh 2011; Chatterjee and Kim 2011; CobbClark and Hildebrand 2006c; Kim et al. 2012; Hao 2007; Painter 2013). Our research informs several policy recommendations. Overall, our research calls attention to the importance of immigrants’ race/ethnicity for wealth inequality. For racial/ethnic inequality, such policies would focus on the relative lack of wealth for blacks and Latinos. Here, we echo the policy recommendations of Sykes (2003) who called for the targeting of specific assets and investments in order to reduce racial/ ethnic wealth inequality. For example, policies that promote and facilitate homeownership—a key asset for wealth attainment—would contribute to the narrowing of racial/ethnic wealth inequality. Other assets including non-primary home real estate, savings accounts, and stocks/bonds would also benefit from policies that increase access to and ownership of these key investments that all contribute to both diversified and balanced portfolios. For immigration policy, our research demonstrates that naturalization, U.S. education, and English language proficiency increase immigrants’ wealth. While it may be more difficult to formulate policies to increase immigrants’ educational attainment in the United States, policies that encourage naturalization and help immigrants improve proficiency in English are more achievable goals. Along with the contributions of this study, we need to acknowledge its limitations. We do not have information on immigrants’ financial well-being at the time of their arrival. This information would be valuable because it would provide insight into the actual processes underlying wealth attainment, rather than provide insight into levels of wealth at a single—and arbitrary—point in time. Although we have information such as educational attainment and whether immigrants received U.S. education as proxies of their pre-immigration socioeconomic position, SIPP does not have direct information on pre-immigration wealth or forms-of-capital; therefore, we cannot completely account for compositional differences—in terms of social, human, and/or financial capital—across immigrants’ racial/ethnic groups as these resources vary by national origin and/or race/ethnicity (see Alba and Logan 123 Wealth Inequality Among Immigrants… 171 1993). Further, SIPP does not contain information on spouses and their wealth. We control for marriage because it is an important factor for wealth. This control also accounts for differences in marital composition between natives and immigrants given that more immigrants are likely to be married than their native counterparts (Qian 2013). Therefore, our results are likely robust to differences in marriage composition, though it would be interesting for future research to explore how wealth differences in nativity are affected by marriage and marital wealth. There is also no information on remittances in the SIPP. Remittances reduce immigrants’ investment capacity in the United States, but may represent investment if immigrants send money to purchase and/or maintain assets back in their home country. The overall impact of remittances on wealth in the United States, however, may actually be small. For example, research examining U.S. wealth attainment among immigrants who recently received LPR status shows that less than 10 % of immigrants report sending more than $500 in the past year to their home country (Painter and Qian Forthcoming; Painter et al. 2016). To close, immigrants move to the United States for an opportunity to expand their life chances. Upon arrival, immigrants’ racial/ethnic status affects their ability to achieve this goal. This paper shows that for some groups, namely, blacks and Latinos, the U.S. social structure serves as a barrier to economic integration and improved financial well-being. Other groups, like Asians, may encounter some obstacles to wealth attainment, but they also are advantaged in key ways (e.g., educational attainment, socioeconomic status) that help facilitate their incorporation into U.S. society. Overall, this study documents persistent racial/ethnic inequality, revealing that even when accounting for key aspects of U.S. experience, wealth parity with whites for racial/ethnic minorities is not attained. This suggests that the very opportunities that immigrants pursue with their relocation to the United States are stratified and this inequality may exist for quite some time. Acknowledgments We thank Ariela Schachter for helpful comments on a previous draft. This research was supported in part by Grant R03HD058693 from the National Institute of Child Health and Human Development (principal investigators: Zhenchao Qian and Matthew Painter). Appendix See Table 6. Table 6 Net worth for full sample and racial/ethnic groups, by select percentiles Percentiles 10th 20th 30th 40th 50th 60th 70th 80th 90th Full sample -$1915 $1288 $9890 $32,926 $66,915 $109,528 $174,390 $278,621 $485,108 Asian $0 $2000 $10,100 $29,372 $70,275 $133,080 $215,660 $335,031 $514,842 Black -$4645 $0 $0 $2100 $8425 $25,251 $50,216 $88,257 $173,310 Latino -$4647 $0 $800 $3600 $9080 $24,790 $53,181 $98,798 $204,444 White -$853 $4560 $22,780 $54,815 $92,917 $142,835 $214,100 $326,680 $547,790 123 172 M. A. Painter II, Z. Qian References Akresh, I. R. (2011). Wealth accumulation among U.S. immigrants: A study of assimilation and differentials. Social Science Research, 40, 1390–1401. Alba, R. (1990). Ethnic identity: the transformation of white America. New Haven, CT: Yale University Press. Alba, R., & Logan, J. (1993). Minority proximity to whites in suburbs: An individual-level analysis of segregation. American Journal of Sociology, 98(6), 1388–1427. Alba, R., & Nee, V. 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Introduction
The United State is one of the many countries in the world that continue to experience
wealth inequality especially the African American and the Latino communities compared to
the whites. The issue of race/ethnicity has affected many immigrant's integrations into the
United States society since they are not offered the same opportunities as the whites primarily
due to their immigration status. Ma...


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