alexa Enduring Advantages: Asian Indian and Chinese Immigrant Wealth | OMICS International
ISSN: 2151-6219
Business and Economics Journal
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Enduring Advantages: Asian Indian and Chinese Immigrant Wealth

Lisa A Keister* and E Paige Borelli

Department of Sociology, Duke University, USA

*Corresponding Author:
Lisa A Keister
Professor, Department of Sociology, 268
Sociology/Psychology Building N 27708
Duke University, USA
Tel: (919) 660-5624
Fax: 919-660-5623
E-mail: [email protected]

Received date: October 05, 2015 Accepted date: October 16, 2015 Published date: October 23, 2015

Citation: Keister LA, Borelli EP (2015) Enduring Advantages: Asian Indian and Chinese Immigrant Wealth. Bus Eco J 6:188. doi:10.4172/2151-6219.1000188

Copyright: © 2015 Keister LA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Visit for more related articles at Business and Economics Journal

Abstract

Since the 1965 immigration reforms that abolished discriminatory immigration quotas, a tremendous influx of immigrants from Latin America, Africa, and Asia to the U.S. has occurred. Accompanying this diversity in immigrant country of origin are new patterns of socioeconomic stratification. Chinese and Asian Indians are two rapidly growing immigrant groups in the U.S., and these groups are notable for attaining significant upward mobility. Scholars often neglect the significant heterogeneity within immigrant groups by focusing on broader stratification patterns between all immigrants, but data from the New Immigrant Survey shows differences in wealth attainment between Chinese and Indian immigrants as well as differences within each nationality. Results indicate that Indian immigrant households have higher wealth than Chinese households, and institutional access explains this difference. In addition, there are unique patterns of wealth attainment by immigrant group, although both Indian and Chinese immigrants with employment visas have significantly higher wealth than co-nationals with other visa status. The correlation between visa status and wealth is mediated by host country SES and financial institutional access for both immigrant groups.

Keywords

Asian immigration; Wealth; Economic incorporation; Legal status

Introduction

Wealth, or total household assets minus total household debts, is an important measure of immigrant economic incorporation and well-being. Immigrant incorporation is typically evaluated through educational attainment [1,2] occupational experience [3,4], residential assimilation [5-7], and earnings [8,9]. However, because wealth includes the total value of family assets, it represents immigrants’ abilities to access or attain saving accounts, homes, and businesses, important milestones in adaptation to life in the U.S. that may be critical to achieving economic stability [10-12]. Additionally, recent scholarship suggests that wealth may provide unique insights into immigrant stratification and assimilation patterns [13,14]. Owning positive wealth can serve as an economic buffer for immigrant households during family crises, including medical emergencies, job losses, and natural disasters; in contrast, households with little or no wealth may experience extreme hardship in times of financial strain. Unlike income, wealth is also inheritable and has the potential to improve mobility prospects for future generations by providing funds for college education or business start-ups, so that owning a moderate amount of wealth can help guard the children of immigrants against downward assimilation [15,16]. As many immigrants have limited credit access, personal and family savings are especially important financial resources for purchasing symbols of the middle class life-style (e.g., homes or businesses) and achieving upward mobility [17].

Asian immigrants, specifically Chinese and Asian Indian immigrants, are one of the fastest growing and most prosperous immigrant groups in the U.S., and studying this population may provide important insights into incorporation patterns central to contemporary immigration research. Since the implementation of the 1965 immigration reforms, Asian Americans have grown from less than 1% to almost 6% of the U.S. population, and in 2009 Asian Americans surpassed Hispanics as the fastest growing immigrant group [18]. Driving this population increase are Chinese and Asian immigrants, who, between 2005 and 2010, were the two most rapidly-increasing immigrant groups to the U.S. [19]. In 2005, Chinese represented 5% and Indians 4% of immigrants to the U.S. Since 2008, Chinese have grown to comprise 9% and Indians 8% of all immigrants arriving in the U.S. [19]. Chinese and Indian immigrants are of particular interest to social scientists because they appear to have higher levels of economic integration than many other immigrants groups, demonstrated by the fact that their average incomes, educational attainment, and occupational experience are often equal to or higher than non- Hispanic white natives [18,20]. High attainment has contributed to the perception of Asian Americans as the “model minority;” however there is evidence that not all Chinese and Indian immigrants are as successful as this label suggests and that significant heterogeneity exists both between and within each of these immigrant groups [14,21]. Previous research has focused on comparing wealth attainment across many different immigrant groups. While this research provides important insights about immigrant stratification patterns, including all immigrants in models of wealth attainment conceals socioeconomic heterogeneity within specific immigrant nationalities. For instance, although marriage is a wealth-building institution, it may be less important in predicting wealth differentials within populations (i.e., Chinese and Indians) who have high marital rates across all SES levels.

This paper compares the wealth attainment of recent Chinese and Indian legal permanent residents and explores variations in wealth attainment within each of these immigrant groups. The focus is exclusively on Chinese and Indian immigrants for two reasons. First, because Chinese and Indian immigrants are the two largest Asian immigrant groups to the U.S., their incorporation patterns will have significant consequences for future research on immigrant stratification. Second, the children of Chinese and Indian immigrants, the 1.5 and second generations, have achieved remarkable educational and occupational success in the U.S., thus motivating explanations of their unique assimilation patterns [22]. Ideas from segmented assimilation and wealth attainment motivate the paper’s model of immigrant wealth accumulation that emphasizes enduring advantages. Enduring advantages are home country or mode of entry characteristics (e.g., pre-migration educational attainment, visa type) that encourage positive selection from immigrants’ home countries and continue to influence an immigrant’s mobility and well-being in their host country. Data from the New Immigrant Survey (NIS) shows how indicators of home country SES (e.g., parental education, education attained abroad), visa status, and context of reception explain both between- and withingroup heterogeneity of Chinese and Indian immigrant wealth.

Enduring Advantages and Immigrant Wealth

To understand differences in wealth accumulation within and between immigrant groups, it is useful to consider a model of immigrant wealth attainment that incorporates ideas from segmented assimilation and wealth attainment models. This model is unique in that it stresses the role of home country context and legal status in immigrant wealth attainment in addition to the role of host country processes (e.g., host country SES and institutional access) – the typical focus of prior research on immigrant stratification. Both segmented assimilation models, in which parental human capital, modes of incorporation, and family structure affect intergenerational patterns of immigrant assimilation, and wealth attainment models – based on status attainment and life course theories – inform our understanding of immigrant wealth accumulation [14,23,24]. Modes of incorporation are the various processes through which immigrant human capital, legal status, and race interact with host country perceptions and institutions to shape generational patterns of assimilation [25]. Wealth attainment models regard wealth accumulation as the result of processes and behaviors occurring throughout the lifespan, which are influenced by family background, human capital, demographic characteristics, and cultural/religious orientations [14,23].

Pre-migration characteristics

Pre-migration characteristics include all of the individual and family level experiences and processes that take place in an immigrant’s country of origin beginning in childhood and continuing until migration. Such characteristics include parents’ educations and occupations, childhood poverty, pre-migration educational attainment, and social networks. Pre-migration characteristics are critical to immigrant wealth accumulation for three reasons. First, premigration characteristics strongly predict the probability of migration. Research has demonstrated that all immigrant groups are positively selected for immigration to the U.S. by education, indicating that, in general, immigrant educational attainment is higher than their home countries’ averages [1,2]. Positive selection is important because immigrant pre-migration educational attainment is strongly correlated with adult wealth and acts as a signal of SES. Educational attainments also indicate home country SES; immigrant pre-migration human and financial capitals are likely to facilitate immigrant wealth accumulation and subsequent economic incorporation [26].

Pre-migration characteristics (e.g., class background and education) play a significant role in establishing immigrant legal status, which includes whether an immigrant has legal documentation to enter the U.S., and if documented, which visa category they have. For instance, parental education is a useful measure of an immigrant’s home country class background and is likely to affect their ability to accumulate wealth in the U.S. Parental education is positively correlated with adult immigrant educational attainment, and therefore predicts adult wealth attainment [23,27]. Similarly, skill-level and education are important because certain visa categories (e.g., employment visas) are restricted to immigrants with high educational attainment and professional experience. Less skilled immigrants with low educational attainment are more likely to enter the U.S. through family reunification or diversity visas or without documentation. Social networks are particularly important for these immigrants, as they distribute information about border crossing, migration destinations, and work opportunities, thus making contact with members of these networks a crucial aspect of undocumented immigration [28,29]. Home country context is also important in determining legal status; for example, immigrants from nations with hostile government regimes sometimes receive refugee status, which provides legal entry and modest government financial support.

Visa status as stratification categories

Visa status is one of the most salient immigrant traits, reflecting premigration financial and human capital; given its effect on participation in labor and financial markets, legal status is an important stratification category and likely to have significant implications for immigrant wealth accumulation [23]. The dichotomy between documented and undocumented immigrants remains the most important legal status among immigrants. For example, undocumented Mexican immigrants have lower human capital than those who are documented, leading to lower wages and more limited prospects for mobility [30-32]. Exclusion from formal labor markets and financial institutions due to lack of U.S. identification forms exacerbates this effect; thus, undocumented immigrants are less likely than documented immigrants are to own assets in the U.S., as they experience greater barriers to opening bank accounts, obtaining mortgages, or otherwise participating in the formal financial system. Legal status also has important long-term effects on the assimilation of future immigrant generations. For instance, the children and grandchildren of undocumented Mexican immigrants suffer from decreased educational attainment compared to documented immigrants due to their parents’ precarious working conditions and the stress of having unauthorized household members [30].

Economic incorporation of documented immigrants also varies by visa category. The major visa categories include those granted to refugees, employment principles, family reunification, and diversity visas. Those seeking political asylum are designated refugees and given relocation assistance by the U.S. government but also experience the most difficult occupational recovery of any visa category [25,33]. In contrast, immigrants with employment visas tend to have higher educations and professional skills compared to other immigrants; these traits facilitate incorporation into both labor and financial markets and may ultimately increase wealth [2,34]. Immigrants entering the U.S. with family reunification visas generally have lower educational attainment and lower rates of occupational recovery than those with employment visas [33]. Educational attainment, labor market skills, and occupational recovery are all strong predictors of asset ownership and immigrant wealth. Given that these characteristics vary by class of admission, visa status is likely to significantly affect immigrant wealth accumulation and economic incorporation.

Host country experiences

Once in the U.S., home country context and visa status provide a foundation for immigrant wealth accumulation, interacting with context of reception, host country SES, and institutional access to affect immigrant wealth. Context of reception refers to the opportunities and constraints immigrants experience in their host country. Constraints include labor market, residential, and social discrimination based on nativity, race/ethnicity, or English ability [14,35,36]. Among these, race may be the most salient trait in determining an immigrant’s context of reception, as research demonstrates that labor market discrimination and residential segregation significantly contribute to the extreme wealth gap between non-Hispanic whites and African Americans in the U.S.[36]. Thus, immigrants who hail from racialized background (e.g., immigrant with African ancestry or dark-skinned Latinos) may experience more discrimination and barriers to incorporation than other immigrants [37-39]. Such exclusion can result in decreased income, low-value housing, and denied access to loans, all of which impede an immigrant’s ability to save and accumulate assets. Conversely, immigrants with higher home country SES and beneficial legal status may encounter educational, employment, business, and financial opportunities in the U.S. In this paper, we consider two types of host country opportunities: host country SES and institutional access.

Access to financial institutions is the ability and desire of immigrant households to use host country institutions. The ability to use host country financial institutions (e.g., opening a bank account or applying for a mortgage) is important for immigrant wealth attainment, as they provide reliable means of saving with the potential of gaining interest. Although there are undeniable benefits to participating in formal financial institutions, there is significant heterogeneity in their use among immigrants to the U.S. [40,41]. Several factors, including foreign asset ownership, English proficiency, and U.S. tenure, contribute to this diversity [40]. Foreign asset ownership is important indicator of access to U.S. financial institutions; growing evidence suggests that owning foreign assets primes immigrants for U.S. asset ownership, as they have increased knowledge of financial instruments, such as bank accounts, retirement funds, or stocks [40,41]. Similarly, access to financial institution grows with U.S. duration and greater English proficiency, as immigrants’ ability to navigate formal U.S. institutions increases [40]. Although immigrants do not enter the U.S. with uniform English proficiency, it is an important marker of an immigrant’s chances of obtaining professional employment and their ability to access formal institutions in their home country.

Receiving formal U.S. education, high household income, and selfemployment independently represent host country SES and are means of upward mobility available to some immigrants; as a result, these factors are highly correlated with wealth attainment [25]. Receiving formal education in the U.S. often leads to wealth attainment, as immigrants attain higher English proficiency, degrees resulting in higher-paid, professional employment, and greater familiarity with U.S. institutions [13]. Similarly, when immigrant households attain higher incomes, facilitated by positive labor market incorporation and educational attainment, they can gradually save and invest in the U.S. and abroad, leading to higher levels of wealth. Self-employment is often the sole mode of upward mobility for immigrants with lower educational attainment and limited English abilities [25,42]. While we expect access to financial institution and host country SES to be highly correlated with immigrant wealth attainment, it is important to note that home country SES and visa status generally form the basis for these factors.

Within-group heterogeneity of Chinese and Indian immigrants

Previous research on immigrant attainment, including research focused on their wealth ownership, has primarily examined betweengroup comparisons, thus obscuring within-group heterogeneity in wealth attainment. Both Indian and Chinese immigrants are socioeconomically diverse groups due to their distinct migration histories and selectivity processes [14,43]. Although Chinese immigration to the U.S. dates back as far as the mid-19th century, significant migration from India did not begin until after the immigration reforms in the mid-1960s [21,44]. Before these reforms, the U.S. had strict quotas restricting the number of people from non- Western European countries who could migrate. The Hart-Cellar immigration reforms that took effect in 1965 facilitated the immigration of 20,000 entrants with employment visas, in addition to those with family preference visas, from both India and China [45,46]. As a result of these and later immigration reforms that continued to prioritize visas allocated to high-skilled migrants and family reunification, the Chinese immigrant community increased from 237,000 in 1960 to 1.6 million in 1990, while the Indian immigrant community grew annually by 24% between the years of 1966 and 1977 [21,44].

Selectivity processes also contribute to within-group variation, as immigrants with different SES prior to migration tend to enter the U.S. with different visa preferences. The two most common visa types obtained by Indian and Chinese immigrants are employment and family reunification visas. In fact, Indian and Chinese immigrants are the two largest immigrant groups receiving employment visas, with 35% of Indian immigrants receiving employment-based LPR status compared to 20% of Chinese immigrants in 2009 [19]. Immigrants who receive employment visas are positively selected to a high degree, as employment visas are reserved for highly skilled individuals who typically have a bachelor’s degree or higher. Because of these patterns of selectivity, Chinese and Indian immigrants with employment visas have much higher educational attainment than their home country’s average [1,14]. However, Indian immigrants received about seven times more employment visas than Chinese immigrants in 2011, suggesting significant differences between the positive selection of these two groups [18].

Compared to Indian immigrants, Chinese are more likely to arrive on family reunification visas, contributing to a greater degree of socioeconomic diversity among Chinese immigrants. At the same time, a large number of Chinese immigrants receive employment visas, with 1 in 10 employment visas going to Chinese immigrants every year [47]. The prevalence of employment visas among Chinese immigrants results in high educational attainment for some. For instance, 9.6% of Chinese immigrants have less than 12 years of education, and 45.4% have attained a bachelor’s degree or higher [14,47]. Nearly twothirds (63%) of Chinese immigrants report having limited English proficiency, which often results in blocked occupational mobility in this population [21,47]. The average age of Chinese immigrants is higher that of Indians because many arrive as parents or grandparents of citizens (15% are over the age of 65) [47]. These older adults generally have limited English language proficiency, lower rates of labor force participation, and lack knowledge about American culture, all of which can lead to isolation and depression [48,49]. Chinese immigrants who are in the labor force are more likely than Indian immigrants to be concentrated in physically intensive, lower-wage service occupations, such as restaurant kitchens [21,50].

In comparison, Indian immigrants began arriving in substantial numbers after the passage of 1965 Immigration Reforms, which abolished country quotas limiting the number of racially and ethnically diverse immigrants to the U.S. and created avenues for those with family or employment in the U.S. [44]. Indian immigrants are highly selected on educational attainment and occupational skills and receive more employment visas than men of any other nationality [18,51]. As a result, 75% of Indian immigrants over the age of 25 having attained a bachelor’s degree or higher and only 2.3% having attained less than 12 years of education [14,51]. Commensurate with these attributes, Indian males have experienced considerable occupational success and are concentrated in the IT sector (29%) and management, business, and finance (21%) [51]. Likewise, Indian females are concentrated within the management and finance (19%) and information technology sectors [51]. Additionally, with 73% between the ages of 25 to 44, the majority of Indian immigrants are in their working years.

Dating back to British colonialism, the English language became widely used in all sectors in India, including education, government, and business sectors. As a result, 70% of Indian immigrants in the U.S. report having strong English language skills, compared to only 49% of all other immigrants [51]. We expect having strong English language skills upon arrival to provide Indian immigrants a wealth advantage, as they are less likely to experience downward occupational trajectories [33]. Considering the SES characteristics of these two immigrant groups and our theory of enduring advantages, we hypothesize:

H1: Indian immigrant households will have significantly higher wealth than Chinese immigrant households.

H2: Indian and Chinese immigrants with higher home country SES will have significantly greater household wealth in the U.S.

H3: Within Indian and Chinese immigrant groups, households with respondents receiving employment visas will have higher wealth than households that have no members with employment visas.

H4: Indian and Chinese immigrants with higher host country SES will have significantly greater household wealth in the U.S.

H5: Indian and Chinese immigrants with greater access to financial institutions will have significantly greater household wealth in the U.S.

Data and Measures

Data

The New Immigrant Survey (NIS) is an ideal data set to test these ideas. The NIS is a cross-sectional, nationally representative sample of immigrants who are legal permanent residents in the U.S. Researchers conducted survey interviews in the respondent’s language of choice as soon as possible after the immigrant received legal permanent residency between May and November of 2003. Respondents in the NIS sample are unique, as their length of stay in the U.S. is much shorter than most samples of surveyed immigrants, and respondents have an average U.S. tenure of 5.6 years. The NIS data is ideal for this research because it includes detailed information on immigrant wealth, including assets held abroad. In addition, the NIS includes retrospective data on respondents’ lives prior to migration, providing valuable data on pre-migration characteristics. NIS data also contains information on immigrant visa categories, home country experiences, educational attainment, work experience, family structure, English abilities, and health. The NIS sampled respondents from the electronic records of the U.S. Immigration and Naturalization Services using four strata based on adult respondent visa category [52]. The NIS randomly selected an equal number of respondents from the following four visa categories: 1) spouses of U.S. citizens, 2) employed by U.S. businesses, 3) diversity lottery winners, and 4) other visa categories. We use multiple imputations for income and asset variables with missing data [53]. As we focus on intergroup heterogeneity, we limit our sample to respondents who reported China or India as their country of birth. Our final sample has 1,204 respondents. The NIS includes only immigrants with LPR status, so our sample does not include those who with extremely low human and social capital who migrated without legal status or who have overstayed their temporary work or travel visas.

Net worth

The main dependent variable is wealth, or net worth. Net worth is total household debt subtracted from total household assets [15,27]. The NIS has a detailed module measuring the ownership, value, and location of all household assets and liabilities. Uniquely, the NIS asks respondents to specify in which country every asset is located. Measuring assets located abroad is essential when calculating immigrant net worth, as it includes any assets owned prior to migration and any investments made abroad after arriving in the U.S. Total household assets consist of real assets plus financial assets. Real assets are the value of any homes, real estate, transportation, and businesses owned by household members. Financial assets are the total value of any easily liquidated assets. This includes saving and checking accounts, Individual Retirement Accounts, Certificates of Deposit, stocks, money markets, and bonds. Total debt is the total of any mortgages, student loans, credit card loans, and any other debt accrued.

The NIS did not top-code asset or liability, leading to some reports of business and real estate valued over $50 billion, therefore we top and bottom code the top and bottom 1% of responses. Researchers interviewed NIS respondents in their native language, and therefore some respondents reported their income and net worth using their home country currency. We converted all currencies to U.S. dollars using 2003 conversion rates. As certain household members are more familiar with family finances, researchers interviewed whichever household member the respondent reported was most knowledgeable about asset ownership and value.

Independent variables

The key independent variable comparing Indian and Chinese wealth is nationality. Nationality is based on whether the respondent reported India (reference) or China (mainland only) as their country of origin consistent with prior research in this area [54,55] . Measures of home country SES, legal status, host country SES, and institutional access are also important variables. Home country SES is foreign education and father’s education. Foreign education is a continuous variable indicating the number of years of education the respondent received in their home country. Father’s education is a dichotomous variable indicating whether the respondent’s father completed 12 or more years of education. Mother’s educational attainment and father’s educational attainment were highly correlated in our sample, so we include only an indicator of father’s education. Models include controls for visa status, a dichotomous variable indicating a respondent’s visa type (1=employment visa; 0= any other type of visa). The majority of Chinese and Indian immigrants received either an employment visa or family reunification visa, thus a dichotomous measure is sufficient for predicting the wealth of these two groups. Household earnings is the total household earned income, logged. U.S. education is a binary variable indicating whether the respondent received any formal education in the U.S. (1=received U.S. education; 0=did not receive U.S. education). Self-employment is a dichotomous variable indicating whether the immigrant was self-employed at the time of the interview (1=self-employed; 0=works for another or is unemployed).

Home country asset ownership, English proficiency, and U.S. tenure measure institutional access. Home country asset ownership is a dichotomous variable indicating whether the respondent household owns any assets in their home country. The NIS asked respondents in which country each asset they own is located, allowing us to estimate whether or not any of their assets (e.g., homes, businesses, farms, or saving accounts) are located in their home country. English proficiency is a dichotomous variable indicating the respondents’ self-reported ability to speak English (1=speaks English very well or well; 0= speaks English poorly). Established immigrant is a dichotomous variable indicating that the immigrant has lived in the U.S. for five years or more (1=U.S. duration is 5 years or more; 0=U.S. duration is less than 5 years). We use five years as a cut-off point because it is the mean U.S. tenure for our sample and because it is a commonly used measure of duration in immigration research.

Control variables

Models include several control variables that are also associated with wealth. Marital status is a dichotomous variable indicating that the respondent is married (1=married; 0=not married). Number of children is a continuous variable, indicating the number of biological children the respondent has. Male is a binary variable indicating respondent gender (1=male; 0=female). Age in years is a continuous variable. U.S. region is a series of continuous variables representing where the respondent currently lives in the U.S.: New York, North East, South, Central, Mountain, California, and Pacific.

Analytic method

Net worth presents a modeling challenge as many recently arrived immigrants in our sample have zero net worth and several respondents own very high values of net worth. In the NIS, the distribution of positive net worth is left-censored with a skewed right tail. Excluding observations without positive net worth from the analyses would introduce selection bias, as they would be non-randomly chosen on their relationship to the dependent variable. For instance, immigrants belonging to specific visa categories (e.g., family reunification) or reporting certain pre-migration characteristics (e.g., low parental educations) may be more likely to have zero wealth. However, using logistic regression to model positive wealth as a dichotomous variable is insufficient, as it does not consider the wide range in net worth values that are likely to have important consequences for immigrant wellbeing. Therefore, in our analyses of net worth, we use Tobit models, or censored regression models, to estimate the relationships between net worth and pre-migration characteristics, legal status, and host country experiences. A Tobit model estimates relationships between continuous dependent variables which are censored and covariates [56]. Tobit models assume normality in the distribution of the dependent variable; however, due to the extreme economic diversity among new Indian and Chinese immigrants, positive net worth is highly skewed with a long right tail.

Findings: Wealth Disparity by Nationality and Visa Status

Descriptive statistics in Table 1 provide preliminary support for the hypotheses. These descriptive statistics indicate that significant differences in wealth exist between nationalities and by visa status for both Indian and Chinese immigrants. Specifically, with median net worth of $7,070, Indian immigrants have greater median net worth than Chinese immigrants, who have zero median net worth. These medians are considerably less than the median household net worth for U.S. natives in 2003, which is likely due to the fact that the immigrants in our sample are recent arrivals, with an average U.S. tenure of 3.29 years. Comparing the means of various factors (e.g., home and host country SES, institutional access, and legal status) by national origin provides insight into the Chinese/Indian wealth gap. To begin, Indian immigrants, on average, have achieved higher educational attainment than Chinese immigrants, and a higher percentage of Indian immigrants have fathers who received 12 or more years of education. In addition, Indian immigrants have higher mean household income than Chinese immigrants, and a slightly higher percent are selfemployed than Chinese immigrants. However, a higher percentage of Chinese immigrants received some type of formal education in the U.S. compared to Indian immigrants. Indian immigrants also have an advantage over Chinese immigrants in terms of institutional access, as a higher percentage of Indian immigrants than Chinese immigrants own assets in their home country and have U.S. tenure greater than 5 years. A much higher percentage of Indian immigrants are proficient in English (69%) than Chinese immigrants (26%), despite the larger proportion of Chinese immigrants educated in the U.S. This is most likely due to the fact that because of India’s history as a British colony, English is the country’s official language. Looking at visa entry category, it seems that Indian immigrants’ advantage over Chinese immigrants related to SES and institutional access factors are due in large part to positive selection, as more than twice as many Indian immigrants hold employment visas (58%) than Chinese immigrants (26%). There is little variation in the family structure of Indian and Chinese immigrants; both national origin groups have high marriage rates and an average of about 1.5 children per household (Table 1).

  All Immigrants Indian Immigrants Chinese Immigrants
  Indian Chinese Employment Visa Family Reunification Employment Visa Family Reunification
Wealth (Median) $7,070 $0.00 $30,000 $ 2.00 $10,000 $0.00
Home Country SES            
Years of foreign education 14.00 11.51 16.40 12.67 14.75 10.61
Father's education ≥ 12 years 57.66 36.30 77.21 30.83 52.94 25.00
Visa Status            
Employment visa 0.58 0.33 1.00 0.00 1.00 0.00
Host Country SES            
Household income (logged) 7.63 5.85 10.17 5.89 9.84 4.56
U.S. education 0.12 0.19 0.21 0.07 0.59 0.10
  Self-employed 0.08 0.06 0.02 0.09 0.02 0.05
Financial Institutional Access            
Home country asset ownership 0.29 0.13 0.23 0.35 0.07 0.14
U.S. tenure > 5 years 33.20 30.22 45.35 11.46 58.82 14.29
Proficient in English 0.69 0.26 0.96 0.50 0.73 0.15
Family Structure            
Married 0.91 0.87 0.95 0.90 0.91 0.88
Number of children 1.66 1.56 0.95 2.15 0.90 1.88
Male 0.42 0.41 0.53 0.34 0.36 0.49
Age 40.91 45.51 0.33 46.51 36.30 49.01
N 744 460 430 253 153 252

Table 1: Descriptive Statistics for New Immigrants by Nationality and Visa Status.

Interestingly, greater variation in net worth exists within immigrant groups by visa type than by nationality. For instance, Indian immigrants with employment visas have a median net worth of $30,000, while Indian immigrants with family reunification visas have $2 median net worth. Significant differences in net worth also exist between Chinese immigrants with employment visas ($10,000) and family reunification visas ($0). Such disparities between visa types is not limited to net worth, but is observed throughout most SES and institutional access characteristics. Both Indian and Chinese immigrants with employment visas have significantly higher educational attainment, more highly educated fathers, higher incomes, longer U.S. tenure, and a greater proportion are proficient in English. While both Indian and Chinese immigrants with employment visas seem, in general, to have much higher SES and institutional access, there are two exceptions to this pattern. First, a higher percent of Indian and Chinese immigrants with family reunification visas are self-employed. This makes intuitive sense, as immigrants with employment visas have jobs upon arrival, and therefore do not need alternate forms of employment. Second, a higher proportion of Indian and Chinese immigrants with family reunification visas own assets in their home country than those with employment visas. The shorter U.S. tenure of immigrants with family reunification visas many explain this difference, as foreign asset ownership and U.S. tenure are negatively correlated [57].

Findings: comparing Chinese and Indian wealth attainment

Multivariate models provide further support for our hypotheses. Table 2 displays estimates from Tobit models of Indian and Chinese immigrant net worth. Consistent with H1, Chinese immigrants have significantly less net worth than Indian immigrants, demonstrated by the negative Chinese coefficient. These results are consistent with previous research demonstrating that Indian immigrants have higher net worth, on average, than Chinese immigrants [14,58], and the fact that Indian immigrants experience higher selectivity in regards to education and are more likely to be employed in professional careers [1,44]. Model 1 provides support for H2, as coefficients for years of foreign education and father’s education are positive and significant, suggesting that immigrants in our sample with additional years of premigration education and/or a father with 12 or more years of formal education both have significantly higher net worth. These findings are consistent with previous research demonstrating that immigrant education is highly correlated with their wealth attainment [14,30], and contributes to the literature by suggesting that high parental education attainment is related to higher immigrant net worth. Model 2 supports H3, as having an employment visa is associated with a significant increase in Chinese and Indian immigrant net worth. Furthermore, estimates from Model 2 suggest that visa status is more highly correlated with immigrant wealth than country of origin, as the absolute value of the coefficient for legal status is greater than the Chinese coefficient (Table 2).

Model 1 Model 2 Model 3 Model 4
Intercept 3.87 8.13 1.77 5.77
(1.61) (1.37) (1.47) (1.29)
Nationality (Reference=Indian)
Chinese -2.87 -3.55 -3.76 -1.10
(0.560 (0.55) (0.54) (0.55)
Home Country SES
Years of foreign education 0.44 -- -- --
(0.07)
Father's education ≥ 12 years 1.41 -- -- --
(0.57)
Visa Status
Employment visa -- 4.08 -- --
(0.63)
Host Country SES
Household income (logged) -- -- 0.72 --
(0.07)
U.S. education -- -- 1.94 --
(0.63)
Self-employed -- -- 1.78 --
(1.02)
Financial Institutional Access
Home country asset ownership -- -- -- 6.96
(0.54)
U.S. tenure > 5 years -- -- -- 2.58
(0.51)
Proficient in English -- -- -- 3.73
(0.60)
Family Structure
Married -0.79 -0.45 -1.14 0.13
(0.78) (0.77) (0.76) (0.71)
Number of children 0.37 0.06 0.06 0.01
(0.21) (0.21) (0.20) (0.19)
(1.52) (1.52) (1.47) (1.38)
Age -0.14 -0.11 -0.04 -0.16
(0.03) (0.03) (0.03) (0.02)
Male 1.50 1.29 1.37 1.04
(0.51) (0.52) (0.49) (0.47)
Sigma 7.86 7.93 7.56 7.16
(0.24) (0.24) (0.23) (0.21)

Table 2: Coefficients from Tobit Models of Indian and Chinese Immigrant Wealth.

Although immigrants in this sample have shorter than average U.S. tenure, our Model 3 provides support for H4, that host country SES is associated with significantly higher wealth for Chinese and Indian immigrants. The finding is nuanced, however, as only household income and receiving formal education in the U.S. are associated with a positive increase in Indian and Chinese immigrant net worth. Interestingly, self-employment is not associated with higher wealth, although previous research has demonstrated that this is an important means of immigrant upward mobility [25,26]. Yet, it is likely that this relationship is not apparent because our sample is comprised mainly of new arrivals, and wealth accumulation facilitated by entrepreneurship can take decades to achieve [14]. Findings from Model 4 indicate that access to financial institutions explains the wealth disparity between new Indian and Chinese immigrants and is related to a significant increase in net worth, thus supporting H5. The Chinese coefficient is no longer significant when home country assets, U.S. tenure, or English proficiency are included in the model. In ancillary analyses, we tried adding these variables one-by-one to the model, but none of them (or combinations of two) explained the wealth gap on their own. Our findings in Model 4 are consistent with immigration wealth research that shows that immigrants steadily accumulate wealth as their U.S. tenure increases [14] and they become more proficient in English [13,58]. Importantly, our results suggest that owning home country assets, living in the U.S. for more than 5 years, and English proficiency are not only associated with higher net worth, but also explain why Indian immigrants have greater wealth than Chinese.

Controls for gender and U.S. region behave as expected, but because of the unique NIS sample, family structure has no significant relationship with Indian and Chinese immigrant wealth. This is unusual, as research has found that marital status and number of children are highly correlated with wealth [59,60]. However, the family structure among Indian and Chinese immigrants in our sample is quite homogenous, as a majority of respondents are married and have an average of less than two children per household. Therefore, lack of variation in marital status and number of children among new Chinese and Indian immigrants is likely the reason that family structure has no significant relationship with net worth in our sample. Additionally, there is evidence that the homogeneous family structure is not unique to our sample, but is common among the general population of Chinese and Indian immigrants in the U.S. [14]. Similarly, in this sample, age is associated with owning significantly less wealth. This is contrary to previous findings on wealth accumulation over the life course, which demonstrates that as individuals age, their wealth increases until retirement, when they begin dissaving [61]. However, previous research has used longitudinal wealth data with predominantly nativeborn samples; our sample differs in that it is a cross-sectional study of new immigrants. Many of the newly arrived older immigrants are parents of citizens, and come to the U.S. to live with and care for their children and grandchildren [62,63]. As they spent the bulk of their working years in their home country, these older immigrants may have transnational assets, but they are likely to have little or no wealth in the U.S. Moreover, older immigrants often have limited English proficiency, which restricts their ability to work outside the home or access social benefits to which they may be entitled [48].

Findings: wealth heterogeneity among Indian immigrants

Limiting the sample to Indian immigrants demonstrates unique wealth patterns. Table 3 displays estimates from Tobit models of Indian immigrant wealth, and in these models our primary independent variable is immigrant visa category. The first three models show that having an employment visa is associated with increased wealth among Indian immigrants in our population. This finding supports H2, and contributes to prior research on legal status and immigrant mobility by demonstrating that there is considerable wealth variation by visa type within our sample of legal Indian immigrants. In addition to family structure and demographic control variables, Model 1 includes indicators of home country SES. Although years of foreign education are associated with significantly higher net worth for Indian immigrants, having a father with 12 or more years of education is not significantly related with net worth. This differs from our findings in Table 2, and highlights the nuanced patterns of wealth ownership within each nationality. Model 2 includes host country characteristics, and again we find distinctive wealth patterns when limiting our sample to Indian immigrants, emphasizing the importance of within group heterogeneity. Similar to Table 2, household income is associated with significantly higher net worth for Indian immigrants in our sample. However, when Indian immigrants are included in the models, U.S. education no longer has a significant relationship with immigrant net worth, but immigrants who are self-employed have significantly higher wealth than those who are not. This finding supports previous research that finds that Indian immigrant entrepreneurs are increasingly concentrated in high-tech, high-capital companies [20,64] (Table 3).

Model 1 Model 2 Model 3 Model 4
Intercept 3.03 0.93 5.73 -1.07
(2.03) (1.86) (1.60) (1.92)
Visa Status
Employment visa 2.60 2.38 2.31 1.32
(0.74) (0.73) (0.70) (0.72)
Home Country SES
Years of foreign education 0.33 -- -- 0.13
(0.09) (0.08)
Father's education ≥ 12 years 0.52 -- -- 0.45
(0.66) (0.58)
Host Country SES
Household income (logged) -- 0.60 -- 0.49
(0.08) (0.07)
U.S. Education -- 0.46 -- 0.22
(0.74) (0.69)
Self-employed -- 2.74 -- 2.00
(1.15) (1.04)
Financial Institutional Access
Home country asset ownership -- -- 6.68 6.09
(0.56) (0.54)
U.S. tenure > 5 years -- -- 1.95 1.69
(0.55) (0.55)
Proficient in English -- -- 1.71 0.95
(0.75) (0.79)
Family Structure
Married -0.95 -1.24 -0.16 -0.91
(0.95) (0.93) (0.86) (0.85)
Number of children 0.74 0.49 0.28 0.41
(0.29) (0.28) (0.26) (0.26)
Age -0.11 -0.04 -0.16 -0.09
(0.03) (0.03) (0.03) (0.03)
Male 1.61 1.53 1.33 0.91
(0.59) (0.57) (0.54) (0.53)
Sigma 7.04 6.79 6.32 6.06
(0.25) (0.24) (0.22) (0.21)

Table 3: Coefficients from Interactive Tobit Models of Indian Immigrant Wealth.

Similar to Table 2, variables related to institutional access are associated with a significant increase in wealth. However, the coefficients for U.S. tenure and English proficiency are smaller in these models, indicating that the relationship between these characteristics and net worth is smaller in the Indian sample. This makes intuitive sense, as many Indian immigrants enter the U.S. with English proficiency, and do not need to live in the U.S. for many years to attain fluency. Model 4 includes variables for legal status, home and host country SES, institutional access, and our controls. When the full model is included, having an employment visa is no longer associated with increased wealth. This supports the proposal that immigrant visa category is a selection mechanism, which doesn’t directly affect immigrant wealth, but mediates the relationship between host country SES and institutional access. Additionally, the coefficients for home country SES (e.g., years of foreign education and father’s educational attainment) are no longer significant when host country SES and institutional access are added to the model. In the full model, only household income, home country asset ownership, and U.S. tenure are associated with significantly higher net worth for Indian immigrants.

Findings: wealth heterogeneity among Chinese immigrants

Intriguing patterns in wealth ownership emerge when we limit our sample to Chinese immigrants. In Table 4, we use models identical to those used in Table 3, but find different results. Model 1 is consistent with Table 3, as we find that having an employment visa is associated with owning significantly higher net worth, but the legal status coefficient is almost twice as large in the Chinese sample as it is in the Indian sample. Home country SES continues to be associated with a significant increase in immigrant wealth; unlike our Indian sample, in addition to years of foreign education being associated with a significantly higher wealth, father’s educational attainment is also associated with significantly higher net worth. These findings suggest that father’s education, and perhaps home country class background, plays a more important role in U.S. wealth attainment for Chinese immigrants than for Indian immigrants (Table 4).

Model 1 Model 2 Model 3 Model 4
Intercept -0.74 -3.94 1.55 -7.20
(2.71) (2.69) (2.29) (2.76)
Visa Status
Employment visa 4.73 2.73 3.15 1.01
(1.37) (1.44) (1.38) (1.40)
Home Country SES
Years of foreign education 0.44 -- -- 0.23
(0.14) (0.13)
Father's education ≥ 12 years 2.42 -- -- 1.19
(1.16) (1.06)
Host Country SES
Household income (logged) -- 0.76 -- 0.65
(0.14) (0.13)
U.S. education -- 3.53 -- 2.74
(1.35) (1.51)
Self-employed -- 1.85 -- 0.63
(2.19) (2.06)
Financial Institutional Access
Home country asset ownership -- -- 7.88 7.69
(1.36) (1.31)
U.S. tenure > 5 years -- -- 2.81 1.38
(1.19) (1.21)
Proficient in English -- -- 5.06 1.80
(1.33) (1.50)
Family Structure
Married -0.01 -1.05 0.34 -0.95
(1.41) (1.37) (1.32) (1.28)
Number of children 0.08 -0.22 -0.15 0.04
(0.34) (0.31) (0.30) (0.30)
Age -0.14 -0.02 -0.11 -0.03
(0.05) (0.05) (0.04) (0.05)
Male 0.77 0.54 0.42 0.31
(1.04) (1.00) (0.98) (0.94)
Sigma 9.31 8.89 8.72 8.23
(0.54) (0.52) (0.50) (0.47)

Table 4: Coefficients from Interactive Tobit Models of Chinese Immigrant Wealth.

Model 2 includes host country SES. Again, we find that consistent with H4, increases in host country SES are associated with having significantly higher net worth, but the pattern for Chinese immigrants is different than the Indian immigrants in our sample in a number of ways. First, in our sample of Chinese immigrants, increases in household income and having some U.S. education are both associated with owning significantly higher net worth, but self-employment is not. This is interesting, as it suggests that for Chinese immigrants receiving U.S. education is more important than self-employment for wealth attainment. There are two possible (and not mutually-exclusive) explanations for this finding. First, U.S. education may be more important for Chinese immigrant wealth than for Indian immigrants because it increases English proficiency, which is important for professional and high-salaried employment. Previous research has shown that low English proficiency often restricts Chinese immigrants to low-paying jobs with few, if any, benefits [50]. Second, although Chinese entrepreneurs are increasingly entering high-capital markets, such as real estate, bio-tech, and online retail, there are still a large number of Chinese immigrants who own low-capital businesses with very low profit-margins, such as restaurants and hair salons [65]. For individuals with limited English, self-employment may be one of the only, and not particularly lucrative, alternatives to working for coethnics for less than minimum wage. Second, when we include host country SES in our model, the coefficient for immigrant visa status is still positive, but is no longer significant. This suggests that although legal status is associated with significantly higher wealth for Chinese immigrants, household income and receiving education in the U.S. mediate that relationship. These findings further support our proposal that legal status is a selection mechanism, but does not directly affect wealth.

In Model 3 institutional access variables are included in the Chinese wealth models. Similar to the Indian models, home country asset ownership, U.S. tenure, and English proficiency are all associated with owning significantly higher wealth. However, the coefficient for English proficiency in the Chinese model is three times greater than the English proficiency coefficient in the Indian model. This again demonstrates the salience of English proficiency for wealth attainment among Chinese immigrants, who as a group have more heterogeneous English skills compared to Indian immigrants. Model 4 is the full model of Chinese wealth and includes legal status, home and host country SES, and institutional access variables, in addition to family structure and demographic controls. Unlike the Indian sample, U.S. tenure is not associated with significantly higher wealth for our full Chinese model. While the positive relationship between household income and wealth is well-established [66,67], the finding that home country asset ownership has a robust relationship with Indian and Chinese immigrant wealth is novel, although it is consistent with previous research emphasizing the importance of transnational resources for immigrant incorporation [64,68].

Conclusion

Immigrants do not arrive in the U.S. as blank slates, nor do they always start off at the bottom of the labor market. They arrive with a unique set of experiences and resources that may promote or hinder incorporation into their host country society. Findings suggest that Indian immigrants experience a wealth advantage over Chinese immigrants, and that this advantage is due to differences in institutional access (i.e., home country asset ownership, U.S. duration, and English proficiency) [14,58]. This work also shows that significant wealth heterogeneity exists within both immigrant groups by visa status, as Chinese and Indian immigrants with employment visas have significantly higher wealth than their co-nationals. Although Indian and Chinese wealth is bifurcated by visa status, the relationship between visa category and wealth is indirect, as having an advantageous visa category (e.g., employment visa) results in highersalaried occupations and greater institutional access, both of which are directly related with higher wealth. These findings support the proposal that visa status is a selection mechanism and contribute to immigration research that emphasizes intra-group heterogeneity [69-73] and the bimodal nature immigrant wealth [14,21]. There are also important intra-group differences in Chinese and Indian wealth attainment. For instance, receiving U.S. education and English proficiency are more salient factors in attaining higher wealth for Chinese immigrants, while self-employment is more important for Indian immigrant wealth attainment [74-76].

Acknowledgement

We appreciate helpful comments from Jody Agius Vallejo, David Eagle, and Hang Young Lee. We acknowledge a grant from the National Science Foundation (SES-1322738) that supported this research. Direct correspondence to Lisa Keister, Department of Sociology, Duke University, Durham, NC 27708, [email protected]

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Article Usage

  • Total views: 12931
  • [From(publication date):
    October-2015 - Dec 13, 2017]
  • Breakdown by view type
  • HTML page views : 8999
  • PDF downloads : 3932
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version