Second-Generation Contextual Mobility: Neighborhood Attainment from Birth to Young Adulthood in the United States

Abstract

This article examines trajectories of neighborhood mobility for the post-1965 second generation in the United States. It advances the concept of second-generation contextual mobility, defined as the change in neighborhood context over the life course among the second generation. This analysis uses unique geocoded longitudinal data over three decades to documents patterns of second-generation neighborhood attainment. Compared to US blacks, the second generation has achieved significant contextual mobility both over time and across generations. Specifically, the second generation in this New York sample lived in better neighborhoods in young adulthood compared to birth neighborhood where their parents once lived. Most groups moved away from the most disadvantaged areas, with the exception of Dominicans. While the second generation has yet to achieve neighborhood parity with US whites, they have already surpassed US blacks in neighborhood attainment. Second-generation contextual mobility is thus an important, but missing, piece in established accounts of neighborhood mobility in the United States.

Introduction

Over the last two decades, a robust literature has focused on how the post-1965 second generation (i.e., the US-born children of immigrants) is incorporating into American society (Portes and Rumbaut 2001; Kasinitz et al. 2008; Bean, Brown, and Bachmeier 2015; Lee and Zhou 2015; Waters and Pineau 2015). At 12 percent of the total US population, the second generation is a diverse group in ethnic origin and racial identity (Pew Hispanic Center 2012). As this post-1965 second generation transitions into young adulthood, intense public and scholarly debates have focused on how the integration of the second generation will transform US society in the coming decades (Alba and Nee 2003; Alba, Kasinitz, and Waters 2011).

While prior research has examined indicators of second-generation individual mobility such as education, occupation, and income (Alba and Nee 2003; Kasinitz et al. 2008), this article shifts the focus to second-generation contextual mobility, defined as the change in neighborhood environment among the second generation over the life course. How does neighborhood context get transmitted across immigrant generations in New York, independent of economic and social mobility? What are key patterns of second-generation contextual mobility from birth to young adulthood? What are the cultural and social determinants of second-generation neighborhood mobility and attainment? How do these patterns vary by ethnic origin? How do neighborhood “starting points” among the immigrant first generation shape trajectories of second-generation neighborhood attainment in young adulthood?

Focusing on second-generation neighborhoods is important for four reasons. First, the size of the post-1965 second generation in the United States has grown substantially over the last decades. In 2013, the adult population of the immigrant second generation was 20 million, with Hispanics and Asians comprising about 70 percent of this population (Pew Research Center 2012). As the post-1965 second generation enters adulthood, their residential decisions have reshaped patterns of ethnoracial segregation in the United States (Brown 2007; Iceland 2009). Second, many neighborhoods across the country have diversified as a result of the post-1965 influx of immigrants in both old and new immigrant destinations (Maly 2005; Winders 2013; Frey 2014; Hall, Tach, and Lee 2016). This new pattern raises questions about how neighborhood attainment and sorting by race and nativity might differ across these geographical regions. Third, many American neighborhoods have become multiethnic, so spatial assimilation into the American mainstream is no longer a unidirectional movement from ethnic enclaves to predominantly white neighborhoods (Alba and Nee 2003). This shift opens up a range of residential options for the second generation, from co-ethnic enclaves and integrated neighborhoods to traditional suburbs and ethnoburbs with significant concentrations of ethnic businesses and services (Logan, Alba, and Zhang 2002; Lung-Amam 2017). Finally, this article moves beyond the usual black-white dichotomy in studies of US neighborhood stratification and contextual mobility (Sampson and Sharkey 2008; Sharkey 2008) by showing how second-generation contextual attainment not only fits within, but also extends beyond, the black-white binary of neighborhood stratification. Put differently, second-generation contextual mobility is a consequential, but missing, puzzle in established accounts of neighborhood mobility in the United States.

This article contributes to scholarship on second-generation assimilation in three ways. First, it adopts a longitudinal perspective on the study of neighborhood attainment and introduces the concept of second-generation contextual mobility to capture the change in neighborhood context over the life course. Second, it uses a unique source of retrospectively longitudinal data with information on second-generation neighborhoods both at birth and in young adulthood from the New York area — one of the most important gateways of immigration. Third, it focuses on four second-generation ethnic groups by benchmarking second-generation neighborhood attainment against US whites, US blacks, and Puerto Ricans.

Spatial Assimilation and Residential Integration

Residential assimilation is a key dimension of immigrant integration because immigrant social mobility can, in turn, facilitate movement into better and more integrated neighborhoods (Alba and Nee 2003; Iceland 2009; White and Glick 2009). The experiences of European ethnic groups from previous waves of US immigration resulted in spatial assimilation theory, which posits a mostly linear trajectory of residential mobility across immigrant generations (Massey and Denton 1985; Alba and Logan 1993). As immigrant groups become integrated into the US mainstream over time, the second and third generation move away from urban ethnic enclaves into predominantly white suburban neighborhoods (Alba and Nee 2003). In contrast to spatial assimilation theory, place stratification theory emphasizes the racial distinctiveness of the post-1965 US second generation and highlights three factors shaping inequality in residential choices (Rosenbaum and Friedman 2007; Charles 2006). First, socioeconomic differences by race mean that minority groups often end up in more disadvantaged neighborhoods because of affordability (Charles 2006). Second, racial prejudice among some whites against having ethnic minorities as neighbors, coupled with established cultural and social preferences for homophily, further limits minority residents’ movement into many white US neighborhoods (Krysan and Crowder 2017). Third, established institutional practices, such as racial discrimination in the housing market, often result in a “dual housing market” in which realtors are more likely to channel minorities into disadvantaged neighborhoods instead of white neighborhoods (Rosenbaum and Freedman 2007). As a result, minorities are often less successful in translating socioeconomic gains into residential mobility over time.

The rise of multi-ethnic neighborhoods further alters established expectations of second-generation neighborhood attainment (Maly 2005). New assimilation theory emphasizes the fact that assimilation is a two-way process and that full integration with whites is no longer the only, or the default, option (Alba and Nee 2003). The US second generation faces a broader range of neighborhood choices. Because of their bicultural and bilingual upbringing, they are in a unique position to fit into and benefit from a richer range of housing and neighborhood options. Such options include co-ethnic, integrated, or multiethnic areas in the urban setting, as well as predominantly white suburbs where good schools remain appealing to many immigrant families with children (Iceland 2009).

Recent research has also shown that the US second generation places significant value on spatial proximity to their parents and other co-ethnics, due to cultural and familial obligations within immigrant families (Yoo and Kim 2013). Among Mexicans, this preference leads to delayed spatial assimilation, a situation in which residential mobility might stall for one or more immigrant generations (Brown 2007). While prior work on residential choices among the second generation has focused on the structural factors that constrain their housing choices, this emerging body of scholarship reaches the opposite conclusion by highlighting the enduring cultural elements that equally shape second-generation preferences in housing choices. Finally, the constant replenishment of new US immigrants continues to strengthen existing ethnic enclaves by contributing the raw elements of ethnic culture from the sending country that further reinforces these preferences (Jiménez 2010).

The rise of intra-racial diversity is also consequential (Waters, Kasinitz, and Asad 2014; Jiménez, Fields, and Schachter 2015). For example, the US black population encompasses three subgroups: native-born blacks whose ancestors have been in the United States for generations, West Indians who mostly arrived in the 1970s and 1980s as voluntary migrants, and African immigrants who are newcomers to the United States and whose internal diversity reflects the 54 African countries from which they hail. Similarly, the category of Hispanic/Latino, which once denoted specific Latino groups based on geographical context (e.g., Mexicans in the Southwest and Puerto Ricans in the Northeast), increasingly includes immigrants from Central and South America. Even though Latinos share a pan-ethnic culture and most speak a common language (i.e., Spanish), they are diverse racially, culturally, politically, and socioeconomically (Mitchell and Tienda 2006). Recognizing this intra-racial diversity, racialized spatial assimilation theory highlights both ethnic and pan-ethnic categories as consequential in shaping residential outcomes among minority groups (Kim and White 2010; Lichter, Parisi, and Taquino 2015).

To be sure, these theoretical perspectives are not mutually exclusive. Each theory might be more relevant for certain ethnic groups, certain cities and regions, or certain historical periods. Yet there is a clear consensus on the role that ethnoracial origin and immigrant generation play in the ethnic spatial sorting process at the neighborhood level. For some groups, life within co-ethnic communities is temporary, and residential integration into the white mainstream might be the end goal. For other groups, living in urban ethnic enclaves or affluent ethnoburbs is a choice, even when spatial assimilation into the predominantly white mainstream is a viable option (Logan, Alba, and Zhang 2002; Wen, Lauderdale, and Kandula 2009). For yet other groups, global neighborhoods — racially integrated and socially stable — have become increasingly widespread and represent a new pathway toward integration (Logan and Zhang 2010). These possibilities highlight the profound consequences of second-generation residential choices and how these choices might reshape neighborhood inequality in the United States in the coming decades.

Second-Generation Contextual Mobility

While spatial assimilation theory and its variants have produced significant knowledge on key patterns and trends of immigrant residential integration, this body of work has mostly relied on cross-sectional analyses of census data. The limitation of this approach is the inability to trace the process of residential attainment over the life course. In contrast to prior work, this analysis adopts a longitudinal approach to examine neighborhood change over time. It advances a conceptual argument on how second-generation contextual mobility can broaden our purview of neighborhood mobility and inequality in the early twenty-first century. In shifting the focus from second-generation individual mobility to contextual mobility, I emphasize neighborhoods’ centrality in shaping life chances and transmitting social inequality across generations. In other words, I draw on one key insight from four decades of urban poverty research that highlights neighborhoods’ role in shaping life chances and asks how it might also apply to the second generation experience (Wilson 1987; Sampson 2012).

While spatial assimilation research has focused on neighborhood attainment as an outcome to be measured, research on contextual mobility makes explicit the intergenerational and longitudinal nature of the process. Here, I draw from Sharkey’s (2008) contextual-mobility perspective, which refers to the process whereby neighborhood (dis)advantages are transmitted across generations, independent of economic and social mobility. While Sharkey documented neighborhood inequality as a rigid dimension of stratification in the United States, given the persistent gaps between black and white areas, second-generation research has documented significant social mobility among the second generation, including Mexicans (Brown 2007; Kasinitz et al. 2008; Bean, Brown, and Bachmeier 2015). The second generation is not only more likely to achieve social mobility than their immigrant parents, but this individual-level mobility also translates into second-generation neighborhood attainment. As a result, second-generation neighborhoods constitute a more fluid dimension of stratification.

Neighborhoods, as a large body of work has shown (e.g., Sharkey 2008; Sampson 2012; Massey et al. 2013; Krysan and Crowder 2017), constitute an important ecological unit of analysis because they form a rigid dimension of stratification for non-immigrant groups, such as US-born blacks. Specifically, neighborhood effects research points to the detrimental impact of growing up in disadvantaged contexts on life chances and the transmission of neighborhood disadvantages across generations (Sampson and Sharkey 2008; Sampson 2012; DeLuca, Clampet-Lundquist, and Edin 2016). From the migration literature, segmented assimilation theory has similarly emphasized the detrimental role of neighborhood-level interactions between the second generation and their native minority peers, especially within disadvantaged and minority neighborhoods. Specifically, second-generation minority youths in inner-city neighborhoods might learn to adopt the “oppositional culture” among native minorities, which can lead to further downward mobility and assimilation into the “urban underclass” (Portes and Zhou 1993; Portes and Rumbaut 2001).

Second-generation contextual mobility is conceptually distinct from spatial assimilation. Because assimilation into US society is an intergenerational process (Alba and Nee 2003), spatial assimilation emphasizes residential mobility both over time and across immigrant generations. In contrast, second-generation contextual mobility bridges research on spatial assimilation and on neighborhood effects to highlight the centrality of neighborhoods in shaping second-generation mobility. To date, spatial assimilation theory and its variants (i.e., place stratification theory, racialized spatial assimilation theory, and delayed spatial assimilation theory) have mostly focused on the first generation’s experiences because the post-1965 second generation in the United States has only entered adulthood in sizable numbers in the last decades (Pew Research Center 2012; Tran 2018b). To test many core hypotheses on spatial assimilation, researchers need better geocoded data on neighborhood environments, ideally over three immigrant generations. To my knowledge, such data are not yet available on post-1965 immigrant cohorts at both national and regional levels (Massey 2018).

Bridging research on spatial assimilation and contextual mobility, the concept of second-generation conceptual mobility draws on research subfields that often have not been in dialogue with each other (Tran 2011). Theoretically, it differs from spatial assimilation in three ways. First, second-generation contextual mobility draws attention to both the reproduction of neighborhood inequality across immigrant generations and the change in trajectories of neighborhood mobility over the life course among the second generation. Second, in contrast to spatial assimilation, which treats neighborhood attainment as a socioeconomic outcome to be measured and assessed across immigrant generations, contextual mobility places emphasis on the mechanisms underlying the process of neighborhood attainment. Third, second-generation contextual mobility shifts the analytic focus from non-immigrant groups to second-generation groups by showing how second-generation experiences not only fit within, but also extend beyond, the black-white binary of neighborhood stratification in the United States.

Data and Methods

This article draws on unique retrospectively longitudinal data from the Immigrant Second Generation in Metropolitan New York (ISGMNY)—a study conducted by Philip Kasinitz, John Mollenkopf, and Mary Waters in 1999–2000. The study included a random telephone survey of 3,415 respondents from the metropolitan New York area (Mollenkopf, Kasinitz, and Waters 2001) and adopted an innovative two-stage sampling methodology. The first stage involved a random digit dialing screening phone call to enumerate household members, along with their ethnic origin and nativity, to identify eligible household respondents. The second stage involved the actual interview with the target respondent. The geographical coverage included four boroughs in New York City (excluding Staten Island), two urban counties in northeastern New Jersey (Hudson and Passaic), and four suburban counties in New York and New Jersey (Essex, Hudson, Nassau, and Westchester). The study focused on young adults between the ages of 18 and 32. It defined the post-1965 second generation as individuals born in the United States to foreign-born parents or born abroad but arriving in the United States by age 12.

ISGMNY is one of only three studies to focus on the post-1965 second generation in the United States.¹ For my purpose, it is the only data source that collected detailed information on the neighborhoods where respondents were born, grew up, and lived in young adulthood. This information provides a unique opportunity to construct retrospective and longitudinal data on the residential history for the sample over three decades. Specifically, I utilize a series of questions about the neighborhoods where respondents were born (“birth neighborhood”), where they lived the longest between the ages of 6 and 18 (“childhood neighborhood”), and where they resided at the time of the survey (“adult neighborhood”). For birth and childhood neighborhoods, the survey collected the cross streets, neighborhoods, borough, city, and state. For respondents’ adult neighborhoods, it collected the exact street addresses. I geocoded respondents’ addresses to identify the census tracts in which they lived. I then merged individual-level data from the survey with contextual-level data from the census between 1970 and 2000 to create a cross-level dataset.

This analysis focuses on four second-generation groups — Chinese, Dominican, South American, and West Indian — and three native groups — US white, US black, and Puerto Rican. “South American” is a composite category that consists of Colombians, Ecuadorans, and Peruvians (Kasinitz et al. 2008). The Puerto Rican sample includes those “who were born on the mainland, but whose parents or grandparents migrated from the island” (Kasinitz et al. 2008, 3). I exclude all 1.5-generation respondents and Russian Jews from this analysis because the majority of them were born outside the United States, making it impossible to geocode their birth neighborhoods. I further limit the sample to only second-generation respondents aged 25 and older, to be consistent with prior research (e.g., Sharkey 2008; Iceland 2009). The final sample size for this analysis is 1,062. For brevity, I refer to the native groups as “white” and “black” and to the second-generation groups by their ethnicity (e.g., Chinese).

ISGMNY has three advantages over similar studies, such as CILS and IIMMLA (see footnote 1). First, it is the only second-generation dataset with retrospectively longitudinal geocoded data. Because the US Census stopped collecting information on parental birthplace in 1970, it is impossible to identify second-generation adults from the Decennial Censuses or American Community Survey (Massey 2018; Tran 2018b). The Current Population Survey contains information about parental birthplace but does not contain neighborhood-level data for geocoding purposes (Tran and Valdez 2017). While CILS and IIMMLA contain geocoded data, the former does not include information on birth neighborhood and the latter only has contextual information for Mexicans (Brown 2007). Second, ISGMNY includes both urban and suburban respondents. This broad coverage is important because spatial assimilation theory predicts movements away from urban enclaves into suburban communities over time and across generations. Given the data’s retrospectively longitudinal nature, second-generation respondents’ birth neighborhood was not only the neighborhood in which they were born but also the neighborhood in which their first-generation parents lived in adulthood. By the same token, respondents’ neighborhood in young adulthood is their current neighborhood. As a result, the trajectory of second-generation neighborhood mobility from birth to adulthood also maps onto the intergenerational transmission of neighborhood from the first to the second generation. Third, ISGMNY captures not only those staying within the city but also those who experienced spatial assimilation and moved into more integrated and suburban communities. Although the sample was drawn from the New York metropolitan area, respondents grew up in many states across the country. However, the sample does not include those who moved to the more distant suburbs or who grew up in New York before moving away from the region. ISGMNY’s focus on the New York region is also important for the study of immigrant integration, given the region’s historic role in incorporating successive waves of immigrants (Kasinitz et al. 2008; Foner 2013).

ISGMNY does, however, have three potential drawbacks. First, the retrospective recall of information on birth and childhood neighborhoods could potentially introduce errors. To address this issue, I triangulated survey data with interview data for respondents who participated in two waves of in-depth interviews (Kasinitz et al. 2008). In the vast majority of cases, respondents not only accurately reported the neighborhoods where they grew up but also recounted vivid experiences from their childhood neighborhood. Respondents often recalled specific streets, and sometimes the actual building number, of their childhood residences. Second, the dataset was collected in 2000 and captured the historical specificity of that period — low unemployment and pro-immigrant sentiments. This positive context, combined with the decline in racial segregation, provided an opportune moment for second-generation integration. In contrast, the recent rise in anti-immigrant sentiments and the Great Recession’s disproportionate impact on immigrant groups might derail the process of neighborhood attainment documented in these data (Tran and Valdez 2017). Third, the dataset is limited to the New York region, and its findings are therefore not generalizable to other parts of the country (or beyond). While the spatial coverage is regional, it does provide an opportunity to examine second-generation contextual mobility in a metropolitan area. This contextual specificity is both a disadvantage and an advantage because national studies of neighborhood stratification often do not account for contextual variations in different regions of the country (South et al. 2016) and, instead, assume that all disadvantaged neighborhoods are internally homogenous (Small 2008). Whereas demographic and socioeconomic measures of neighborhood characteristics available at the census-tract level are compatible across the country, the benchmarks of neighborhood attainment vary across local communities. Future work should, thus, expand this analysis to include other immigrant gateways and new immigrant destinations in which the spatial sorting of immigrants into local neighborhoods might unfold differently (Singer, Hardwick, and Brettell 2008; Winders 2013; Krysan and Crowder 2017).

For census data, I use the Neighborhood Change Database from 1970 to 2000 — a product jointly developed by GeoLytics and the Urban Institute. The database standardizes census-tract boundaries across the four decennial censuses to facilitate effective comparison of census-tract data over time in normalized 2000 boundaries (GeoLytics 2003). Because ISGMNY respondents were born between 1966 and 1982, reached early adolescence between 1978 and 1994, and were in their young adulthood between 1998 and 2000, the 1970 to 2000 censuses are the closest match. Specifically, I geocoded birth neighborhoods with census data from 1970 and 1980, childhood neighborhoods with data from 1980 and 1990, and adult neighborhoods with data from 2000. For every intervening year between two decennial censuses, I used linear interpolation to estimate the census-tract-level characteristics (Brown 2007; Sharkey 2008). I matched neighborhood-level data to individual-level data based on the exact year when the respondent was born, at age 12, and at the time of the survey in young adulthood. To determine childhood neighborhoods, I used the survey question on “the neighborhood in which the respondents lived the longest between the ages of 6 and 18.” For geocoding procedures, I centered respondents’ age at 12. In line with the approach taken in previous research, I used census tract as a proxy for neighborhood (Logan and Zhang 2010; Hall, Tach, and Lee 2016; South et al. 2016). Although I have information on respondents’ census block groups, data availability and compatibility across four decades dictated the choice of census tracts as the unit of aggregation. Figure 1 illustrates ISGMNY respondents’ neighborhood in young adulthood based on the geocoded information.

Figure 1.

ISGMNY respondents’ adult neighborhood in the New York Metropolitan Area.

Dependent Variables

This analysis focuses on neighborhood mobility trajectories over time and neighborhood attainment in young adulthood. The dependent variables are two continuous variables measured at the tract level: mean household income and percent non-Hispanic white. To account for inflation across decades, the mean household income for the tract was adjusted, using the Consumer Price Index, and benchmarked, using constant dollars in 2000. I focus on these two neighborhood measures for three reasons. First, neighborhood mean income is the key measure used in research on contextual mobility and neighborhood attainment (Sharkey 2008; South et al. 2016). Second, neighborhood white composition is the key measure used in research on spatial assimilation and locational attainment (Alba and Logan 1993; Brown 2007; Iceland 2009; Logan and Zhang 2010). Third, these two tract-level measures are consistent across the decennial censuses, allowing for effective comparisons over time. In contrast, other measures such as neighborhood crime rate or neighborhood amenities at the census-tract level are often not collected using a standardized process and are also unavailable for many parts of the country.

Independent Variables

The key independent variable is ethnic origin, with whites as the reference group.² The control variables consist of a set of covariates for family background (parental education, work status, and female-headed household) and respondents’ own attainment (education, income, and home ownership). I include both fathers’ and mothers’ education to better capture the growing-up experiences of these ethnic groups. The correlation between the two measures of parental education is 0.37. The family background covariates further control for the level of resources available in the household in which the respondent grew up. Respondents’ education and income are included to examine how different ethnic groups translate their socioeconomic gains into residence in better neighborhoods. All measures of socioeconomic background are categorical in the survey. Finally, I control for whether respondents grew up in public housing, how frequently they moved between the ages of 6 and 18, how long they had lived in their neighborhood, whether they lived with parents at the time of the survey,³ and whether they grew up in New York. All descriptive statistics are in Supplemental Table A1 in the Supplemental Appendix (available in the online version of this article).

Analytic Methods

To capture change in the neighborhood environment from birth to young adulthood, I use a series of growth-curve models. Growth-curve models belong to a class of statistical models that account for a nested data structure (Singer and Willet 2003). They are appropriate here because multiple observations are “nested” within the same respondent over time. In this article, full maximum likelihood estimation is used to model two distinct types of variations: changes within the same individual in neighborhood characteristics over time and differences among individuals in neighborhood attainment. In essence, growth-curve models are a linked pair of hierarchical statistical models in which a level-1 model describes how a particular outcome within the same respondent changes over time and a level-2 model relates differences across respondents to the change in the explanatory variables (Singer and Willet 2003). Models for neighborhood mean income and neighborhood white composition are fitted separately to estimate parameters for the fixed effects and random components (Rabe-Hesketh and Skrondal 2012). To increase the coefficients’ interpretability in the growth curve models, respondents’ age is centered at 12 — the average age for the determination of respondents’ childhood neighborhoods. Specifically, a series of four nested models was fitted for each dependent variable: the unconditional means model; the unconditional growth model; a growth curve model controlling for ethnicity, gender, and age; and a growth curve model with all additional controls. Although growth-curve models can accommodate random slope models, I did not pursue them here because of data limitations and small sample size. The taxonomy for these models, including the specifications of level-1 and level-2 models, is below.

The unconditional means model:

Level-1: y_{t i} = β_{0 i} + e_{t i}

Level-2: β_{0 i} = γ_{00} + μ_{0 i}

The unconditional growth model:

Level-1: y_{t i} = β_{0 i} + β_{1 i} (A g e_{t i} - 12) + e_{t i}

Level-2: β_{0 i} = γ_{00} + μ_{0 i}

β_{1 i} = γ_{10} + μ_{1 i}

Growth curve models with covariates:

Level-1: y_{t i} = β_{0 i} + β_{1 i} (A g e_{t i} - 12) + e_{t i}

Level-2: β_{0 i} = γ_{00} + γ_{01} (Ethnicity) + γ_{002} (Female) + γ_{003} (Controls) + μ_{0 i}

β_{1 i} = γ_{10} + μ_{1 i}

Specifically, y _ti represents the neighborhood characteristics for respondent i at time t. e _ti is the residual component from the level-1 equation. μ _0i and μ _1i are the residual components from the level-2 equations. Age, ethnicity, and gender are the demographic characteristics for respondent i. These models assume that the random within-person error term e _ti is normally distributed and that the level-2 residual components μ _0i and μ _1i have a multivariate normal distribution.

Given the complexity of the sampling process and survey design, the analyses are adjusted, using the sampling weights provided by the survey firm SRBI Inc. All missing data were imputed, using a multiple imputation procedure. Multiple imputation is a flexible, simulation-based statistical technique for handling missing data (Rubin 1987; StataCorp 2015). To lessen the Monte Carlo error from the simulation, I generated two separate datasets based on 20 and 50 imputations, but the results are substantively similar, suggesting that findings are robust with regards to the missing data that were imputed. The predictors used in the imputation equation include the individual-level covariates in the final growth curve models. This procedure rests on the assumption that data are missing at random, conditional on the observable individual-level covariates (StataCorp 2015).

Findings

Contextual Mobility from Birth to Adulthood

On neighborhood mean income, Figure 2 shows that whites lived in the most advantaged neighborhoods from birth to young adulthood, compared to other ethnic groups. (For detailed neighborhood statistics by ethnic group, see Supplemental Table A2 in the Supplemental Appendix.) For example, the average white respondent was born in a neighborhood with an average household income of $52,530, grew up in a neighborhood with an average household income of $58,370, and lived in a neighborhood with an average household income of $77,410 in young adulthood. Among the second generation, Chinese were the most advantaged, and Dominicans were the least advantaged. In young adulthood, Chinese lived in neighborhoods with mean income of $56,430, higher than South Americans ($51,190) and West Indians ($51,930). In contrast, Dominicans, Puerto Ricans, and native blacks lived in neighborhoods with the lowest mean income.

Figure 2.

Mobility trajectory for neighborhood mean income by ethnic group.

On the one hand, these findings mirror prior research that documented neighborhood advantage among whites and disadvantage among blacks (Sharkey 2008; South et al. 2016). On the other hand, they point to important intra-racial differences. For example, South Americans reported living in better neighborhoods than Dominicans and Puerto Ricans, whereas West Indians reported living in better neighborhoods than blacks. Furthermore, West Indians, a racially black group, lived in more advantaged neighborhoods than Dominicans and Puerto Ricans, both Latino groups. These paired contrasts highlight the centrality of race in shaping neighborhood context across racial groups, while also pointing to the significance of ethnicity and nativity in shaping the heterogeneity in neighborhood environment within the same racial group. Furthermore, the neighborhood income gap between whites and other ethnic groups grew substantially from birth to young adulthood. For example, the neighborhood income gap between whites and blacks more than doubled from $15,200 at birth to about $36,330 in young adulthood. Compared to whites, Chinese and South Americans both report small gaps in birth neighborhood mean income, whereas the same gaps are quite substantial among West Indians and Dominicans.

On neighborhood white composition, race matters. Figure 3 shows that blacks and West Indians were the most segregated from whites, whereas Chinese and South Americans were the most integrated with whites. At birth, the neighborhood white composition was 72.8 percent for Chinese and 82.6 percent for South Americans, but only 45.6 percent for West Indians. These proportions steadily declined throughout childhood for all groups, but the declines were more dramatic for blacks and West Indians, pointing to increasing segregation over time. For example, the white composition in the current neighborhood was 40.5 percent for Chinese, 35.2 percent for South Americans, 20.6 percent for Dominicans, but only 16.1 percent for West Indians.

Figure 3.

Mobility trajectory for neighborhood white composition by ethnic group.

This decline in neighborhood white composition reflects both the growth in ethnoracial diversity and the persistence of segregation over the last decades (Iceland 2009; Rugh and Massey 2014; Hall, Tach, and Lee 2016). This trend is true not only in many metropolitan areas but also in New York City, which welcomed many immigrants during this time, including black immigrants such as the West Indians (Waters 1999; Kasinitz et al. 2008; Foner 2013). The in-migration of post-1965 immigrants into New York City also coincided with white out-migration into surrounding suburbs (Kasinitz et al. 2008). At the same time, the concentration of poverty in urban neighborhoods in the 1970s and 1980s led to higher segregation among blacks (Haynes and Solovitch 2017).

To summarize, whites not only lived in the most advantaged neighborhoods at birth but also lived in significantly better neighborhoods than other groups in young adulthood. There was also improvement in neighborhood environment among the second generation. Although the second generation has not achieved parity with whites in neighborhood attainment, it has moved away from the more disadvantaged neighborhoods where their parents once lived. At the same time, blacks are mired in the most disadvantaged neighborhoods compared to whites and to the second generation. Put differently, one unintended consequence of post-1965 immigration has been the solidification of white neighborhoood advantage at the top of the neighborhood hierarchy. Finally, there is heterogeneity among second-generation groups. Chinese reported the most gains, whereas Dominicans experienced the most disadvantage.

Neighborhood Attainment in Young Adulthood

The next analyses explored the role of family background, socioeconomic attainment, and neighborhood dynamics in shaping second-generation neighborhood attainment. Table 1 presents results from four nested models on neighborhood mean income. Model 1 indicates that the average respondent lived in a neighborhood with mean household income of $45,624 in young adulthood (γ₀₀ = 45.624, p < 0.001). The intra-class correlation coefficient (ρ = 130.6 / [603.1 + 130.6] = 0.178) indicates that about 17.8 percent of the variation lies between individuals and 82.2 percent within the same individual over time. Model 2 introduces age in its linear form as the key time-varying predictor.⁴ On average, each additional year in age is associated with a gain of $759 in neighborhood mean income (γ₁₀ = .759, p < 0.001). Comparing Model 1 and Model 2, the within-person random effects declined, suggesting that 69 percent of the within-person variation in neighborhood mean household income is associated with respondents’ age (R _ε ² = 0.69). In Model 3, the estimated neighborhood mean income for a white male respondent is $63,453 (γ₀₀ = 63.453, p < 0.001). Among the second-generation groups, significant differences exist, with Chinese reporting the smallest gap ($8,318) and Dominicans reporting the largest gap ($16,416) in neighborhood income compared to whites. More specifically, these gaps range from 13 to 26 percent of the neighborhood mean income for whites. At the same time, the second-generation groups fare better than Puerto Ricans and blacks, for whom the gaps with whites are $18,232 and $18,038, respectively. Model 4 introduces the full set of controls as additional level-2 predictors of neighborhood mean income. The coefficients for the ethnic dummies slightly decline, whereas the effects of age and gender remain roughly the same. The second generation reported a disadvantage in neighborhood mean income compared to whites, ranging from $8,936 among Chinese to $12,886 among Dominicans. The second generation also reported an advantage in neighborhood income compared to blacks, ranging from $5,104 among Chinese to $1,154 among Dominicans. Finally, the second generation’s advantage over their proximal host of the same race was clear. West Indians lived in more advantaged neighborhoods compared to blacks. South Americans and Dominicans lived in more advantaged neighborhoods compared to Puerto Ricans.

Table 1.

Growth-Curve Models for Neighborhood Mean Income.

Variables	Model 1	Model 2	Model 3	Model 4
Intercept	45.624***	50.615***	63.453***	51.381***
	(0.561)	(0.887)	(1.603)	(2.243)
Age		0.759***	0.763***	0.763***
		(0.071)	(0.072)	(0.072)
Female			–1.533	–1.226
			(0.998)	(0.992)
Ethnicity (ref: native white)
Chinese			–8.318***	–8.936***
			(2.258)	(2.189)
South American			–12.051***	–10.028***
			(1.771)	(1.809)
West Indian			–12.735***	–11.296***
			(1.769)	(1.874)
Dominican			–16.416***	–12.886***
			(1.632)	(1.710)
Puerto Rican			–18.232***	–13.289***
			(1.582)	(1.636)
Native black			–18.038***	–14.039***
			(1.580)	(1.653)
Random effects
Mean rate of change, σ² ₁		4.285***	4.285***	4.285***
		(0.217)	(0.215)	(0.063)
Mean initial level, σ² ₀	130.557***	711.289***	590.004***	552.250***
	(0.697)	(2.352)	(2.577)	(2.735)
Within-person, σ² _ε	603.102***	184.688***	184.145***	184.145***
	(0.381)	(0.581)	(0.579)	(0.577)
Full set of controls	No	No	No	Yes
Average RVI^a	0.021	0.016	0.028	0.028
Largest FMI^b	0.034	0.022	0.044	0.058
N (observations)	3,186	3,186	3,186	3,186
N (respondents)	1,062	1,062	1,062	1,062

Source: Geocoded ISGMNY (2000).

^aRelative variance increase test.

^bUnrestricted fraction missing information test.

*p < 0.05. **p < 0.01. ***p < 0.001.

Table 2 presents results on neighborhood white composition. Model 1 indicates that the average respondent lived in a neighborhood with 47.9 percent of non-Hispanic whites in young adulthood (γ₀₀ = 47.9, p < 0.001). The intra-class correlation coefficient (ρ = 275.2 / [1,046.4 + 275.2] = 0.208) indicates that about 20.8 percent of the variation lies between individuals and 79.2 percent within the same individual over time. Model 2 introduces age in its linear form as the key time-varying predictor. On average, each additional year in age is associated with a decline of 1.3 percent in neighborhood white composition (γ₁₀ = –1.33, p < 0.001). Comparing Model 1 and Model 2, the within-person random effects also declined, suggesting that 44.1 percent of the within-person variation in neighborhood white composition is associated with respondents’ age (R _ε ² = 0.441). In Model 3, the estimated mean neighborhood white composition for a white male respondent is 67.9 percent (γ₀₀ = 67.925, p < 0.001). In contrast, the other groups lived in neighborhoods where whites accounted for a minority of the population. Among the second generation, Chinese reported the highest level of residential integration with whites, whereas West Indians reported the highest level of residential segregation from whites. Model 4 introduces the full set of controls as additional level-2 predictors of neighborhood white composition. Although the coefficients for the ethnic dummies slightly decline, the second generation was still less likely to live in white neighborhoods than whites were. Chinese and South Americans were the most integrated with whites, whereas West Indians and blacks were most segregated from whites. Among Latinos, South Americans reported living in neighborhoods with approximately 8 percent more whites than did Dominicans and Puerto Ricans.

Table 2.

Growth-Curve Models for Neighborhood White Composition.

Variables	Model 1	Model 2	Model 3	Model 4
Intercept	47.961***	39.180***	67.925***	60.718***
	(0.771)	(0.900)	(1.367)	(2.947)
Age		–1.330***	–1.329***	–1.337***
		(0.063)	(0.063)	(0.063)
Female			–2.417*	–1.889
			(1.222)	(1.174)
Ethnicity (ref: native white)
Chinese			–20.001***	–19.032***
			(2.855)	(2.838)
South American			–21.633***	–18.226***
			(2.200)	(2.249)
West Indian			–44.385***	–40.343***
			(2.324)	(2.435)
Dominican			–32.393***	–27.224***
			(1.968)	(2.265)
Puerto Rican			–32.694***	–26.298***
			(1.830)	(2.107)
Native black			–47.259***	–41.621***
			(1.576)	(1.812)
Random effects
Mean rate of change, σ² ₁		1.299	1.464***	1.563***
		(0.073)	(0.068)	(0.069)
Mean initial level, σ² ₀	275.198***	500.864***	157.503***	130.416***
	(0.892)	(0.539)	(0.663)	(0.672)
Within-person, σ² _ε	1,046.377***	584.672***	573.411***	568.346***
	(0.501)	(0.557)	(0.529)	(0.526)
Full set of controls	No	No	No	Yes
Average RVI^a	0.022	0.042	0.029	0.025
Largest FMI^b	0.032	0.035	0.044	0.042
N (observations)	3,186	3,186	3,186	3,186
N (respondents)	1,062	1,062	1,062	1,062

Source: Geocoded ISGMNY (2000).

^aRelative variance increase test.

^bUnrestricted fraction missing information test.

*p < 0.05. **p < 0.01. ***p < 0.001.

Table 3 decomposes the impact of both individual and neighborhood characteristics on neighborhood attainment by presenting the relevant covariates from Model 4 in Tables 1 and 2. On neighborhood income, the fathers’ and the respondents’ education are significant and positive predictors of neighborhood attainment. Both social class and individual mobility matter for the sorting of the second generation into more advantaged areas. Among other covariates, there are no significant differences between those still living with parents and those who left the parental household. Respondents who grew up in New York also reported living in more disadvantaged neighborhoods compared to those who migrated to New York. On neighborhood composition, growing up in public housing was correlated with lower concentration of whites in adult neighborhood.

Table 3.

Decomposition of Neighborhood Attainment in Young Adulthood.

Variables	Mean Household Income		% Non-Hispanic White
	Model 1		Model 2
	Coefficient	SE	Coefficient	SE
Age	0.763***	0.071	–1.337***	0.063
Female	–1.226	0.992	–1.889	1.174
Mother’s education, high school	1.167	1.144	0.029	1.580
Mother’s education, some college	–0.620	1.393	–0.886	1.881
Mother’s education, college graduate	1.851	1.844	0.606	2.156
Mother’s education, graduate degree	3.143	2.318	2.771	2.764
Father’s education, high school	1.435	1.133	1.645	1.616
Father’s education, some college	–0.548	1.464	2.106	2.087
Father’s education, college graduate	6.445**	2.089	3.961	2.212
Father’s education, graduate degree	10.900***	2.706	4.499	2.388
Mother worked full time	0.767	1.009	0.908	1.169
Father worked full time	0.880	1.219	2.303	1.664
Female-headed household	0.476	1.219	–0.392	1.773
Respondent’s education, high school	3.806*	1.602	0.379	2.195
Respondent’s education, some college	3.137**	1.119	–0.509	1.894
Respondent’s education, college graduate	6.276***	1.438	3.818	2.353
Respondent’s income, less than $8,000	3.211	1.737	–4.168*	1.869
Respondent’s income, $8,000–$19,999	0.031	1.520	–4.979*	1.938
Respondent’s income, $20,000–$29,999	2.447	1.348	–3.196	1.837
Respondent’s income, $30,000–$49,999	2.815*	1.389	0.649	1.781
Respondent’s income, more than $50,000	2.744	1.817	3.128	2.335
Own vs. rent	0.501	1.223	1.481	1.549
Grew up in housing project	–2.528	1.506	–5.401**	1.986
Times moved between 6 and 18	–0.190	0.174	–1.103	0.228
Years in current neighborhood	–0.074	0.065	–0.099	0.091
Currently live with parents	1.026	1.439	–3.081	1.886
Grew up within New York Metropolitan Area	–4.333*	2.192	–0.612	1.946
Constant	51.381***	2.243	60.718	2.947
Mean rate of change, σ² ₁	4.285***	0.214	1.562***	0.069
Mean initial level, σ² ₀	552.250***	2.735	130.416***	0.672
Within-person, σ² _ε	184.145***	0.577	568.346***	0.526
Average RVI^a	0.028		0.025
Largest FMI^b	0.058		0.042
N (observations)	3,186		3,186
N (respondents)	1,062		1,062

Note: Full growth-curve models also control for respondent’s ethnic origin. The reference category for education is “no high school degree.” The reference category for income is “respondent’s income, missing.”

Source: Geocoded ISGMNY (2000).

^aRelative variance increase test.

^bUnrestricted fraction missing information test.

*p < 0.05. **p < 0.01. ***p < 0.001.

Change Score Models on Neighborhood Characteristics

How do the changes in neighborhood characteristics from birth to young adulthood vary by ethnic origin? How do the differences in neighborhood starting point at birth shape these life course trajectories? To address these questions, I used a series of change score models in which the dependent variable was the difference in neighborhood mean income or white composition between birth and young adulthood (Allison 1990; Dalecki and Willets 1991). A positive value on the change score signals that the respondent lived in a neighborhood with a higher average income and a higher white composition in adulthood (compared to at birth). The key independent variable is ethnic origin. For each dependent variable, I ran two models. The first controlled for the full set of independent variables in Table 3. The second further controlled for the birth neighborhood’s characteristics to account for neighborhood starting point. The full results from these ordinary least squares (OLS) regressions are in Supplemental Table A3 in the Supplemental Appendix. For ease of interpretation, Figure 4 graphs the predicted values of the change in neighborhood characteristics by ethnic origin from the full models from Supplemental Table A4 in the Supplemental Appendix, holding other covariates at their mean level.

Figure 4.

Predicted probabilities of change in neighborhood attainment from birth to adulthood.

On change in neighborhood mean income, the top left panel in Figure 4 shows that whites made the most gains, whereas Chinese made the least gains from birth to young adulthood. The gains in neighborhood income for West Indians and Puerto Ricans were similar to whites, whereas South Americans and Dominicans made smaller gains compared to whites. Controlling for neighborhood mean income at birth, the top right panel shows a clear white advantage, with the average white respondent reporting a gain of $27,000 in neighborhood income from birth to adulthood. The predicted value for the gain in neighborhood income among whites is twice the size for South Americans and West Indians and four times the size for Chinese and Dominicans during the same time period. In contrast, blacks gained the least.

On change in neighborhood white composition, the lower left panel in Figure 4 shows that whites experienced the lowest increase in neighborhood diversity, whereas Dominicans experienced the highest increase. The decrease in neighborhood white composition for Chinese was similarly small compared to whites, whereas South Americans, West Indians, and Dominicans experienced a significant decrease in neighborhood white composition compared to whites. Controlling for neighborhood white composition at birth, the white advantage was clear. White was the only ethnic group that experienced no change in neighborhood white composition, whereas all others reported a decrease in neighborhood white composition. As expected, the predicted values for the decrease in neighborhood white composition were steepest among West Indians and blacks. The increase in neighborhood diversity was also high for all other groups: 11 percent among South Americans, 16 percent among Chinese, and 24 percent among Dominicans.

Assimilation into What and Progress Compared to Whom?

The questions of “assimilation into what?” and “progress compared to whom?” remain central to our assessment of second-generation neighborhood attainment. The analyses thus far have implicitly used whites as the reference group to benchmark neighborhood attainment. What if blacks and Puerto Ricans are used as the reference instead? Table 4 summarizes the results on second-generation neighborhood attainment in relation to the three native-born reference groups: whites, blacks, and Puerto Ricans. This comparison builds directly on recent work that argues for a more contextualized benchmark to assess second-generation progress (Park, Myers, and Jiménez 2014; Tran and Valdez 2017; Valdez and Tran 2019). Here, I compare four second-generation groups to whites, South Americans and Dominicans to Puerto Ricans, and West Indians to blacks. Based on this analysis of trends in the late twentieth century, second-generation adults have yet to achieve parity with the majority group, but they have surpassed minority groups of the same race, with the exception of West Indians. Compared to blacks, West Indians lived in similarly disadvantaged and segregated areas. Compared to Puerto Ricans, South Americans lived in more advantaged and integrated neighborhoods, whereas Dominicans lived in neighborhoods similar to those of Puerto Ricans.

Table 4.

Benchmarking Second-Generation Neighborhood Attainment.

Neighborhood Attainment	Neighborhood Mean Income			Neighborhood White Composition
Neighborhood Attainment	Native White	Puerto Rican	Native Black	Native White	Puerto Rican	Native Black
Chinese	–			–
South Americans	–	+		–	+
Dominicans	–	0		–	0
West Indians	–		0	–		0

The three proximal host reference groups are white, black, and Puerto Rican. Comparisons of relative advantage or disadvantage in a particular outcome are based on the coefficients for the second-generation groups from the multivariate analyses with the corresponding reference group, adjusting for observable covariates in the full models in Table 3. These full results are not shown here, but available upon request from the author. Specifically, “+” denotes a significant advantage of a second-generation group relative to a native group; “–” denotes a significant disadvantage of a second-generation group relative to a native group; and “0” denotes no significant differences between a second-generation group and a native group.

How Selectivity Matters for Neighborhood Attainment

Four sources of selectivity might affect these findings: in-migration into New York, out-migration from the New York region, out-migration from New York metropolitan area into more distant suburbs, and migration away from the parental home. I address each topic in turn below. First, on selectivity due to in-migration, respondents who grew up outside New York are highly selected because many moved to the city after college to work in its financial industry (Kasinitz et al. 2008). However, this selectivity is mostly concentrated among whites, accounting for over a third of the white sample. In contrast, in-movers accounted for fewer than 9 percent of respondents in other ethnic groups (See Supplemental Table A1, last row). In light of this selectivity due to in-migration, Supplemental Table A2 provides the descriptive statistics for neighborhood attainment by ethnic group, separating white respondents into two groups: those growing up in and outside New York. At birth, white respondents from outside New York reported lower neighborhood mean income than those from New York, reflecting the higher mean household income in the area compared to the majority of the country. This pattern was reversed in adulthood, with white respondents from outside New York reporting a significant neighborhood advantage over those from New York. These results further confirm the selectivity of white in-movers. However, the white advantage over the second generation remains, even when second-generation neighborhood attainment is benchmarked using only white respondents who grew up in the New York area.

What about individuals who moved away from New York? How might their exclusion from the sample affect these findings? In their analysis of census data, Kasinitz et al. (2008) find that lower-income households were more likely to leave New York and that native-born groups were more likely to leave than were second-generation groups. Lower-income households report moving away from New York in search of more affordable housing within the metropolitan area (Matsumoto 2018). Similarly, immigrant groups tend to stay closer to New York because of existing co-ethnic and familial ties (Yoo and Kim 2013; Lung-Amam 2017). Since the second-generation groups were more likely to be in the lower-income category than whites, the gap in neighborhood attainment between the second generation and native whites in the sample would have been even greater if we take this selectivity in out-migration into consideration. However, addressing this issue fully goes beyond the scope of these data and is a topic for future research.

While the sample includes both urban and suburban respondents, it does not include those who moved to the distant suburbs within the New York metropolitan area. These individuals are likely to be among the most assimilated members of the second generation, so their absence from the sample underestimates the extent of second-generation contextual mobility that has occurred. These distant suburbs are not only predominantly white but also more affluent, so the exclusion of native-born individuals from these areas underestimates neighborhood attainment among whites. Given the second generation’s relatively smaller presence in these distant suburbs (Singer, Hardwick, and Brettell 2008), the net effect of these trends is an underestimation of second-generation neighborhood attainment, and the actual white neighborhood advantage is likely to be greater.

Finally, might respondents living with their parents at the time of the survey differ from those who had left the parental home? To address this issue, I replicated the same growth-curve models in Table 1 and Table 2, limiting the sample to those who left the parental home. For this analysis, I do not distinguish between respondents who moved away from and those who chose to remain in the neighborhood where they grew up. While this is a conceptual distinction, it is not an empirical one because the measures of neighborhood characteristics are tied to their adult neighborhood. These results are presented in full in Supplemental Table A4. First, these results do not differ significantly from those for the full sample, and the neighborhood advantage among whites over the second generation remains substantial. Second, the gap in neighborhood income among Chinese and whites decreases whereas the gaps between other ethnic groups and whites increase. Third, the gap in neighborhood white composition among Chinese and whites shrinks whereas the gaps between the other groups and white remain unchanged.

These findings suggest that the high prevalence in intergenerational living among the Chinese underestimated the extent of contextual mobility among Chinese if a higher proportion of the Chinese second generation chose to leave the parental home. Put differently, many second-generation Chinese often lived with their parents into adulthood not by necessity, but by choice. For example, the second generation might use this intergenerational living arrangement to finish graduate school with less debt, to save for a down payment for a future home purchase, or to rely on their immigrant parents for childcare support. The opposite is true for other second-generation groups, for whom the gaps in neighborhood attainment with whites increased for respondents who left the parental home. Put differently, non-Chinese second-generation groups in this sample often had to live with their parents in adulthood out of necessity, not by choice. As a result, non-Chinese second-generation respondents often ended up in more disadvantaged areas when they managed to find a place of their own.

Sensitivity Analyses and Robustness Checks

I rely on three sensitivity tests to confirm these results’ robustness. First, the largest value from the unrestricted fraction missing information (FMI) model tests is 0.058. According to the guidelines from StataCorp (2015), the minimum recommended number of imputations is at least equal to M = 100 × FMI = 100 × 0.058 = 5.8. As a result, the number of imputations used here (20) is more than adequate for the reproducibility of this multiple imputation analysis. A second sensitivity measure is the average relative variance increase (RVI), which reports on the average relative increase (averaged over all coefficients) in variance of the estimates because of information loss due to missing values. The RVI values range from 0.016 to 0.042, so the effect of missing data on the estimate’s variance is very small. Third, the Monte Carlo error (MCE) estimates of coefficients in these models is less than 10 percent of the standard errors of the coefficients, providing further evidence on these results’ reproducibility (White, Royston, and Wood 2011). While this analysis is limited to respondents aged 25 and older, related analyses applied to the entire sample aged 18 and older yield results that are substantively similar to those reported here (results available upon request). However, I chose to focus on respondents aged 25 and older because they are more likely to have completed the initial stages of neighborhood attainment.

Discussion and Conclusion

This article presents a new framework to understand second-generation contextual mobility in the context of post-1965 immigration to the United States. Focusing on second-generation neighborhood attainment from birth to adulthood reveals the persistence of advantages among whites and disadvantages among blacks at the two extreme ends of the neighborhood distribution. The second generation has yet to achieve neighborhood parity with whites but has surpassed blacks in neighborhood attainment, with the exception of West Indians. These findings extend prior work on second-generation individual mobility to second-generation contextual mobility and reveal the latter process as an important dimension of assimilation and inequality. Such a life-course approach to neighborhood attainment further highlights the crucial role of “starting points” in shaping neighborhood trajectories over time for ethnic groups in the United States. Among whites, the significant neighborhood advantage at birth contributes substantially to their neighborhood advantage in adulthood. Among the second generation, ethnic groups with high levels of human capital (e.g., Chinese and South Americans) also experienced significant gains over the life course. In contrast, ethnic groups with low levels of human capital among the first generation not only started in the most disadvantaged neighborhoods but continued to live in them in adulthood (e.g., Dominicans and West Indians). To be sure, these trends also captured the role of race in shaping residential choices and neighborhood mobility. It is not coincidental that blacks and working-class Latino groups lived in more segregated and disadvantaged areas compared to Asians and middle-class Latino groups.

While prior work has argued that neighborhood constitutes a particularly rigid dimension of inequality for native groups, this analysis of second-generation contextual mobility reaches a different conclusion. It shows significant contextual mobility among the second generation over the life course. Instead of being stuck in similarly disadvantaged neighborhoods where the first generation once lived, many moved away into more integrated and better neighborhoods. While there is more fluidity in second-generation neighborhood mobility, neighborhood remains a key dimension of inequality for the second generation. To be sure, young adults in the second generation still fall short of neighborhood parity with whites, but they have mostly fared better than native groups of the same race. In light of spatial assimilation theory, intergenerational progress has been quite substantial and the second generation can achieve even higher levels of neighborhood attainment outcomes as they move from young to middle adulthood in the next decade. Therefore, extending the study of contextual mobility to include second-generation experiences provides a more complete assessment of neighborhood mobility in the United States while highlighting how prior conclusions about neighborhood rigidity have yet to consider the transformative potential of the new second generation. Second-generation contextual mobility, thus, is an important, but missing, puzzle in established accounts of neighborhood inequality in the United States.

Decades of research have revealed race’s centrality in shaping residential choices (Maly 2005; Iceland 2009; Krysan and Crowder 2017). For example, West Indians — the only black second-generation group in this study — report no neighborhood advantage over blacks, indicating the enduring significance of race in neighborhood spatial sorting. This set of analyses not only confirms this sentiment but also extends it to highlight the rise in intra-racial and intra-ethnic diversity. For example, there are differences among Colombians, Ecuadorians, and Salvadorans within the South American category. Among Chinese, the ISGMNY sample includes three subgroups from Mainland China, Taiwan, and Hong Kong; their small sample sizes, however, does not allow for within-group analyses.

This article has important implications for theories of spatial assimilation and of neighborhood effects. For immigration scholars, its findings confirm neighborhood attainment as a key dimension of socioeconomic attainment and second-generation assimilation. While spatial assimilation theory and second-generation contextual mobility both focus on neighborhood as an outcome variable, the former often compares residential indicators across immigrant generations at one point in time, whereas the latter adopts a more dynamic and longitudinal approach to residential mobility. While spatial assimilation focuses on intergenerational change among the immigrant population, contextual mobility further benchmarks neighborhood gains against native reference groups of the same race in reaching conclusions about neighborhood parity. In a racially stratified society, the latter framework provides a contextualized assessment of spatial progress. While immigration research has focused on neighborhood mobility as an assimilation outcome (e.g., Massey and Denton 1985; Alba and Nee 2003), there has been scant attention to how neighborhood disadvantage shapes second-generation life chances — a topic of great consequence that awaits further research. For neighborhood effects researchers, one key insight is the relative fluidity of neighborhoods, both over time and across generations among the second generation. This fluidity is in stark contrast to the relative rigidity of neighborhoods documented among native minority groups (Sharkey 2008). How this rigidity and fluidity affect the experiences of adolescents from second-generation and native minority groups matters for researchers’ understandings of key processes and mechanisms behind “neighborhood effects” research.

This set of analyses examined second-generation contextual mobility in young adulthood in the New York City area in the late twentieth century. By focusing on respondents aged 25 to 32, its findings capture the early stages of neighborhood attainment. Early gains in neighborhood attainment bode well for the future of residential integration and mobility but are based on only one cohort of respondents who came of age during a period of declining racial segregation, increasing ethnic diversity, and rising income inequality (Timberlake and Iceland 2007; Waters and Pineau 2015; Hall, Tach, and Lee 2016; South et al. 2016). Future research should, thus, expand this inquiry’s scope to a range of cohorts, ideally using high-quality geocoded data with large samples of the second generation, and should also examine how neighborhood attainment might unfold as the second generation reaches middle adulthood (Feliciano and Rumbaut 2018).

While the New York metropolitan area is unique in its history, ethnic diversity, and neighborhood composition (Kasinitz et al. 2008; Foner 2013), the process of neighborhood attainment is likely to be similar in other established metropolitan areas with a history of integrating immigrants, such as Los Angeles or Miami (Bean, Brown, and Bachmeier 2015; Lee and Zhou 2015). By comparison, there is no research on neighborhood attainment in new immigrant destinations, where segregation rates between immigrants and natives are higher (Lichter et al. 2010; Hall 2013). As a result, second-generation young adults might face additional barriers in neighborhood attainment in these destinations.

Future work can extend this research to include other measures of neighborhood quality, such as concentrated disadvantage, ethnic diversity, local amenities, or crime rates. An important measure would be the level of co-ethnic concentration. While this analysis has examined second-generation neighborhood attainment using proximity to whites as a benchmark, it has not shown whether the second generation prefers to live in neighborhoods that are mostly white, integrated, ethnic, or pan-ethnic. As a result, we do not have a full grasp of second-generation neighborhood preferences and what features of a neighborhood they will prioritize as they make the decisions on where to live. Qualitative research can shed light on second-generation neighborhood choices and preferences behind quantitative trends on neighborhood attainment (Winders 2013). Because the second generation constantly navigates two cultural worlds, their neighborhood preferences will likely differ from both their parents and their US peers. While the first generation is more likely to live among co-ethnics, the second generation is more likely to grow up in multi-ethnic neighborhoods and to value diversity (Pew Research Center 2012; Yoo and Kim 2013). Moreover, the second generation might also choose to stay close to their parents or co-ethnics, due to financial necessity, cultural expectations, or familial obligations, even when they can afford to move away (Brown 2007; Yoo and Kim 2013; Spring et al. 2017; Tran 2018a).

Furthermore, examining how these cultural factors shape residential decisions among the second generation as they transition from young to middle adulthood in the coming decades will provide insights into how second-generation contextual mobility might vary across ethnic groups, life course, and geographical regions. While prior work on contextual mobility has documented significant black-white neighborhood inequalities (Sharkey 2008), this body of work has not yet examined the cultural processes underlying this transmission of neighborhood disadvantage across generations. Future work can incorporate survey measures that explicitly ask about residential preferences by ethnicity and race, along with other cultural elements that shape residential choice, including intergenerational support, filial expectations, co-ethnic networks, and interpersonal ties (Krysan and Crowder 2017).

In sum, this set of analyses broadly supports three main findings. In comparison to their birth neighborhood, second-generation groups live in better neighborhoods in young adulthood, but the extent of neighborhood mobility varies across groups. Chinese and South Americans gained the most, whereas West Indians and Dominicans gained the least, but most groups have moved away from the most disadvantaged neighborhoods. The second generation has yet to close the neighborhood gap with whites, but it has surpassed blacks in neighborhood attainment, with the exception of West Indians. Finally, contextual mobility is more substantial among second-generation adults compared to US blacks and Puerto Ricans, who remained deeply mired in the most disadvantaged neighborhoods. Although the post-1965 second generation’s future remains in flux, their significant presence in local communities across the United States will likely promote further integration as they enter middle adulthood in the decades ahead.

Supplemental Material

Supplemental Material, MRX832235_Supplemental_Appendix - Second-Generation Contextual Mobility: Neighborhood Attainment from Birth to Young Adulthood in the United States

Supplemental Material, MRX832235_Supplemental_Appendix for Second-Generation Contextual Mobility: Neighborhood Attainment from Birth to Young Adulthood in the United States by Van C. Tran in International Migration Review

Footnotes

Acknowledgments

I am extremely grateful to Philip Kasinitz, John Mollenkopf, and Mary Waters for providing me access to the restricted, geocoded version of the ISGMNY data which made this analysis possible. Richard Alba, Andrew Deener, Tom DiPrete, David Goodwin, Philip Kasinitz, Peter Marsden, Orlando Patterson, Wendy Roth, Mary Waters, Chris Winship, and Bill Wilson, along with the editor and three anonymous reviewers for International Migration Review, provided critical comments that significantly improved the final draft. I also thank audiences at Columbia, Stanford, NYU, UCLA, UC-San Diego, University of Michigan, and University of Pennsylvania for their feedback on this work. All remaining errors are my own.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author gratefully acknowledges funding support from the Paul and Daisy Soros Fellowships for New Americans, the National Science Foundation Graduate Research Fellowship, and the Horowitz Foundation for Social Policy for this research.

Supplemental Material

The Supplemental Appendix is available in the online version of this article at .

Notes

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