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Contents lists available at ScienceDirect Journal of Adolescence journal homepage: www.elsevier.com/locate/adolescence Intergenerational transmission of socio-economic status: The role of neighborhood effects Igor Ryabov Department of Sociology The University of Texas-Rio Grande Valley, 1201 W. University Drive, Edinburg, TX, 78539-2999, USA A R T I C L E I N F O Keywords: Socio-economic status Race/ethnicity Intergenerational transmission Social mobility Neighborhood effects A B S T R A C T Introduction: Little attention has been paid to the role of neighborhoods as a predictor of inter- generational transmission of socio-economic status. This study uses a nationally representative, longitudinal sample from the United States to examine how neighborhoods impact racial/ethnic disparities in the intergenerational transmission of socio-economic status. Methods: The study sample is derived from Waves 1 and 4 of the National Longitudinal Study of Adolescent to Adult Health. The sample size comprises 13,738 participants (aged 15 at Wave 1; 51% female). Multilevel regression is used to predict racial/ethnic disparities in intergenerational transmission of socio-economic status. Six neighborhood-level indicators are regressed on the indicators of intergenerational transmission of education, income and occupational prestige, while controlling for a range of individual socio-demographic variables. Results: Results reveal that: (1) African-American and Hispanic children are less likely to surpass their parents’ levels of education, income and occupational prestige than Asian-Pacific Islander and non-Hispanic white children; (2) these racial/ethnic differences in intergenerational trans- mission of socio-economic status are attenuated but not fully explained by neighborhood-level indicators; (3) all but one of the neighborhood-level factors examined were significant in pre- dicting the pace of intergenerational transmission of socio-economic status. Conclusions: The implication of these results is that policies aimed at reducing inequalities be- tween residential neighborhoods are likely to beneficially affect all racial/ethnic groups but are not sufficient in reducing racial/ethnic disparities in intergenerational transmission of socio- economic status. 1. Introduction Over the past century, American racial and ethnic minorities have experienced a noticeable boost in educational attainment and income, the key indicators of socio-economic status (SES) (Gamoran, 2001; Gittleman & Wolff, 2000). However, the striking racial/ ethnic gaps in SES persist (Collins & Margo, 2000; Fryer, Pager, & Spenkuch, 2013; Reskin, 2012). For example, African-Americans (AAs) are consistently shown to lag behind other racial/ethnic groups in academic achievement and wealth accumulation (Musu- Gillette et al., 2017; Sullivan et al., 2015). Because the U.S. population becomes increasingly diverse, especially among its youth, there is an increasing awareness in the scientific community of the race and ethnic gaps in status attainment (Kao & Thompson, 2003). Not surprisingly, numerous studies have examined the socio-economic differences between racial/ethnic groups and found that these differences, at least in part, reflect the social context in which these groups are embedded (Breen & Jonsson, 2005; Buu et al., 2009; Gamoran & An, 2016; Moren-Cross https://doi.org/10.1016/j.adolescence.2020.02.007 Received 13 August 2019; Received in revised form 11 February 2020; Accepted 12 February 2020 E-mail address: igor.ryabov@utrgv.edu. Journal of Adolescence 80 (2020) 84–97 0140-1971/ © 2020 Published by Elsevier Ltd on behalf of The Foundation for Professionals in Services for Adolescents. T http://www.sciencedirect.com/science/journal/01401971 https://www.elsevier.com/locate/adolescence https://doi.org/10.1016/j.adolescence.2020.02.007 https://doi.org/10.1016/j.adolescence.2020.02.007 mailto:igor.ryabov@utrgv.edu https://doi.org/10.1016/j.adolescence.2020.02.007 http://crossmark.crossref.org/dialog/?doi=10.1016/j.adolescence.2020.02.007&domain=pdf & Lin, 2008; Noguera, 2008; Rankin & Quane, 2000). One of such social contexts is a neighborhood. Neighborhoods are not just places where people reside, but they represent the geographically bound social interactions that result in certain behaviors (Sampson, Morenoff, & Gannon-Rowley, 2002; Sampson, Raudenbush, & Earls, 1997). As such, neighborhoods are implicated in shaping so- cioeconomic outcomes of its residents. Therefore, neighborhoods constitute a fundamental unit of interest for many social scientists (Dietz, 2002; Dupere, Leventhal, Crosnoe, & Dion, 2010; Harding, 2003; Ludwig et al., 2012; Urban, Lewin-Bizan, & Lerner, 2009). Following the convention of earlier research (Coulton, Korbin, Chan, & Su, 2001; Morland, Wing, Roux, & Poole, 2002; Nazmi, Roux, Ranjit, Seeman, & Jenny, 2010), the neighborhood is defined in the present study as the census tract of residence. Using the National Longitudinal Study of Adolescent to Adult Health (Add Health), this study examines several pathways of neighborhood influence through which intergenerational advantage (or disadvantage) passes from parents to children. This work intends to demonstrate that, compared to their non-Hispanic black counterparts, Asian-Pacific Islander (API), Hispanic-American (HA) and non-Hispanic white (NHW) children are more likely to experience upward social mobility in adulthood, and neighborhood conditions is one of the contributing factors to the differences in intergenerational transmission of SES by race/ethnicity. This study is also an attempt to update the results of prior research that demonstrates how neighborhood conditions shape educational and employment outcomes of its residents (Collins & Margo, 2000; Jencks & Mayer, 1990; Pong & Hao, 2007; Sampson et al., 1997, 2002). In contrast in prior research, however, the focus of this investigation is not on educational and occupational outcomes per se, but on the transmission of educational attainment, income and occupational status from parents to children. Thus, the outcomes of interest are labeled accordingly as intergenerational transmission of education (ITE), intergenerational transmission of income (ITI) and intergenerational transmission of occupational prestige (ITOP). Uniquely, our main focus lies on examining how young people's attained statuses differ from their parents' statuses by using discrepancy in levels of education, occupational prestige and income. When approaching the question of intergenerational transmission of status, the analytic strategy almost exclusively adopted by prior research has been to control for parents' attained social position. This strategy is based on the assumption that the distributions of income, educational attainment and occupational prestige between older and younger generations of Americans are the same. However, this assumption is not realistic, given the generational trends in all dimensions of SES, but especially in educational attainment. There are more college graduates now than ever (McFarland et al., 2018). Among Millennials ages 25 to 37, around 39% have a bachelor's degree or higher, compared with 29% of the generation Xers, their parent generation, when they were the same age (Bialik & Fry, 2019). Therefore, this analysis utilizes a different analytic strategy designed to measure a change in social status from parents to children, while simultaneously controlling for the change in the distribution of income, educational attainment and occupational prestige between generations. The present study improves on prior research in other respects, as well. Much of the research has been devoted to status at- tainment of specific minority groups using predominantly cross-national data (Feliciano & Lanuza, 2017; Kao & Thompson, 2003; Portes & Rumbaut, 2006). In contrast, this study's data are longitudinal and encompass all major racial/ethnic groups. Finally, unlike prior research, this examination of intergenerational transmission of SES is based on multilevel mediation model, whereby neigh- borhood-level effects mediate the influence of race/ethnicity, an individual-level predictor, on intergenerational transmission of SES. To summarize, the research presented here extends our knowledge by using longitudinal data from Add Health to examine a number of neighborhood effects on racial/ethnic differences in intergenerational transmission of SES, conceptualized as three different variables, ITE, ITI and ITOP. By using multilevel modeling, these analyses examine the following questions: 1) Are there racial/ethnic disparities in intergenerational transmission of SES, and, if yes, how large are they? 2) Do neighborhood-level factors matter for intergenerational transmission of SES and, if so, which of them are more important than the others? 3) Do neighborhood-level factors, at least, partially explain the relationship between race/ethnicity and transmission of SES in young adulthood? 2. Theoretical background 2.1. Racial/ethnic gaps in intergenerational transmission of socio-economic status This article is not the first to ask whether there are racial and ethnic differences groups in intergenerational status attainment. The fact that pace of social mobility differs between racial and ethnic groups is well known and extensively documented (see, for example, Bloome & Western, 2011; Ciocca Eller & DiPrete, 2018; Torche, 2015). Specifically, AAs have consistently been found to lag well behind other groups in intergenerational mobility (Ciocca Eller & DiPrete, 2018, p. 2018; Musu-Gillette et al., 2017; Woldoff & Ovadia, 2009). It is worth noting that AAs are unique among other minority groups due to their experience of slavery. Moreover, they have historically endured the most savage forms of discrimination and segregation in the American society (Massey & Denton, 1993; Omi & Winant, 2014; Woldoff & Ovadia, 2009). The prospects for socio-economic advancement for AAs who have always been at the bottom of racial hierarchy in the United States are still limited. There is no wonder that black underachievement, in comparison to other racial groups, have been given much consideration in sociological research (Massey & Denton, 1993; Noguera, 2003, 2008). The weight of scholarly evidence suggests that blacks are still the most disadvantaged minority group in the United States. It has been documented that rates of intergenerational income mobility among non-black minority groups have been higher than those of blacks (Chetty, Hendren, Jones, & Porter, 2018; Hardaway & McLoyd, 2009). Literature also suggests that the racial differentials in the I. Ryabov Journal of Adolescence 80 (2020) 84–97 85 transmission of socio-economic standing from parents to children may be due to environmental factors, among which neighborhood effects play an important role (Chetty, Hendren, & Katz, 2016; Crane, 1991; Sampson et al., 2002; Urban et al., 2009). 2.2. Adolescence and neighborhood context This study is guided by Bronfenbrenner (1977, 2005) ecological theory of development that focuses on the association between the person and their social environment throughout the life course. This theory views individual development as being influenced by the qualities of the social settings in which they are embedded (Bronfenbrenner, 1977; Neal & Neal, 2013; Tudge, Mokrova, Hatfield, & Karnik, 2009). According to Bronfenbrenner (1979, 2005), human development is affected by five contexts: the microsystem (e.g., family, school), the mesosystem (which links two microsystems), the exosystem (e.g., parents' employers, school administrators), the macrosystem (e.g., culture, society) and the chronosystem (time). A neighborhood is an example of a microsystem because it supports every-day interactions between adolescents and their proximal others (parents, peers). There are several reasons to believe that the neighborhood may be most salient in adolescence as a social milieu responsible for differential developmental trajectories among youth (Leventhal & Brooks-Gunn, 2000). First, adolescence is a developmental stage characterized by the increasing relevance of social contexts beyond the home (Espelage, 2014; Neal & Neal, 2013). Adolescents spend more time outside of the household, compared to either children or adults. The neighborhood is a social setting where adolescents interact with adults and peers, and such day-to-day interactions foster the development of collective bonds among youth (Browning, Leventhal, & Brooks-Gunn, 2004). Second, social identity formation is one of the primary developmental tasks during adolescence. Not only do adolescents begin to express their identity to others, but they also have a strong need for self-concept consistency (hence the importance of the neighborhood's normative environment) (Sampson et al., 1997, 2002). Third, apart from having a direct effect on child development, the neighborhood influences tend to accumulate over time to affect adolescent transitions into adulthood (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993). Thus, the lagged neighborhood effects can be observed well into adulthood (Brenner, Zimmerman, Bauermeister, & Caldwell, 2013; Wheaton & Clarke, 2003). In sum, these reasons suggest that neighborhoods in which adolescents live may either put them at risk or provide a protective environment for development (Brooks-Gunn et al., 1993; Sampson et al., 1997, 2002). Some studies have shown that negative neighborhood conditions may expose youth to more stressors while equipping them with limited resources (Brenner et al., 2013; Hurd, Stoddard, & Zimmerman, 2013). Moreover, adverse neighborhood environment can disrupt life course trajectories for ado- lescents and contribute to the delayed adoption of adult roles (Collins, 2001; Gutman & Sameroff, 2004). On the other hand, positive neighborhood environment can foster resilient youth outcomes (Leventhal & Brooks-Gunn, 2000). Most importantly, there is general agreement in the literature that the neighborhood adds significantly to inequality of outcomes in young adulthood (Brenner et al., 2013; Sampson, 2008; Sampson et al., 2002). 2.3. Models of neighborhood influence on adolescent outcomes At present, there is no comprehensive theory to describe neighborhood influence on social mobility in a unified way. Moreover, there has been a dearth of theoretical studies that examine the mechanisms through which the neighborhood exerts an effect on adolescent development (Jencks & Mayer, 1990; Sampson et al., 2002). Arguably the most comprehensive scholarly attempt to develop a taxonomy of ways in which neighborhoods might affect transitions to adulthood has been undertaken by Jencks and Mayer (1990). They systematically reviewed a range of mechanisms of neighborhood influence and organized them into four major cate- gories: (1) Contagion theory, which assumes adolescent behavior to be contagious, and the neighborhood effect is conditioned on the presence of antisocial young people to spread problem behaviors. Accordingly, social norms to which adolescents are exposed are influenced by the proportion of deviant groups in the population (Crane, 1991; Jencks & Mayer, 1990). (2) Collective socialization theory, in which neighborhood social control and monitoring are deemed to be essential for adolescent socialization. This theory suggests that continuity and stability in the neighborhood could facilitate enforcement of pro-social norms among youths (Brooks-Gunn et al., 1993; Elliott et al., 1996). (3) Institutional theory, which emphasizes the importance of successful adult role models in the neighborhood for adolescent de- velopment. In this theory, successful adult role models are seen as agents of change that help young people to internalize social norms and to learn the boundaries of acceptable behavior (Jencks & Mayer, 1990; Small & Newman, 2001). (4) Relative deprivation theory, in which individuals evaluate their own position in the neighborhood by comparing themselves with their more affluent neighbors. The original idea behind this model is that children from families with relatively low standing but living in an affluent neighborhood are likely to develop a feeling of deprivation. For poor children, comparing themselves with their more affluent counterparts could provoke resentment and this, in turn, may result in underperformance in school and, later, in the occupational arena (Nieuwenhuis et al., 2017; Smith, Pettigrew, Pippin, & Bialosiewicz, 2012; Vilhjalmsdottir, Gardarsdottir, Bernburg, & Sigfusdottir, 2016). After Jencks and Mayer (1990), there have been no serious attempts to synthesize relevant studies and create a single theory of neighborhood influence on social mobility. Therefore, Jencks and Mayer's (1990) work forms the basis for our exploration of neighborhood effects and guides our selection variables. More specifically, we derived four independent variables from four theories identified by Jencks and Mayer (1990). To begin with, we conceptualized contagion influence as the percentage of disconnected I. Ryabov Journal of Adolescence 80 (2020) 84–97 86 youth in the neighborhood. This approach is supported by research showing that disconnected youth project negative role models and spread deviant lifestyle, while inducing other adolescents to follow them (Crane, 1991). Residential stability has been used as an indicator of collective socialization in this study. This is because the collective socia- lization process can be offset by the destabilizing potential of rapid population change (Jencks & Mayer, 1990). A high rate of residential instability weakens the means of social controls over youth. It has been shown that neighborhoods with high residential instability offer little opportunities for social control (Sampson et al., 2002; Small & Newman, 2001). The average educational status of the neighborhood is used in this study as a relevant indicator of institutional influence. According to institutional theory, the presence of successful adult role models other than a child's own parents or the lack of thereof determines adolescent outcomes. Without doubt, educational attainment is a valid measure of success. Furthermore, studies show that youth who are surrounded by well-educated adults are more likely to value education, adhere to pro-social norms, and work hard (Jencks & Mayer, 1990; Small & Newman, 2001). Relative deprivation theory assumes that neighborhood economic status affects children's development and behavior (Jencks & Mayer, 1990). Therefore, we conceptualized relative deprivation as average neighborhood income. In addition to Jencks and Mayer's (1990) work, two other theoretical perspectives were used to model neighborhood influence in this study. One of them is social disorganization theory (Kubrin & Weitzer, 2003; Shaw & McKay, 1942). This theory which was first introduced by Shaw and McKay (1942) states that the breakdown of social norms in the neighborhood leads to higher rates of crime, more especially delinquency. Social organization is hence viewed as the ability of a neighborhood to fulfill community values and maintain effective social controls. Because the original focus of social disorganization theory is on crime and deviance, we include in our analysis an indicator of criminality (e.g., crime rate) and indicators of population density and immigrant concentration. These indicators were combined into an index of neighborhood vulnerability. The last perspective that merits discussion here is social capital theory (Coleman, 1988; Sampson et al., 2002). This theory has been extensively applied in the study of neighborhood influence on individual behavior (e.g., Boyce, Davies, Gallupe, & Shelley, 2008; Byun, Meece, Irvin, & Hutchins, 2012; Dika & Singh, 2002; Dufur, Parcel, & Troutman, 2013; Stanton-Salazar, 2011). Unlike social disorganization theory which views neighborhood influence as guided by normative structures, social capital theory looks at a neighborhood as a locus of social interaction. Although social capital is a complex construct with much debate about its measurement and interpretation (Burt, 2000; Subramanian, Lochner, & Kawachi, 2003), we rely on Coleman’s (1988) concept of parent-child closure to conceptualize social capital. The parent-child (or intergenerational) closure refers to the interconnection of parents' and children's personal networks. Prior research has shown that network closure maintains and enhances trust, norms and solidarity in the neighborhood (Kawachi, Kennedy, & Glass, 1999; Leventhal & Brooks-Gunn, 2000; Subramanian et al., 2003). 3. Hypotheses This study involves analyses of Waves 1 and 4 of data from Add Health in pursuit of two goals. The first goal was to estimate racial/ethnic gaps in ITE, ITI and ITOP. The second goal was to test whether a range of neighborhood-level factors can explain these gaps. Based on the review of literature above, the following specific hypotheses were tested: Hypothesis 1. After controlling for individual- and neighborhood-level variables, all indicators of intergenerational social mobility (ITE, ITI, and ITOP) will be lower for AAs than for other racial/ethnic groups. Hypothesis 2. Adolescents raised in neighborhoods with high concentration of disconnected youth are less likely to surpass their parents' levels of education, income and occupational prestige as adults than their counterparts who had lived in neighborhoods with fewer disconnected youth. Hypothesis 3. Living in a neighborhood with high residential instability negatively affects one's chances of getting ahead of their parents in terms of educational attainment, income, and occupational prestige. Hypothesis 4. The higher the educational status of the neighborhood, the more likely individuals who lived in this neighborhood as adolescents are to surpass their parents in educational attainment, income and occupational prestige in young adulthood. Hypothesis 5. Residing in a higher-income neighborhood will have a negative effect on intergenerational transmission of educational attainment, income and occupational prestige. Hypothesis 6. Adolescents who lived in a vulnerable neighborhood are less likely to surpass their parents' educational attainment, income and occupational prestige in young adulthood as compared to those who did not. Hypothesis 7. Young adults who, as adolescents, lived in neighborhoods with high levels of intergenerational closure are more likely than those who did not to surpass their parents' levels of education, income and occupational prestige. 4. Methods 4.1. Data The current study draws upon the Waves 1 (1994–1995) and 4 (2008) of the National Longitudinal Study of Adolescent to Adult Health (Add Health), the most comprehensive survey of adolescents and young adults ever undertaken in the United States. The Add I. Ryabov Journal of Adolescence 80 (2020) 84–97 87 Health team of researchers representing different social sciences collected a nationally representative sample of adolescents in grades 7 through 12 during the 1994–1995 school years and continued following them into adulthood. Wave 4 in-home interviews were conducted in 2008–2009 when the respondents were between ages 24 and 32. In brief, Add Health used a school-based sample design. The primary sampling frame included 26,666 U. S. High Schools. From this frame Add Health selected a stratified sample of 132 high schools with probability of selection proportional to school size. Additional information about the sample and sampling procedures is available elsewhere (e.g., Harris, 2011). During Wave 1 all students at the participating schools were surveyed (N = 90,118). A subset of students was randomly selected from these schools (N = 20,745) for in-home interviews. The Wave 4 in-home sample consisted of those Wave 1 respondents who could be located and reinterviewed nine years later. The response rates for Waves 1 and 4 Add Health were 79% and 80%, corre- spondingly. Of the 15,701 respondents who completed the Wave 4 interviews, 14,800 had a grand sample cross-sectional weight. Our analytic sample included 13,738 respondents who reported who participated in both the in-school and in-home surveys and for whom non-missing information on the outcome variables was available. 4.2. Variables 4.2.1. Outcome variables The dependent variables in this analysis are Intergenerational Transmission of Education (ITE), Intergenerational Transmission of Income (ITI) and Intergenerational Transmission of Occupational Prestige (ITOP). They were operationalized as absolute differences between respondent's standardized scores of education, income and occupational status, correspondingly, and those of their parent (s). Calculation of ITE involved several steps. First, we calculated educational attainment of adolescents' parents. The Add Health respondents were asked to report the highest year of schooling for their biological or adoptive mother and father at Wave 1. When adolescents reported information for one parent only, that value was used as the parents' level of education. If the information for both parents was available, the highest attainment of either of the parents was recorded as the parent's level of education. Respondents reported their own educational attainment at Wave 4. Respondents' and parents' education were measured on the same scale ranging from 1 = ‘8th grade or less’ to 13 = ‘completed post baccalaureate professional education’. Second, we standardized the educational attainment of children and that of their parents. Finally, we subtracted the parents' standardized scores from their children scores. Intergenerational Transmission of Income (ITI) was measured as the difference between the standardized scores of the re- spondent's and their parents' incomes. Data on household income was obtained from the parent interview in the first wave of data collection, while data on respondent's income comes from Wave 4. Family income earned in 1994 (at Wave 1) was an interval variable with a range between $0 and $999,000. Similarly to the other two outcome variables, ITOP was constructed as the difference between the standardized scores of the respondent's and their parents' occupational statuses. Following earlier studies (Feliciano & Lanuza, 2017; Hahm, Lahiff, & Guterman, 2003), the original occupations in Add Health were coded according to a five-point ordinal scale: professionals, office/sales workers, blue-collar workers, crafts/military/farm/other workers, and unemployed. This was done due to the non-linearity of the original occupational listing, which is overly extensive and is subject to potential measurement error. The highest occupational status of the two parents (wherever applicable) was chosen to represent parental occupational status. 4.2.2. Predictor variables As mentioned above, the first four neighborhood-level variables in this analysis are derived from epidemic, collective sociali- zation, relative deprivation and competition models of neighborhood influence outlined by Jencks and Mayer (1990). In addition to the theoretical considerations outlined above, the variance inflation factor (VIF) method was used to identify those variables which were most responsible for collinearity. After eliminating those variables, we applied a factor analysis to create composites that are consistent with the theoretical models. Information on all neighborhood-level variables was obtained from the Add Health contextual file (Wave 1). Table l contains the definitions of all variables used in the analysis. The disconnected youth, an index that captures neighborhood epidemic influence, is borrowed from Pong and Hao (2007). Disconnected youth were identified as those who were neither enrolled in school nor working at the time of Wave 1 interview. The composite has three components: the share of co-racial/co-ethnic peers among all adolescents, the share of disconnected adolescents, and the share of co-racial/co-ethnic disconnected adolescents in the neighborhood. In order to identify co-racial/co-ethnic peers, we matched the respondent's race/ethnicity to the census-tract information on youth of their race/ethnicity. Variables belonging to this composite were standardized and then combined by taking the average. Collective socialization model of neighborhood influence is represented by residential (in)stability, that is by the proportion of housing units moved into the neighborhood between 1985 and 1990. Relative deprivation is indicated by the neighborhood edu- cational status, which is measured by standardizing and then averaging the share of 25+ years old without high school diploma and the share of 25+ years old with college degree or above. The income of a neighborhood was constructed by taking an arithmetic average of household incomes of its residents. These two indicators, residential stability and educational status, were partially derived from Pong and Hao (2007). Parent-child closure was borrowed from Haynie, Petts, Maimon, and Piquero (2009). Three measures that tap the degree to which parents know members of their child's network and two measures of parents' participation in community were standardized and averaged to produce the index of parent-child closure. I. Ryabov Journal of Adolescence 80 (2020) 84–97 88 Vulnerability is also an index containing three items: neighborhood crime rate; percentage of foreign-born; and population density. Similarly to all above indices, vulnerability was created by standardizing and averaging the aforementioned three compo- nents. This index was borrowed from Gallupe (2017). Parents' age, respondents’ gender and age and family structure are individual-level variables included as controls. Gender is a dummy variable with male serving as the reference category. Age is measured in complete years at the time of the interview. Family structure is indicated by whether or not the respondents were living in a two-parent family at the time of Wave 1 administration. 4.3. Data analysis Survey commands in STATA (svy mean, svy regress) were used to account for sample stratification and clustering. Multilevel linear regression was conducted to test the research hypotheses. Treating individuals as level-one units and neighborhoods as level- two units, the multilevel regression model used in this study can be conceptualized as a two-level linear model. All individual-level continuous variables were group-mean centered to avoid multicollinearity between individual and neighborhood characteristics. In order to calculate the estimates for each outcome (ITE, ITI and ITOP), the following formula was used: = − = + + + + − − − − − − IT SS SS α β B γ G δ D ε( ) ,i ic ip i0 (1 3) (1 3) (1 6) (1 6) (1 4) (1 4) where ITI is intergenerational transmission of i-th dimension of social status (i = education, income or occupational prestige); SS(ic) is the standardized score of the i-th dimension of social status of children; SS(ip) is the standardized score of the i-th dimension of social status of parents; α0 is a constant; β (1-3) are regression coefficients for race/ethnicity variables (n = 3; AA is the reference category) B(1-3); γ(1-6) are regression coefficients of neighborhood factors (n = 6) G(1-6); δ(1-4) are regression coefficients of control variables (n = 4) D(1-4); and εi is the error term exhibiting a normal distribution. For each set of multivariate models, individual sociodemographic characteristics are first entered, followed by a model with all neighborhood-level variables. To determine whether mediating effects of neighborhood influence are statistically significant across models, we employ a method suggested by Baron and Kenny (1986) and Judd and Kenny (1981). This is the one of the most commonly used methods to test mediation (MacKinnon, Taborga, & Morgan-Lopez, 2002). In this approach, neighborhood factors are said to mediate the effect of race/ethnicity on intergenerational transmission of SES if and only if: (1) the race/ethnicity coefficients are statistically significant in the regression without neighborhood-level factors; (2) race/ethnicity coefficients are no longer sta- tistically significant after the introduction in the regression of neighborhood-level factors; and (3) neighborhood-level factors are statistically significant in a mediation regression with the race/ethnicity variables as predictors. Given that the analytic sample was stratified by region, state, and school, respondents were not necessarily equally distributed across smaller geographic units. For this reason, the smallest census unit, the census block, was not practicable as a proxy for neighborhoods. Almost half of census blocks had only one respondent, which makes a multilevel regression model difficult to apply. Therefore, the census tract is the most proximate level of aggregation available that also provides enough distribution of the sample across geographic units to be able to detect variation both within and across neighborhoods. In this study, two thousand one hundred census tracts were used for analysis. In all regression models, the intraclass correlation coefficient (ICC) was used to determine the percentage of the variation in outcome variables that can be attributed to differences between neighborhoods. ICC was calculated by dividing the neighborhood- level random effect variance by the total variance. Auxiliary analyses (not shown) using ICC of the unconditional models indicated that 17.2, 16.8 and 16.4% of the variation of ITE, ITI and ITOP, correspondingly, was attributable to differences between neigh- borhoods (see Table 1). 5. Results 5.1. Univariate results The means of all study variables for the total sample and by race/ethnicity are shown in Table 2. Overall, approximately 46% of respondents achieved a relatively higher educational standing than their parents. However, there is considerable variation in ITE between racial/ethnic groups. Only 41% of AAs and HAs had surpassed their parents’ educational level. At the same time, the corresponding value for APIs is 55%, even higher than that for NHWs (52%). It also appears that AAs and HAs lag behind NHWs and APIs with respect to the other two indicators of intergenerational transmission of status – ITI and ITOP. However, only multivariate analyses can ascertain whether there exists an association between race/ethnicity and intergenerational transmission of social status. Predictor variables, especially those capturing neighborhood context, also show a pattern of variation between racial/ethnic groups. For example, AA and HA respondents were raised in neighborhoods where indices of disconnected youth, parent-child closure and vulnerability were higher than the average for the sample. At the same time, neighborhoods where NHW and API adolescents lived seem to have higher educational status and income. It is also noteworthy that, compared to AAs and HAs, higher shares of NHW and API adolescents came from two-parent families. The bivariate correlations between the major study variables are displayed in Table 3. It is also worth noting that ITE, ITI, and ITOP are moderately intercorrelated, with the correlation coefficients varying in magnitude from 0.32 to 0.57. This means that there is a certain overlap, albeit not perfect, between intergenerational transitions of education, income and occupational status. Second, neighborhood-level factors (group-mean centered for the correlation analysis) also exhibit a degree of interconnectedness, although I. Ryabov Journal of Adolescence 80 (2020) 84–97 89 the correlation coefficients vary more between neighborhood-level variables than between ITE, ITI, and ITOP. Third, and most importantly, these correlational analyses reveal significant (at p < 0.05) associations between all three outcomes, ITE, ITI, and ITOP, on the one hand, and the six indicators of neighborhood influence, on the other. Specifically, ITE, ITI, and ITOP are positively correlated with measures of educational status, income and parent child closure and negatively correlated with disconnected youth, residential instability and vulnerability. These findings concord with our predictions (see Hypotheses 2–7). Although these bivariate analyses can shed light on the nature of the relationship between neighborhood influence and intergenerational status transmission, they are not the most robust tests of our hypotheses. Only the multivariate analyses that follow can be regarded as conclusive. Table 1 Description of study variables. Variable Name Description Outcome Measures Intergenerational Transmission of Education (ITE) The difference between the standardized score of the respondent's educational attainment at Wave 4 to that of their parents' at Wave 1. Intergenerational Transmission of Income (ITI) The difference between the standardized score of the respondent's income at Wave 4 to that of their parents' at Wave 1. Intergenerational Transmission of Occupational Prestige (ITOP) The difference between the standardized score of the respondent's occupational prestige at Wave 4 to that of their parents' at Wave 1. The original occupational categories of the survey were grouped into 5 categories: (1) professionals; (2) office/sales workers, (3) blue-collar workers, (4) crafts/military/ farm/other workers; (5) unemployed. Explanatory Measures Race/ethnicity A series of dummy variables distinguishing Asians, non-Hispanic blacks, non-Hispanic whites, and others. Neighborhood Context Disconnected Youth The composite contains the following components: (1) Percentage of co-racial/co-ethnic peers among adolescents aged 16–19; (2) Percentage of adolescents aged 16–19 not in school or in LF; (3) Percentage of co-racial peers aged 16–19 not in school or in LF. Cronbach's α = 0.92. Residential Instability Proportion of housing units moved into the neighborhood from 1985 to 1990. Educational Status The composite contains the following components: (1) Proportion of 25+ years old without HS diploma (reverse coded); (2) Proportion of 25+ years old with college degree or above. Pearson's r = 0.83. Income Average income of the neighborhood. Intergenerational Closure The composite contains the following components: (1) The respondent's parent met child's best friend in person; (2) The respondent's parent met child's best friend's parents; (3) The respondent's parent has talked to child's best friend's in past 4 weeks; (4) The respondent's parent has been involved in PTA during the past year; (5) The respondent's parent has participated in civic associations during the past year. Cronbach's α = 0.65. Vulnerability The composite contains the following components: (1) Neighborhood crime rate; (2) Percentage of foreign-born; (3) Population density. Cronbach's α = 0.67. Control Variables Two-Parent Household 1 = Having been raised in two-parent families; 0 = Else. Parents' Age Average age of both parents in years as of Wave 1 Respondent's Gender 1 = Male; 0 = Female. Respondent's Age Respondent's age in years. Table 2 Descriptive statistics of study variables by race/ethnicity. a Overall (100%) NHW (65.3%) AA (15.6%) HA (13.9%) API (5.3%) Outcome Measures Intergenerational Transmission of Education (ITE) 0.46 0.52 0.41 0.41 0.55 Intergenerational Transmission of Income (ITI) 0.35 0.36 0.30 0.32 0.38 Intergenerational Transmission of Occupational Prestige (ITOP) 0.32 0.33 0.29 0.28 0.32 Explanatory Measures Neighborhood Context Disconnected Youth 0.00 −0.12 0.25 0.32 −0.09 Residential Instability 0.50 0.47 0.45 0.55 0.53 Educational Status 0.00 0.11 −0.23 −0.31 0.13 Income per capita (in thousands) 23.7 26.6 17.4 16.1 25.8 Parent-Child Closure 0.00 0.11 −0.29 −0.24 0.13 Vulnerability 0.00 −0.12 0.32 0.24 −0.15 Control Variables Two-Parent Household 0.63 0.70 0.37 0.53 0.79 Parents' Age 41.3 41.2 41.6 41.0 41.5 Respondent's Gender (Male) 0.49 0.49 0.50 0.49 0.49 Respondent's Age 15.0 15.0 15.1 15.0 14.9 a N = 13,738. I. Ryabov Journal of Adolescence 80 (2020) 84–97 90 5.2. Multivariate results Table 4 shows multilevel regression models predicting ITE. Model 1 includes the race/ethnicity dummy variables and controls for family structure of origin, parents' average age, respondent's age and gender. The regression coefficients for NHWs and APIs are positive and significant. The reference group for the analysis are AAs, which means that NHW and API young adults were more likely than AAs to surpass their parent’ level of education. At the same time, the difference in ITE between AAs and HAs was not significant in both models of Table 4. Thus, representatives of these two racial/ethnic groups tend to be equally disadvantaged in the trans- mission of educational status. Overall, these findings support Hypothesis 1 that predicts disadvantage of AAs in ITE relative to other racial/ethnic groups. It is worth noting that the coefficients for NHW and API remain significant in Model 2 that enters six measures of neighborhood influence. Nevertheless, both the magnitude and significance level of racial/ethnic differentials in ITE subsides in Model 2 in comparison to Model 1. Consistent with our prediction, all neighborhood-level indicators strongly affect ITE, albeit in different directions. Disconnected youth, residential instability and vulnerability all have a negative association with ITE. The finding yields considerable support to Hypotheses 2, 3 and 6. At the same time, two neighborhood predictors—educational level and parent-child closure—were found to have a positive association with ITE. This finding is also consistent with Hypotheses 4, and 7. However, contrary to Hypothesis 5, the effect of neighborhood income on ITE was positive. Thus, based on these findings, five hypotheses about the neighborhood influence found support in this analysis. It is also important to note that the addition of the neighborhood-level variables to Model 1 reduced the variance at the neighborhood level, as evidenced by the decline of ICC from 0.074 in Model 1 to 0.012 in Model 2. At the same time, pseudo R2 has increased from 0.237 to 0.258, meaning that the explanatory power of Model 2 was greater than Model 1. Among the control variables, household structure and the age of the respondent were significant predictors in the full model of Table 4. In the next table, Table 5, we show the analysis of factors that may be associated with ITI. The racial/ethnic differences in ITI Table 3 Bivariate correlations between neighborhood-level variables. 1 2 3 4 5 6 7 8 9 1. ITE 1 2. ITI 0.57 1 3. ITOP 0.41 0.32 1 4. Disconnected Youth −0.44 −0.52 −0.47 1 5. Residential Instability −0.17 −0.13 −0.15 0.28 1 6. Educational Status 0.51 0.48 0.43 −0.35 −0.42 1 7. Income 0.54 0.41 0.28 −0.23 −0.28 0.27 1 8. Parent-Child Closure 0.37 0.55 0.41 −0.45 −0.32 0.36 0.39 1 9. Vulnerability −0.38 −0.34 −0.26 0.31 0.27 −0.33 −0.31 −0.35 1 Note: All correlation coefficients were significant at p ≤ 0.05. Table 4 Unstandardized coefficients and standard errors (in parenthesis) of neighborhood- and individual-level predictors of Intergenerational Transmission of Education (ITE). Model 1 Model 2 Race/Ethnicity1 NHW 6.30 (1.74) *** 3.42 (1.87) ** HA −2.52 (2.08) −1.24 (2.10) API 7.26 (2.57) *** 5.17 (2.51) * Control Variables Two-Parent Household 2.35 (0.69) *** 1.89 (0.73) ** Parents' Age −1.24 (0.41) ** −0.59 (0.47) Respondent's Gender (Male) −1.23 (0.67) * 0.40 (0.62) Respondent's Age 0.78 (0.23) ** 0.63 (0.25) * Neighborhood Context Disconnected Youth −14.64 (4.17) *** Residential Instability −1.59 (0.67) * Educational Status 10.96 (2.84) *** Income per capita (in thousands) 5.81 (1.69) ** Parent-Child Closure 12.05 (3.88) *** Vulnerability −9.87 (2.19) *** Intraclass Correlation 0.074 ** 0.012 -Pseudo log-likelihood 1,561 1,365 Pseudo R2 0.237 0.258 Notes: 1-AAs are the reference group. *p ≤ 0.05. **p ≤ 0.01. ***p ≤ 0.001. I. Ryabov Journal of Adolescence 80 (2020) 84–97 91 seem to follow a similar pattern as those in ITE observed in Table 4. Compared to AAs, NHWs, HAs and APIs had higher levels of ITI in Model 2. The coefficient for HAs, albeit significant (P < 0.1) in Model 1, was statistically insignificant in the full model (Model 2) of Table 5. In the full model, ITI was higher for NHWs and APIs than for AAs, thus confirming Hypothesis 1. All factors representing neighborhood context, except residential instability, have been statistically significant to explain variation in ITI. Disconnected youth, educational level, vulnerability, and parent-child closure were all in predicted directions. However, the effect of neighborhood income on ITI was positive, which is contrary to relative deprivation theory (Hypothesis 5). It is also worth noting that two individual-level control effects were consistently significant in both models of Table 5 (two-parent household and parental age), albeit in the opposite direction. Specifically, the results show that living with two parents as an adolescent was conductive to ITI, while having older parents was not. The last table, Table 6, examines predictors of ITOP. As in the previous tables, AAs were used as the reference group for the estimation of racial/ethnic differences in social status transmission from parents to children. The regression results presented in Model 1 indicate that NHW and API parents were more likely than AA parents to pass their occupational prestige to their children. However, in the full model (Model 2) of Table 6, only one statistically significant racial/ethnic differential in ITOP was found—the one between AAs and NHWs. This suggests that neighborhood-level factors in Model 2 explain the gap in ITOP between APIs and AAs. Table 5 Unstandardized coefficients and standard errors (in parenthesis) of neighborhood- and individual-level predictors of Intergenerational Transmission of Income (ITI). Model 1 Model 2 Race/Ethnicity1 NHW 4.45 (1.19) *** 3.77 (1.14) *** HA 2.62 (1.77) * 1.96 (1.81) API 6.31 (2.06) *** 4.75 (2.18) ** Control Variables Two-Parent Household 3.14 (0.84) *** 2.52 (0.88) ** Parents' Age −2.15 (0.76) *** −1.60 (0.83) * Respondent's Gender (Male) 0.74 (0.50) 0.48 (0.54) Respondent's Age 1.32 (0.47) ** 0.79 (0.51) Neighborhood Context Disconnected Youth −11.35 (3.22) *** Residential Instability −0.80 (0.76) Educational Status 9.43 (2.60) *** Income per capita (in thousands) 7.45 (2.37) *** Parent-Child Closure 12.03 (3.32) *** Vulnerability −8.61 (2.27) *** Intraclass Correlation 0.068 ** 0.010 -Pseudo log-likelihood 1,558 1,370 Pseudo R2 0.236 0.255 Notes: 1-AAs are the reference group. *p ≤ 0.05. **p ≤ 0.01. ***p ≤ 0.001. Table 6 Unstandardized coefficients and standard errors (in parenthesis) of neighborhood- and individual-level predictors of Intergenerational Transmission of Occupational Prestige (ITOP). Model 1 Model 2 Race/Ethnicity1 NHW 3.14 (0.87) *** 2.63 (1.02) * HA −0.69 (0.57) 0.27 (0.68) API 2.44 (0.97) ** 1.76 (1.11) Control Variables Two-Parent Household 2.25 (0.74) *** 1.82 (0.79) * Parents' Age −1.24 (0.38) ** −1.00 (0.36) * Respondent's Gender (Male) 0.89 (0.43) * 0.68 (0.49) Respondent's Age 2.26 (0.73) *** 1.67 (0.72) ** Neighborhood Context Disconnected Youth −10.96 (2.65) *** Residential Instability −0.78 (0.65) Educational Status 6.80 (2.56) * Income per capita (in thousands) 5.26 (2.19) * Parent-Child Closure 11.25 (3.55) ** Vulnerability −12.76 (3.54) *** Intraclass Correlation 0.065 ** 0.014 -Pseudo log-likelihood 1,567 1,384 Pseudo R2 0.233 0.249 Notes: 1-AAs are the reference group. *p ≤ 0.05. **p ≤ 0.01. ***p ≤ 0.001. I. Ryabov Journal of Adolescence 80 (2020) 84–97 92 Disconnected youth, educational status, parent-child closure and vulnerability were found to be significant predictors of ITOP, thus corroborating Hypotheses 2, 4, 6 and 7. However, one neighborhood effect, neighborhood income, was counter to our expectations. This effect was positive, meaning that those respondents who lived resided in a higher-income neighborhood as adolescents were more likely to surpass their parents’ occupational prestige as young adults. Residential instability was the only neighborhood-level predictor that did not have a significant effect on ITOP. In addition, some control variables in both Models 1 and 2 had an influence on ITOP. First of all, living in a two-parent household as an adolescent is conducive to ITOP. Further, parents' age was negatively while respondent's age was positively associated with ITOP. The latter finding makes sense: older respondents are more likely than younger respondents to achieve higher occupational prestige. However, we also found that older parents are less likely to pass their occupational prestige to their children. In order to estimate the racial/ethnic differentials in ITE, ITI and ITOP, additional analyses (not shown for parsimony) have been conducted using the same predictors but different reference categories for race/ethnicity (APIs, HAs and NHWs). These analyses were almost identical to those above with the difference that the reference categories were other than AAs. The results did not find significant differences in the levels of ITE, ITI and ITOP between APIs and NHWs. Likewise, AAs and HAs did not differ on any of the outcome variables. Thus, it is safe to say that, after controlling for all individual- and neighborhood-level variables, AA and HA parents were less likely to pass their educational attainment, income and occupational prestige onto their children than their API and NHW counterparts. 6. Discussion and conclusion Racial/ethnic inequality in relative socio-economic standing remains a substantial problem in American society (Collins & Margo, 2000; Duncan & Magnuson, 2005). Although racial gaps in income and education have declined after the passage of the Civil Rights Act of 1965, the advancement of racial and ethnic minorities has been uneven, and since 1980s there have been signs of the trend reversal (Reskin, 2012; Sullivan et al., 2015). Studies show that racial/ethnic gaps in SES persist largely due to the intergenerational inequality in status transmission between racial/ethnic groups (Musu-Gillette et al., 2017; Sullivan et al., 2015). That is, some minority groups are more successful than others in catching up with the dominant group (NHWs) primarily because of their higher rates of intergenerational transmission of SES. Against this backdrop, the present study examined neighborhood factors that contribute to the racial/ethnic differences in in- tergenerational transmission of education, income and occupational prestige in the contemporary U.S. This study started with the premise that, as documented by prior research (Dietz, 2002; Dupere et al., 2010; Pong & Hao, 2007; Sampson et al., 2002; Wodtke, Elwert, & Harding, 2016), neighborhood conditions is one of the contributing factors to the existing racial/ethnic differences in intergenerational social mobility. The study's purpose was twofold: (1) to show that there are non-trivial racial/ethnic differences in the rates of intergenerational transmission of SES; and (2) to find out which neighborhood effects contribute to racial/ethnic dif- ferences in intergenerational transmission of SES. On a theoretical plan, this study drew from social capital (Coleman, 1988; Sampson et al., 2002) and social disorganization (Kubrin & Weitzer, 2003; Shaw & McKay, 1942) theories, but primarily from the compre- hensive theoretical framework of neighborhood influence by Jencks and Mayer (1990). This work tested epidemic, collective so- cialization, institutional and relative deprivation theoretical models described by Jencks and Mayer (1990) and parent-child closure and vulnerability models based on social capital and social disorganization theories, respectively. Our choice of the predictor variables was derived from these theoretical models. Thus, six indicators—disconnected youth, residential instability, educational status, income, parent-child closure and vulnerability—were regressed on three indicators of intergenerational transmission of SE- S—ITE, ITI and ITOP. The results show that, all else being equal, AAs and HAs were the most disadvantaged minority groups, as both groups lagged behind APIs and NHWs in all three dimensions of intergenerational transmission of SES. In other words, AA and HA young adults were more likely than their API and NHW counterparts to surpass their parents' levels of education, income and occupational prestige. However, these effects were not consistent across models, as we found some evidence of mediation by neighborhood-level factors. According to the criteria of mediation suggested by Baron and Kenny (1986) and Judd and Kenny (1981) and outlined above, the entry of the mediators (neighborhood level factors) into the model must eliminate the impact of the independent variables (NHW, HA and API) on the dependent variables (ITE, ITI and ITOP). Baron and Kenny's (1986) mediation criteria were met in two cases. The effects of HA on ITI and API on ITOP were eliminated in final models of Tables 5 and 6, respectively. Other racial/ethnic differences in intergenerational transmission of SES persisted even after individual- and neighborhood-level variables were taken into account. Although the neighborhood-level effects did not fully explain the racial/ethnic differences in intergenerational transmission of SES, five out of six neighborhood indicators that we tested proved to be relevant predictors of ITE, ITI and ITOP. These indicators included disconnected youth, educational status, income, parent-child closure and vulnerability. Hence, our results identified multiple neighborhood-level influences on intergenerational transmission of SES. One important mechanism of neighborhood-level influence is contagion. Contagion theory predicts the spontaneous spread of anti-social behaviors, like smoking, drinking, delinquency, and sexuality, from idle teens to their peers (Crane, 1991; Jencks & Mayer, 1990). Our analyses support contagion theory by showing that lower concentration of disconnected youth in a neighborhood was conducive to higher levels of intergenerational SES transmission among its residents. The next two pathways of neighborhood influence that we tested are institutional and relative deprivation theories. Institutional theory asserts that the presence of positive adult role models in the neighborhood is crucial for adolescent development, while relative deprivation theory focuses on the feeling of deprivation resulting from being poor and living in an affluent neighborhood. The average educational status and average income of the neighborhood served as indicators of institutional influence and relative I. Ryabov Journal of Adolescence 80 (2020) 84–97 93 deprivation, respectively. In line with our expectations (Hypothesis 4) based on institutional theory, educational status of a neigh- borhood was directly related to ITE, ITI and ITOP. The multilevel regression analyses confirmed a positive association between the average income of a neighborhood and inter- generational SES transmission, which is contrary to what is expected from relative deprivation model. Relative deprivation theory predicts a negative association between neighborhood income and adolescent outcomes because it assumes that neighbors compete for scarce neighborhood resources and the more affluent the neighborhood, the fiercer the competition. Although there is some empirical support for relative deprivation theory (Nieuwenhuis et al., 2017; Smith et al., 2012; Vilhjalmsdottir et al., 2016), it can also be argued that children from low-income families benefit from living in high-income neighborhoods by enjoying better op- portunities for acquisition of social skills and superior infrastructure, including higher-quality schools. Numerous studies have shown that poor children growing in high-income neighborhoods have higher cognitive ability and school achievement than their coun- terparts living in low-income neighborhoods (Chetty, Hendren, & Katz, 2016; Crowder & South, 2003; Elliott et al., 1996; Leventhal & Brooks-Gunn, 2000; Wodtke & Parbst, 2017). It is also possible to interpret neighborhood income within the framework of in- stitutional theory. Neighborhood income, on par with neighborhood educational status, can imply the presence of successful role models in the neighborhood, which confers benefits on children, especially low-income children. Finally, we found support for social capital and social disorganization theories. Both theories have been extensively applied in the study of neighborhood effects (Byun et al., 2012; Dika & Singh, 2002; Dufur et al., 2013; Kubrin & Weitzer, 2003). Social capital theory looks at the neighborhood as the site of interpersonal ties formation and reciprocal social action (Dika & Singh, 2002; Pong & Hao, 2007). Parent-child closure, an index that shows how closely intertwined are the parent's and child's social networks, was used as an indicator of social capital. The results indicate that respondents who, as adolescents, lived in neighborhoods with higher parental-child closure were advantaged in comparison to their counterparts who did not. Social disorganization theory asserts that adolescents who live in neighborhoods with higher rates of delinquency, crime, di- versity, are less likely to complete a successful transition to adulthood than those who do not (Kubrin & Weitzer, 2003; Shaw & McKay, 1942). In order to adequately capture the content of this theory, we have incorporated the composite index of neighborhood vulnerability comprising items of crime rate, population density and immigrant concentration. According to our analyses, the index of vulnerability had a strong and negative effect on all three outcomes – ITE, ITI and ITOP. It is also worth mentioning that the effects of control variables were either insignificant or in directions predicted from earlier research. For example, respondents who had lived with two parents as adolescents were significantly more likely to surpass their parents’ levels of levels of education, income and occupational prestige than those who had not. This finding is generally in agreement with past research showing that living in a two-parent family increases educational attainment (e.g., Davis-Kean, 2005; Dufur et al., 2013; Kao & Thompson, 2003). One puzzling finding was the negative relationships between the parents' age, on the one hand, and ITI and ITOP, on the other. At the same time, respondent's age was found to be directly related to both ITI and ITOP. Thus, these analyses show that a persons' age is a contributing factor to ITE and ITOP, but parental age is an inhibitive factor in ITI and ITOP. Prior research uniformly finds that person's age is directly related to their SES (Furnham & Cheng, 2013; Katz-Gerro & Yaish, 2003; Klein, 2016). However, our results do not run counter to this evidence because we did not regress parent's age on SES, but on intergenerational transmission of SES. What our results do show is that the larger the age gap between parents and children, the greater the income and occupational status penalty for children. Although it is difficult to speculate why this should be the case, the negative effect of parental age on ITI and ITOP can be explained by a higher probability of intergenerational conflict and lower investment in children by older parents (Salmon, 2005). Nevertheless, we urge caution against a simplistic interpretation of this finding. This study is not without limitations. First, Add Health was conducted at the turn of the 21 century and, therefore, the racial/ ethnic disparities in intergenerational transfer of SES might have changed since then. Second, it is unclear if our results would have been altered by using an alternative analytic strategy. This study adopted discrepancy scores in operationalizing the intergenerational transmission of socioeconomic status. Although there are obvious advantages to this method, which have been outlined above, discrepancy scores are not sensitive to the extreme values of the distribution. For example, in the case when both the parent and the child could have the highest level of education, the child would not be considered to surpass his or her parent's education level. Third, the original occupational status categories in Add Health were fitted into a five-point ordinal scale. This strategy which was borrowed from prior studies (Feliciano & Lanuza, 2017; Hahm et al., 2003) allows for a closer alignment of the occupational structure with educational stratification. Nevertheless, the disadvantages of simplifying the original occupational structure can result in loss of variation, which, in turn, can affect the results. Fourth, our choice of neighborhood indicators could have influenced our results. Should we have chosen a different set of predictor variables, our results would have been different. Fifth, selection bias, or un- observed heterogeneity, is also a problem. It is no secret that people make a conscious choice where to live. Therefore, we are unclear about the extent to which racial/ethnic differences in intergenerational transmission of SES are attributable to residential preferences and choices. In this work, we did not control for changes of residence between waves of data collection. Thus, some respondents might have changed place of residence together with their parents immediately before or after Wave 1, while some respondents moved out of the parental home sometime between Waves 1 and 4 (during the study period). Moreover, it is possible some Add Health participants have returned back home to co-reside with their parents at Wave 4, while others continued living with them uninterruptedly between Waves 1 and 4. In other words, given the contains of the Add Health methodology, we could not control for the length of period the respondents were exposed to the neighborhood conditions that were observed at Wave 1. Finally, there may exist other variables that affect intergenerational transmission of SES but were not included in the current study. Therefore, future research should explore additional variables that can explain the relationship between race/ethnicity and intergenerational trans- mission of SES. Such variables may include nativity status, religiosity, delinquency, etc. I. Ryabov Journal of Adolescence 80 (2020) 84–97 94 Despite these limitations, we are able to draw some important conclusions. First, when examined from the developmental per- spective, our results unambiguously confirm that neighborhood environment in adolescence affects one's outcomes in young adulthood. That is, the influence of neighborhood environment appears to extend beyond the adolescence into adulthood. Moreover, neighborhood conditions play an important role in transmission of SES from parents to children, and these conditions cannot be simply reduced to one factor. In fact, intergenerational transmission of SES is influenced by the combined effect of a variety of neighborhood-level factors, ranging from ‘hard’ and established predictors such as average neighborhood income to ‘soft’ predictors such as parent-child closure. Consequently, a broad spectrum of neighborhood-centered policies and institutional arrangements for neighborhood change is needed to facilitate the transmission of SES from parents to children. These policies can range from neighborhood anti-poverty initiatives to community networking that provides opportunities for the formation of local social ties. Education policies aimed at reducing family SES disadvantage should also be a useful means to address the neighborhood inequality. Because attainment of education is one of America's most important values, schooling have always been an area in which the most sincere commitment to equality of opportunity has been made. Emerging evidence also suggests that schools, especially public schools, tend to reduce inequality (Downey, von Hippel, & Broh, 2004; Downey & Condron, 2016; Alexander, Entwisle, & Olson, 2001; Vartanian & Buck, 2005). Therefore, providing children from disadvantaged neighborhoods access to less segregated, better funded schools in any possible manner should remain an important policy objective for generations to come. Second, as mentioned above, neighborhood-level factors do not completely explain the racial/ethnic differences in ITE, ITI and ITOP. That is, AA and HA adolescents are more likely to lag behind their API and NHW counterparts in terms of the intergenerational transmission of SES even after neighborhood-level differences in income, educational level and other important indicators are taken into account. Consequently, policies aimed at equalizing differences between neighborhoods can alleviate but cannot solve the problem of racial/ethnic disparities in intergenerational transmission of education, income and occupational prestige. Particularly, reducing neighborhood-level inequalities via residential desegregation is not sufficient to eliminate the persistence of racial/ethnic socio-economic inequality in the U.S. In summary, we would like to emphasize that, racial/ethnic gaps in intergenerational transmission of SES are readily observable and violate the norm of meritocracy that is dominant in most western societies. Meritocratic ideals are deeply entrenched in the consciousness of the U.S. public (Newman, Johnston, & Lown, 2015). Moreover, many people from abroad view the United States is a country where socio-economic attainment is dependent on individual effort and merit (Kurasawa, 2008). This study clearly shows that this view is deeply flawed. It is certainly true that social and economic advantages and disadvantages are passed from one generation to the next in all societies. 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