Disentangling the role of income in the academic achievement of migrant children

Social Science Research xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Social Science Research journal homepage: www.elsevier.com/locate/ssresearch

Disentangling the role of income in the academic achievement of migrant children Monique Gagnéa,∗, Magdalena Janusa,b, Nazeem Muhajarinec, Anne Gadermanna, Eric Dukub, Constance Milbratha, Anita Minha, Barry Forera, Carly Mageea, Martin Guhna a

University of British Columbia, Canada McMaster University, Canada c University of Saskatchewan, Canada b

A R T IC LE I N F O

ABS TRA CT

Keywords: Migrant Academic achievement Refugee children Immigrant children Income Poverty

Poverty has a well-established association with poor developmental outcomes in children but is often found to be a weak predictor of outcomes for migrant children. Building on theory focused on the developmental competencies of minority children, the current study used a systematic and novel analytic approach to disentangle the relationship between income and developmental outcomes for different groups of migrant children. Utilizing a population-based cohort of children in British Columbia, Canada (N = 23,154), the study examined whether income differently predicted the kindergarten to Grade 7 (K-7) literacy and numeracy trajectories of migrant children (economic, family, and refugee groups), in comparison to non-migrants. By applying GroupBased Trajectory Modeling (GBTM), the study found that lower income was generally associated with lower K-7 literacy and numeracy achievement trajectories. The relationship between income and achievement did not differ for migrant children in comparison to non-migrant children, with the exception of one sub-group of high-achieving economic class migrant children, which appeared to be less impacted by low income levels. Follow-up binomial logistic regression analysis found that parental education levels at migration and English language ability predicted which migrant children would be high literacy and numeracy achievers despite low income. The results suggest that basic associations between poverty and the outcomes of migrant children mask an underlying complexity: For most migrant children, poverty was just as predictive of detrimental academic outcomes as it was for non-migrant children and being in the exceptional sub-group of high-achieving, low-income migrant children was partly accounted for by other protective factors.

Children who migrate are more likely to live in poverty - across Canada and in the world's richest nations (Smeeding et al., 2012). Poverty has a well-established association with a range of long-lasting effects on the outcomes of children, from health and well-being to learning and behaviour (Chen et al., 2010; Duncan and Brooks-Gunn, 1997; Hertzman and Boyce, 2010; Keating and Hertzman, 1999). However, a growing number of studies have found that indicators of socioeconomic status, such as low income, seem to be weaker predictors of negative outcomes for migrant children (Archambault et al., 2017; Beiser et al., 2002; Garcia Coll and Marks, 2012; Georgiades et al., 2007; McAndrew et al., 2009). Despite this reoccurring pattern, there is a paucity of research that has focused

∗

Corresponding author. E-mail address: [email protected] (M. Gagné).

https://doi.org/10.1016/j.ssresearch.2019.102344 Received 12 July 2018; Received in revised form 24 June 2019; Accepted 28 August 2019 0049-089X/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Monique Gagné, et al., Social Science Research, https://doi.org/10.1016/j.ssresearch.2019.102344

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

on the unique relationship between income and the developmental outcomes of migrant children. As we enter a period of unprecedented global migration (UNHCR, 2017) coupled with rising income inequality in Canada and other countries (Green et al., 2016; OECD, 2011), we need to better understand the unique role of low income in the lives of immigrant and refugee children (whom we more generally refer to in this study as migrant children). Systematically investigating this relationship is important for two main reasons: First, migrant children come from wide-ranging backgrounds and can have fundamentally different migration experiences and it is not clear whether the relationship between income and child outcomes is consistent across different sub-groups. Second, the better than expected outcomes of migrant children may be a result of selectivity associated with the immigration process (Citizenship and Immigration Canada, 2016). Resulting from this selectivity, some children who migrate may be more likely to have other positive socio-demographic factors and this could help to predict which migrant children show an attenuated association between income and developmental outcomes. Disentangling this relationship warrants a theoretical approach that specifically considers the factors that may play a role in the relationship between poverty and the developmental outcomes of migrant children as well as a unique analytical approach that moves away from general associations and that is able to account for heterogeneity within and between migrant groups. Our understanding of this relationship would also be improved by accounting for change over time. Children who migrate may be undergoing a period of rapid change as part of their adaptation process. As such, knowing how income is associated with achievement at one time point offers an incomplete picture of the relationship as it may miss important information about the nature and valence of their trajectories (e.g., whether they are consistently thriving, improving or declining). Addressing these concerns, the current study draws on theory specific to the development of minority children and takes a novel analytic approach that can account for within-group heterogeneity in order to develop a clearer understanding of the relationship between income and the developmental outcomes of migrant children. Utilizing a longitudinal dataset that follows a cohort of migrant (immigrant and refugee) and non-migrant children who were born between 1994 and 1998 to their Grade 7 year (2006–2010), the current study applies group-based trajectory modeling to focus on one indicator of positive development for migrant children, academic achievement over time, and investigates: (1) the association between income and the kindergarten to Grade 7 (K-7) academic trajectories of three groups of migrant children (economic, family, and refugee), in comparison to nonmigrant children and (2) whether other socio-demographic factors (parental education levels at migration, English language ability, and generation status) help to predict which migrant children are able to maintain high K-7 academic trajectories, despite low income. 1. Socioeconomic status and developmental outcomes of migrant children Child poverty and lower socioeconomic status have a well-established association with a range of poor developmental outcomes, from health and well-being to learning and behaviour, at least in the context of developed countries (Duncan and Brooks-Gunn, 1997; Duncan et al., 2010; Hertzman and Boyce, 2010). This association is often termed the socioeconomic gradient of developmental health (Keating and Hertzman, 1999). Yet, there is growing evidence to suggest that this association is attenuated for migrant children (Pong and Landale, 2012) and despite being from poorer families overall, migrant children often show better outcomes than their nonmigrant peers (Archambault et al., 2017; Beiser et al., 2002; Garcia Coll and Marks, 2009, 2012; Georgiades et al., 2007; McAndrew et al., 2009; Zhou, 1997). The evidence for this weakened association has been reported in various ways. Some researchers have highlighted anomalies in the outcomes of immigrant children – with many achieving remarkably high levels of educational success, despite their socioeconomic backgrounds (Zhou, 1997). Other studies have reported that indicators of socioeconomic status, such as median family income, had weak (or not significant) associations with academic achievement (Fuligni, 1997; McAndrew et al., 2009; Pong and Hao, 2007). Beiser and colleauges (2002) found that despite being twice as likely to live in poverty, migrant children showed lower levels of emotional and behavioural problems than non-migrants. The authors furthermore found that the factors that mediated the relationship for Canadian-born children, such as family dysfunction and parental depression, did not play the same role for the foreign-born children in the study. Accounting for a range of factors at the child, family, and neighbourhood level, Georgiades and colleagues (2007) found evidence for an attenuated effect of family poverty on externalizing behaviour outcomes for children from recent immigrant families (lived in Canada for 15 years or less), but not for internalizing behaviour or school performance outcomes. In addition to family-level poverty, Georgiades et al. (2007) were also able to account for neighbourhood-level disadvantage and capture concentration of immigrant families within a neighbourhood. This was an important consideration because though some neighbourhoods may be economically disadvantaged, they may also have advantages such as stronger cultural communities (Crosnoe and Lopez Turley, 2011). Georgiades et al. (2007) teased apart this relationship by accounting for both the concentration of immigrants within neighbourhoods as well as levels of neighbourhood disadvantage. In doing so, they found that neighbourhoods with higher concentrations of immigrants were associated with positive outcomes for immigrant children (i.e., lower emotional-behavioural problems) and the opposite was true for non-immigrants; however, neighbourhood disadvantage remained associated with emotional-behavioural problems and poor school performance for both immigrant and non-immigrant children. Building on this, Milbrath and Guhn (2019) found that neighbourhood cultural density appeared to buffer the association between poverty and children's developmental outcomes, but only in the case of certain cultural groups. Although we are starting to build our understanding of neighbourhood-level protective factors, we continue to lack clarity about why some migrant children might be more protected against the deleterious effects of poverty and low income. Immigrant and refugee children come from wide-ranging backgrounds and there continues to be a paucity of research that accounts for withinmigrant group diversity. Accounting for differing individual and family level migration circumstances is likely to play an important role in understanding why some migrant children may have the capacity to adapt to the low income circumstances they may face. 2

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

2. Varying capacities to adapt: migrant children and low income Theoretically, we draw from Garcia Coll and colleagues' (1996) integrative model for the study of developmental competencies in minority children to conceptualize how economic disadvantages may differently impact the development of migrant children. Garcia Coll and colleagues posit that developmental competencies of minority children are impacted by a person or group's capacity to cope and adapt to the inequalities (such as lower income) they may face – a construct they term adaptive culture. The adaptive culture response is said to be the product of a group's prior collective history (cultural, political, and economic) in tandem with the contextual demands (which can be promotive or inhibitory) of their environments. We might best understand the response of low income migrant children and families by considering their capacity to draw upon their collective cultural, political, and economic history as well as the appropriateness and responsiveness of their new countries and communities in meeting their needs. This capacity may vary greatly by migration class (economic, family, or refugee) and may depend upon additional socio-demographic factors related to migration such as parental education levels at migration, host country language ability (e.g., English), and generation status (first- or second-generation), each of which may help to predict which migrant children will be able cope and adapt in order to achieve positive adaptation outcomes, despite low-income circumstances. 2.1. Migration class Individuals generally migrate to Canada under one of three main migration classes (economic, family, and refugee classes). These classes described below are associated with different migration experiences and therefore we hypothesize that they have different capacities to cope and adapt to challenges, such as low income. Economic class migrants go through an application process and are selected based upon their skill and potential to contribute to the Canadian economy (Citizenship and Immigration Canada, 2016). By very nature of this economic selectivity, we might expect that children who migrate under the economic class would collectively have wide-ranging socioeconomic advantages such as high levels of education that would increase their likelihood of positive developmental outcomes. They also represent the largest group of migrants (Citizenship and Immigration Canada, 2016), predominantly arriving from China, India, and the Philippines (Citizenship and Immigration Canada, 2016), with large, well-established cultural communities to welcome them in Canada (see Guo, 2004). In comparison, children who migrate under the family class have joined family members who are already Canadian citizens or permanent residents (Citizenship and Immigration Canada, 2016). Given this, we might expect that they would be privy to established family social support in Canada, helping them to broker and access community supports that meet their needs. However, it should also be noted that those who arrive under the family class process are not selected based upon their ability to be successful in the Canadian labour market and as such, they may have fewer socioeconomic advantages to draw upon in comparison to the economic class group. Finally, children who arrive in Canada under the refugee class have fled their home countries and are accepted based upon their need for protection and, similar to the family class, they were not selected based upon their ability to succeed in the Canadian labour market (Citizenship and Immigration Canada, 2016). Given the circumstances, we might expect that children who arrive under the refugee class are likely to have faced multiple hardships – from experiences of violence and family loss to gaps in education and socioeconomic hardships (Stewart, 2011). Furthermore, refugees represent the smallest proportion of migrants in Canada and, proportionally-speaking, arrive from many different countries, and finding culturally appropriate support and resources may be much more challenging. 2.2. Parental education Parental education is a strong predictor of child developmental outcomes (Brooks-Gunn et al., 1997; Hernandez and Darke, 1999). Migrant parents tend to have higher levels of education than would typically be expected given their income levels (Fuligni, 1998). This may be attributed to the generally lower rates of employment in several settlement countries for migrant parents (Hernandez, 2012), likely the result of social and systemic challenges to occupational integration for migrants, such as language barriers and discrimination (Simich et al., 2005). Perhaps because of these income and education discrepancies, there is some evidence to indicate that parental education may play a stronger role in outcomes than other SES-related factors for young migrants (Hernandez, 2004). In fact, education has also been shown to influence child outcomes independent of income; possibly because parents who are more educated may have more capacity to offer their children richer learning environments at home and more effective support in navigating the demands of school (Brooks-Gunn et al., 1997). These findings tentatively suggest parental educational level may be an important factor in predicting the achievement of low income migrant children. 2.3. English language ability Developing competence in a new language can take many years to develop (Cummins, 1991). In the context of British Columbia, Canada, where English is the official language spoken, the ability to communicate in English is critical to the integration process. English language competency is key to achieving academically for young migrants (Suarez-Orozco et al., 2009). Migrant children who lack proficiency in English are less likely to engage at school, which in turn, has been found to be associated with lower academic achievement (Kim and Suarez-Orozco, 2015). More generally, lacking fluency with the language of the majority can lead to interpersonal challenges (Tsai, 2006). For migrant children in Canada then, competence in English is likely to offer greater ability to navigate the dominant culture, increasing one's capacity to adapt and cope with the challenges they face, including economic 3

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

challenges. 2.4. Generation status A number of studies have pointed to a pattern of generational decline in outcomes for migrant children: first-generation migrant children tend to do better academically than their second-generation counterparts, which is often termed the immigrant paradox (Marks et al., 2014). This is paradoxical given that second-generation children and their parents would have had a greater length of time to culturally adapt but it does highlight the notion that one's capacity to cope and adapt to the inequalities faced does not simply hinge upon the length of time in the country of reception. Time may facilitate one's ability to learn and adapt to contextual demands, but it does not necessarily enhance the ability to draw upon one's collective cultural, political, and economic history. In fact, through the process of acculturation, second-generation migrants may find themselves with a diminishing ability to leverage their cultural heritage in times of stress (Schwartz and Montgomery, 2002). Distancing from one's heritage culture as part of the acculturation process has been associated with a number of detrimental outcomes, such as depression and lower psychological well-being (Harker, 2001). 3. The present study Understanding the unique role of low income in the lives of migrant children has become ever more important as we are faced with record high global migration rates (UNHCR, 2017) and with the knowledge that migrant children are far more likely to live in poverty (Smeeding et al., 2012). The current study is designed to advance our understanding of the unique role of low income in the lives of migrant children by drawing on theory that is specific to the development of minority children and adopting a novel analytic approach that is able to account for heterogeneity within the migrant group. Using a large, population-based cohort of children in British Columbia, Canada, the study addressed two research questions: 1) What is the association between income (family and neighbourhood) and the kindergarten to Grade 7 (K-7) literacy and numeracy trajectories of economic, family, and refugee class migrant children, in comparison to their non-migrant peers? (2) Do additional socio-demographic factors (parental education levels at migration, English language ability, and generation status) predict high academic achievement over time in low income migrant children? We expected that economic class migrants, with greater socio-demographic resources at their disposal, would have a greater capacity to buffer the impact of low income and do well academically, in comparison to family and refugee class migrant groups. 4. Methods The study used a retrospective longitudinal population-based cohort design. Data linked at the individual child-level across four sources were used: 1) Data from the Early Development Instrument (EDI), a teacher-assessed measure of children's developmental outcomes in the middle of kindergarten (Janus and Offord, 2007), were sourced from the Human Early Learning Partnership (HELPHuman Early Learning Partnership, 2014) to define the study population and to determine teacher reports of literacy and numeracy skills in Kindergarten. 2) Data from Immigration, Refugees, and Citizenship Canada (IRCC; Immigration, Refugees, and Citizenship Canada, 2014) were used to capture migration-related information, 3) BC Ministry of Education's (MEDMinistry of Education, 2014) Foundational Skills Assessment (FSA) data were used to determine Grade 4 and Grade 7 standardized literacy and numeracy skill test scores, and 4) BC Ministry of Health (MOHBritish Columbia Ministry of Health, 2013) Medical Services Plan (MSP) data were used to capture income-related information at the family- and neighbourhood-level. The individual level linkage was accomplished by a trusted third party for linkage in BC (Population Data BC), using a hybrid (probabilistic-deterministic) approach (see Population Data BC, 2014). 4.1. Study sample The study included a cohort of children from 10 large, urban/suburban and ethnically diverse school districts in British Columbia who were born between 1994 and 1998 and for whom EDI data were collected in their kindergarten year (N = 23,154). EDI data are routinely collected by HELP in multi-year waves across the Canadian province of British Columbia, resulting in province-wide population-level data on children's developmental well-being in the middle of kindergarten. The study cohort was then further divided into a first- and second-generation migrant cohort, which included children (or children of parents) who had migrated to Canada, based upon federal immigration (IRCC) records (N = 4,143). All other children were included in the non-migrant cohort (N = 19,011). 4.2. Measures 4.2.1. Literacy and numeracy achievement from kindergarten to Grade 7 Literacy and numeracy scores were collected at three time points (kindergarten, Grade 4, and Grade 7; corresponding to average ages of 5, 9, and 12). Note that literacy and numeracy skills were based upon teacher assessments at kindergarten (EDI) and student test scores at Grade 4 and 7 (FSA). A number of studies have found teacher-assessed EDI academic scores to be strongly associated with academic achievement in later grades (e.g., Guhn et al., 2016; Davies et al., 2016). Furthermore, past research has found 4

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

evidence to support the appropriateness of using the EDI and FSA scores together in trajectory analyses (Lloyd and Hertzman, 2009). Teacher-assessed EDI scores on advanced literacy and numeracy were used to determine each child's literacy skill level and numeracy skill level at Kindergarten (Time 1 in the trajectory models). Provincial standardized FSA test scores in Grade 4 and Grade 7 (Time 2 and 3 in the trajectory models) were used to determine the literacy skill level (based upon the population-centered reading and writing subscale scores) and numeracy skill level (based upon the population-centered numeracy subscale score) for each child. 4.2.2. Income Data from the Medical Services Plan (MSP), the provincial universal health insurance program, were used to develop indicators of income (see Milbrath and Guhn, 2019 who used a similar methodology). Neighbourhood income quintiles were used to capture neighbourhood-level income. Neighbourhood income quintiles were assigned to each child based upon their residential postal code, as documented by their MSP registration in their kindergarten year (post-kindergarten neighbourhood changes were not accounted for in the current study). Neighbourhood income quintiles, which are aggregated at the postal code level and adjusted for household size using Canadian census data, are converted from postal codes using the Statistics Canada Postal Code Conversion File (PCCF+; Statistics Canada, 2011). Neighbourhood income quintiles divide neighbourhoods into five equal parts, from 1 (lowest income quintile) to 5 (highest income quintile). The second indicator of income was defined at the family level. In British Columbia, individuals/families in the lowest adjusted net income category in a given year qualify for 100% subsidies. Therefore, for any child who at any time in the study period (K-7) qualified for full (100%) subsidy on their BC Medical Services Plan health insurance premiums, this variable was coded as 1, and 0 for children who did not qualify for subsidy at any time. As will be discussed further in the analysis section, the similar patterns of association for the neighbourhood- and family-level income variables subsequently led to the creation of a combined neighbourhood- and family-level indicator of income. This was done by adding a centered version of the neighbourhood income quintiles (−2, −1, 0, 1, 2) to the family-level low-income indicator, which was recoded as −1 = low-income indicator and 1 = no low-income indicator. The addition of the two variables resulted in a combined indicator of (neighbourhood and family) low income that ranged from −3 (lowest combined income) to +3 (highest combined income). 4.2.3. Migration class We included variables for the three main admission categories (migration classes) in Canada: economic, family, and refugee classes (Statistics Canada, 2016). These were provided in the IRCC file. Three binary (dummy-coded) variables were created for the economic, family, and refugee class with the non-migrant group used as the reference group (i.e., 0) in all cases. 4.2.4. Parental education Parents who were documented as having 0–9 years of schooling in the IRCC data were coded as 1. Parents with 10–12 years of schooling were coded as 2, and parents with 13 or more years of school (including those with a trade certificate, non-university diploma, or a bachelor's, master's, or doctorate degree) were coded as 3. Parental education was based upon the mean score of mother and father levels of education at the time of migration, as reported in the IRCC data. 4.2.5. English language status (ELL) For students who were required to take coursework to improve their English proficiency at any point between kindergarten and Grade 7, the ELL status was coded as 1; non-ELL status was coded as 0. These were determined based on school-records data from the BC Ministry of Education. 4.2.6. Generation status Children who were born outside of Canada, or “first-generation migrants”, were coded as 1. Second-generation migrant children, who were born in Canada with at least one parent who migrated, were coded as 0. This was based upon IRCC records. 4.2.7. Age and sex EDI data were used to capture the age and sex of each child. Females were coded as 0 and males were coded as 1 in the analyses. Age was calculated at the time of cohort entry (i.e., EDI data collection). 4.3. Data analysis Group-Based Trajectory Modeling (GBTM) (Nagin, 2005) was used to answer our research questions. GBTM is a statistical modeling technique used to identify groups of individuals who follow a similar pattern of outcomes over time and then to characterize the individuals who have the highest probability of belonging in those groups (Frankfurt et al., 2016; Nagin, 2005). We do not make the assumption that the individuals in each trajectory group comprise literally distinct sub-populations and this was a key theoretical reason for choosing a GBTM approach over similar techniques, such as latent growth modeling (see Frankfurt et al., 2016 for a discussion on distinction between the two approaches). Using a GBTM approach in SAS (Proc Traj; Jones and Nagin, 2007), the study modeled trajectories of literacy and numeracy skills in kindergarten (Time 1; standardized EDI literacy and numeracy scores), Grade 4 (Time 2; standardized Grade 4 FSA literacy and numeracy scores) and Grade 7 (Time 3; standardized Grade 7 FSA literacy and numeracy scores). This was based upon censored normal models, which are appropriate for modeling data that are continuous and approximately normally distributed (Nagin, 2005). 5

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

We follow an iterative and stepwise process, as recommended by Nagin (2005), to determine best model fit and the optimal numbers of trajectory groups based upon four criteria: (1) the Bayesian Information Criterion (BIC) values - a criterion that is recommended to determine best model fit. (2) We calculated average posterior probabilities – the probability that individuals belong to their assigned trajectory group to ensure they were sufficiently high. Consistent with Nagin (2005), we used 0.7 as a cut-off. (3) For the sake of parsimony, we ensured the resulting number of trajectory groups added explanatory value to the model and had a sample size large enough for subsequent analyses. Upon determining the optimal number of trajectory groups and probabilistically establishing individual membership in the literacy and numeracy trajectory groups, multinomial logistic regression analyses were used to predict group membership based upon the factors of interest: migration class (economic, family, and refugee class), neighbourhood-level income, and family-level low-income (controlling for age and sex). Interaction terms that combined each migration class variable with income were entered separately into each regression model to determine whether income differently predicted academic trajectory group membership for each migrant group. Only significant interaction terms were retained in the final models. The Lowincreasing literacy and numeracy groups (characterized by low but improving trajectories over time) were used as the reference groups for the sake of consistency and ease of interpretation across models. In order to answer the second research question, we identified low-income migrant children based on the combined low-income variable (income scores of −3, −2, or −1 were considered low-income; children with moderate to high income scores of 0–3 were excluded). The low-income migrant group was further divided into two groups: High-achieving low-income migrants who were members of both the high literacy and high numeracy K-7 trajectory groups (n = 410) and low-income migrants who were members of the low or declining K-7 literacy and numeracy skill trajectories (n = 452). Then, a binomial logistic regression model was used to determine whether parental education level at migration, English Language Learner (ELL) status, and generation status predicted being in the high-achieving, low-income migrant group (controlling for migration class). 5. Results 5.1. Descriptive statistics Children from both the migrant and non-migrant cohorts were 5.7 years of age on average at the time of EDI data collection. The migrant and non-migrant cohorts had an equal proportion of females (48.5% vs 48.6%, respectively). Just over 50.5% of the migrant group were economic class migrants (n = 2096), 34.2% were family class (n = 1421), and 15.3% were refugee class (n = 634). Migrant children lived in neighbourhoods in their kindergarten year with significantly lower mean income quintiles than the nonmigrant children (M = 2.51, SD = 1.30; M = 3.10, SD = 1.38, respectively, p < .001). A significantly larger proportion of the migrant cohort qualified for full subsidy at some point within the study period (62.4%), in contrast to 40.6% of the non-migrant cohort (p < .001). For the combined neighbourhood- and family-level indicator of income, the migrant cohort had on average lower income than the non-migrant peers (M = −0.75, SD = 1.69; M = 0.29, SD = 1.90, respectively). Breaking down the migrant group, the economic, family, and refugee groups also differed on the combined income indicator (M = −0.36, SD = 1.79; M = −1.11, SD = 1.49; M = −1.14, SD = 1.56, respectively). Both fathers and mothers of the migrant cohort had 10–12 years of schooling on average (M = 2.40, SD = 0.78 and M = 2.34, SD = 0.76, respectively). The majority of the migrant cohort (85%) were English Language Learners (n = 3534 versus n = 617). First-generation migrants accounted for 25.9% (n = 1077) of the migrant cohort. Second-generation migrants represented 69.8% (n = 3066). The migrant cohort had scores below the population centered mean on both literacy and numeracy in kindergarten (Mlitk = −0.03, SD = 0.99; Mnumk = −0.11, SD = 1.11, respectively), in comparison to the non-migrant cohort whose average score was above the centered mean (Mlitk = 0.01, SD = 1.00; Mnumk = 0.02, SD = 0.97, respectively). The migrant group had scores slightly above the centered mean on the Grade 4 and Grade 7 composite literacy skill scores (Mlit4 = 0.05, SD = 0.97; Mlit7 = 0.10, SD = 0.96, respectively), whereas the non-migrant group had scores slightly below (Mlit4 = −0.01, SD = 1.01; Mlit7 = −0.02, SD = 1.01, respectively). On the Grade 4 and Grade 7 composite numeracy skill scores, the migrant group had scores slightly above the centered mean on the Grade 4 and Grade 7 numeracy skill scores (Mnum4 = 0.03, SD = 1.06; Mnum7 = 0.23, SD = 0.96, respectively), whereas the non-migrant group had scores lower in comparison (Mnum4 = −0.06, SD = 1.00; Mnum7 = 0.01, SD = 0.97, respectively). 5.2. K-7 literacy and numeracy trajectories 5.2.1. Trajectory model selection Three literacy skill trajectory groups and four numeracy skill trajectory groups were found to most optimally fit the data (see Figs. 1 and 2). We chose a three-group literacy model (BIC3grp = −79040.77) even though the BIC value was optimized with a fourgroup model (BIC4grp = −75981.95; BIC2grp = −80966.20). Similarly, we selected a four group numeracy model (BIC4grp = −70082.50) over the three- or five-group models (BIC3grp = −73213.62; BIC5grp = −68508.50). Although the BIC values for the models that contained the higher number of groups was more favourable in the case of both the literacy and numeracy models, the more parsimonious three- and four-group models were chosen, respectively. In both models, the additional groups were small (less than 4%) and had slopes and intercepts that were similar to other groups in the model. The decision in each case included the consideration of average posterior probability values, which were all well above 0.70 for the more parsimonious models (which has been recommended as an appropriate cut-off; see Nagin, 2005). For the literacy trajectory groups, 35.2% of the study population fell into the Low-Increasing literacy group (n = 7465). This 6

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

Fig. 1. High, Low-increasing, and Declining literacy skill trajectories at Kindergarten, Grade 4, and Grade 7 for the migrant and non-migrant study population. Kindergarten, Grade 4, and Grade 7 literacy scores are scaled based upon the full population (a score of 0 indicates the mean score on the scale).

Fig. 2. High, Average, Low-increasing, and Declining numeracy skill trajectories at Kindergarten, Grade 4, and Grade 7 for the migrant and nonmigrant study population. Kindergarten, Grade 4, and Grade 7 numeracy scores are scaled based upon the full population (a score of 0 indicates the mean score on the scale).

group (linear estimate = 0.45, SE = 0.01, p < .001) represents the individuals with the lowest literacy skills, which increases over the K-7 timeframe but remains below average. The High group comprises the majority of the study population (50.6%; n = 11,189) and is characterized by above average literacy skill levels over K-7 (linear estimate = −0.12, SE = 0.01, p < .001). The Declining group represents 14.3% (n = 3027) of the study population and is marked by literacy skill levels that are approximately average at Kindergarten but decline consistently over K-7 (linear estimate = −0.71, SE = 0.01, p < .001). As can be seen in Fig. 2 and 8.8% of the study population fell into the Low-Increasing numeracy group (n = 1892). This group (linear estimate = 1.04, SE = 0.01, p < .001) represents the individuals with the lowest numeracy skills, which increase over the K7 timeframe but remain below average. The Declining group represents 11.8% (n = 2352) of the study population and is marked by numeracy skill levels that are approximately average at Kindergarten but decline consistently over K-7 (linear estimate = −0.97, SE = 0.01, p < .001). The majority of the study population are represented by the Average group (55.8%; n = 13,328), which is characterized by average numeracy skills over K-7 (linear estimate = −0.01, SE = 0.01, p = .013). The High group comprises 23.6% (n = 4109) and is characterized by above average numeracy skill levels over K-7 (linear estimate = 1.04, SE = 0.01, p < .001). 5.3. Predicting trajectory group membership by income and migration class 5.3.1. Age and sex Age was a significant predictor of membership in all groups, whereby older children were consistently less likely to belong in the Low-increasing literacy and numeracy reference groups. Males were less likely to belong to the High literacy group but sex was not significantly associated with High numeracy group membership. Males were also less likely to be in the declining literacy and numeracy groups, in comparison to the Low-increasing reference groups (see Tables 1 and 2 for more detail). 5.3.2. Income After noting that both neighbourhood income and family-level low income variables showed the same pattern of association in each model (including the interaction terms), our final analysis used the combined (neighbourhood and family) income variable for the sake of parsimony and to reduce multi-collinearity (see the measures section for more on the creation of combined income variable). Income was significantly associated with children's memberships in groups other than the comparison groups. Higher income was associated with a greater likelihood of membership in the literacy and numeracy groups characterized by higher levels of achievement (see Tables 1 and 2 for further details). 7

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

Table 1 Multinomial logistic regression results in predicting K-7 literacy skill group membership. High Literacy (n = 11,189)

Age Sexa Incomeb Economic Class Family Class Refugee Class EconBYIncomec

Declining Literacy (n = 3,027)

B

SE

OR

0.47*** −1.06*** 0.25*** 0.63*** −0.42*** −0.52*** −0.15***

0.02 0.04 0.01 0.08 0.09 0.14 0.04

1.60 0.35 1.28 1.88 0.66 0.59 0.86

[1.56,1.64] [0.27,0.43] [1.26,1.30] [1.72,2.04] [0.48,0.84] [0.32,0.86] [0.78,0.94]

B

SE

OR

0.33*** −0.30*** −0.07*** −0.40** −0.71*** −0.30* −0.12

0.03 0.05 0.01 0.13 0.11 0.14 0.07

1.39 0.74 0.93 0.67 0.49 0.74 0.89

[1.33,1.45] [0.64,0.84] [0.91,0.95] [0.42,0.92] [0.27,0.71] [0.47,1.01] [0.75,1.03]

Note. The low-increasing literacy group is the reference group (n = 7,465). *p < .05; **p < .01; ***p < .001. a Females coded as 0 in the analyses (males = 1). b Income = Neighbourhood and family-level income combined. c EconBYIncome = The Economic Class* Income interaction term (see Fig. 3 for an illustration). Table 2 Multinomial logistic regression results in predicting K-7 numeracy skill group membership.

Age Sexa Incomeb Economic Family Refugee EconBYIncc

High Numeracy (n = 4,109)

Average Numeracy (n = 13,328)

Declining Numeracy (n = 2,352)

B

SE

OR

B

SE

OR

B

SE

OR

0.53*** 0.14 0.52*** 1.63*** −0.26 −0.49* −0.23***

0.04 0.07 0.02 0.14 0.15 0.22 0.07

1.70[1.62,1.78] 1.15[1.01,1.29] 1.68[1.64,1.72] 5.10[4.83,5.37] 0.77[0.48,1.06] 0.61[0.18,1.04] 0.79[0.65,0.93]

0.24*** −0.38*** 0.25*** −0.11 −0.30*** −0.62*** −0.06

0.03 0.06 0.02 0.15 0.09 0.13 0.07

1.27[1.21,1.33] 0.68[0.56,0.80] 1.28[1.24,1.32] 1.07[0.78,1.36] 0.90[0.72,1.08] 0.54[0.29.0.79] 0.94[0.80,1.08]

0.33*** −0.21** 0.18*** −0.08 −0.86*** −0.85*** −0.06

0.03 0.07 0.16 0.16 0.14 0.19 0.09

1.39[1.33,1.45] 0.81[0.77,0.95] 1.20[0.89,1.51] 0.92[0.61,1.23] 0.42[0.15,0.69] 0.43[0.06,0.80] 0.94[0.76,1.12]

Note. The low-increasing numeracy group is the reference group (n = 1,892). *p < .05; **p < .01; ***p < .001. a Females coded as 0 in the analyses (males = 1). b Income = Neighbourhood and family-level income combined. c EconBYIncome = The Economic Class* Income interaction term (see Fig. 4 for an illustration).

5.3.3. Migration class Economic class status was associated with a greater likelihood of membership in the high literacy and numeracy trajectory groups, in comparison to the Low-increasing groups. By contrast, the family class and refugee class were more likely to belong in the literacy and numeracy Low-increasing groups (the reference groups) than all other groups, including the high groups and the declining groups (see Tables 1 and 2). 5.3.4. Moderator terms Neither refugee class nor family class were found to moderate the relationship between income and academic (literacy and numeracy) group membership. That is to say, these migration groups showed the same relationship with income as their non-migrant peers (see Tables 1 and 2). However, the economic class-by-income interaction term was found to be a significant predictor of membership in both the high literacy and high numeracy trajectory groups, indicating that economic class migrants showed a different relationship between income and high academic achievement trajectory membership. To illustrate the interactions, Fig. 3 plots the probability of membership in the High literacy group and Fig. 4 plots the probability of membership in the High numeracy group, as a function of income, for the migrant (economic, family, and refugee classes) and non-migrant groups. Both figures illustrate a buffering effect, meaning the economic class migrant group demonstrates an attenuated relationship between income and high literacy and numeracy group membership, in comparison to the non-migrant cohort. Figs. 3 and 4 also illustrate the similarity in the relationship between income and High literacy and numeracy group membership for the family and refugee class groups. 5.4. Predicting high literacy and numeracy trajectory membership for low income migrant children The results of the binomial logistic regression analysis suggested that socio-demographic factors explained a significant amount of the variance in High literacy and numeracy achievement group membership for low-income migrant children (R2Cox & Snell = 0.35; R2Nagelkerke = 0.47). As can be seen in Table 3, English Language Learner status was associated with a lower likelihood of being in the High literacy and numeracy trajectory groups for low-income migrant children. Higher education levels of both mother and father at the time of migration were associated with a greater likelihood of membership in the High literacy and numeracy groups. Refugee 8

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

Fig. 3. Probability of membership in the High literacy group as a function of income for the migrant (economic, family, and refugee classes) and non-migrant cohorts. The Low-increasing group is the reference group. aNote that Income is a composite of family-level poverty and neighbourhood median income.

Fig. 4. Probability of membership in the High numeracy group as a function of income for the migrant (economic, family, and refugee classes) and non-migrant cohorts. The Low-increasing group is the reference group. aNote that Income is a composite of family-level poverty and neighbourhood median income. Table 3 Binomial logistic regression results for predicting of high literacy and numeracy trajectory membership for low income migrant children. R2Cox

& Snell =

.35; R2Nagelkerke = .47

Sexa English Language Learner Generation Status (1st vs. 2nd)b Parent Education Level Refugee Class (vs. Economic)c Family Class (vs. Economic)c

B

SE

OR

95%CIOR

−0.33 −0.92** 0.13 0.58*** −2.81*** −2.37***

0.18 0.33 0.21 0.15 0.30 0.22

0.72 0.40 1.14 1.80 0.06 0.09

[0.51, [0.21, [0.75, [1.33, [0.03, [0.06,

1.02] 0.75] 1.72] 2.40] 0.11] 0.15]

Note. 862 low-income migrant children were included in the analysis. 410 low income migrant children were members of both the high literacy and numeracy groups (the reference group included 452 low-income migrant children who were not in the high literacy nor the high numeracy group. *p < .05; **p < .01; ***p < .001. a Females coded as 0 in the analyses (males = 1). b 1st generation migrant children were coded as 1 (2nd generation was coded as 0). c Dummy coded.

and family class migration were associated with a lower likelihood of membership in the High literacy and numeracy groups, in comparison to economic class migration. Sex and Generation status (i.e., being a first-versus second-generation migrant) were not significantly predictive of membership.

9

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

6. Discussion Drawing upon theory specific to the development of minority children (Garcia-Coll et al., 1996) and an analytic approach that could account for heterogeneity within the migrant children group, the findings from the current study provide a more nuanced understanding of the relationship between income and the developmental outcomes of migrant children. As expected, the study found that lower income was generally associated with lower K-7 literacy and numeracy achievement trajectories. Importantly, the relationship between income and achievement did not differ for refugee and family class migrant children in comparison to nonmigrant children. This relationship was also consistent for economic class migrant children with the exception of one sub-group of high-achieving economic class migrant children whose academic outcomes were less associated with income levels. Building on this, the study found that, after accounting for migration class, parental education levels at migration and English language ability predicted which low-income migrant children would be in the high literacy and numeracy achievement groups. The results of the study offer evidence to suggest that most migrant children seem to be impacted by low income to the same degree as their non-migrant peers. The low-income migrant children that showed high academic achievement over time were more likely to be those with additional, socio-demographic resources (i.e., parental education and English language ability) at their disposal. One of the most important study findings to highlight is that there were no statistical differences in the way that income predicted literacy and numeracy trajectory membership for the majority of the migrant children in the study in comparison to non-migrant children. This was found consistently for family class and refugee class migrants and for a sub-group of economic class migrants. This finding suggests that the attenuated relationships between SES and the developmental outcomes for migrant children overall may be driven by one sub-group. This is an important caution for researchers studying the association between SES variables and developmental outcomes in migrant children: As we found in the current study, general associations between income and outcomes may be masking a good deal of variation amongst migrant children and important sub-group differences. To reiterate, understanding the complexity underlying this relationship is important because migrant children are far more likely to live in poverty – this was true not only in the current study, but also well-documented in Canada and in a number of other countries (Smeeding et al., 2012). The current study findings suggest that the assumption that migrant children are generally less impacted by poverty is inaccurate. Given this, strategies for poverty reduction for migrant families could have important pay-offs for low-income migrant children. Indeed, a sub-group of high-achieving economic class migrants did show an attenuated relationship between income and academic (literacy and numeracy) achievement over time. Coming back to Garcia Coll and colleague's (1996) concept of adaptive culture, the results are consistent with the notion that certain groups of migrant children will have a greater capacity to adapt to low income circumstances and maintain high academic scores from kindergarten to Grade 7. We expected to find that economic class migrant children would be more likely to adapt to low income circumstances and do well academically, given the potential collective cultural, political, and economic resources that this group may experience: The economic class is by far the largest migration class accepted into Canada (Statistics Canada, 2017), which means that many of the economic class migrant groups have large, established ethno-cultural communities in Canada. This is in contrast to refugee class groups, which represent a great diversity of ethno-cultural groups and who are often, in essence, the minority groups within the minority. There is some evidence of protective effects for immigrant children who live in communities with a higher concentration of other immigrants (Georgiades et al., 2007); higher concentrations of others with similar ethno-cultural and migration backgrounds are thought to bolster supportive community and family processes (García Coll and Szalacha, 2004; Sampson et al., 2002). This is also consistent with the work of Beiser and colleagues (2002) who found that many of the family vulnerability factors that typically mediate the relationship between poverty and child outcomes, such as family dysfunction and parental depression, were not found for migrant children. Future research that is able to account for contextual and family processes as well as migration class (economic, family, and refugee classes) will be important in order to better understand the underlying mechanisms whereby certain groups of migrants (or migration experiences) lead to the maintenance of positive family processes despite poverty – and whether this explains why some low-income migrant children (e.g., some economic class migrants) show positive outcomes whereas others do not. As economic class migrants are selected to enter Canada based upon particular labour market criteria such as education and skill levels, they are also likely to have additional socio-demographic advantages, which can help with the adaptation process, even in the presence of low-income circumstances. This explanation is in keeping with research conducted in the U.S., which has found that the socioeconomic status and social privileges that certain migrant groups experience prior to migration persist in their impact on children after migration, even when post-migration socioeconomic status declines (Pong and Landale, 2012). The findings from our second research question help to unpack this notion in more detail to understand what broader socio-demographic advantages certain migrants may have that help to predict their high academic achievement outcomes, despite low income circumstances post-migration. Indeed, pre-migration parental education and English language ability did contribute to the prediction of high academic trajectories despite low income, even after accounting for migration class. In keeping with expectations, parental level of education at migration helped to predict whether low-income migrant children would have high academic achievement over time. This is consistent with the work by Pong and Landale (2012), which found that pre-migration parental education was the strongest predictor of children's academic achievement - stronger than post-migration education and work status. The researchers argue that while higher parental education levels may not lead to higher occupational status after migration, it nevertheless equates to more human capital; in the form of knowledge and practical skills that are associated with rich cognitive home environments for children. Migrant parents with higher levels of education may also be in a better position to help their children navigate their schoolwork and the education system (Hernandez, 2012). Notably, the parental education levels varied in the current study. Providing opportunities and resources for migrant parents with more limited education experiences to learn about the education system may go a long way to help prepare their children for academic success. As suggested by Pong and 10

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

Landale (2012), this could include more education upgrading opportunities for migrant parents, such as adult education and literacy programs. English Language Learner status was negatively associated with high academic trajectory membership for low-income migrant children, suggesting that English language proficiency can act as a protective factor. This was expected given that English language proficiency has emerged as a strong predictor of academic achievement (Kim and Suarez-Orozco, 2015; Suarez-Orozco et al., 2010). Kim and Suarez-Orozco (2015) found that this relationship was partly explained by relational engagement (indirectly) and behavioral engagement (directly) at school. This suggests that migrant children who have a command of the English language have more opportunities to engage at school in a way that offers greater access to the supports and resources required to meet the demands and expectations of school; this may help to bolster their ability to do well academically, even in the face of low-income circumstances. Importantly, generation status (first-versus second-generation) did not contribute to predicting whether low-income migrant children would be members of the high academic achievement trajectory groups. Although second-generation migrant children may have had more time to adapt to mainstream culture, this may be combined with a loss of heritage culture and the associated protective effects (Schwartz and Montgomery, 2002). The extent to which migrant children participate in mainstream culture as well as their heritage cultural backgrounds may offer more insight into which first- and second-generation migrant children are able to buffer the effects of low income circumstances. Aside from this, it is also possible that generation status did not emerge as significant due to the study design. Given that the study followed children from kindergarten onwards, all children in the cohort were necessarily in Canada by the age of 5 (their kindergarten year), including those children who were born outside of Canada (i.e., the firstgeneration cohort). Given this, the first-generation children in the study may have had adaptation experiences that were reasonably similar to their second-generation peers and this may have contributed to an attenuated relationship. Even after accounting for additional socio-demographic factors, migration class remained a powerful predictor of high academic achievement over time for low-income migrant children. As expected, economic class migrants were far more likely to show high academic achievement despite low income, in comparison to both the family and refugee class groups. This underscores the fact that economic class migrant children likely have protective factors that are unaccounted for in the present study. One promising direction that would likely help to expand upon the current findings is the study of wealth or net worth (e.g., investments and homeownership). Researchers are beginning to highlight the heterogeneity in the economic circumstances of migrant families (Joo, 2013) and this includes a growing interest and acknowledgement that there are substantial variations in wealth (net worth) across migrant groups (Painter and Qian, 2016). Researchers are also starting to highlight the role of wealth as a useful indicator of family financial security and therefore an important predictor for child developmental outcomes (Blumenthal and Rothwell, 2018). Accounting for wealth discrepancies in future research may provide meaningfull insight as to why economic class migrant children in the current study were more likely to show better academic outcomes, despite low income. 6.1. Strengths and limitations Results of our study need to be interpreted in light of certain limitations. To start, income captures only one aspect of the socioeconomic status of children and there are other dimensions that may help to further explicate the reasons for which some migrant children seem to be less impacted by low income (wealth is one of example of this, as already discussed). Furthermore, our measure of family income relies on health insurance subsidy, a binary variable that lacks nuance and which carries important administrative data limitations. Notably, there is evidence to suggest that not all individuals who are eligible for health care subsidies (due to low income) receive them (Warburton, 2005). Given this, it may be that the number of low-income migrant children in the present study has been under-reported. The study focused on the academic domain of functioning as the outcome of interest. Academic achievement is indeed a valuable indicator of adaptation for young migrants – Boyd and Tian (2016) have recently found that academic achievement for those who migrate is associated with important long-term outcomes, such as greater gains in professional employment and higher earnings in the Canadian context. However, academic achievement is only one aspect of functioning and it does not fully capture the larger picture of functioning and adaptation for migrant children. Broader investigation into the impact of economic disadvantages on other important domains, such as the social and emotional functioning of migrant children, would add important insight and dimension to the current work. Furthermore, the study focused on economic, family, and refugee migration classes but it should be noted that these groups also comprise several sub-categories in and of themselves. Whereas the current study has contributed to unpacking some of the heterogeneity within the migrant children classification, it does not explain heterogeneity within each group and this is an important area of investigation in future research. That we were able to account for a number of socio-demographic factors associated with this relationship is a study strength as it offers policy-relevant insight into the factors that may help to protect against low income circumstances for migrant children. For example, children from migrant families who speak English and have higher levels of parental education appear to be in a better position to overcome obstacles associated with low income to achieve academically. In Canada, these factors are relatively common in migrant families because the Canadian immigration system considers both education levels and English language ability as part of the selection process (Citizenship and Immigration Canada, 2016). Still, many migrant children do not have highly educated parents or strong English language skills. Where the Canadian immigration system remains largely untested is in its ability to successfully integrate migrant children who are less likely to overcome their low income circumstances to achieve academic success. The protective factors identified in this study (parental education and English language ability) lend themselves to modification and therefore interventions that could be designed to place low income migrant children on a positive academic path. However, the current study only scratches the surface of our understanding of how to most effectively support low income 11

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

migrant children so they can achieve academically and more work is required to understand the individual, family, and community processes that drive the associations found. There are also other potential moderating factors that have not been accounted for in the present study but deserve attention. For example, it would be beneficial to better understand how within country transitions for migrant families (particularly transitions related to income/employment prospects) may play a role in the relationship between economic disadvantage and positive outcomes for migrant children. Lastly, there is complexity that remains to be understood with respect to how these protective factors may operate together and whether they have an additive or accumulative impact on academic achievement for low income migrant children. Certain protective factors may prove to be more powerful and more modifiable than others. It behoves us to study these complexities in more detail and with a highly practical lens to create the most effective, evidencebased interventions possible. Drawing on theory specifically related to the development of minority children, the study contributed a more nuanced understanding of the impact of low income circumstances on migrant children. We also found theoretical support for our hypotheses stemming from Garcia Coll et al. (1996) concept of adaptive culture. These are important steps in developing theories of child development that are inclusive and account for the development of all children, including children from minority groups. Nevertheless, our study findings could benefit from in-depth qualitative work to enrich our theoretical understanding of the role of poverty in the lives of migrant children and how family and community processes can offer insight into how some migrant children are able to cope and adapt in low income circumstances. Early childhood has long been considered a key period for reducing the impact of poverty on children (Brooks-Gunn and Duncan, 1997) and although migrant children disproportionately live in poverty, the specific role of poverty in the lives of migrant children has garnered little theoretical and empirical attention. The current study helps to fill this gap and provides a more nuanced understanding of the relationship between income and the developmental outcomes of migrant children. The results of the study provide insight into which groups of migrants will be most likely to struggle with rising above low income circumstances. Early interventions, which bolster English language ability and a greater understanding of what will be expected in a school context may prove helpful in offering a step up for low income migrant families to counteract some of the economic challenges they face, particularly for migrant sub-groups who may not be able to access well-established, culturally appropriate community resources and supports. Author note Monique Gagné, Martin Guhn, Magdalena Janus, Anne Gadermann, Constance Milbrath, Anita Minh, Barry Forer and Carly Magee, Human Early Learning Partnership, School of Population and Public Health, University of British Columbia, Vancouver, British Columbia; Magdalena Janus and Eric Duku, Offord Centre for Child Studies, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario; Nazeem Muhajarine, Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Saskatchewan; Anne Gadermann, Centre for Health Evaluation & Outcome Sciences (CHEOS) at the University of British Columbia. We gratefully acknowledge the Social Sciences and Humanities Research Council (SSHRC) of Canada for funding this research. The authors are also grateful for the support of the University of British Columbia and Population Data BC and they furthermore acknowledge the support of the data stewards (the Ministry of Health, the Ministry of Education, Immigration, Refugees, & Citizenship Canada, the Human Early Learning Partnership) who permitted access to the data in order to undertake the study. Please note that all inferences, opinions, and conclusions drawn in this study are those of the author, and do not reflect the opinions or policies of the data stewards. Study correspondence should be addressed to Monique Gagné, University of British Columbia. Email: [email protected]. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssresearch.2019.102344. References Archambault, I., Janosz, M., Dupéré, V., Brault, M.C., McAndrew, M.M., 2017. Individual, social, and family factors associated with high school dropout among low-SES youth: differential effects as a function of immigrant status. Br. J. Educ. Psychol. 87 (3), 456–477. https://doi.org/10.1111/bjep.12159. Beiser, M., Hou, F., Hyman, I., Tousignant, M., 2002. Poverty, family process, and the mental health of immigrant children in Canada. Am. J. Public Health 92, 220–227. https:// doi.org/10.2105/AJPH.92.2.220. Blumenthal, A., Rothwell, D.W., 2018. The measurement and description of child income and asset poverty in Canada. Child Indic. Res. 1–27. https://doi.org/10.1007/s12187017-9525-0. Boyd, M., Tian, S., 2016. Educational and labour market attainments of the 1.5- and second- generation children of East Asian immigrants in Canada. Am. Behav. Sci. 60 (56), 705–729. British Columbia Ministry of Health [creator], 2013. Consolidation Files (MSP Registration & Premium Billing). Population Data BC [publisher]. Data Extract. MOH (2013). http://www.popdata.bc.ca/data. Brooks-Gunn, J., Duncan, G.J., 1997. The effects of poverty on children. Future Child.: Child. Poverty 7, 55–71. Brooks-Gunn, J., Duncan, G.J., Maritato, N., 1997. Poor families, poor outcomes: the well-being of children and youth. In: Duncan, G.J., Brooks-Gunn, J. (Eds.), The Consequences of Growing Up Poor. Russell Sage Foundation, NY, pp. 1–17. Chen, J., Hetzner, N., Brooks-Gunn, J., 2010. Growing up in poverty in developed countries. In: second ed. In: Bremner, G., Wachs, T.D. (Eds.), Wiley-Blackwell Handbook of Infant Development, vol. 2. pp. 115–139. Citizenship and Immigration Canada, 2016. Glossary. Retrieved January 18, 2017 from. http://www.cic.gc.ca/english/helpcentre/glossary.asp#f. Crosnoe, R., Lopez Turley, R.N., 2011. K-12 educational outcomes of immigrant youth. Future Child. 21, 129–152. https://doi.org/10.1353/foc.2011.0008. Cummins, J., 1991. Language learning and bilingualism. Sophia Linguistica 29, 1–194.

12

Social Science Research xxx (xxxx) xxxx

M. Gagné, et al.

Davies, S., Janus, M., Duku, E., Gaskin, A., 2016. Using the Early Development Instrument to examine cognitive and non-cognitive school readiness and elementary student achievement. Early Child. Res. Q. 35, 63–75. https://doi.org/10.1016/j.ecresq.2015.10.002. Duncan, G.J., Brooks-Gunn, J., 1997. Consequences of Growing up Poor. Russell Sage Foundation, New York. Duncan, G.J., Ziol-Guest, K.M., Kalil, A., 2010. Early-childhood poverty and adult attainment, behavior, and health. Child Dev. 81 (1), 306–325. https://doi.org/10.1111/j.14678624.2009.01396.x. Frankfurt, S., Frazier, P., Syed, M., Jung, K.R., 2016. Using group-based trajectory and growth mixture modeling to identify classes of change trajectories. Counsel. Psychol. 44 (5), 622–660. Fuligni, A., 1997. The academic achievement of adolescents from immigrant families: the roles of family background, attitudes, and behavior. Child Dev. 68, 261–273. Fuligni, A., 1998. The adjustment of children from immigrant families. Curr. Dir. Psychol. Sci. 7, 99–103. https://doi.org/10.1111/1467-8721.ep10774731. Garcia Coll, C., Marks, A.K., 2009. Immigrant Stories: Ethnicity and Academics in Middle Childhood. Oxford University Press, New York. Garcia Coll, C., Marks, A.K., 2012. The Immigrant Paradox in Children and Adolescents: Is Becoming an American a Developmental Risk? American Psychological Association, Washington, DC. García Coll, C., Szalacha, L.A., 2004. The multiple contexts of middle childhood. Future Child. 14 (2), 81–97. Garcia Coll, C., Lamberty, G., Jenkins, R., McAdoo, H.P., Crnic, K., Wasik, B.H., Garcia, H.V., 1996. An integrative model for the study of developmental competencies in minority children. Child Dev. 67, 1891–1914. https://doi.org/10.2307/1131600. Georgiades, K., Boyle, M.H., Duku, E., 2007. Contextual influences on children's mental health and school performance: the moderating effects of family immigrant status. Child Dev. 78, 1572–1591. https://doi.org/10.1111/j.1467-8624.2007.01084.x. Green, D.A., Riddell, C.W., St-Hilaire, F., 2016. Income Inequality: the Canadian Story. Institute for Research on Public Policy, Montreal, Quebec. Guhn, M., Milbrath, C., Hertzman, C., 2016. Associations between child home language, gender, bilingualism and school readiness: a population-based study. Early Child. Res. Q. 35, 95–110. https://doi.org/10.1016/j.ecresq.2015.11.003. Guo, S., 2004. Responding to the Changing Needs of the Chinese Community in Vancouver: The Contribution of SUCCESS (1973-1998). http://mbc.metropolis.net/assets/ uploads/files/wp/2004/WP04-08.pdf Retrieved April 5, 2013, from. Harker, K., 2001. Immigrant generation, assimilation, and adolescent psychological well-being. Soc. Forces 79, 969–1004. https://doi.org/10.1353/sof.2001.0010. Hernandez, D.J., 2004. Demographic change and the life circumstances of immigrant families. Future Child. 14 (2), 17–47. https://doi.org/10.2307/1602792. Hernandez, D.J., 2012. Resources, strengths, and challenges for children in immigrant families in eight affluent countries. In: Masten, A.S., Liebkind, K., Hernandez, D.J. (Eds.), Realizing the Potential of Immigrant Youth. Cambridge University Press, NY, pp. 63–89. Hernandez, D.J., Darke, K., 1999. Socioeconomic and demographic risk factors and resources among children in immigrant and native-born families: 1910, 1960, and 1990. In: Hernandez, D.J. (Ed.), Children of Immigrants: Health, Adjustment, and Public Assistance. National Academies Press, Washington, DC, pp. 19–125. Hertzman, C., Boyce, T., 2010. How experience gets under the skin to create gradients in developmental health. Annu. Rev. Public Health 31 (1), 329–347. https://doi.org/10. 1146/annurev.publhealth.012809.103538. Human Early Learning Partnership [creator], 2014. Early Development Instrument. Population Data BC [publisher]. Data Extract. Human Early Learning Partnership, vol. 2013 University of British Columbia, School of Population and Public Health, Vancouver, BC. http://www.popdata.bc.ca/data. Immigration, Refugees, and Citizenship Canada, 2014. Immigration, Refugees, and Citizenship Canada (IRCC) Permanent Residents File. Retrieved from: http://www.popdata. bc.ca/data. Janus, M., Offord, D.R., 2007. Development and psychometric properties of the Early Development Instrument (EDI): a measure of children's school readiness. Can. J. Behav. Sci. 39 (1), 1. https://doi.org/10.1037/cjbs2007001. Jones, B.L., Nagin, D.S., 2007. Advances in group-based trajectory modeling and a SAS procedure for estimating them. Sociol. Methods Res. 35 (4), 542–571. https://doi.org/10. 1177/0049124106292364. Joo, M., 2013. Explaining heterogeneity in the child poverty rate among immigrant families: differences by parental citizenship. Child. Youth Serv. Rev. 35 (4), 668–677. https:// doi.org/10.1016/j.childyouth.2013.01.005. Keating, D.P., Hertzman, C., 1999. Modernity's paradox. In: Keating, D.P., Hertzman, C. (Eds.), Developmental Health and the Wealth of Nations: Social, Biological, and Educational Dynamics. Guilford Press, NY, pp. 1–18. Kim, H.Y., Suarez-Orozco, C., 2015. The language of learning: the academic engagement of newcomer immigrant youth. J. Res. Adolesc. 25 (2), 229–245. https://doi.org/10. 1111/jora.12130. Lloyd, J.E., Hertzman, C., 2009. From kindergarten readiness to fourth-grade assessment: longitudinal analysis with linked population data. Soc. Sci. Med. 68 (1), 111–123. https://doi.org/10.1016/j.socscimed.2008.09.063. Marks, A.K., Ejesi, K., Garcia Coll, C., 2014. Understanding the U.S. Immigrant paradox in childhood and adolescence. Child Develop. Perspect. 8 (2), 59–64. https://doi.org/10. 1111/cdep.12071. McAndrew, M., Ait-Said, R., Ledent, J., Murdoch, J., Anisef, P., Brown, R., Sweet, R., Walters, D., Aman, C., Garnett, B., 2009. Educational Pathways of Youth of Immigrant Origin: Comparing Montreal, Toronto, and Vancouver. Canadian Council on Learning. Milbrath, C., Guhn, M., 2019. Neighbourhood culture and immigrant children's developmental outcomes at kindergarten. Early Child. Res. Q. 48, 198–214. Ministry of Education, 2014. Ministry of Education. Edudata [publisher]. Data Extract. MED. https://www.edudata.ca/site/home/ 2012. Nagin, D., 2005. Group-based Modeling of Development. Harvard University Press, Cambridge, Massachusetts. Organization for Economic Co-operation and Development, 2011. Divided We Stand: Why Inequality Keeps Rising. OECD Publishing, Paris. Painter, M.I., Qian, Z., 2016. Wealth inequality among new immigrants. Sociol. Perspect. 59 (2), 368–394. Pong, S., Hao, L., 2007. Neighborhood and school factors in the school performance of immigrants children. Int. Migr. Rev. 41 (1), 206–241. https://doi.org/10.1111/j.17477379.2007.00062.x. Pong, S., Landale, N.S., 2012. Academic achievement of legal immigrants' children: the roles of parents' pre- and postmigration characteristics in origin-group differences. Child Dev. 83 (5), 1543–1559. https://doi.org/10.1111/j.1467-8624.2012.01790.x. Population Data BC, 2014. The data linkage process. Retrieved from: https://www.popdata.bc.ca/datalinkage/process, Accessed date: 30 August 2019. Sampson, R.J., Morenoff, J.D., Gannon-Rowley, T., 2002. Assessing “neighborhood effects”: social processes and new directions in research. Annu. Rev. Sociol. 28, 443–478. Schwartz, S.J., Montgomery, M.J., 2002. Similarities or differences in identity development? The impact of acculturation and gender on identity process and outcome. J. Youth Adolesc. 31, 359–372. Simich, L., Beiser, M., Stewart, M., Mwakarimba, E., 2005. Providing social support for immigrants and refugees in Canada: challenges and directions. J. Immigr. Health 7 (2005). https://doi.org/10.1007/s10903-005-5123-1. Smeeding, T.S., Robson, K., Wing, C., Gershuny, J., 2012. Income poverty and income support for minority and immigrant households with children in rich countries. In: Masten, A.S., Liebkind, K., Hernandez, D.J. (Eds.), Realizing the Potential of Immigrant Youth. Cambridge University Press, NY, pp. 63–89. Statistics Canada, 2011. Postal code conversion file (PCCF; catalogue 92F0153GIE). Retrieved July 1, 2011 from. http://www5.statcan.gc.ca/bsolc/olc-cel/olc-cel?lang=eng& catno=92F0153X. Statistics Canada, 2016. Classification of admission category of immigrant. Retrieved June 26, 2018 from. http://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD& TVD=323293&CVD=323294&CLV=0&MLV=4&D=1. Statistics Canada, 2017. Annual report to parliament on immigration. https://www.canada.ca/en/immigration-refugees-citizenship/corporate/publications-manuals/annualreport-parliament-immigration-2017 2017 Retrieved June 27, 2018 from. Stewart, J., 2011. Supporting Refugee Children: Strategies for Educators. University of Toronto Press. Suarez-Orozco, C., Rhodes, J., Milburn, M., 2009. Unraveling the immigrant paradox: academic engagement and disengagement among recently arrived immigrant youth. Youth Soc. 41, 151–185. https://doi.org/10.1177/0044118X09333647. Suarez-Orozco, C., Gaytan, F.X., Bang, H.J., Pakes, J., O'Connor, E., Rhodes, J., 2010. Academic trajectories of newcomer immigrant youth. Dev. Psychol. 46, 602–618. https:// doi.org/10.1037/a0018201. Tsai, H.-S.,J., 2006. Xenophobia, ethnic community, and immigrant youths' friendship network formation. Adolescence 41, 285–298. United National High Commission for Refugees, 2017. Global Trends – Forced Displacement in 2016. UNHCR, Geneva. Warburton, R.N., 2005. Takeup of income-tested health-care premium subsidies: evidence and remedies for British Columbia. Can. Tax J. 53 (1), 1. Zhou, M., 1997. Growing up American: the challenge confronting immigrant children and children of immigrants. Annu. Rev. Sociol. 23, 63–95. https://doi.org/10.1146/ annurev.soc.23.1.63.

13