Educational Expectations of Immigrant Students: Does Tracking Matter?

Abstract

Immigrants are known to have high expectations to matriculate into college and achieve a college degree. Yet the majority of the studies that examine the educational expectations of immigrant youth focus only on one country. Furthermore, researchers have not yet examined whether the high educational expectations of immigrants are promoted or hampered by the characteristics of educational systems in immigrants’ host countries. This paper examines the relationship between one such feature, tracking, and the educational expectations of immigrant youth in Europe. It shows that cross-nationally, immigrant students have higher educational expectations than nonimmigrant youth. However, for first-generation immigrants, this advantage is not as pronounced in tracked systems as compared with nontracked systems. This suggests that immigrants and nonimmigrants respond differently to the educational contexts that they encounter and that certain features of educational systems can stymie immigrant advancement.

Keywords

education educational systems immigration

Introduction

There are many factors that either exacerbate or alleviate inequality between immigrants and nonimmigrants. More specifically, in trying to explain the disparities between immigrant and nonimmigrant populations, researchers have analyzed how the welfare state (Filindra, Blanding, and Coll 2011; Fossati 2011; Morissens and Sainsbury 2005) and policies that intend to promote integration (Filindra et al. 2011; Fossati 2011; Manatschal and Stadelmann-Steffen 2013) influence immigrant advancement. They have also examined how the educational systems in immigrants’ host countries might shape their schooling outcomes (Alba and Silberman 2009; Borgna and Contini 2014; Crul and Vermeulen 2003; Dronkers, Levels, and de Heus 2014; Schneeweis 2011).

In fact, research on how the institutional features of educational systems influence immigrant student achievement and attainment is an established strand of the literature. This literature typically focuses on examining the effects of preschool and early compulsory schooling (Borgna and Contini 2014; Schneeweis 2011), as well as school programming specifically designed to help immigrants integrate (Crul and Vermeulen 2003; Gandara and Hopkins 2010). Another prominent factor in this literature is whether the host country features an educational system that separates students in different tracks during their progression through schooling (Alba and Silberman 2009; Borgna and Contini 2014; Dronkers et al. 2014). These studies typically examine the effect of tracking on either student achievement or the socioeconomic gradient in achievement and how these effects vary according to immigrant background. Much less attention has been paid to how the characteristics of educational systems might affect the educational plans of immigrant students and their expectations to achieve a college degree.

This line of inquiry, however, is very important. One reason why is because college graduates enjoy higher salaries than those who have not attained this credential (Goldin and Katz 2007; Haveman and Smeeding 2006). They also tend to have better health (Easterbrook, Kuppens, and Manstead 2016; Grossman 2006) and be more politically engaged (Easterbrook et al. 2016; Emler and Frazer 1999). Aspiring to and expecting to achieve a college degree is a necessary precursor to eventually attaining one. As the educational expectations of immigrants are linked to their educational attainment, this means that understanding the factors that influence the development of these expectations is extremely important.

This paper examines how one characteristic of educational systems—tracking—might affect the educational expectations of immigrant students. More specifically, it provides cross-national evidence that first- and second-generation immigrant students have higher educational expectations than nonimmigrant youth. This advantage, however, is reduced for first-generation immigrants in tracked systems. I argue that this reduction happens because immigrant families lack sufficient knowledge about how tracking works. By showing that tracking can be a vehicle for attenuating the educational expectations of first-generation immigrants, this paper provides additional evidence that educational institutions can have perverse effects on immigrant students.

Theory

Educational Expectations of Immigrant Youth

The expectations of immigrants to complete a college degree have been the focus of numerous studies. The most consistent finding in this literature is that immigrant youth generally have higher educational expectations than their nonimmigrant peers, holding their achievement and socioeconomic background constant (Brinbaum and Cebolla-Boado 2007; Feliciano and Lanuza 2016; Goyette and Xie 1999; Hao and Bonstead-Bruns 1998; Krahn and Taylor 2005; Portes and Rivas 2011). One possible reason for this gap is that immigrant students come from families that possess positive characteristics that nonimmigrant families do not. One such characteristic might be that immigrant parents have high levels of optimism for their children’s prospects in their new countries (Kao and Tienda 1995; Tjaden and Hunkler 2017). Other important factors might include immigrants’ higher motivation to better their fortunes—often referred to as “immigrant drive”—and their “grit” (Portes and Rumbaut 2001). Immigrant parents might be able to transmit these characteristics to their children, who as a result have an array of positive dispositions, including increased expectations to achieve a college degree (Kao and Tienda 1995).

The majority of studies that examine the educational expectations of immigrant youth focus on the United States (Feliciano and Lanuza 2016; Goyette and Xie 1999; Hao and Bonstead-Bruns 1998; Portes and Rivas 2011). Studies that examine educational expectations outside of the United States generally focus only on one country, such as Austria (Pásztor 2016), Canada (Krahn and Taylor 2005), France (Brinbaum and Cebolla-Boado 2007), the Netherlands (Pásztor 2009), or Spain (Portes et al. 2010). These studies typically find empirical support for the idea that immigrant students have higher educational expectations than nonimmigrant youth. One potential limitation with these findings, though, is that we do not know whether this relationship exists across a wider range of countries. This is important if we are interested in the generalizability of these findings.

Prior studies also largely disregard the observed tendency for immigrants to take on the values of natives as they assimilate (Waters and Pineau 2015). This means that the parents of second-generation youth are likely to be more similar to nonimmigrant parents than the parents of first-generation youth. If a first-generation and a second-generation student are of the same age, the parents of the latter have stayed in their host country longer. One empirical consequence of this is that the qualities that researchers expect to drive the increased educational expectations of immigrant youth should be more pronounced in the parents of first-generation immigrants than in the parents of second-generation immigrants. This line of argument leads me to expect that immigrant students will have higher educational expectations than nonimmigrant students, and that first-generation immigrants will experience a stronger positive inflation of educational expectations than second-generation immigrants. I outline this theoretical claim in the Immigrant Expectations Hypothesis.

Immigrant Expectations Hypothesis: Immigrant students will have higher educational expectations than nonimmigrant students. This immigrant gain will be higher for first-generation students than for second-generation students.

Tracking and Educational Expectations of Native and Immigrant Youth

Educational systems vary in the academic pathways that they provide to students. One way in which educational systems differ is the degree to which they provide differentiated curriculum and instruction for different student groups. Differentiation, broadly defined, is a mechanism that distributes students with certain abilities or career preferences to courses or programs that are expected to fit their preferences and aptitudes (Ainsworth and Roscigno 2005; Sorensen 1970). G. K. LeTendre, B. K. Hofer, and H. Shimizu (2003) define five mechanisms for how this differentiation occurs: (1) by school type, (2) by course of study, (3) by stream, (4) by ability grouping, and (5) by geographic location. When educational systems use the first two mechanisms, students are assigned to either a distinct type of school (e.g., academically oriented vs. vocationally oriented) or a distinct type of program (e.g., technical course of study within one school), respectively. In both cases, some tracks are more competitive to get into than others and movement between tracks is extremely limited (Dupriez, Dumay, and Vause 2008). Differentiation by stream occurs when students are assigned to lower or higher level classes, such as college preparatory classes, on a course-by-course basis (Chmielewski 2014). Ability grouping occurs when students work within higher and lower ability groups within one classroom (LeTendre et al. 2003). Differentiation by geographic location occurs when there are differences in curricular offerings and opportunity to learn based on where the school is located (LeTendre et al. 2003). In this paper, I focus on differentiation by distinct school programs and school types. Although scholars oftentimes use the term “tracking” to refer to any of the five types of differentiation mentioned above, I use the term “tracking” to only refer to differentiation by distinct school programs and school types. In line with that, I also empirically examine only these two types of differentiation. This is because scholars have argued that the difficulty of moving between tracks, which is unique to the types of differentiation that I examine, is more detrimental to student outcomes than other types of differentiation, such as ability grouping within classrooms (Hallinan 1994; Stadelmann-Steffen 2012). The lack of possible movement across tracks can erode student self-efficacy and lead students to think that their postsecondary outcomes have been predetermined (Buchmann and Park 2009). My focus on this type of differentiation follows an established tradition in comparative education (Bodovski et al. 2017; McDaniel 2010; Pfeffer 2008; Schlicht, Stadelmann-Steffen, and Freitag 2010).

Although there is a large research program that shows how tracking can undermine student achievement and equality of educational opportunity (Bol et al. 2014; Burger 2016; Lavrijsen and Nicaise 2015; Schlicht et al. 2010; Stadelmann-Steffen 2012), only a handful of studies have examined how tracking might affect student expectations to matriculate into college and attain a college degree (Buchmann and Dalton 2002; Buchmann and Park 2009; Lee 2014; McDaniel 2010; Pásztor 2009, 2016). B. Lee (2014) shows that tracking has a negative effect on the educational expectations of all students and also finds that once students are placed on either the academic track or vocational track, their educational expectations do not differ along socioeconomic lines. C. Buchmann and H. Park (2009) show that socioeconomic status is a much stronger predictor of educational expectations in tracked systems than in comprehensive systems. A. McDaniel (2010) uses data from 29 Organisation for Economic Co-operation and Development (OECD) countries and finds that although tracking does not affect female students differently than male students, it has a generally negative effect on the educational expectations for all students.

This negative relationship, however, might be conditional on the immigrant status of the students. A. Pásztor (2009, 2016), in qualitative studies of students in the Netherlands and Austria, provides some initial evidence that this is the case. More specifically, the author discusses the interplay between choosing different tracks, student ethnicity, and the socioeconomic status of student families and examines how this interplay conditions immigrants’ thoughts about college matriculation (Pásztor 2009, 2016). In line with this, I theorize that the relationship between tracking and educational expectations will differ for immigrant and nonimmigrant students.

More specifically, tracking might be more detrimental to immigrant students as compared with native-born students. This occurs because immigrant students and their parents are less likely than native-born students and their parents to be familiar with the educational system of their new country, in general, and with tracking systems in particular (Alba, Sloan, and Sperling 2011; Barban and White 2011; Goldenberg et al. 2001; Rosenbaum and Rochford 2008). In fact, E. Rosenbaum and J. A. Rochford (2008) and C. Goldenberg et al. (2001) theorize that this lack of parental knowledge might inadvertently hamper the educational careers of immigrant students. There is some evidence that immigrant students whose parents are not knowledgeable in how the school system works might enroll in lower tracks or less academically oriented schools because they or their parents fear failure in academic schools, even if their school achievement indicates that they should succeed (Pásztor 2009; Sattin-Bajaj 2014; Yonezawa 1999). As such, prior studies have shown that a lack of knowledge about how educational systems work does not result in immigrants painting a rosy picture of their opportunities in school; instead, it results in immigrant parents and students overestimating how hard it would be to move through academic tracks of formal schooling. Furthermore, even if placed in higher tracks, immigrants often report being silenced in their classes, and feeling uncomfortable about speaking up (Gibson and Carrasco 2009).

Also, as I outline above, scholars have theorized that the higher educational expectations of recent immigrants can be partly explained by the more positive outlook and stronger work ethic of immigrant parents (Kao and Tienda 1995; Portes and Rumbaut 2001). C. Buchmann and B. Dalton (2002), however, show that while the attitudes of friends and family are highly important to the formation of educational expectations in more comprehensive educational systems with weak tracking, they are unimportant in educational systems with strong tracking. This happens because in more comprehensive systems, parents have a stronger ability to steer student aspirations as the educational system itself imposes few constraints on the postsecondary educational options of students. As “immigrant optimism” has been theorized to be an essential factor leading to higher educational expectations for immigrant students, immigrants’ educational expectations might be attenuated more by the presence of rigid tracking as compared with nonimmigrants, as immigrant parents might not be able to positively influence their children’s expectations as much.

Although a decrease in educational expectations is possible due to the factors outlined above, it is also possible that this attenuation will be stronger for first-generation immigrants than for second-generation immigrants. This is because as immigrant parents stay in their new countries for a longer period of time, they learn more about how tracking works. Furthermore, they likely face less of a language barrier that could hamper their ability to become engaged in schools and thus learn more about tracking (Terriquez 2012). They also might have stronger networks to count on if they need help navigating the school system. As such, I hypothesize that tracking will negatively influence immigrants’ educational expectations to a greater degree than it influences nonimmigrants’ educational expectations. This decrease in educational expectations should be greater for first-generation immigrants than for second-generation immigrants.

Conditional Tracking Hypothesis: The positive relationship between immigrant status and educational expectations will be weaker in countries that have a tracked educational system. First-generation immigrants will experience a stronger decline in educational expectations than second-generation immigrants.

Data

To test my hypotheses, I use multiple sources of data. All individual-level variables come from the Program for International Student Assessment (PISA) 2003.¹ PISA is a survey of 15-year-old students in approximately 70 countries and economies conducted by the OECD. In my sample, I include students from 11 European immigration countries: Austria, Belgium, France, Germany, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and United Kingdom (Bauer, Lofstrom, and Zimmermann 2001; de la Rica, Glitz, and Ortega 2013; Holzmann and Munz 2004). I focus only on European immigration countries as these countries are more likely to have mechanisms and policies in place that regulate immigrant integration, thus making immigrant experiences within their host countries more homogeneous. Also, these countries have a sufficient stock of immigrants to allow for robust statistical analyses. Below, I outline how I construct my variables of interest, as well as their data sources.

Dependent Variable

The outcome variable is Educational Expectations for a college degree or more. It takes a value of 1 if students indicate that they expect to complete a four-year college degree or more and 0 otherwise.

Independent Variables

I have three primary independent variables: First-generation student, Second-generation student, and Tracking. First-generation immigrant status is coded 1 for students who were born outside of their host country and 0 if otherwise; Second-generation immigrant status is coded 1 for students who were born inside their host country but have at least one parent who was born outside of the host country and 0 if otherwise. As I include these variables in my empirical models, the reference category for my results is students who were born inside their respective analyzed country and whose parents were born there as well. I refer to these students as nonimmigrant students.

Tracking is coded 1 for a country where students are tracked into separate distinct educational programs or school types before the age of 16 years, and 0 for more comprehensive systems where students are not tracked into such programs or schools during their progression through compulsory schooling. In employing this approach to measuring tracking, I follow E. A. Hanushek and L. Woessmann (2006) and M. Jakubowski (2010). For example, I code Germany as 1 because after elementary school (or in some cases during lower secondary schooling), the most academically able students in Germany are tracked into Gymnasium; others are tracked into Realschule, which is less academic and more vocational; and yet others are tracked into Hauptschule, which is the most vocational and least academic track (Hillmert and Jacob 2010; Muhlenweg 2007). The curriculum that students are exposed to in each of these tracks varies widely, and while movement between tracks is possible, it is very hard due to the differences in curriculum that these students are taught (Hillmert and Jacob 2010; Muhlenweg 2007). Sweden, on the contrary, is coded as 0 because in this country, students progress through schooling without being tracked into distinct program types and schools (Hanushek and Woessmann 2006; Jakubowski 2010). Table 1 details whether each country features a tracked educational system.

Table 1.

Tracking in Educational Systems.

Country	Tracking
Norway	0
Spain	0
Sweden	0
United Kingdom	0
Austria	1
Belgium	1
France	1
Germany	1
Ireland	1
Italy	1
The Netherlands	1

Note. The table displays how Tracking varies across the countries in my sample. These data are collected from Learning for Tomorrow’s World: First Results from PISA 2003 (Organisation for Economic Co-operation and Development 2004), United Nations Educational, Scientific and Cultural Organization’s (UNESCO 2007) World Data on Education, and M. Jakubowski (2010).

I collect information on the number of distinct programs and distinct school types available to students within each country and the age when tracking begins from Learning for Tomorrow’s World: First Results from PISA 2003 (OECD 2004). For France, I obtain additional information from UNESCO’s (2007) World Data on Education and from Jakubowski (2010).

Control Variables

Previous studies have pointed to numerous factors that affect the educational outcomes of immigrant students as well as the relationship between tracking and the educational outcomes of immigrants. In line with this research, I include controls for gender (Feliciano and Rumbaut 2005), prior achievement (Goyette and Xie 1999), parental education (Haller and Portes 1973; Lee and Zhou 2015), parental occupation (Haller and Portes 1973; Hao and Bonstead-Bruns 1998), and student family structure (Hampden-Thompson 2009; Hao and Bonstead-Bruns 1998). I also control for the type of school program that the student attends (Dronkers, Van der Velden, and Dunne 2012). At the country level, I control for gross domestic product (GDP) and the level of unemployment (Clark 2011), as well as the degree to which government policies are welcoming to immigrants (Manatschal and Stadelmann-Steffen 2013).

Regarding the measurement of control variables, Female is coded 1 for females and 0 for males. To measure Achievement, I combine each student’s math and reading scores across all five plausible values² for each subject. When constructing this measure, I follow Buchmann and Park (2009), M. Levels, J. Dronkers, and G. Kraaykamp (2008), and H. Park and S. Y. Byun (2015). Two-parent Family is coded 1 for students who live with a biological father and mother, and 0 for all other family arrangements. I measure Parental Education as the highest number of years of education completed by either parent. Parental Occupation is measured by the highest level of occupational status attained by a parent on the ISEI (International Socio-economic Index) scale (Ganzeboom, De Graaf, and Treiman 1992); as with parental education, if the occupational status is provided for both parents, the higher status is used. Program Designation denotes the type of study program that a student is currently enrolled in. It ranges from 1 to 3, where 1 indicates general programs that provide students with access to the next program level, 2 indicates programs that provide students with access to the next level of vocational studies, and 3 indicates programs that designate students to exit directly into the labor market upon completion.³ Should the vast majority of immigrant students in my sample be tracked into lower program designations, it might mean that the effects that I observe between tracking and the immigrant status of students are driven by the possibility that most immigrant students in my sample are segregated in lower program designations. Table A1 in the appendix demonstrates that such segregation does not occur.

At the country level, I control for the national GDP, the national unemployment rate as a percent of total labor force, and a nation’s position on the Multiculturalism Policy Index (MPI) constructed by E. Tolley (2011). I collect measures of GDP per capita and Unemployment from the World Bank DataBank. I use the 2002 estimates for both variables to ensure that economic performance and unemployment are measured prior to educational expectations. MPI is a summative index of whether a country has multicultural policies in place. It is used to capture the policy environment wherein immigrants reside. The policies included in the index range from affirmative action for disadvantaged immigrant groups to state funding of bilingual education or mother-tongue instruction (Tolley 2011). I use estimates for 2000. The descriptive statistics for all variables are provided in Table 2.

Table 2.

Descriptive Statistics.

Variable	M	SD	Minimum	Maximum
Educational expectations	0.389	0.487	0	1
Immigrant background
First-generation	0.049	0.217	0	1
Second-generation	0.102	0.302	0	1
Female	0.504	0.500	0	1
Achievement	506.867	87.238	74.404	770.920
Parental education	12.821	3.242	0	17
Parental occupation	48.898	16.557	16	90
Two-parent Family	0.750	0.433	0	1
Program designation
Next level of vocational studies	0.061	0.239	0	1
Exit into the labor market	0.179	0.383	0	1
Tracking	0.591	0.492	0	1
GDP	25,771.210	59,90.971	17,019.500	43,061.200
Unemployment	7.206	2.762	2.600	11.600
MPI	2.457	1.611	0	5

Note. The table displays descriptive statistics for the countries in my sample. GDP = gross domestic product; MPI = Multiculturalism Policy Index.

Analyses and Results

I test my hypotheses using multilevel modeling to take advantage of the nested structure of my data (i.e., students, the level-1 units, are nested within schools, the level-2 units, and within countries, the level-3 units). I include random intercepts at the school and at the country levels. This type of modeling has two primary advantages: (1) It allows me to account for dependence among error term components and (2) it allows me to control for unobserved heterogeneity (Gelman and Hill 2006; Raudenbush and Bryk 2002).

Given that my outcome measure is a binary variable and follows a Bernoulli distribution, I estimate a random effects logistic regression model (Wooldridge 2010). The probability model is Prob(Educational Expectations = 1|βj) = φ (Raudenbush and Bryk 2002). The logit model uses a logistic link function, g′(η) = log(φ / (1 − φ)), which transforms any outcome variable bounded on [0, 1] into an unbounded variable that ranges from [−∞, ∞] (Wooldridge 2010). The results of the analyses are presented in Table 3. Model 1 tests Immigrant Expectations Hypothesis and model 2 tests Conditional Tracking Hypothesis.

Table 3.

The Estimated Relationships between Tracking, First-generation and Second-generation Immigrant Status, and Educational Expectations.

Dependent variable: Educational expectations	Model 1		Model 2
Dependent variable: Educational expectations	Coefficient	OR^a	Coefficient	OR^b
Immigrant background (reference category: Nonimmigrant Students)
First-generation	0.843***	2.323	1.078***	2.939
First-generation	(0.054)		(0.084)
Second-generation	0.453***	1.573	0.492***	1.636
Second-generation	(0.036)^c		(0.057)^d
Tracking	−0.569		−0.546
Tracking	(0.537)		(0.536)
First-generation × Tracking			−0.397***	0.672
First-generation × Tracking			(0.109)
Second-generation × Tracking			−0.067
Second-generation × Tracking			(0.074)^e
Achievement	0.013***	1.013	0.013***	1.013
Achievement	(0.0002)		(0.0002)
Female	0.465***	1.592	0.465***	1.592
Female	(0.022)		(0.022)
Parental education	0.101***	1.106	0.101***	1.106
Parental education	(0.004)		(0.004)
Parental occupation	0.015***	1.015	0.015***	1.015
Parental occupation	(0.001)		(0.001)
Two-parent family	0.120***	1.127	0.120***	1.127
Two-parent family	(0.025)		(0.025)
Program designation (reference category: Next General Program Level)
Next level of vocational studies	−1.515***	0.220	−1.512***	0.220
Next level of vocational studies	(0.113)		(0.113)
Exit into the labor market	−0.540***	0.583	−0.538***	0.584
Exit into the labor market	(0.074)		(0.074)
GDP	−0.0001*	0.9999	−0.0001*	0.9999
GDP	(0.00007)		(0.00007)
Unemployment	−0.191		−0.192
Unemployment	(0.157)		(0.156)
MPI	−0.129		−0.131
	(0.168)		(0.168)
			(0.0736)
Constant	−4.598		−4.590
Constant	(3.224)		(3.216)
Between-school variance component	0.519		0.517
Between-school variance component	(0.025)		(0.025)
Between-country variance component	0.523		0.521
Between-country variance component	(0.225)		(0.225)
Number of students	62,738		62,738
Number of schools	2,662		2,662
Number of countries	11		11

Note. The table displays the results of two three-level models that estimate the relationship between Tracking, First-generation and Second-generation immigrant status, and Educational Expectations. Random intercepts are used at the school and country levels. Standard errors in parentheses. OR = odds ratio; GDP = gross domestic product; MPI = Multiculturalism Policy Index.

Only statistically significant odds ratios are reported.

I conduct a Wald test to examine whether there is statistically significant difference between the First-generation and Second-generation coefficients in model 1. The coefficients are statistically different (χ² = 40.28, p < .001).

I conduct a Wald test to examine whether there is statistically significant difference between the First-generation and Second-generation coefficients in model 2. The coefficients are statistically different (χ² = 36.71, p < .001).

I conduct a Wald test to examine whether there is statistically significant difference between the First-generation × Tracking and Second-generation × Tracking coefficients in model 2. The coefficients are statistically different (χ² = 6.95, p < .01). A. Gelman and H. Stern (2006) discuss why a formal test between a statistically significant coefficient and a nonstatistically significant coefficient still needs to be conducted to determine whether the two coefficients are statistically distinct.

p < .1. **p < .05. ***p < .01, two-tailed.

In model 1, the coefficients of interest are First-generation and Second-generation. These coefficients are positive and statistically significant. Holding other factors constant, being a first-generation immigrant is associated with a 132 percent increase in the likelihood that a student expects to achieve a college degree, and being a second-generation immigrant is associated with a 57 percent increase in such likelihood. This is in line with my Immigrant Expectations Hypothesis.

I also conduct a Wald test to examine whether the coefficients for First-generation and Second-generation in model 1 are different from each other. The Wald test shows that the difference is statistically significant (χ² = 40.28, p < .001). This indicates that first-generation immigrants experience a higher gain in educational expectations than second-generation immigrants. This provides additional evidence for my Immigrant Expectations Hypothesis.

I test the Conditional Tracking Hypothesis in model 2. Here, I am interested in the coefficient for two cross-level interactions: First-generation × Tracking and Second-generation × Tracking. The first interaction term is statistically significant but the second one is not. Model 2 shows that a first-generation immigrant in an untracked system is 194 percent more likely to expect to attain a college degree than a nonimmigrant student. In a tracked system, however, the odds decrease to a 98 percent advantage.⁴ However, the decrease that second-generation immigrants experience is not statistically significant. This relationship is depicted in Figure 1.

Figure 1.

A marginal effects plot that shows how the association between tracking and educational expectations changes under different immigrant status conditions.

In sum, the positive association between immigrant background and educational expectations is attenuated for first-generation immigrants in tracked systems but not for second-generation immigrants. As such, I find partial support for my Conditional Tracking Hypothesis.

Furthermore, while my theory does not focus on the relationships between educational expectations and the control variables that I include in my models, it is worth noting that being a female, having higher achievement scores, having parents that are more educated and hold more prestigious jobs, as well as coming from a family where a student lives with two biological parents, are all associated with an increase in educational expectations. At the same time, attending school programs that lead to the next level of vocational studies or expect students to exit directly into the labor market are associated with a decrease in educational expectations. The results also indicate that GDP is negatively associated with educational expectations; this association, however, is substantively trivial.⁵

Another interesting question is whether there is variation in how immigrant students react to tracking depending on their gender, the education of their parents, and the type of school program they attend. In analyses not presented here,⁶ I subset my data only to immigrant students (both first- and second-generation students) and estimate additional models that include the following interaction terms: Female × Immigrant Background⁷ × Tracking; Parental Education × Immigrant Background × Tracking; and Program Designation × Immigrant Background in tracked countries.⁸ I find that none of these interactions are significant. In other words, the marginal effect of tracking on educational expectations of first- and second-generation students does not differ by student gender and parental education. Furthermore, the associations between first- and second-generation status and educational expectations do not vary depending on the program type that students attend.

Discussion

This paper examines the relationship between tracking and the educational expectations of immigrant and nonimmigrant students in Europe. It shows that both first- and second-generation students expect to achieve a college degree at a higher rate than nonimmigrant students. This finding is encouraging because expectations to achieve a college degree are generally regarded as a positive indicator for eventual degree attainment (Jacob and Wilder 2010). However, this paper also shows that first-generation immigrants tend to moderate their educational expectations in tracked educational systems, while second-generation and nonimmigrant students do not. I argue that the moderation of educational expectations results from it being harder for immigrant parents to navigate their child through tracked systems due to a lack of knowledge about how tracking works. The lack of a finding for second-generation students can then be attributed to the fact that this information asymmetry is likely more pronounced in the families of first-generation students than of second-generation students.

The findings of this paper also extend existing scholarship on how educational institutions serve to promote or hamper immigrants’ opportunities to realize their potential (Alba and Holdaway 2013; Crul et al. 2012; Dronkers et al. 2014). Although the finding that the educational expectations of immigrants are higher than those of nonimmigrants is reassuring, the fact that first-generation students have lower educational expectations in tracked systems speaks to the literature on how educational institutions might serve to marginalize minority youth (Delpit 2012; Faist 1993; Gonzales 2010; Noden, Shiner, and Modood 2014). This is especially problematic given the characteristics of immigrants currently arriving to Europe. As compared with immigrants who arrived in the previous decade, recent immigrants to Europe are less likely to be proficient in the language of their host country, possess an understanding of local customs and standards, and have educational qualifications that are recognized in their host country (OECD 2016). The net result of all these differences is that entry into the job market is more difficult for these immigrants (OECD 2016). In addition, many have also experienced emotional trauma from violence and deprivation in their homelands and on the way to Europe (Global Migration Data Analysis Center 2016). Due to these disadvantages, recent immigrant parents are perhaps worse suited to figure out the educational systems of their new host countries than those who arrived in the previous decade. This means that for current immigrants, the negative effects of tracking are likely to be stronger and harder to overcome.

This paper also provides the first cross-national test of how educational systems might influence the educational expectations of immigrant students. At a time when obtaining a college degree is associated with increased earnings, better health, and a stronger concept of self (Baker 2014), making sure that immigrants who achieve at the levels that enable them to matriculate into college do so is of utmost importance. Furthermore, anti-immigrant sentiment is often fueled by a perception that immigrants do not wish to assimilate into their host countries and that they overburden the welfare state (Feagin 1997; Schneider 2008). More educated immigrants are ostensibly at a lower likelihood of being perceived this way. By hampering first-generation immigrants’ educational expectations and thus their educational attainment, tracking might indirectly contribute to a strengthening of the anti-immigrant sentiment. In light of these potential negative outcomes, this paper adds an additional piece of evidence in favor of the OECD’s long-standing recommendation that countries should scale down tracking practices (OECD 2012).

Footnotes

Appendix

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Volha Chykina

Author Biography

Volha Chykina is a postdoctoral fellow at the Donia Human Rights Center at the University of Michigan. Her research interests include comparative and international education and education of immigrant and minority children. In her research, she examines how the characteristics of educational systems influence educational expectations, achievement, and attainment across countries. She is also interested in how immigrant and minority students’ educational outcomes are shaped by the characteristics of their communities.

References

Ainsworth

J. W.

Roscigno

V. J.

2005. “Stratification, School-work Linkages and Vocational Education.” Social Forces 84(1):257–84.

Alba

J. Holdaway. eds. 2013. The Children of Immigrants at School: A Comparative Look at Integration in the United States and Western Europe. New York: New York University Press.

Alba

Silberman

2009. “The Children of Immigrants and Host-society Educational Systems: Mexicans in the United States and North Africans in France.” Teachers College Record 111(6):1444–75.

Alba

Sloan

Sperling

2011. “The Integration Imperative: The Children of Low-status Immigrants in the Schools of Wealthy Societies.” Annual Review of Sociology 37:395–415.

Baker

2014. The Schooled Society: The Educational Transformation of Global Culture. Stanford, CA: Stanford University Press.

Barban

White

M. J.

2011. “Immigrants’ Children’s Transition to Secondary School in Italy.” International Migration Review 45(3):702–26.

Bauer

T. K.

Lofstrom

Zimmermann

K. F.

2001. Immigration Policy, Assimilation of Immigrants and Natives’ Sentiments towards Immigrants: Evidence from 12 OECD-countries. Working Paper No. 33. Retrieved January 24, 2019 (http://bit.ly/1NE3i5r).

Bodovski

Byun

S. Y.

Chykina

Chung

H. J.

2017. “Searching for the Golden Model of Education: Cross-national Analysis of Math Achievement.” Compare: A Journal of Comparative and International Education 47:722–41. Retrieved January 24, 2019 (https://www-tandfonline-com.web.bisu.edu.cn/doi/abs/10.1080/03057925.2016.1274881).

Bol

Witschge

Van de Werfhorst

H. G.

Dronkers

2014. “Curricular Tracking and Central Examinations: Counterbalancing the Impact of Social Background on Student Achievement in 36 Countries.” Social Forces 92(4):1545–72.

10.

Borgna

Contini

2014. “Migrant Achievement Penalties in Western Europe: Do Educational Systems Matter?” European Sociological Review 30(5):670–83.

11.

Brinbaum

Cebolla-Boado

2007. “The School Careers of Ethnic Minority Youth in France: Success or Disillusion?” Ethnicities 7(3):445–74.

12.

Buchmann

Dalton

2002. “Interpersonal Influences and Educational Aspirations in 12 Countries: The Importance of Institutional Context.” Sociology of Education 75(2):99–122.

13.

Buchmann

Park

2009. “Stratification and the Formation of Expectations in Highly Differentiated Educational Systems.” Research in Social Stratification and Mobility 27(4):245–67.

14.

Burger

2016. “Intergenerational Transmission of Education in Europe: Do More Comprehensive Education Systems Reduce Social Gradients in Student Achievement?” Research in Social Stratification and Mobility 44:54–67.

15.

Cebolla-Boado

Radl

Salazar

2016. “Educational Disadvantage in a Changing Economic Context.” Anuario de la Facultad de Derecho de la Universidad Autónoma de Madrid 20:279–304.

16.

Chmielewski

A. K.

2014. “An International Comparison of Achievement Inequality in Within- and between-School Tracking Systems.” American Journal of Education 120(3):293–324.

17.

Clark

2011. “Do Recessions Keep Students in School? The Impact of Youth Unemployment on Enrolment in Post-compulsory Education in England.” Economica 78(311):523–45.

18.

Crabtree

C. D.

Fariss

C. J.

2015. “Uncovering Patterns among Latent Variables: Human Rights and de Facto Judicial Independence.” Research & Politics 2(3):1–9.

19.

Crul

Schnell

Herzog-Punzenberger

Wilmes

Slootman

Aparicio Gómez

2012. “School Careers of Second-generation Youth in Europe: Which Education Systems Provide the Best Chances for Success.” Pp 101–64 in The European Second Generation Compared: Does the Integration Context Matter? edited by Crul

Schneider

Lelie

Amsterdam, The Netherlands: Amsterdam University Press.

20.

Crul

Vermeulen

2003. “The Second Generation in Europe.” International Migration Review 37(4):965–86.

21.

de la Rica

Glitz

Ortega

2013. “Immigration in Europe: Trends, policies and empirical evidence.” Discussion Paper No. 7778. Retrieved January 24, 2019 (http://bit.ly/1PLnul5).

22.

Delpit

L. D.

2012. “Multiplication Is for White People”: Raising Expectations for Other People’s Children. New York: The New Press.

23.

Dronkers

Levels

de Heus

2014. “Migrant Pupils’ Scientific Performance: The Influence of Educational System Features of Origin and Destination Countries.” Large–Scale Assessments in Education 2(3):1–28.

24.

Dronkers

Van der Velden

Dunne

2012. “Why are Migrant Students Better off in Certain Types of Educational Systems or Schools than in Others?” European Educational Research Journal 11(1):11–44.

25.

Dupriez

Dumay

Vause

2008. “How Do School Systems Manage Pupils’ Heterogeneity?” Comparative Education Review 52(2):245–73.

26.

Easterbrook

M. J.

Kuppens

Manstead

A. S.

2016. “The Education Effect: Higher Educational Qualifications are Robustly Associated with Beneficial Personal and Socio-political Outcomes.” Social Indicators Research 126(3):1261–98.

27.

Emler

Frazer

1999. “Politics: The education effect.” Oxford Review of Education 25(1–2):251–73.

28.

Faist

1993. “From School to Work: Public Policy and Underclass Formation among Young Turks in Germany during the 1980s.” International Migration Review 27:306–31.

29.

Feagin

1997. “Old Poison in New Bottles: The Deep Roots of Modern Nativism.” Pp. 13–43 in Immigrants Out! the New Nativism and the Anti-immigrant Impulse in the United States, edited by Perea

New York: New York University Press.

30.

Feliciano

Lanuza

(2016). “The Immigrant Advantage in Adolescent Educational Expectations.” International Migration Review 50(3):758–92.

31.

Feliciano

Rumbaut

R. G.

2005. “Gendered Paths: Educational and Occupational Expectations and Outcomes among Adult Children of Immigrants.” Ethnic and Racial Studies 28(6):1087–1118.

32.

Filindra

Blanding

Coll

C. G.

2011. “The Power of Context: State-level Policies and Politics and the Educational Performance of the Children of Immigrants in the United States.” Harvard Educational Review 81(3):407–38.

33.

Fossati

2011. “The Effect of Integration and Social Democratic Welfare States on Immigrants’ Educational Attainment: A Multilevel Estimate.” Journal of European Social Policy 21(5):391–412.

34.

Gandara

Hopkins

2010. Forbidden Language: English Learners and Restrictive Language Policies. New York, NY: Teachers College Press.

35.

Ganzeboom

H. B.

De Graaf

P. M.

Treiman

D. J.

1992. “A Standard International Socio-economic Index of occupational status.” Social Science Research 21(1):1–56.

36.

Gelman

Hill

2006. Data Analysis Using Regression and Multilevel / Hierarchical Models. New York, NY: Cambridge University Press.

37.

Gelman

Stern

2006. “The Difference between “Significant” and “Not Significant” Is Not Itself Statistically Significant.” The American Statistician 60(4):328–31.

38.

Gibson

M. A.

Carrasco

2009. “The Education of Immigrant Youth: Some Lessons from the U.S. and Spain.” Theory into Practice 48(4):249–57.

39.

Global Migration Data Analysis Center. 2016. Dangerous Journeys—International Migration Increasingly Unsafe in 2016. Retrieved January 24, 2019 (https://missingmigrants.iom.int/sites/default/files/gmdac_data_briefing_series_issue4.pdf).

40.

Goldenberg

Gallimore

Reese

Garnier

2001. “Cause or Effect? A Longitudinal Study of Immigrant Latino Parents’ Aspirations and Expectations, and Their Children’s School Performance.” American Educational Research Journal 38(3):547–82.

41.

Goldin

Katz

L. F.

2007. “Long-run Changes in the US Wage Structure: Narrowing, Widening, Polarizing.” Working Paper No. 13568. Retrieved January 24, 2019 (http://www.nber.org/papers/w13568).

42.

Gonzales

R. G.

2010. “On the Wrong Side of the Tracks: Understanding the Effects of School Structure and Social Capital in the Educational Pursuits of Undocumented Immigrant Students.” Peabody Journal of Education 85(4):469–85.

43.

Goyette

Xie

1999. “Educational Expectations of Asian American youths: Determinants and Ethnic Differences.” Sociology of Education 72(1):22–36.

44.

Grossman

2006. “Education and Non-market Outcomes.” Pp. 577–633 in Handbook of the Economics of Education, edited by Hanushek

Welch

, vol. 1. Amsterdam, The Netherlands: Elsevier.

45.

Haller

A. O.

Portes

1973. “Status Attainment Processes.” Sociology of Education 46(1):51–91.

46.

Hallinan

M. T.

1994. “Tracking: From Theory to Practice.” Sociology of Education 67(2):79–84.

47.

Hampden-Thompson

2009. “Are Two Better than One? A Comparative Study of Achievement Gaps and Family Structure.” Compare 39(4):517–34.

48.

Hanushek

E. A.

Woessmann

2006. “Does Educational Tracking Affect Performance and Inequality? Differences-in-differences Evidence across Countries.” The Economic Journal 116(510):63–76.

49.

Hao

Bonstead-Bruns

1998. “Parent-child Differences in Educational Expectations and the Academic Achievement of Immigrant and Native Students.” Sociology of Education 71(3):175–98.

50.

Haveman

R. H.

Smeeding

T. M.

2006. “The Role of Higher Education in Social Mobility.” The Future of Children 16(2):125–50.

51.

Hillmert

Jacob

2010. “Selections and Social Selectivity on the Academic Track: A Life-course Analysis of Educational Attainment in Germany.” Research in Social Stratification and Mobility 28(1):59–76.

52.

Holzmann

Munz

2004. “Challenges and Opportunities of International Migration for the EU, Its Member States, Neighboring Countries, and Regions: A Policy Note.” Discussion Paper No. 30160. Retrieved January 24, 2019 (http://bit.ly/1LuFRvU).

53.

Jacob

B. A.

Wilder

2010. “Educational Expectations and Attainment.” Working Paper No. 15683. Retrieved January 24, 2019 (http://www.nber.org/papers/w15683).

54.

Jakubowski

2010. “Institutional Tracking and Achievement Growth: Exploring Difference-in-Differences Approach to PIRLS, TIMSS, and PISA Data.” Pp. 41–81 in Quality and Inequality of Education: Cross-national Perspectives, edited by Dronkers

Dordrecht, The Netherlands: Springer.

55.

Kao

Tienda

1995. “Optimism and Achievement: The Educational Performance of Immigrant Youth.” Social Science Quarterly 76(1):1–19.

56.

Krahn

Taylor

2005. “Resilient Teenagers: Explaining the High Educational Aspirations of Visible-minority Youth in Canada.” Journal of International Migration and Integration 6(3/4):405–34.

57.

Lavrijsen

Nicaise

2015. “New Empirical Evidence on the Effect of Educational Tracking on Social Inequalities in Reading Achievement.” European Educational Research Journal 14(3–4):206–21.

58.

Lee

2014. “The Influence of School Tracking Systems on Educational Expectations: A comparative study of Austria and Italy.” Comparative Education 50(2):206–28.

59.

Lee

Zhou

2015. The Asian American Achievement Paradox. New York: Russell Sage Foundation.

60.

LeTendre

G. K.

Hofer

B. K.

Shimizu

2003. “What is tracking? Cultural Expectations in the United States, Germany, and Japan.” American Educational Research Journal 40(1):43–89.

61.

Levels

Dronkers

Kraaykamp

2008. “Immigrant Children’s Educational Achievement in Western Countries: Origin, Destination, and Community Effects on Mathematical Performance.” American Sociological Review 73(5):835–53.

62.

Manatschal

Stadelmann-Steffen

2013. “Cantonal Variations of Integration Policy and Their Impact on Immigrant Educational Inequality.” Comparative European Politics 11(5):671–95.

63.

McDaniel

2010. “Cross-national Gender Gaps in Educational Expectations: The Influence of National-level Gender Ideology and Educational Systems.” Comparative Education Review 54(1):27–50.

64.

Morissens

Sainsbury

2005. “Migrants’ Social Rights, Ethnicity and Welfare Regimes.” Journal of Social Policy 34(4):637–60.

65.

Muhlenweg

2007. Educational Effects of Early or Later Secondary School Tracking in Germany. Retrieved January 24, 2019 (https://www.econstor.eu/bitstream/10419/24664/1/dp07079.pdf).

66.

Noden

Shiner

Modood

2014. “University Offer Rates for Candidates from Different Ethnic Categories.” Oxford Review of Education 40(3):349–69.

67.

Organisation for Economic Co-operation and Development. 2004. Learning for Tomorrow’s World: First Results from PISA 2003. Retrieved January 24, 2019 (https://bit.ly/2KUvnZs).

68.

Organisation for Economic Co-operation and Development. 2012. Equity and Quality in Education: Supporting Disadvantaged Students and Schools. Retrieved January 24, 2019 (http://bit.ly/1k5pObg).

69.

Organisation for Economic Co-operation and Development. 2016. “Labour Market Outcomes of Recent Migrants and Integration Policies in OECD Countries.” In International Migration Outlook 2016. Paris, France: Organisation for Economic Co-operation and Development. Retrieved January 24, 2019 (http://www.oecd.org/els/international-migration-outlook-1999124x.htm).

70.

Park

Byun

S. Y.

2015. “Why Some Countries Attract More High-ability Young Students to Teaching: Cross-national Comparisons of Students’ Expectation of Becoming a Teacher.” Comparative Education Review 59(3):523–49.

71.

Pásztor

2009. “Different Settings, Different Choosers? Applying Ball’s framework on the case of second-generation Turks.” International Studies in Sociology of Education 19(3–4):203–15.

72.

Pásztor

2016. “Divergent Pathways: The Road to Higher Education for Second-generation Turks in Austria.” Race Ethnicity and Education 19(4):880–900.

73.

Pfeffer

F. T.

2008. “Persistent Inequality in Educational Attainment and Its Institutional Context.” European Sociological Review 24(5):543–65.

74.

Portes

Aparicio

Haller

Vickstrom

2010. “Moving ahead in Madrid: Aspirations and Expectations in the Spanish Second Generation.” International Migration Review 44(4):767–801.

75.

Portes

Rivas

2011. “The Adaptation of Migrant Children.” The Future of Children 21(1):219–46.

76.

Portes

Rumbaut

2001. Legacies: The Story of the Immigrant Second Generation. Berkeley, CA: University of California Press.

77.

Raudenbush

Bryk

2002. Hierarchical Linear Models: Applications and Data Analysis Methods. Vol. 1. Thousand Oaks, CA: Sage.

78.

Rosenbaum

Rochford

J. A.

2008. “Generational Patterns in Academic Performance: The Variable Effects of Attitudes and Social Capital.” Social Science Research 37(1):350–72.

79.

Rubin

D. B.

1987. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley & Sons.

80.

Sattin-Bajaj

2014. “Two Roads Diverged: Exploring Variation in Students’ School Choice Experiences by Socioeconomic Status, Parental Nativity, and Ethnicity.” Journal of School Choice 8(3):410–45.

81.

Schlicht

Stadelmann-Steffen

Freitag

2010. “Educational Inequality in the EU: The Effectiveness of the National Education Policy.” European Union Politics 11(1):29–59.

82.

Schneeweis

2011. “Educational Institutions and the Integration of Migrants.” Journal of Population Economics 24(4):1281–1308.

83.

Schneider

S. L.

2008. “Anti-immigrant Attitudes in Europe: Outgroup Size and Perceived Ethnic Threat.” European Sociological Review 24(1):53–67.

84.

Sorensen

A. B.

1970. “Organizational Differentiation of Students and Educational Opportunity.” Sociology of Education 43(4):355–76.

85.

Stadelmann-Steffen

2012. “Education Policy and Educational Inequality—Evidence from the Swiss Laboratory.” European Sociological Review 28(3):379–93.

86.

Terriquez

2012. “Civic inequalities? Immigrant Incorporation and Latina Mothers’ Participation in Their Children’s Schools.” Sociological Perspectives 55(4):663–82.

87.

Tjaden

J. D.

Hunkler

2017. “Optimism, Information, or Discrimination? Explaining Ethnic Choice Effects in Germany’s Secondary Education System.” Social Science Research 67:213–28. Retrieved January 24, 2019 (http://www.sciencedirect.com/science/article/pii/S0049089X15302052).

88.

Tolley

2011. Multiculturalism Policy Index: Immigrant Minority Policies. Rev. ed. Kingston, ON: Queen’s School of Policy Studies. Retrieved January 24, 2019 (Retrieved from http://bit.ly/2jDvFXD).

89.

United Nations Educational, Scientific and Cultural Organization. 2007. World Data on Education. France. Retrieved January 24, 2019 (https://bit.ly/2Kj18KG).

90.

Waters

Pineau

2015. The Integration of Immigrants into American Society. Washington, DC: The National Academies Press.

91.

Wooldridge

2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: Massachusetts Institute of Technology Press.

92.

Yonezawa

1999. “Unpacking the Black Box of Tracking Decisions: Critical Tales of Families Navigating the Course Placement Process.” Pp. 109–37 in Schooling Students Placed at Risk: Research, Policy, and Practice in the Education of Poor and Minority Adolescents, edited by Sanders

Mahwah, NJ: Routledge.