Effectively maintaining inequality in Toronto: predicting student destinations in Ontario universities.
Davies, Scott ; Maldonado, Vicky ; Zarifa, David 等
INTRODUCTION: UNEQUAL ACCESS TO HIGHER EDUCATION
UNIVERSITY ENROLLMENTS AROUND the world have been expanding for
half a century, and policy makers see this growth continuing into the
foreseeable future. In most nations, including Canada, virtually all
social groups have boosted their access to higher education over this
period. But sociologists have questioned whether this expansion has
meaningfully reduced inequalities in attainment (Goldthorpe and Jackson
2008; Lucas 2001; Shavit, Arum, and Gamoran 2007; Walters 2000). Sizable
disparities in access to Canadian higher education across a variety of
social groupings were initially documented in the 1970s when modern
university expansion was still in its infancy (e.g., Anisef 1974;
Porter, Porter, and Blishen 1979), and yet despite much further
expansion over the next 40 years, studies reveal mixed improvements at
best.
On the one hand, gender and racial patterns have changed somewhat
in Canada. For the past quarter century, women have attended
universities in greater proportions relative to men (Frenette and Zeman
2007), though gender segregation across fields of study continues to be
marked (Andres and Adamuti-Trache 2007). Female advantages in
enrollments appear to be mediated largely by academic mechanisms; male
underachievement is evident from the earliest primary grades and is
quite pronounced by high school (Anisef et al. 2010; Bowlby and McMullen
2002; Brown 2006). Disparities by race are mixed. Immigration has
altered the face of the Canadian undergraduate population, with
Asian-origin youth enjoying above-average rates of attendance, though
black and especially aboriginal students have far lower rates (Anisef et
al. 2010, 2011; Thiessen 2009). Some argue that Asian students'
higher academic achievement is mediated by their superior academic
performance, which in turn is rooted in cultural factors such as their
parents' high expectations (Goyette and Xie 1999; Thiessen 2009).
On the other hand, disparities in access to higher education
clearly persist by socioeconomic status (SES) (de Broucker 2005; Looker
and Lowe 2001; Willms 2004). While Canadian postsecondary participation
continues to rise and be relatively high in international comparison,
the proportion of participants from lower income families has remained
static for the past two decades (Deller and Oldford 2011). These SES
inequalities appear to arise through inequalities in resources (e.g.,
parental financial support), academic processes (e.g., grades), and
status culture practices (e.g., information gathering, planning, and
encouragement for postsecondary study; Berger, Motte, and Parkin 2007;
Frenette 2007).
Some theorists characterize this simultaneous process of ongoing
expansion and persisting socioeconomic inequality as a form of
"maximally maintained inequality" (MMI). MMI suggests that
advantaged groups migrate to more advanced credential tiers when less
advantaged groups are able to access lower tiers en masse (Raferty and
Hout 1993). In the decades following WWII, the near-universal entry of
youth into high schools prompted a great wave of higher education
expansion. MMI theorists contend that when higher status groups sense
that competition for better-paying jobs is intensifying, they pursue
higher tiers of education to retain their advantages. Today, proponents
of MMI would trace the expansion of professional and graduate tiers in
part to widened access to university baccalaureate programs among lower
status groups. (1)
But in today's context, an additional dimension of educational
inequality may be emerging within higher education. Building on past
Canadian research that linked social origins to postsecondary entry
(e.g., Anisef 1974), newer work is premised on the notion that
educational organizations within any tier are stratified by resources
and prestige (Davies and Zarifa 2012). Well-positioned schools,
institutions, fields of study, and programs can confer to their
graduates more social recognition, cognitive resources, and/or labor
market advantages than can other entities. When this stratification
congeals to a certain level, higher education institutions and fields
can form a hierarchy. (2) Clearly arrayed hierarchies then encourage
students to jockey for the most favorable positions within them.
Lucas (2001) describes EMI as a process in which widened access to
a credential tier encourages privileged groups to migrate toward its
most advantageous, selective, and prestigious sectors. If MMI describes
how inequality is maintained through upward movements between credential
tiers, EMI describes how inequality is maintained through lateral
movements within a tier. For instance, wealthy Canadians may
increasingly seek entry into elite private high schools in order to
attain a more advantageous education than one obtained from a public
high school.
This sorting process can produce disparities. Studies of
America's highly stratified higher education system have examined
race, class, and gender influences on access to and payoffs from
institutions and fields of varying rank (Ayalon and Yogev 2005; Davies
and Guppy 1997; Espenshade and Radford 2009; Gerber and Cheung 2008;
Goyette and Mullen 2006; Grodsky and Jones 2007; Jacobs 1999; Mullen and
Goyette 2010; Mullen, Goyette, and Soares 2003; Zarifa 2012a). Entrants
into the top-ranked institutions tend to be elite in both academic and
social terms (Espenshade, Chung, and Walling 2004), while female, black,
and lower SES origin students tend to attend less elite institutions,
though race and gender associations sometimes disappear after academic
factors are controlled (Gerber and Cheung 2008:310). (3)
EMI may be intensified in today's academic climate. As
national systems of higher education around the world continue to
expand, many states are pushing them to undergo an additional transition
under the banners of human capital formation and wealth creation. Policy
makers are urging universities to become more differentiated,
competitive, and entrepreneurial, whether by building partnerships with
corporations, bidding for research contracts, winning massive private
donations, or luring more and more tuition-paying students (for a
Canadian discussion, see Clark et al. 2011). These competitive forces
can combine with enrollment expansion to intensify processes of EMI in
several ways.
First, some expansion occurs through the founding of new
institutions, such as university colleges, for-profit and online
universities, or new teaching-oriented campuses (Clark and Mayer 2011;
Ruch 2001). Such institutions typically survive by digging niches in
vocationally oriented programs that are used to court
"nontraditional" students--older pupils with less salutary
academic profiles. This process of "anticipatory
subordination" allows new institutions to survive while minimizing
direct competition with higher ranked institutions for top students
(Brint and Karabel 1989). Second, these forces create incentives for
universities to vie for status, resources, and high rankings, whether by
recruiting high-profile researchers, erecting prestigious professional
schools, or mounting ambitious fundraising campaigns (e.g., Brint,
Riddle, and Hanneman 2006; Kirp 2004; Tuchman 2009). Third, once in
motion, these processes can simultaneously encourage the most
academically and socially advantaged students to self-select into the
most resource-rich and reputation-rich universities, while at the same
time encouraging those universities to become more selective in their
admissions. (4) These processes can further entrench institutional
stratification and hierarchies.
In the United States, these mechanisms are generating a polar
pattern of enrollment. While most of the growth in American universities
has been in "broad access" institutions that are not
particularly selective (Astin and Oseguera 2004; Kirst, Proctor, and
Stevens 2011), students with the highest GPA's and SAT's are
increasingly concentrating themselves in the most selective institutions
(Hoxby 2009). Top-ranked Ivy League schools have become increasingly
selective in recent years while many other institutions have become less
selective than they were even 50 years ago (Hoxby 2009).
APPLYING EMI THEORY TO CANADIAN HIGHER EDUCATION
Originating in the United States, EMI theory assumes the existence
of clear hierarchies of institutions and fields within any credential
tier. At the K-12 level, American proponents point to elite private
schools and newer public schools of choice that are better endowed, more
socially exclusive, and offer surer routes to prestigious universities
than standard public schools (Espenshade and Radford 2009; Mullen and
Goyette 2010). A Canadian proponent could likewise point to this
country's growing private sector in K-12 education, along with
publicly subsidized independent schools and other schools of choice
(Davies and Aurini 2011; Davies and Quirke 2007). Yet, applying EMI
theory to Canadian higher education requires rethinking some important
details.
Most importantly, Canadian universities are less hierarchically
arrayed than American universities (Davies and Hammack 2005). Almost all
Canadian universities are publicly-governed and, until recently,
received provincial funding premised on norms of institutional parity.
Moreover, Canada's undergraduate market is mostly local or
provincial in scope, with relatively few students crossing provincial
borders to attend baccalaureate programs (notwithstanding notable
exceptions of Queens, McGill, and some Nova Scotia universities). And
there is no Canadian equivalent to the world-renowned Ivy League, whose
budgets can rival those of many entire nations, not to mention
universities. As a result, Canadian universities are far less stratified
by resources and selectivity than are American universities (Davies and
Zarifa 2012). Canadian students enter a less-clearly established
hierarchy of universities. Still, there are reasons to suspect EMI-like
processes are operating in Canadian higher education, and perhaps
increasingly so.
First, Canada may be a prime candidate for EMI in international
perspective, given its relatively large proportion of university
graduates and comparatively weak association between socioeconomic
background and educational attainment (Beller and Hout 2006). According
to Beller and Hout (2006), greater numbers in the baccalaureate tier
serve to intensify competition, prompting higher SES students to seek
advantages in lateral directions, perhaps by attending better-resourced
institutions. Similarly, less-differentiated higher education systems
tend to have higher rates of enrollment and weaker social exclusivity
(Shavit, Arum, and Gamoran 2007). Canadian higher education is
differentiated by several sectors--community colleges, private career
colleges, and universities--but the latter has expanded the most. This
combination of expansion and differentiation may produce less
stratification in access to higher education (MMI) but may also trigger
considerable amounts of EMI.
Second, while resources are distributed relatively evenly among
Canadian universities compared to those in other nations, particularly
the United States, Canada's stratification is nonetheless
substantial and appears to be slowly growing (Davies and Zarifa 2012).
The presidents of Canada's best-resourced research
universities--the "U15"--have for 20 years pressured
governments to fund them at levels higher than other universities, to
permit them to raise their tuition to unprecedented levels, and/or to
become semiprivate. In Ontario, policy makers are urging their
universities to "differentiate" in ways that might further
exacerbate these inequalities (Clark and Mayer 2011; Weingarten and
Deller 2010). Also, Canadian universities are enjoying vastly differing
successes in their private fundraising ventures, which tend to advantage
older and more established institutions. (5)
Third, Canadian undergraduates, particularly from relatively
privileged origins, may be increasingly attuned to these hierarchies. An
Ontario study from 1979 found that established institutions such as the
University of Toronto, McMaster, Waterloo, and Queens tended to recruit
more students from higher status origins than did less-established
institutions (Anisef 1982). (6) But over the past 20 years, the rising
visibility of the Maclean's rankings (7) may have moulded a
"rank consciousness" among some students. While those rankings
are routinely criticized by professors and administrators, they
correlate highly with university resources (discussed below). Further,
Ontario institutions that are higher ranked by Maclean's,
particularly in the medical-doctoral category, tend to receive more
applications, as do institutions that raise their rank (Mueller and
Rockerbie 2005), though this may occur only among smaller institutions
(Drewes and Michael 2006). Canada-wide, Kong and Veall (2005) find some
evidence that medical-doctoral universities that raise their rank then
receive applicants with higher high school averages. Thus, if
academically and/or socially elite students are particularly prone to
consult rankings when choosing universities, they might increasingly
concentrate themselves in better-resourced institutions.
Qualitative evidence from Ontario suggests affluent youth indeed
perceive a hierarchy of universities in terms of their social cache
and/or academic standing. (8) Likewise, the geography of student choice
of university across Toronto illustrates how affluent youth avoid lesser
ranked institutions. Figure 1 maps the home location (using postal code)
of students who eventually attended Queen's and York in 2006. Among
the many Torontonians who chose York (signified by dots), relatively few
lived in the city's most affluent neighborhoods, while the converse
was true for those who attended Queen's (signified by triangles).
The concentrations of Queen's attendees near the center, west end,
and southeast correspond to the Yonge Street corridor, Bridal Path,
Kingsway, and Beaches--all among the wealthiest neighborhoods in the
Greater Toronto Area (GTA). In contrast, almost no Queen's
attendees hailed from humbler areas like York, Rexdale, Downsview, and
Scarborough. York attendees are scattered throughout the city, but are
less concentrated in the aforementioned affluent areas.
[FIGURE 1 OMITTED]
TESTING EMI THEORY IN TORONTO
We test EMI theory in a Canadian setting by asking the following
questions: First, what kinds of students in Toronto attend
Ontario's higher ranked universities? Are there differences by SES,
gender, or race? Older American studies find that racial minorities and
women are less likely to attend higher ranked universities (Gerber and
Cheung 2008), suggesting that EMI tends to reinforce older forms of
educational inequality. In contrast, a plausible counterhypothesis is
that since today's MMI is mixed--women and some racial minorities
are overrepresented in higher education, while SES disparities have
remained stagnant over time--we may similarly observe greater
proportions of women and minorities in higher ranked universities.
Second, what variables mediate student entry into higher ranked
universities? In particular, do academic measures such as high school
grades predict such entry? If highly ranked universities require higher
grade averages, we would expect grades to mediate some of the effects of
social origins on entry. However, if those effects remain partly
unexplained, they may also reflect status-group processes, that is, the
tastes, expectations, and/or social pressures that shape high-SES
groups' preferences for universities, or shape mobility strategies
for nontraditional university students.
Toronto provides an excellent testing ground for EMI theory. As
Canada's largest school board, the Toronto District School Board
(TDSB) offers the large numbers needed to examine subgroups by race,
class, and gender. TDSB graduates have ample choice among universities,
including 18 institutions within their province, four of these within
their city, and several more within commuting distance. Among these
universities there are striking differences in institutional age,
ranging from the venerable University of Toronto (founded in 1850) to
institutions that have acquired university status much more recently
(Ryerson University in 1993, and the Ontario University Institute of
Technology and the Ontario College of Art and Design University each in
2002), as well as sizable variations in research intensiveness and
endowments. By comparing university destinations among students from a
single city, we control for geographic proximity between their homes and
their institution, and thus analyze how other factors shape outcomes.
Our design has some limits and inherent trade-offs. One limitation
is its focus on one city. We do not know if we can generalize findings
from Toronto to other Canadian regions. Similar processes are likely at
work in other large cities, since there are no Canadian studies (to our
knowledge) that show radically different patterns of higher education
access. But different processes may be operating in less populated
regions with fewer immediate universities. In such locales, student must
weigh different issues when choosing, such as the psychic and financial
costs of moving and switching communities (Looker and Naylor 2009). But
EMI could be even stronger in outlying regions, at least among more
affluent youth. Since those youth must leave home to attend any
university, the certainty of those costs may motivate them more to
attend a higher status institution. Future research is needed to verify
either scenario. Also, Canadian provinces have different ways of
governing colleges and universities: Ontario has a relatively rigid
divide between its colleges and universities that restricts flows of
students between those sectors, while other provinces such as British
Columbia and Alberta have larger such flows (see Alberta Enterprise and
Advanced Education 2012; Heslop 2012). These varying arrangements might
complicate EMI processes, and empirical data from other provinces is
needed to sort out their effects. We also lack information on the small
percentage of TDSB graduates who applied to universities outside of
Ontario, and for graduates from Toronto private and Catholic schools.
Furthermore, we have to use a proxy for a crucial SES variable--family
income--at the neighborhood level, which undoubtedly adds measurement
error to our estimates. However, these limitations likely cause our
models to underestimate the current extent of EMI, especially for SES.
Students in Toronto private schools are likely to be far more affluent
than those in the TDSB, given the cost of private tuition, which Ontario
does not subsidize. And Toronto high school graduates who attend
American universities, as well as out-of-province universities like
McGill, are also likely to be affluent, due to costs of tuition and
moving. (9)
METHODS
Data Sources and Sample
We merged four data sets to track an entire cohort of TDSB students
from the ninth grade to an Ontario postsecondary institution. The first
database consisted of official records for all students in the TDSB who
were 17 years of age in the fall of 2006 and attended a regular day
school. (10) This cohort totalled 19,082 students. It omitted students
who earlier dropped out of school or transferred out of the TDSB later
that year. This core data set was merged to a second data set from the
Ontario Universities' Application Centre, which records all
applications and confirmed attendance for all Ontario postsecondary
students in Ontario universities. Using student identification numbers,
we linked any student from the TDSB cohort who confirmed attendance at
an Ontario university in any one of three years (2007, 2008, and 2009).
A third merged data set came from the TDSB "Student Census."
In the fall of 2006, all TDSB students in Grades 7 to 12 were surveyed
about their demographics and school attitudes. Useable data were
collected for approximately three-quarters of the 17-year-old cohort.
And finally, to measure the hierarchy of Ontario universities, we merged
data on each university's resources and ranking, the bulk of which
was compiled by David Zarifa (2008). The institution-level data set
contains measures of 2006 financial resources--annual income and
expenditures--for almost all Ontario universities. These data come from
several annual Statistics Canada surveys: the Financial Information of
Universities and Colleges (FIUC), Tuition and Living Accommodation Costs
Survey (TLAC), and the University Student Information System (USIS).
(11) These resources are critical for research, teaching, and
operations, and can indirectly shape quality of education by affecting
student-teacher ratios, physical plants, ranges of courses and programs,
faculty salaries (which is correlated with productivity), and capacity
to recruit students. We then supplemented these data with measures of
university endowments in 2006 and with three years of Maclean's
rankings.
This merged data set has several advantages for testing EMI theory.
First, it uses a three-year time frame after graduation, thus capturing
any students who did not apply to university immediately after
graduation. Second, and most crucially, it identifies which Ontario
university students eventually attended. Such data are rare in Canada.
Available versions of the National Graduate Survey and Youth in
Transition Survey, for instance, do not identify universities. Third,
these data are also comprehensive. They contain an extensive set of
measures from students' official high school academic records,
including their residential postal code (used to calculate distance
between their home and their university of choice) and grades. Coming
from administrative sources, these variables have far less measurement
error than would those that relied on survey self-reports. Fourth, these
variables have a host of demographic and attitudinal measures. And
finally, by using multiple outcome measures, we were able to explore
several related dimensions of institutional inequality, providing a
robust test of the EMI thesis and enhancing the reliability of our
conclusions.
Our analytic sample has several restrictions, however. Since we are
testing EMI theory for the university tier, we exclude students who were
admitted to Ontario community colleges or who did not apply to any
postsecondary institution. Further, these data do not include any
registrants at universities beyond Ontario. The TDSB estimates that 2 to
3 percent of its students attend postsecondary institutions outside
Ontario; those students tend to be concentrated in a small number of
socioeconomically advantaged schools (Brown 2009). (12) After these
restrictions, our original analytic sample consisted of 8,614 students
who were 17 years old in 2006 and were admitted to an Ontario university
within three years (2007-2009). Of these, approximately 75 percent wrote
the student census. Thus, our analytic samples range from 6,479 to 6,420
students across our models. See Table 4 for frequencies of attendance at
each Ontario university.
Our statistical analyses consist of multilevel regression models
that estimate the effects of social background on student attendance
within a hierarchy of universities. Our focal interest is on tracing the
effects of student background (SES, gender, and race). Our models first
estimate effects of SES, gender, and race, while controlling for other
demographics. Model 1 includes controls for a series of additional
demographic variables, namely whether or not students attended the same
high school between Grades 9 and 12 and whether or not they live with
two parents, and distance between their home in high school and their
eventual university. These are good control variables because studies
show that each is related to the likelihood of attending a postsecondary
institution. Models 2 and 3 proceed to examine how those effects are
mediated by measures of students' academic experience and
achievement. These are important mediating variables since both are
related to social background and to postsecondary attendance. We have
separate models for four related outcomes: average Maclean's rank
over three years, expenditures per full-time equivalent (FTE) student,
income per FTE student, and endowments per FTE student. (13) In
addition, we estimated multilevel logits to examine which students are
more likely to attend one of two highly ranked universities outside of
Toronto: Queen's and McMaster. These two institutions have the
greatest resources per student among universities outside of Toronto.
(14)
Measures
Table 1 presents descriptive statistics for all variables in our
models. Our independent variables include gender, self-identified race,
SES, and several key controls. SES is measured by two variables: whether
or not at least one parent had completed university, and the median
family income in each student's neighborhood, measured at the
dissemination area level, using the student's postal code. (15)
Other demographic controls include a dummy variable for whether students
had previously switched high schools and a continuous measure of the
distance between students' homes and the universities they
attended.
Distance is an important control variable. Given the high cost of
attending an institution far from home, distance can affect whether or
not students attend a university and, if so, which one they attend.
Lopez-Turley (2009) found that the odds of applying to a U.S. four-year
college increased significantly as the number of nearby colleges
increased, even controlling for student and neighborhood-level factors.
In Canada, Frenette (2009) examined the impact of newly created
universities on postsecondary participation rates. The emergence of a
new university served to increase university participation rates among
local youth, though this trend occurred at the expense of college
participation rates. Lower income families experienced the greatest
increases in university participation, suggesting that greater distance
does indeed represent a financial barrier. Other studies show that
Canadians in rural areas not in close proximity to a university are less
likely to attend a university, controlling for gender, family income,
and parental education (Frenette 2003; Looker and Naylor 2009). Our
expectation for TDSB students is somewhat different: Since all Toronto
youth are in close proximity to several universities, advantaged
students may be likelier to attend higher ranking universities outside
of Toronto, due to their greater costs.
To investigate the extent to which student experiences mediate the
effects of social background, three attitudinal scales about student
experiences at school were created from survey items. Each has an
acceptable level of internal consistency (Cronbach's alphas are
reported in Table 1). Students' social engagement at school was
constructed from three survey questions about how well students get
along, how accepting students are of each other, and how accepting
adults in the school are of students. Another scale addresses
students' perceptions of instruction using items on whether
students thought teachers expected them to succeed, their satisfaction
with the teaching at their school, how much they felt supported and
comfortable sharing with teachers, how well they felt staff respected
their cultural, racial, or religious background, and the extent of extra
help available. This variable was dropped from analyses because it did
not improve model fit. A school climate scale was devised from measures
of how much students enjoy school, whether school is a friendly and
welcoming place, and whether the school building is an attractive place
to learn.
The final model adds several measures of educational achievement,
including students' average grades from Grade 11 or 12, and their
Grade 9 math mark. Grade 11 and 12 marks are central to students'
applications to university. Grade 9 math achievement has been found to
be a fairly reliable predictor of later academic achievement (Brown
2010).
We use several related outcome variables: the dollar value of each
university's income and expenditure per FTE in 2006, the financial
endowment per FTE of each university in 2008, each university's
Maclean's rank within its own category, averaged over three years
(2007, 2008, and 2009), and whether or not students attended particular
institutions. (16) Table 2 shows that these measures are strongly
intercorrelated; despite their small number of cases, all correlations
are highly significant.
Table 3 displays the Ontario hierarchy by cross-tabulating a
variety of institutional-level measures for each university and
converting them to ranks within the province. Looking across columns of
Table 3, many universities have fairly consistent ranks across each
measure. Queen's is highly ranked--either first or second on all
measures --followed by U of T, which is ranked between second and fourth
on each. McMaster, Western, and Waterloo are also ranked highly across
these measures. In contrast, the lowest tiers of Ontario universities
appear to be occupied by Nipissing and Brock.
Data Analysis
This study uses a series of multilevel models to examine the
effects of the individual-level variables and school-level variables.
These data have a hierarchical structure, with students nested in
secondary schools, and some variables measured at the school level.
Since the errors are likely to be correlated within schools, we use
multilevel techniques (Raudenbush and Bryk 2002; Snijders and Bosker
2012). Our analysis begins by fitting null models to examine
between-group effects for each dependent variable (not shown). This
model can be expressed as the following:
[Y.sub.ij] = [[gamma].sub.00] + [U.sub.0j] + [R.sub.ij],
where the dependent variable Y for student i nested in school j is
the sum of a general mean, [gamma.sub.00], [U.sub.0j] representing a
random effect at the school level, and [R.sub.ij] representing a random
effect at the student level (Snijders and Bosker 2012). We then proceed
to estimate linear mixed-effects models with a random intercept by
sequentially adding four successive blocks of variables. (17) Thus, the
null model can be extended by adding individual- and school-level
covariates:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
where [x.sub.1], ..., [x.sub.p] represents independent variables at
the individual level, and [z.sub.1], ..., [z.sub.q] represents
independent variables at the school level (Snijders and Bosker 2012).
We also estimate an additional series of mixed-effects logistic
regressions using the xtmelogit procedures in Stata 12 (StataCorp LP,
College Station, TX). These models are appropriate when estimating
multilevel models for dichotomous dependent variables. The logistic
random intercept model for a dichotomous dependent variable for student
i nested in school j can be expressed in the following way:
log it ([P.sub.ij]) = [[gamma].sub.0] + [r.summation over (h=1)]
[[gamma].sub.h][x.sub.hij] + [U.sub.0j],
where the logit of [P.sub.ij] is expressed as a sum of a linear
function of the independent variables ([x.sub.1], ..., [x.sub.1]) and a
random group-dependent component, [U.sub.0j] (Snijders and Bosker 2012).
(18)
Results
We begin by exploring simple counts and percentages. Table 4
reports the Ontario universities attended by TDSB graduates. The most
striking pattern is that almost 62 percent chose to remain in Toronto to
attend university. Their top choice was the University of Toronto,
followed by York and Ryerson. This pattern is consistent with the
contention that Canadian markets for undergraduate education tend to be
relatively local and dominated by commuters, unlike those in some
European nations and U.S. regions (Davies and Hammack 2005). The next
most popular choices tend to have high status within province: Guelph,
Waterloo, Western, Queen's, and McMaster, which combine for another
25 percent of choices. The two universities thought to be most preferred
by students from more socio-economically advantaged backgrounds--Western
and Queen's--combine for 8 percent. About 13 percent of all TDSB
graduates chose one of the remaining Ontario universities, and very few
attended the lowest ranked institutions outside of Toronto: only 1
percent attended Nipissing, Lakehead, Laurentian, or Windsor. Toronto
high school graduates prefer universities that are either nearby or of
relatively high status.
Table 5 shows multilevel regression results for entering an Ontario
university that has been scored by its Maclean's rank averaged over
three years. (19) Ranks are reverse-coded (bottom rank = 1 and the top
rank = 18), so that positive coefficients signal that increases in that
variable are associated with higher ranks. An analysis of the variance
components in the null model (not shown) suggests only about 4 percent
of the variance lies among schools, and the remainder lies among
students. However, the intercept is highly statistically significant,
suggesting there are considerable differences among schools in the rates
that their graduates apply to top-ranked universities. Model 1 examines
the effects of social background, controlling for distances between a
student's home address and their university of choice. The
significant and negative coefficient for the "male" variable
(p < .01) signals that females are more likely to attend higher
ranked universities. This finding differs from older American studies,
which found that males were slightly more likely to attend highly
selective institutions. Our finding may be tapping newer gender patterns
in which EMI outcomes simply mirror those of MMI; a second possibility
is that higher ranked Ontario universities, unlike those in the United
States, tend to offer a general array of programs, including those with
high female enrollments like nursing and education (Jacobs 1999). In
terms of student SES, the strong, positive parent-education coefficient
(p < .001) suggests that students with highly educated parents tend
to attend higher ranked institutions. In terms of race and ethnicity,
students who identify as South Asian or East Asian (p < .001) tend to
attend higher ranked institutions, while those who self-identify as
black (p < .001) are less likely to do so.
Model 2 adds measures of perceived school climate and social
relations. Their inclusion has very little effect on the existing
patterns of coefficients. School climate has an independent effect (p
< .001), suggesting that students who have a more positive image of
their school's climate are more likely to attend higher ranked
universities. Model 3 adds measures of student academic achievement. As
expected, students with higher grades are significantly more likely to
attend higher ranked universities (p < .001), since those
universities typically have higher entry requirements. The addition of
these academic measures decreases the gender coefficient substantially
so that it is no longer statistically significant. This pattern suggests
that males' disadvantage is largely explained by their academic
achievement. By contrast, the parent education coefficient shrinks by
only 40 percent, and remains statistically significant (p < .001).
Thus, SES effects on entering the university hierarchy are not explained
away by academic performance. Race continues to play a role in
determining university selection, despite the addition of academic
characteristics. While the negative effect for black students is no
longer statistically significant, the effects for South Asians and East
Asians remain significant (p < .001). (20)
Overall, Table 5 suggests that many of the disadvantages faced by
male and black students are largely due to academics, while the
advantages of higher SES and Asian students remain net of their academic
performance alone. As further discussed in the conclusion, these net
effects may be due to unmeasured status cultural processes among some
dominant groups, and strategies for upward social mobility for others.
Table 6 shows regressions for income per FTE of each university in
2006. Positive coefficients signal that increases in independent
variables are associated with attending a university with greater
resources. In Model 1, a sizable and significant gender effect again
emerges. Males are significantly less likely than females to attend a
well-resourced university. Similarly, all three SES
coefficients--neighborhood income, parental education, and family
structure--are positive and significant. Students from higher SES
families are more likely to enter well-resourced universities. As in
Table 5, students who identify as South Asian and East Asian are more
likely to enter universities that have higher incomes, while
self-identified blacks are less likely. Model 2 that adds attitudinal
measures has only one additional significant and independent effect--for
school climate (p < .001). In Model 3, adding academic measures
substantially shrinks the male coefficient by about 75 percent, making
it no longer statistically significant. The effects for neighborhood
income, family structure, and education are also reduced, though income
and education remain significant. A similar pattern occurs for the
Asian-ancestry coefficients, while the black disadvantage is no longer
significant after controlling for grades. (21)
Table 7 investigates a measure of the wealth of each institution:
their financial endowments per FTE. As in the previous sets of models,
Model 1 shows males are also less likely to enter wealthy universities.
That effect is reduced by almost one-half once academic variables are
entered in Model 3. Model 1 also shows a statistically significant
effect for parent education (p < .001). The effect shrinks by 40
percent but remains significant, once academic performance is taken into
consideration (Model 3). In terms of race, Models 1 and 2 suggest blacks
may be less likely to attend wealthy institution, yet this effect
greatly weakens and is no longer statistically significant in Model 3.
Students with Asian ancestry are more likely to enter wealthy
universities, even controlling for academic performance.
Table 8 shows the results of several multilevel logistic
regressions predicting the likelihood of a student attending one of two
top-ranked Ontario universities outside of the GTA: Queen's and
McMaster. These models are estimated on only those students who attended
institutions outside of the GTA. Many of the previous results hold.
Males are significantly less likely than females to attend those higher
ranked universities (p < .01), even controlling for academic
performance in Model 3, though the effect does shrink. Similarly,
parental education initially has a strong and positive effect (p <
.05), but it shrinks and is no longer significant once academic
variables are included in Model 3. That is, for students who left the
GTA, parent education played less of a direct role in shaping which
university they attended, once academic factors were taken into
consideration. South Asians are significantly more likely to attend a
highly ranked school (p < .01) controlling for all other factors.
Interestingly, neighborhood income is also positive and significant (p
< .05) and appears to have an importance influence on whether
students attend a highly ranked institution outside of the GTA.
In summary, an array of statistical models for different
measures-university rankings, income, expenditures, endowments, and
attending a highly ranked institution outside of Toronto--yield quite
similar patterns of effects. Males tend to attend lower status
universities, but those effects are mostly mediated through academic
processes. Self-identified blacks are similarly disadvantaged, but that
effect too is largely mediated by academics. In contrast, students that
have higher SES and Asian ancestry are more likely to attend higher
status institutions, and their advantages are only partly mediated by
their higher grades.
CONCLUSION: EMI AS AN EMERGING MIRROR OF REPRODUCTION AND MOBILITY
This paper offers a first Canadian look at EMI. It reveals an
additional dimension of inequality in higher education. Advantaged youth
in Toronto are not only overrepresented in Ontario universities, they
are also over-represented in higher ranked institutions. EMI thus tends
to mirror MMI. Some of these patterns emerged largely through academic
processes. But we found a more complicated link between EMI and broader
processes of social reproduction and mobility. Much reproduction occurs
in Toronto, as suggested by the robust SES effects on our outcomes, and
these were only partly explained by academic variables. We interpreted
these effects as reflecting the status cultures of higher SES youth. Our
map of applicants to Queen's University and Baker's
(forthcoming) ethnography of elite Toronto private schools both support
that view. But we also found that some groups that were overrepresented
in higher ranked institutions are, in historical perspective, relative
newcomers to Canadian higher education--women and students of Asian
ancestry. For them, EMI represents social mobility, not reproduction per
se, at least in the realm of education. Their academic advantages may
not necessarily translate into large advantages in the labor market,
since women's growing educational attainment in Canada is yet to be
matched by a comparable economic payoff (Davies and Guppy 2013). We have
yet to see whether choice of institution offers an effective vehicle of
upward mobility for women or Asian-origin minorities.
If these processes of EMI are robust within and beyond Toronto,
they raise a key question for Canadian stratification research: How
consequential are they for larger patterns of social inequality? It is
well known that graduating from a university boosts a variety of life
chances, and that a student's field of study has a powerful
influence on their future earnings. But does a university's
position in a hierarchy have comparable effects? Ontario's (and, by
implication, Canada's) hierarchy may not be steep in international
comparison, but it is nonetheless a further layer of stratification, and
it appears to sort Toronto students by their SES, gender, and race.
American research clearly shows that graduates from highly selective
universities have the best incomes and access to elite corporate and
government positions, though it is less clear whether these advantages
persist net of students' prior social and academic characteristics
(Gerber and Cheung 2008). In Canada, comparable research is in its
infancy, partly due to a lack of longitudinal data that identifies where
students attend university. As a result, we do not know if attending one
Canadian university or another has marked influences on students'
life chances. But EMI may at least have an experiential component. An
additional American literature is describing stark variations across
institutions of higher and lesser status. Quantitative research suggests
that top-ranked universities are becoming more and more exclusive, both
socially and academically, while "broad access" colleges are
becoming less selective, and may even have falling academic standards
(Arum and Roksa 2011; Espenshade and Radford 2009; Hoxby 2009).
Qualitative research is describing how students' experience of
university life varies widely between institutions of higher and lesser
status, and may be diverging with time (Mullen 2010; Stuber 2011).
Top-ranked schools continue to be breeding grounds for elites (Karabel
2005; Stevens 2007) while many students elsewhere suffer from high rates
of attrition and low levels of engagement (Professor X 2010). Might we
need a similar literature for Canadian universities?
One could argue that EMI is an unsurprising by-product of
expansion. In a continually growing region like the GTA, higher
education has expanded massively over the past half century, with the
building of York University, UT Mississauga, and UT Scarborough and, in
the past 10 to 20 years, the granting of university status to Ryerson,
OCAD University, and UOIT. This higher education expansion has surely
offered much social mobility for hundreds of thousands of students. But
it may have also forged new kinds of social inequality.
If EMI is consequential for higher education students' life
chances and/or experiences, our findings then have a key implication for
current policies that call for expanded access and differentiation
(Clark et al. 2011). While greater access will surely boost
opportunities for many, including thousands of"first
generation" students, a greater variety of institutional mandates,
missions, funding, and/or program offerings may also generate disparate
university experiences and later outcomes. If this scenario does emerge,
our thinking about higher education inequality may need to change in
step. In the 1950s, massive numbers of youth began to attend high
schools for several years, so researchers and policy makers became
increasingly sensitive to sorting processes within that credential tier,
and paid more attention to unequal rates of streaming, dropping out, and
myriad other factors that shaped student experiences and outcomes.
Today, as near majorities of youth cohorts enter postsecondary
education, we need to be similarly sensitized to sorting processes
within that tier. Being placed into fields and institutions of varying
rank is a kind of "streaming" that can affect student
experiences and outcomes. Future research is needed to further uncover
this additional layer of stratification.
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SCOTT DAVIES
McMaster University
VICKY MALDONADO
McMaster University
DAVID ZARIFA
Nipissing University
(1.) For a recent examination of MMI at graduate levels in Canada,
see Zarifa (2012b).
(2.) An additional dimension of effectively maintained inequality
(EMI) is hierarchies among fields of study. We will explore that
dimension in future research. For Canadian studies on access to various
fields, see Waiters and Frank (2010) and Zarifa (2012a).
(3.) SES patterns are produced by both academic and status cultural
processes. For instance, Mullen's (2009 2010) comparison of
students at Yale and a nearby nonelite university identified among the
former a status culture in which families, peers, and high schools long
nurtured aspirations and competitive academic strategies. Many
sociologists, including Suzanne Bianchi, Scan Reardon, Annette Lareau,
and Peggy McDonough, to name a few, have highlighted how upper SES
groups in recent decades have intensified their educationally oriented
parenting by gathering information about school options and learning
strategies (see Davies and Guppy 2013).
(4.) Student self-selection can occur through two mechanisms.
Academically ambitious students may perceive that well-resourced
institutions offer superior educations, whether due to higher quality of
instruction, smaller classes, or superior classmates and their peer
effects. Or, status-conscious students may seek highly ranked,
reputable, and resourced institutions not for educational benefits per
se, but for their social recognition and exclusivity.
(6.) Disparities in endowments, for instance, are huge. In 2010,
the University of Toronto endowment was $1,538,820,000, UBC's was
$1,045,829,000, and McGill's was $941,112,000, compared to
Huntington University ($2,416,000) and Athabasca ($2,358,000; see CAUT
2012:51). Canadian universities are more unequal in endowments than in
operating incomes and expenditures; thus, endowments represent a
component of rising institutional stratification.
(6.) That study lacked a sampling frame that could generate
representative samples from each university, however, and therefore this
statement was considered suggestive rather than definitive (Anisef,
1982:5).
(7.) The Maclean's Guide to Canadian Universities
(Maclean's 2013) provides annual rankings of Canada's
universities in three categories: "medical doctoral,"
"comprehensive," and "primarily undergraduate."
Ranks are based on multiple indicators across six dimensions: (1)
student characteristics (awards and scholarships) and classroom
experiences; (2) faculty caliber (teaching fellowships, awards, and
grants); (3) financial resources and expenditures; (4) student aid and
services; (5) library holdings; and (6) reputation among university
officials, high school guidance counselors, and heads of other
organizations.
(8.) Baker (forthcoming) found that students in two elite Toronto
private schools were most impressed by the prospect of attending top
American universities, though not all thought seriously about applying
to those schools. Beyond the top U.S. universities, those students
deemed McGill, Queen's, and Western to be far more desirable than
other Ontario universities, including those in Toronto.
(9.) A suggestive anecdote comes from the first author's
research leave at Harvard University in 2006. When lunching with
Harvard's Canadian Undergraduate club, a show of hands from the 12
students gathered revealed all were graduates of' Canadian private
high schools.
(10.) Seventeen is the age when students are typically in Grade 12
and most frequently apply to postsecondary institutions, though some in
this cohort were in Grade 11 that year.
(11.) The FIUC provides annual information on income and
expenditures for all universities and degree-granting colleges. The TLAC
surveys provide annual financial information (e.g., tuition fees, living
accommodation costs) on all degree-granting universities and colleges.
The USIS provides annual information on student background (e.g.,
gender, citizenship, age) and level and type of education.
(12.) TDSB data show that 743 students in an earlier cohort applied
to universities outside Ontario, most of whom (466) applied to McGill.
Another 264 did not later confirm admission to Ontario universities,
likely meaning that they attended an institution outside the province.
Among those 264 students, over three-quarters came from schools in
neighborhoods that were in the two highest deciles of income, 57 percent
were in the highest decile, and two-thirds were female.
(13) Results for income and expenditures were nearly identical, and
so we present a table only for the former.
(14.) We explore this outcome since the effects of social
background may be especially strong for attending highly ranked
universities beyond commuting distance.
(15.) The latter measure is an aggregate-level proxy for
students' actual family income that undoubtedly introduces
measurement error into our models.
(16.) Due to moderate negative skew evident in graphical displays
of the original distributions, we performed square root transformations
on the institutional income, expenditures, and endowment variables to
satisfy the assumption of normality (Tabachnick and Fidell 2007).
(17) The intraclass correlation coefficient for the models ranged
between 4 and 5 percent across all unrestricted models, suggesting that
roughly 5 percent of the variance may be attributable to school traits.
(18.) Given the large sample size and large number of level 2
units, linear mixed-effects models were fitted using full
maximum-likelihood estimation. The mixed-effects logit models were
estimated using a penalized quasi-likelihood procedure. Therefore, the
reported deviance, Akaike information criterion (AIC), and Bayesian
information criterion (BIC) for these models should be interpreted with
caution.
(19) Pseudo [R.sup.2] values for random intercept models are
reported for each model to assess the proportional reduction of
prediction error (Snijders and Bosker 2012).
(20) For all outcomes, a fourth model (not shown) including several
school-level measures of social assistance, family structure, and
language at school was estimated. None of these variables were
statistically significant, altered the broad patterns of coefficients
described above, or improve the overall model fit.
(21.) Additional models were also estimated for the total
expenditures per FTE for each university in 2006. Their results were
nearly identical to those for income, and so were not reproduced here,
but are available upon request.
The authors thank Robert Brown from the Toronto District School
Board for providing access to the data and running the models. We also
thank the SSHRC funded Postsecondary Pathways Project team Paul Anisef,
Chris Conley, Kristyn Frank, Maria Adamuti-Trache, Robert Sweet, David
Walters, and Gillian Parekh for their feedback. Portions of this study
have been presented at the 12th, 13th, and 14th National Metropolis
Conference and Congress 2012.
Scott Davies, Department of Sociology, McMaster University, Kenneth
Taylor Hall, Room 627, 1280 Main Street West, Hamilton, Ontario L8S 4M4,
Canada. E-mail:
[email protected]
Table 1
Descriptive Statistics of Study Variables
(N = 8,614 students)
Dependent variables Variable description Mean/proportion (SD)
Maclean's rankings Maclean's rankings 6.07 (3.33)
averaged for 2007,
2008, and 2009 (min
= 2.00; max =
20.00)
Total income per Thousands of 29.30 (9.13)
full-time dollars in income
enrollment university could
(FTE) student spend per student
based on student
FTE for 2006 (min =
13.9; max = 41.96)
Total expenditures Thousands of 27.41 (8.09)
per FTE student dollars university
spends per student
based on FTE for
2006 (min = 13.75;
max = 39.99)
Total endowments per Dollar value of 16,822.73 (12,973.81)
FTE student university
endowment per
student for 2008
(min = 2,113.29;
max = 36,300.32)
Level 1 variables
Male Dummy variable = 0.46 (0.50)
1 if male
Same school Dummy variable = 1 0.85 (0.35)
if student present
in same school
between 2003-2004
and 2006-2007
Parent completed Dummy variable = 1 0.62 (0.49)
university if one or more
parent completed
university
Lives with two Dummy variable = 1 0.82 (0.39)
parents if both parents
living in household
Self-identified race Dummy variables
(reference = white)
Black 0.04 (0.20)
Latino/a 0.01 (0.09)
Southeast Asian 0.03 (0.16)
South Asian 0.23 (0.40)
East Asian 0.30 (0.46)
Mixed 0.04 (0.19)
Middle eastern 0.04 (0.20)
Other 0.01 (0.08)
Distance Distance (km) from 57.08 (88.05)
student residence
to university
institution (min =
0.25; max = 936.00)
Social engagement
Social relations Scale: Chronbach's 4.13 (0.76)
alpha = 0.73;
Combination of
three questions:
(1) students
getting along with
other students in
the school, (2)
students feeling
accepted by
students in the
school, (3)
students feeling
accepted by adults
in the school.
Unstandardized
scale range 1-5; 5
= never
Student perceptions Scale: Chronbach's 3.96 (0.85)
of instruction alpha = 0.77;
Combination of six
questions: (1)
teacher expects
student to succeed
in school, (2)
satisfied with the
way teachers teach,
(3) feeling
supported and
encouraged by
teachers, (4)
feeling comfortable
discussing problems
with teachers, (5)
school's staff
respect background
(e.g., cultural,
racial, religious),
(6) extra help is
available when
needed.
Unstandardized
scale range 1-5; 5
= none of them; for
extra help 5 =
never
School climate Scale: Chronbach's 3.47 (0.87)
alpha = 0.82;
Combination of
three questions:
(1) enjoying
school, (2) school
is a friendly and
welcoming place,
(3) school building
is an attractive
and great place to
learn.
Unstandardized
scale range 1-5; 5
= never
Grades
Average Grade 11/12 Average from all 76.81 (9.55)
mark courses in 2006-
2007 (min = 13.00;
max = 99.00)
Grade 9 math mark Grade 9 math or 75.30 (15.11)
first math mark
(min = 0.00; max =
100.00)
Level 2 variables
School level
Neighborhood family Median family 61.49 (24.14)
income income from 2001
census in thousands
of dollars (min =
15.00; max =
110.00)
Social assistance The proportion of 76.79 (23.55)
families in a
student's
neighborhood whose
income comes from
government sources
(based on data
about families with
children) (min =
3.00; max = 109.00)
Lone parents The proportion of 74.83 (28.93)
families families in a
neighborhood where
the parent does not
live with either a
spouse or common
law partner (based
on data about
families with
children) (min =
1.00; max = 109.00)
Language Proportion of 58.11 (20.77)
students speaking
English only (and a
language other than
English) in the
school (min = 9.17;
max = 92.24)
Note: Standard deviations are in parentheses.
Table 2
Correlations among University-Level Measures (n = 18)
Average Income (2006,
Maclean's rank $ thousands/FTE)
Average Maclean's rank
Income (2006, $ -0.641 **
thousands/FTE)
Expenditures (2006, $ -0.635 ** 0.960 ***
thousands/FTE)
Endowment (2006, $ -0.561 * 0.822 ***
thousands/FTE)
Expenditures (2006,
$ thousands/FTE)
Average Maclean's rank
Income (2006, $
thousands/FTE)
Expenditures (2006, $
thousands/FTE)
Endowment (2006, $ 0.769 ***
thousands/FTE)
Notes: Pearson coefficients are reported; calculating Spearman
coefficients yields similar results. Highly ranked universities are
assigned low values, that is, top rank = 1. Two-tailed * p < .05; ** p
< .01; *** p < .001.
Table 3
Rankings of University-Level Measures, Ontario Universities
Rank by
total income
Institution Institution per FTE
name type student
Queen's Medical doctoral 1
Waterloo Comprehensive 6
Toronto Medical doctoral 3
Guelph Comprehensive 5
Trent Primarily undergraduate 15
Wilfrid Laurier Primarily undergraduate 16
McMaster Medical doctoral 2
Carleton Comprehensive 10
Western Medical doctoral 4
York Comprehensive 14
Ottawa Medical doctoral 7
Windsor Comprehensive 12
Lakehead Primarily undergraduate 11
Laurentian Primarily undergraduate 9
Ryerson Primarily undergraduate 13
Brock Primarily undergraduate 17
Nipissing Primarily undergraduate 18
Rank by total
Rank by total income from
expenditures endowments Maclean's
Institution per FTE per FTE ranking
name student student 2007
Queen's 1 1 2
Waterloo 8 9 3
Toronto 3 2 4
Guelph 6 8 4
Trent 14 13 4
Wilfrid Laurier 16 18 6
McMaster 2 3 6
Carleton 11 6 7
Western 5 4 7
York 13 10 8
Ottawa 7 11 8
Windsor 12 14 10
Lakehead 15 5 11
Laurentian 9 15 10
Ryerson 10 17 11
Brock 17 19 15
Nipissing 18 16 20
Maclean's
averaged
Maclean's Maclean's rankings
Institution ranking ranking 2007, 2008,
name 2008 2009 2009
Queen's 2 2 2
Waterloo 3 3 3
Toronto 4 2 3
Guelph 4 4 4
Trent 4 6 5
Wilfrid Laurier 6 5 6
McMaster 6 6 6
Carleton 7 7 7
Western 7 10 8
York 8 9 8
Ottawa 8 10 9
Windsor 10 8 9
Lakehead 11 11 11
Laurentian 10 17 12
Ryerson 11 13 12
Brock 15 14 15
Nipissing 20 20 20
Table 4
Confirmed Attendance by Ontario University (n = 8,614)
Distance from TDSB
headquarters (5050
Yonge Street, Toronto)
Institution Frequency Percent (km)
Toronto 2,820 32.7 10.3
York 1,473 17.1 12.9
Ryerson 1,077 12.5 10.6
Waterloo 663 7.7 101
Guelph 468 5.4 72.4
Western 391 4.5 186
McMaster 365 4.2 63.5
Queen's 315 3.7 268
Wilfrid Laurier 226 2.6 98.8
Brock 127 1.5 109
Carleton 123 1.4 455
Ottawa 85 1 457
Trent 83 1 151
Windsor 56 0.7 360
Lakehead 20 0.2 1,372
Nipissing 16 0.2 338
Laurentian 14 0.2 377
Notes: Three years of postsecondary confirmations (2007, 2008, 2009).
W e do not have complete institutional-level data for the University
of Ontario Institute of Technology and have none for the Ontario
College of Art and Design. Neither of these institutions is included
in the Maclean's rankings.
Table 5
Linear Mixed-Effects Models with Random Intercept of Maclean's
Rankings on Student Characteristics, Social Engagement, and
Grades
Model 1
Coefficient (SE)
Fixed effects
Intercept 13.737 (0.221) ***
Student
characteristics
Male -0.214 (0.081) **
Same school 0.027 (0.118)
Neighborhood 0.001 (0.002)
family income
Parent completed 0.711 (0.089) ***
university
Lives with two 0.155 (0.108) ***
parents
Self-identified race
(ref: white)
Black -0.811 (0.220) ***
Latino/a -0.238 (0.462)
Southeast Asian 0.236 (0.268)
South Asian 0.521 (0.135) ***
East Asian 1.335 (0.120) ***
Mixed 0.340 (0.221)
Middle eastern 0.047 (0.223)
Other 0.575 (0.525)
Distance -0.288 (0.000)
Social engagement
Social relations
School climate
Rounded mark
Math
Random effects
Intercept 0.289 (0.079) ***
Residual 10.373 (0.183)
Pseudo [R.sup.2] 0.032
Deviance 33,627.28
AIC 33,661.29
BIC 33,776.49
Model 2
Coefficient (SE)
Fixed effects
Intercept 13.079 (0.321) ***
Student
characteristics
Male -0.205 (0.082) *
Same school 0.060 (0.119)
Neighborhood 0.002 (0.002)
family income
Parent completed 0.697 (0.089) ***
university
Lives with two 0.157 (0.108) ***
parents
Self-identified race
(ref: white)
Black -0.813 (0.222) ***
Latino/a -0.210 (0.461)
Southeast Asian 0.234 (0.267)
South Asian 0.500 (0.135) ***
East Asian 1.344 (0.120) ***
Mixed 0.333 (0.221)
Middle eastern 0.050 (0.224)
Other 0.866 (0.541)
Distance -0.288 (0.000)
Social engagement
Social relations -0.050 (0.063)
School climate 0.238 (0.055) ***
Rounded mark
Math
Random effects
Intercept 0.273 (0.076) ***
Residual 10.324 (0.183)
Pseudo [R.sup.2] 0.036
Deviance 33,370.88
AIC 33,408.88
BIC 33,537.50
Model 3
Coefficient (SE)
Fixed effects
Intercept 6.681 (0.445)
Student
characteristics
Male -0.007 (0.080)
Same school -0.086 (0.116)
Neighborhood 0.002 (0.002)
family income
Parent completed 0.421(0.087) ***
university
Lives with two 0.002 (0.105) ***
parents
Self-identified race
(ref: white)
Black -0.247 (0.216)
Latino/a 0.211 (0.447)
Southeast Asian 0.092 (0.259)
South Asian 0.538 (0.131) ***
East Asian 0.989 (0.120) ***
Mixed 0.506 (0.214) *
Middle eastern 0.273 (0.218)
Other 0.571 (0.524)
Distance -0.288 (0.000)
Social engagement
Social relations -0.082 (0.061)
School climate 0.154 (0.054) **
Rounded mark 0.073 (0.005) ***
Math 0.021 (0.003) ***
Random effects
Intercept 0.292 (0.078) ***
Residual 9.657 (0.172)
Pseudo [R.sup.2] 0.099
Deviance 32,865.90
AIC 32,907.89
BIC 33,050.00
Notes: N = 6,479 students (Model 1), 6.436 students (Model 2), and
6,420 students (Model 3) in 84 schools. Additional models (not shown)
included census measures of proportions of families in students'
neighbor hood on social assistance. proportions of single parents, and
proportions speaking English. None of these predictors significantly
improved overall model fit. Wald tests are reported adjacent to
variable names for blocks of dummy regressors. The distance
coefficient has been multiplied by 1,000.
* p < .05; ** p < .01; *** p < .001.
Table 6
Linear Mixed-Effects Models with Random Intercept of
Institutional Income per FTE Student on Student
Characteristics, Social Engagement, and Grades
Model 1 Model 2
Coefficient (SE) Coefficient (SE)
Fixed effects
Intercept 4.033 (0.090) 4.348 (0.130)
Student
characteristics
Male -0.132 (0.033 -0.125 (0.033)
Same school 0.032 (0.048) 0.045 (0.048)
Neighborhood 0.002 (0.001) 0.002 (0.001)
family income
Parent completed 0.295 (0.036) 0.288 (0.036)
university
Lives with two 0.076 (0.043) 0.073 (0.043)
parents
Self-identified race
(ref: white)
Black -0.338 (0.089) -0.332 (0.089)
Latino/a -0.151 (0.186) -0.143 (0.185)
Southeast Asian 0.090 (0.108) 0.089 (0.107)
South Asian 0.291 (0.054) 0.279 (0.055)
East Asian 0.498 (0.049) 0.505 (0.049)
Mixed 0.112 (0.089) 0.107 (0.089)
Middle eastern 0.074 (0.090) 0.070 (0.090)
Other 0.182 (0.210) 0.270 (0.217)
Distance 0.193 (0.000) 0.193 (0.000)
Social engagement
Social relations 0.002 (0.025)
School climate 0.086 (0.022)
Average Grade
11/12 mark
Grade 9 math mark
Random effects
Intercept 0.061 (0.015) 0.059 (0.015)
Residual 1.669 (0.030) 1.664 (0.183)
Pseudo [R.sup.2] 0.055 0.059
Deviance 21,805.12 21,639.80
AIC 21,839.12 21,677.80
BIC 21,954.31 21,806.42
Model 3
Coefficient (SE)
Fixed effects
Intercept 6.967 (0.179)
Student
characteristics
Male -0.047 (0.032)
Same school -0.010 (0.047)
Neighborhood 0.002 (0.001)
family income
Parent completed 0.182 (0.035)
university
Lives with two 0.014 (0.042)
parents
Self-identified race
(ref: white)
Black -0.104 (0.087)
Latino/a 0.024 (0.179)
Southeast Asian 0.034 (0.104)
South Asian 0.296 (0.053)
East Asian 0.360 (0.048)
Mixed 0.177 (0.086)
Middle eastern 0.169 (0.088)
Other 0.157 (0.210)
Distance 0.193 (0.000)
Social engagement
Social relations -0.009 (0.024)
School climate 0.051 (0.022)
Average Grade 0.030 (0.002)
11/12 mark
Grade 9 math mark 0.008 (0.001)
Random effects
Intercept 0.059 (0.015)
Residual 1.554 (0.028)
Pseudo [R.sup.2] 0.12
Deviance 21,148.52
AIC 21,190.53
BIC 21,332.64
Notes: N = 6,479 (Model 1), 6,436 (Model 2), and 6,420 students
(Model 3) in 84 schools. Additional models (not shown) included
measures of social assistance, family structure, and language at
school, but none of these additional predictors significantly
improved the overall model fit. Wald tests are reported adjacent to
variable names for blocks of dummy regressors. The distance
coefficient has been multiplied by 1,000.
* p < .05; ** p < .01; *** p < .001.
Table 7
Linear Mixed-Effects Models with Random Intercept of
Endowments per FTE Student on Student Characteristics, Social
Engagement, and Grades
Model 1 Model 2
Coefficient (SE) Coefficient (SE)
Fixed effects
Intercept 140.611 (3.880) 154.091 (5.626)
Student
characteristics
Male -7.355 (1.423) -7.085 (1.426)
Same school 1.010 (2.069) 1.625 (2.081)
Neighborhood 0.057 (0.035) 0.058 (0.035)
family income
Parent completed 9.814 (1.548) 9.506 (1.553)
university
Lives with two 1.646 (1.882) 1.339 (1.890)
parents
Self-identified race
(ref: white)
Black -11.863 (3.851) -11.751 (3.877)
Latino/a -2.468 (8.070) -2.093 (8.057)
Southeast Asian 4.366 (4.674) 4.336 (4.667)
South Asian 8.665 (2.363) 8.234 (2.366)
East Asian 16.583 (2.109) 16.815 (2.112)
Mixed 4.664 (3.856) 4.316 (3.857)
Middle eastern 6.607 (3.895) 6.447 (3.914)
Other 1.823 (9.159) 4.339 (9.459)
Distance -0.043 (0.008) -0.043 (0.008)
Social engagement
Social relations -0.170 (1.094)
School climate 4.001 (0.963)
Average Grade
11/12 mark
Grade 9 math mark
Random effects
Intercept 100.629 (25.830) 98.440 (25.418)
Residual 3,161.398 (55.910) 3,149.382 (55.884)
Pseudo [R.sup.2] 0.027 0.031
Deviance 70,690.76 70,196.36
AIC 70,724.77 70,234.36
BIC 70,839.96 70,362.99
Model 3
Coefficient (SE)
Fixed effects
Intercept 253.877 (7.839)
Student
characteristics
Male -3.987 (1.407)
Same school -0.767 (2.046)
Neighborhood 0.055 (0.034)
family income
Parent completed 5.338 (1.534)
university
Lives with two 1.029 (1.850)
parents
Self-identified race
(ref: white)
Black 3.100 (3.812)
Latino/a 4.289 (7.864)
Southeast Asian 2.380 (4.560)
South Asian 8.996 (2.323)
East Asian 11.643 (2.117)
Mixed 7.007 (3.765)
Middle eastern 10.297 (3.842)
Other 0.041 (9.229)
Distance -0.042 (0.008)
Social engagement
Social relations -0.670 (1.070)
School climate 2.721 (0.944)
Average Grade 1.194 (0.093)
11/12 mark
Grade 9 math mark 0.264 (0.057)
Random effects
Intercept 102.694 (26.215)
Residual 2,992.675 (53.181)
Pseudo [R.sup.2] 0.077
Deviance 69,699.00
AIC 69,741.00
BIC 69,883.11
Notes: N = 6,479 students (Model 1), 6,436 students (Model 2), and
6,420 students (Model 3) in 84 schools. Additional models (not
shown) included measures of social assistance, family structure, and
language at school, but none of these additional predictors
significantly improved the overall model fit. Wald tests are
reported adjacent to variable names for blocks of dummy regressors.
* p < .05; ** p < .01; *** p < .001.
Table 8
Mixed-Effects Logistic Regression with Random Intercept of
Attending a Top-Ranked Ontario University Outside of the
Greater Toronto Area on Student Characteristics, Social
Engagement, and Grades
Model 1
Coefficient (SE)
Fixed effects
Intercept -2.012 (0.282) ***
Student
characteristics
Male -0.411 (0.101) ***
Same school -0.007 (0.148)
Neighborhood 0.004 (0.002)
family income
Parent completed 0.282 (0.120)
university
Lives with two 0.046 (0.141)
parents
Self-identified race
(ref: white)
Black -0.337 (0.389)
Latino/a -0.251 (0.801)
Southeast Asian -0.497 (0.549)
South Asian 0.380 (0.172) *
East Asian 0.232 (0.139)
Mixed 0.099 (0.256)
Middle eastern 0.492 (0.372)
Other -0.226 (0.641)
Distance 0.002 (0.000) ***
Social engagement
Social relations
School climate
Averaged Grade
11/12 mark
Grade 9 math
mark
Random effects
Intercept 0.068 (0.045) **
Deviance 2,538.00
AIC 2,570.00
BIC 2,662.95
Model 2
Coefficient (SE)
Fixed effects
Intercept -2.608 (0.416) **
Student
characteristics
Male -0.390 (0.102) ***
Same school 0.003 (0.150)
Neighborhood 0.004 (0.002)
family income
Parent completed 0.261 (0.121) *
university
Lives with two 0.029 (0.142)
parents
Self-identified race
(ref: white)
Black -0.327 (0.390)
Latino/a -0.290 (0.801)
Southeast Asian -0.515 (0.549)
South Asian 0.345 (0.174) *
East Asian 0.237 (0.140)
Mixed 0.076 (0.261)
Middle eastern 0.454 (0.373)
Other 0.013 (0.653)
Distance 0.002 (0.000) **
Social engagement
Social relations 0.051 (0.079)
School climate 0.117 (0.070)
Averaged Grade
11/12 mark
Grade 9 math
mark
Random effects
Intercept 0.069 (0.046) *
Deviance 2,520.80
AIC 2,556.80
BIC 2,661.28
Model 3
Coefficient (SE)
Fixed effects
Intercept -6.877 (0.658)
Student
characteristics
Male -0.287 (0.104) **
Same school -0.059 (0.155)
Neighborhood 0.005 (0.002) *
family income
Parent completed 0.120 (0.125)
university
Lives with two 0.001 (0.146)
parents
Self-identified race
(ref: white)
Black 0.018 (0.396)
Latino/a 0.118 (0.831)
Southeast Asian -0.458 (0.555)
South Asian 0.471 (0.177) **
East Asian 0.029 (0.147)
Mixed 0.204 (0.266)
Middle eastern 0.745 (0.385)
Other -0.212 (0.665)
Distance 0.002 (0.000) ***
Social engagement
Social relations 0.017 (0.082)
School climate 0.079 (0.071)
Averaged Grade 0.050 (0.008) ***
11/12 mark
Grade 9 math 0.008 (0.005)
mark
Random effects
Intercept 0.040 (0.034)
Deviance 2,417.57
AIC 2,457.57
BIC 2,573.57
Notes: N = 2,464 students (Model 1), 2,451 students (Model 2), and
2,441 students (Model 3) in 84 schools. Additional models (not shown)
included measures of social assistance, family structure, and language
at school, but none of these additional predictors significantly
improved the overall model fit. Wald tests are reported adjacent to
variable names for blocks of dummy repressors.
* p < .05; ** p < .01; *** p < .001.
