The power of a strong research report is the way it changes our civil discourse. In Toronto, Poverty by Postal Code, the Strong Neighbourhoods Taskforce Report, MISWAA, and University of Toronto/St. Christopher House research reports on neighbourhood change have all played a robust part in recent public policy discussions. Such reports re-frame the way we think about our city and each other.
So, when the TDSB’s Inner City Advisory Committee (ICAC) asked the board’s research staff to do a comparative analysis tracking students’ academic achievement patterns against the Neighbourhood Change CURA’s “Three Cities” report, it seemed like a good idea. The Three Cities report had splashed over the front pages of our daily newspapers and underscored the growing inequality and geographic separations within our city. ICAC expected the results would provide further insight into schools in low-income neighbourhoods.
On first analysis, however, the results were disappointing.
Several measures of educational achievement were tested, including:
- EQAO Grade 3 Math scores
- EQAO Grade 6 Math scores
- Grade 9 science results
- Grade 9-10 Academic program
- Ontario Secondary School Literacy Test (OSSLT)
- Access to Ontario post-secondary institute
Yet, the correlation between the “Three Cities” and students’ academic performance was weak — likely for two reasons: first, the Neighbourhood Change/Three Cities analysis used average incomes in its comparisons of neighbourhoods, a known, weaker predictor of academic performance; and, secondly, almost half of the TDSB’s highest-need schools are actually located outside the areas identified as the “third city” or lowest-income areas.
Nevertheless, the school board’s researcher charged with the task, Dr. Rob Brown, persevered in his analysis.
The “three cities,” described by Dr. Hulchanski et. al., break down into further categories. For instance, high income areas are comprised of Elite neighbourhoods which were rich and have remained rich and Gentrifying neighbourhoods which have become high-income in recent decades.
Poor areas of the city break out into four main areas:
- Youngest suburbs (Lower density, homeowners, larger families, white-collar jobs, high visible minority population, higher Chinese population)
- Older suburbs (Lower density, more seniors, lower education levels, higher White population)
- Renters (Immigrant reception areas, highest density, apartment towers, high levels of education, low incomes, more South Asian)
- Lowest incomes (Highrise rental and social housing, low incomes, lower education, manual labour jobs, higher Black population, more single parents)
So, when Brown looked to see whether academic achievement tracked with these categories, the patterns were more interesting. What he found gives new insight into some of the debates at the school board around race and poverty.
Predictably, the highest performing students were almost consistently the students who lived in the Elite neighbourhoods. However, in two instances they were beaten, in Grade 3 Math and Grade 9 Science — both times by students, in the “third city,” from the Youngest Suburbs. In fact, in all but two of the measures, students in the Youngest Suburbs also out-performed the Gentrifying group of students in “city one”: Taking academic program in Grade 9-10, and the OSSLT.
University admissions tracked a similar path. 53% of Elite students confirmed attendance at an Ontario university, followed by 49% of students in the Youngest Suburbs. These two groups were also the most likely to have applied to post-secondary education. Students in every other neighbourhood type lagged behind in the 33% – 36% range, except for high school students in the Lowest-income neighbourhoods, where only 25% confirmed university attendance (and where 57% did not apply to any level of higher education).
In comparison, students from the other parts of the “third city,” Older Suburbs and Renters, were often within a few percentage points of each other and approaching, or occasionally surpassing, the performance of middle-income students in “city two.” The lowest academic performers were the Lowest Income, except in the case of Grade 3 math, where they beat the Gentrifying neighbourhoods.
So, the analysis shows that while income, or the lack there-of, can be an important predictor of students’ academic performance, it is not a determinant. While Brown himself doesn’t speculate, the interesting part of this work is to imagine what protective factors might be helping some low-income students to compete.
A perfunctory analysis might note that the distinguishing factors between the different “cities” are the racial and ethnic compositions of them. Buttressing the weight of this is the first release of the TDSB’s Student Census which made headlines when it was published because of the analysis which how students of various ethno-cultural backgrounds were performing in school. But that initial report stopped there at these correlations, ipso facto, not looking to control other factors, such as poverty, lone parent status, low education levels and other risk factors found in each of these neighbourhoods.
I would argue a deeper, more nuanced picture emerges from Brown’s ICAC study, one which outlines the structuralist nature of educational achievement. Because the neighbourhood categories were more homogenous, it was possible to examine some of the complex interplays of income and race and, more importantly, the social capital students were able to access.
Within the context of the City of Toronto, these factors play out along a racial dimension, in other places, they may play out along other lines of identity, of accent or class or another form of “othering.” We need to think though the root cause of the barriers. For instance, racism, rather than race, per se, may be a barrier, but so is limited access to social and economic capital or access to strong, supportive social networks. Race, ethnicity and culture are the shorthand for a much more complex picture, which encapsulates access to resources and opportunities, individual and systemic racism, community expectations and a wide range of other social determinants.
So, for instance, students in the Youngest Suburbs were part of a cultural heritage that holds scholarship in esteem, where white-collar jobs were more common, and where family structures were wider. In contrast, students in the Lowest Income neighbourhoods were more likely to live in low-quality (rental, crowded) housing, with poorer job prospects, fewer family supports, and fewer role models who had attended higher education. Students in the Youngest Suburbs and the Renters have also more likely been exposed to a second language, which can improve learning.
These apparent racial divisions are the evidence of deeper divides within the city. They represent the unequal division and distribution of resources among us. These racial divides allow the easy concentration of resources within family, kinship, and friendship networks, encasing the economic and social capital that families and neighbourhoods bring to bear on its own young. The result is that those with the fewest resources are least likely to apply to university, whereas those who still have a strong sense of aspiration, positive supports, and role models are more likely to have better outcomes.
This peer effect is underscored by the work of David Harding at the University of Michigan. He found that “disadvantaged neighborhoods exhibit greater heterogeneity in college goals and that adolescents in more heterogeneous neighborhoods are more likely to change educational goals over time and are less likely to act in concert.” Essentially, more kids in richer neighbourhoods attend university because they are expected to do so.
What Brown’s research underscores is that poverty is about more than income. It’s about the inoculative supports which many lack.
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