Summary Of: Are Immigrants Buying to Get In?: The Role of Ethnic Clustering on the Homeownership Propensities of 12 Toronto Immigrant Groups, 1996-2001
Bibliographic record
Abstract
This article summarizes findings from the research paper entitled Are immigrants buying to get in? The role of ethnic clustering on the homeownership propensities of 12 Toronto immigrant groups, 1996-2001. Spatial assimilation theory is a model of status attainment that links the spatial and social positions of minority group members (Massey and Denton 1985). If applied to immigrants, the model would suggest that immigrants would first cluster in typically poor neighbourhoods with high concentrations of co-ethnics, but that ethnic concentration should be temporary and of declining utility. Once an immigrant family's socioeconomic status improves, they should merge into the residential 'mainstream' by moving to a better, and typically less segregated, neighbourhood (Massey and Denton 1985). Further, although housing tenure is not an explicit dimension of spatial assimilation theory, given the well-established relationship between income, human capital and homeownership (Balakrishnan and Wu 1992; Laryea 1999), and the importance of homeownership as an indicator of well-being and residential assimilation (Myers and Lee 1998), part of an immigrant family's socioeconomic ascent should be a shift from tenant to homeowner (Alba and Logan 1992). Spatial assimilation theory would further predict that same-group concentration should be inversely related to homeownership since ethnic enclaves are typically conceived of as poor rental zones (Fong and Gulia 1999; Myles and Hou 2004). Recent research (Alba and Nee 2003; Logan, Alba, and Zhang 2002), however, finds that some immigrant groups may be choosing against spatial assimilation to form more durable 'ethnic communities' (Logan, Alba, and Zhang 2002), giving rise to a positive and growing 'enclave effect' on homeownership (Borjas 2002). In this paper, an enclave effect is evaluated as an explanation for the 1996-2001 homeownership patterns of Toronto's 12 largest recent immigrant groups. Using longitudinally-consistent and temporally-antecedent 1996 neighbourhood ethnic composition data this paper aims to determine if immigrants buy homes outside their enclaves or prefer an owner-occupied neighbourhood of same-group members. To this end, the paper discusses the potential benefits of living and buying in an enclave; it develops a predictive framework for determining which groups might benefit from owner-occupied ethnic communities; it also examines the issue of 'neighbourhood disequilibrium' and evaluates the enclave effect on homeownership using a sample of recent (1996-2001) movers, their 1996 neighbourhood ethnic characteristics, and bivariate probit models with sample selection corrections (Van de Ven and Van Praag 1981).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".