RECENT
IMMIGRANTS AND UNDERCLASS
POPULATION IN CANADIAN CITIES
Conclusion
The project have looked at recent immigrant
population and poverty in three Canadian cities; Montreal, Vancouver
and Toronto. Relevant socio-economic factors that characterize an
underclass population have been identified as low income economic
families and persons without income with regards to their association
with RIP. Though these socio-economic variables indicated strong
association in census tracts with high proportion of RIP, the overall
poverty index has little dependence on RIP since their proportion in
census tracts are minimum relative to the individual census tracts.
Overall poverty index with regards to RIP have declined from 1996 to
2001. However, RIP has varying degrees of influence on poverty index in
different cities. Montreal has experienced the highest influence though
with relatively low proportion of RIP followed by Toronto then
Vancouver. The study also revealed patterns of recent immigrant
attributes in Canadian cities that are consistent with earlier work by
David Ley and Heather Smith.
Finally, the project examined the
results of ecological fallacy (or discrepancy in spatial distribution
due to different area and scale of aggregation). The exact estimates of
error incurred through such procedures have not being verified.
Aggregation of census data at Census Tract (large area) and Enumeration
Area (relatively small area) levels does uncover different spatial
distribution and variability of the same data and that brings into
question previous conclusions about the correlation between immigrants
and urban underclass.
A number of questions remain for
further research. The study has worked with aggregate data; census
tract and enumeration area scaled data, focusing on the big picture.
Subsequent study could extend this analysis to disaggregated or small
scale data to unearth in-depth results at fined spatial resolution.
Besides, socio economic variables could be gathered with respect to
specific groups to enable objective prediction of future occurrences. A
finally necessary extension of this present study is a more qualitative
research to quantify the exact discrepancies incurred in aggregating
census data at large areas.