Data As Evidence
Many people believe that their lives are shaped by the
individual and by the decisions that they make over time, however many
sociologists have researched this topic and have developed the theory that
people are the products of their environments. Last night I decided to explore
this idea by picking three of the forty largest counties in Illinois to
research some of their demographics. First I chose the three counties based on their
median family incomes, and I wanted to see large difference in social data so I
chose the county with the highest (DuPage-$98,000), with the lowest (Jefferson-$54,000),
and a happy median (Grundy-$78,000). I questioned what may have caused these variations
in income so I turned to education looking the ratio of the populations that
had achieved a college degree. The results were rather linear with the income
data, DuPage County once again achieved the highest (49.6%), Jefferson with one
of the lowest (16.9%), and Grundy sits in the middle again (26.9%). This data
correlation of DuPage having the best statics and Jefferson having the worst
ran all through a number of areas such as Teen Births, Child Abuse, and Child
Poverty. I then thought that this might have to do with a difference in ethnic
populations being the driving force, but in fact all three counties have almost
identical data on race with white being the largest (60%-80%) and black being the
lowest (about 10%). So I then turned to
geographic locations and I found that DuPage was actually the closest county to
Chicago without actually being in Chicago, Grundy was nearby but not as close
to Chicago, and Jefferson is actually all the way down by Kentucky.
After reviewing
all of the data the only thing that I could hypothesis from it was that all of
these statistics were caused by the geographic location of the counties through
the communitive property. Since Chicago is one of the largest and most
economically active areas in both the US and world, some of best jobs are
within the city. People come from all over to work in Chicago, most of whom are
highly educated and work high paying corporate jobs. Since people tend to shape
a community these areas have great resources and programs that assist with the
average problems a family/community may be facing.
This
correlation of data and its analysis may be able to change the effects of this geographical
differences. More troubled counties can look at this data for answers on how to
better their communities by modeling their programs and resources available
after those closer to the city. This data could also serve as an instigator for
families who want to break the “cycle of poverty” and move to these areas to
provide better lives for future generations.
While
the data appears to support my hypothesis, geographic location in relation to
the city may not be the main cause for the variation in these counties. Often
times people with more privileged backgrounds tend to live in privileged areas
and vice versa, so the variations may be caused by a “cycle of poverty” or even
a “cycle of wealth” which shape this counties. I believe that the best way to
further explore this correlation is by case studies of those who have had their
families in a single area for multiple generations, and those who have moved
between communities. The results of these case studies will help determine if
lives truly are products of environment or if they are bounded by the cycle of
poverty.
Sources
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