Data Management
Culminating Project
Factors That Affect Income of Canadians
By: Jodi Morden & Mike Curridor
Sacred Heart High School, Ottawa
Factors Investigated
 Location,
Sex, Age, and Education
Hypothesis


All of these factors contribute to the fluctuation of one’s
income
Therefore living in a big city, being male, between 25-35
years of age, and having a university level education.
Causes one to have a higher income.
Location!!!
Location: City
Average Income of the 5 largest cities in Canada: $54,469
(Toronto, Montreal, Vancouver, Ottawa, Calgary)
Average Income of the 20 Other Largest Cities in Canada: $47,952
Average Income of Cities
60000
Average
Income
40000
20000
0
The Big 5
Other 20
City
Average
Income
The Big 5
80000
60000
Income 40000
Toronto
$60,110
20000
0
Montreal
$44,593
Vancouver
$54,055
Ottawa
$56,760
Calgary
$56,829
Toronto
Montreal
Vancouver
Ottaw a
Calgary
We can conclude that living in a big city affects
one’s income. However, your income may also
affect one living in a big city. Therefore we can
conclude that there is a relationship, but that it is
causal. Also the city has a direct correlation with
the type of work. Due to a uniform graph, we
can see no major difference between the cities.
Measures of Spread
•Mean:
$54,469
•Median: Ottawa ($56,670)
•Standard Deviation: 5298.3
Location: Province
Province
Average Income
Newfoundland
$41,064
PEI
$42,028
Nova Scotia
$41,446
New Brunswick
$41,090
Quebec
$42,229
Ontario
$54,291
Manitoba
$43,404
Saskatchewan
$42,685
Alberta
$51,118
British Columbia
$50,667
Yukon
$54,953
Northwest Territories
$60,506
Province
Average Provincial Income
NWT
Yuk
BC
Altb
Sask
Man
Ont
Que
NB
NS
PEI
NFLD
60506
54953
50667
51118
42685
43404
54291
42229
41090
41446
42028
41064
We can see that the Yukon and the Northwest Territories have the highest
income. Although this may appear odd at first, if investigated it makes quite a
bit of sense. There is essentially no unemployed people in these provinces,
because most of the unemployed moved south to find work. The employed
people who do live there are given special incentives by their employer to live
there ( i.e. more money). Therefore the small number of people that do live
there are making a large sum of money.
Measures of Spread
•Median: $43,044.50
•Mean:
$47,123.42
•Standard Deviation: 6513.37
Sex!!!
Sex
Income VS. Sex
$100,000+
$80,000-$99,999
$60,000-$79,999
$40,000-$59,999
$20,000-$39,999
$0-$19,999
0
Males
50
Females
100
150
Frequency
200
250
Income
Males Females
$0-$19,999
93
141
$20,000-$39,999
107
100
$40,000-$59,999
73
28
$60,000-$79,999
25
7
$80,000-$99,999
6
1
$100,000+
3
2
The number of males and females
earning a high income ($100,000+) are
relatively low, but equal. As we move
down the income brackets the division
becomes more and more. Eventually
leveling back off at the lower income
brackets. We can interrupt this in
saying, woman either have a very small
or very large income. Therefore the
majority of the middle earners are
male.
Measures of Spread

Skewed Right

Mean:
Males: $33,810.58
Females: $23,619.58

Median:
Males: $0-$19,999
Females: $20,000-$39,999

Mode:
Males: $20,000-$39,999
Females: $0-$19,999

Standard
Deviation:
Males: 21 220.21
Females: 17 022.43
Age!!!
Age
Source: 1991 Census microdata
Income Vs. Age
140000
120000
Income
100000
80000
60000
40000
20000
0
0
20
40
60
Age
80
100
The income of people increases from 18,
and peaks around 50. Then takes a
sharp decline. Except for certain
outliers, we can conclude that once a
person passes the age of 50 their income
declines. This is mainly because of
inability to work full days and the
retirement factor. All these can
contribute to the decline in wages and
number of hours worked daily. This
graph shows a strong positive
correlation, which further substantiates
the affect that age has on one’s income.
Education!!!
Education
Income of $0-$39,999
19%
16%
Highschool
Less than Highschool
25%
40%
Less than University
University
Income of $40,000-$79,999
18%
13%
Highschool
Less than Highschool
33%
36%
Less than University
University
Income of $80,000+
8%
8%
Highschool
25%
Less than Highschool
Less than University
59%
University
Measures of Spread
•Mode:
$0-$39,999- Less than University 40%
$40,000-$79,999- Less than University 36%
$80,000+- Less than High school 59%
*Mode is only applicable to this data set.
The data was surprising with respect to the high income
earners. This was because 56% of the high income earners
had education of less than high school. Even within the
middle income bracket, the majority of earners had an
education of less than university. Therefore we can see no
evidence which supports our original prediction.
Summarization





To recap- we predicted that the most important factors that affect a
person’s income are location, sex, age, and education. We predicted
that living in a big city, being male, being between the age of 35 and
45, and having a university level education.
The first factor that we looked at was location, we concluded that if
you live in a big city, to be more precise Toronto, you are more likely
to earn a higher income. Also we determined that this relationship was
causal.
The second factor that we looked at was sex, we discovered that males
earn more on average than females.
The third factor that we looked at was age, we found that at the age of
18 a person’s income increased steadily. Peaked around 50 years old,
and began to decrease afterwards.
The final factor that we looked at was education. From the
examination we discovered a surprising twist in the data, it showed
that the majority of high income earners had an education less than
high school.
Conclusions

From the data that we have analyzed, we can predict that
being male, living in a big city, being 35-45 years of age,
increase one’s chance of high income.

We were incorrect with our initial prediction of education.
A possibility to explain the number of high income earners
who had a less than high school education could be, taking
over a family run company. The person knows they have a
well paying job once they leave school, therefore they may
see school as unnecessary.
Sources

Statistics Canada 2001 Census – Average
Income based on location (province, CMA)

Statistics Canada Micro data from the 1991
Census – Income based on Sex, Education,
and Age
The End
Thank You