Thunder Bay and District Poverty Reduction Strategy: Annual Report
Saku Pinta, PhD and Mike Jones, MSW
The Thunder Bay and District Poverty Reduction Strategy is pleased to present its first
Annual Report. The Report draws on data drawn from a variety of sources including
original data generated from the Lakehead Social Planning Council’s Volunteer Income
Tax clinic. The objective is to identify broader social trends, provide data that can be
compiled on an annual basis in order to track progress in the Strategy, as well as to
provide information that will help to inform evidence-based policy making.
Section 1, Defining Poverty, provides the standard statistical measure of poverty used
in this report as the Low Income Measure (After-Tax) income threshold. This
information is given visual representation over a period of more than 10 years, and
correlated with the unemployment rate .................................................................. Page 3
Section 2, the 2013 Income Tax Data Sample Analysis, uses a representative sample
of non-identifying data collected from the Volunteer Income Tax Clinic to provide a
snapshot of the low income population in Thunder Bay. The data was analyzed using
Statistical Package for the Social Sciences (SPSS) software to determine statistically
significant correlations in a number of areas including demographics, income, and
housing................................................................................................................... Page 4
Section 3, the Wage Calculation Comparison, contextualizes the Low Income
Measure by representing a number of different income sources as before-tax annual
and hourly wages. Importantly, the minimum wage is shown to be insufficient to meet
the Low Income Measure threshold ..................................................................... Page 15
Section 4, the Canadian Centre for Policy Alternatives Living Wage Calculation,
uses a standardized methodology to provide a living wage calculation specific to
Thunder Bay......................................................................................................... Page 16
Section 5, Comparable Cities to Thunder Bay, provides a peer-group of
Canadian cities and statistical information that can be tracked for comparative
purposes. ............................................................................................................. Page 16
Section 6, Housing, shows the correlation between Thunder Bay vacancy rates and
social housing waitlists, and the related rise in emergency shelter occupancy .... Page 20
Section 7, Conclusions and Recommendations, argues that while measures of
relative poverty are trending downward, the position of the poorest and most
marginalized residents of Thunder Bay are declining dramatically. Three
recommendations are proposed that can help to make a difference in the lives of low
income individuals ................................................................................................ Page 21
1. Thunder Bay and District Poverty Reduction Strategy: Annual Report
Saku Pinta, PhD and Mike Jones, MSW
The Thunder Bay and District Poverty Reduction Strategy is pleased to present its first
Annual Report. The Report draws on data drawn from a variety of sources including
original data generated from the Lakehead Social Planning Council’s Volunteer Income
Tax clinic. The objective is to identify broader social trends, provide data that can be
compiled on an annual basis in order to track progress in the Strategy, as well as to
provide information that will help to inform evidence-based policy making.
Section 1, Defining Poverty, provides the standard statistical measure of poverty used
in this report as the Low Income Measure (After-Tax) income threshold. This
information is given visual representation over a period of more than 10 years, and
correlated with the unemployment rate .................................................................. Page 3
Section 2, the 2013 Income Tax Data Sample Analysis, uses a representative sample
of non-identifying data collected from the Volunteer Income Tax Clinic to provide a
snapshot of the low income population in Thunder Bay. The data was analyzed using
Statistical Package for the Social Sciences (SPSS) software to determine statistically
significant correlations in a number of areas including demographics, income, and
housing................................................................................................................... Page 4
Section 3, the Wage Calculation Comparison, contextualizes the Low Income
Measure by representing a number of different income sources as before-tax annual
and hourly wages. Importantly, the minimum wage is shown to be insufficient to meet
the Low Income Measure threshold ..................................................................... Page 15
Section 4, the Canadian Centre for Policy Alternatives Living Wage Calculation,
uses a standardized methodology to provide a living wage calculation specific to
Thunder Bay......................................................................................................... Page 16
Section 5, Comparable Cities to Thunder Bay, provides a peer-group of
Canadian cities and statistical information that can be tracked for comparative
purposes .............................................................................................................. Page 16
Section 6, Housing, shows the correlation between Thunder Bay vacancy rates and
social housing waitlists, and the related rise in emergency shelter occupancy .... Page 20
Section 7, Conclusions and Recommendations, argues that while measures of
relative poverty are trending downward, the position of the poorest and most
marginalized residents of Thunder Bay are declining dramatically. Three
recommendations are proposed that can help to make a difference in the lives of low
income individuals ................................................................................................ Page 21
2
Defining Poverty
While there is no officially recognized poverty line calculation in Canada, the Low
Income Measure (LIM) is one the most frequently used metrics used to determine how
many people in a community live in poverty. The LIM is calculated as 50% of the
median income, adjusted for family size. The LIM is approximately $21,000 for an
individual (based on the 2011 LIM After-Tax calculation, adjusted for inflation). Based
on this calculation, approximately 15,100 thousand people in Thunder Bay live below
the LIM.
Aside from the LIM, it is useful to draw a distinction between “relative” and “absolute”
poverty. Relative poverty is the lack of the minimum amount of income required in order
to maintain the average standard of living in a given society. Absolute poverty, on the
other hand, is the lack of one or more basic needs over a period long enough to
endanger life or to cause harm.
This distinction is important in the context of this report as the data collected shows a
very gradual decrease in social indicators of relative poverty (for example, the LIM and
unemployment rate) and a dramatic rise in social indicators of absolute poverty (for
example, increases in emergency shelter usage).
The graphs below show the gradual downward trend of the percentage of people in low
income in Thunder Bay and this trend in relation to the unemployment rate.
Percentage of People in Low Income Measure After-Tax in Thunder Bay
14.5
14
Percentage
13.5
13
12.5
12
11.5
2000
2001
2002
2003
2004
2005
2006
Year
3
2007
2008
2009
2010
2011
2012
Percentage of People in Low Income-After Tax in Thunder Bay
14.5
9
8
14
Percentage
13.5
6
5
13
4
12.5
3
2
Percentage in LI-AT
12
Unemployment Rate
1
Linear (Percentage in LI-AT)
Linear (Unemployment Rate)
11.5
2000
2001
2002
2003
2004
2005
2006
2007
2008
0
2009
2010
2011
2012
Year
2. 2013 Income Tax Data Sample Analysis
For more than ten years the Lakehead Social Planning Council's Community Volunteer
Income Tax Program (CVITP) has been completing tax returns for low income members
of the Thunder Bay community. As the program has grown, so has its impact. While the
program has a clear, positive financial impact on members of the community, it has also
opened other possibilities for data collection and analysis.
When the Canadian Government made the decision to rescind the long-form census
and begin use of a voluntary National Household Survey, the ramifications for
researchers were and continue to be damaging. Low income individuals were already
one of the most underrepresented groups and the changes to census data only
reinforce this reality.
4
Unemployment Rate
7
In 2013, the CVITP collected and completed over 4000 tax returns for low income
individuals in the Thunder Bay community and surrounding areas. Low income as
defined by the tax clinic is a single individual making $20,000 or less or a couple making
a combined $30,000 or less (special circumstances notwithstanding).
The data contained within the over 4000 tax returns contributes to a significant annual
sample of low income members of Thunder Bay that can be used to introduce more
evidence-based policies that benefit those who need it most. The data made available
by the CVITP will be collected annually and will help the LSPC's social planners and
researchers identify, assess and contextualize trends year-over-year.
Of the 4000+ tax returns of low income members of the Thunder Bay community and
district that have been completed by the CVITP, we have selected a random sample of
1204. The 1204 tax returns will provide a more than adequate sample for assessing,
analyzing and extrapolating data to the low income community in Thunder Bay and
district.
Although the CVITP does not represent a true “random sampling,” it is nonetheless a
representative sample of the low income population in Thunder Bay. This is
demonstrated by the maps below. The map of the left represents CVITP users and
their locations by postal code in the Thunder Bay Census Metropolitan Area. The map
on the right is from Statistics Canada and shows the distribution of people living below
the Low Income Measure by census tract. The two maps are nearly identical in terms of
the distribution of low income individuals.
5
2.1 Demographics
Status vs. Non-status
Status Canadian Indian
Frequency
Valid
Percent
Valid Percent
Cumulative Percent
Non-Status
537
44.6
44.6
44.6
Status
667
55.4
55.4
100.0
Total
1204
100.0
100.0
The sample revealed that the majority (55.4%) of the individuals using the tax clinic are
Status (Aboriginal) Canadians.
Birth Dates
In graphing every birth-date within the 1204 person sample, there is a distinct negative
skew of the data towards a younger population.
6
Age Ranges
Age Ranges
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
1920-1940
44
3.7
3.7
3.7
1941-1960
275
22.8
22.8
26.5
1961-1980
447
37.1
37.1
63.6
1981-2000
438
36.4
36.4
100.0
1204
100.0
100.0
Total
The sample revealed that 73.5% of the individuals who returned their taxes were born
after 1960 (under 55 years old). The largest category of individuals using the tax clinic
was those born between 1961 and 1980 (ages 35-55) with 37% of the sample, while the
lowest was individuals born between 1920 and 1940 at 3.7%.
2.2 Income
Pension Income
Pension Income Amount
Valid
183
N
Missing
Mean
Median
Std. Deviation
0
10567.1768
9776.0700
6493.29728
The median income amongst individuals receiving pension income (including Canada
Pension Plan and Survivor’s Benefit) was $9,776.07, with 68% of individuals receiving
between $3,283 and $16,269.
7
Employment Income
Employment Income Amount
Valid
1204
N
Missing
Mean
0
1991.10
Median
.00
Std. Deviation
5653.838
Minimum
0
Maximum
35720
In assessing the entire sample, 21.9% of individuals received some form of employment
income. The mean of all 1204 individuals was $1991.10 while the median was 0,
indicating the majority of individuals claimed no employment income.
Employment Income
Employment Income Amount
Valid
264
N
Missing
0
Mean
9080.61
Median
5500.32
Std. Deviation
9032.978
Minimum
39
Maximum
35720
In assessing only the individuals who claimed employment income on their tax return,
there was a strong positive skew towards those receiving under $5000 in employment
income. The median employment income amount claimed was $5,500.32, while the
minimum claimed was $39 and the maximum $35,720.
8
Identifying whether status or non-status plays a role in employment income amount
Descriptives
Employment Income Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Non-Status
536
2524.09
6398.718
276.383
1981.16
3067.01
0
33767
Status
668
1563.43
4939.772
191.125
1188.15
1938.71
0
35720
Total
1204
1991.10
5653.838
162.941
1671.42
2310.78
0
35720
ANOVA
Employment Income Amount
Sum of Squares
Between Groups
df
Mean Square
274442885.475
1
274442885.475
Within Groups
38180519175.031
1202
31764159.047
Total
38454962060.506
1203
F
Sig.
8.640
.003
There was a statistically significant difference between groups as determined by a
one-way ANOVA (F(1,1202) = 8.64, p = .003). Non-Status individuals claimed more
employment income than status individuals.
Identifying whether status or non-status plays a role in social assistance amount received
Descriptives
Social Assistance Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Non-Status
536
6799.5349
6192.47928
267.47437
6274.1061
7324.9636
.00
23284.00
Status
668
6938.1468
6111.54296
236.46270
6473.8460
7402.4477
.00
25613.00
Total
1204
6876.4392
6145.52937
177.11121
6528.9580
7223.9204
.00
25613.00
ANOVA
Social Assistance Amount
Sum of Squares
Between Groups
df
Mean Square
5713685.951
1
5713685.951
Within Groups
45428626362.139
1202
37794198.305
Total
45434340048.090
1203
9
F
Sig.
.151
.697
There was no statistically significant difference between the amount of social
assistance claimed by status and non-status individuals as determined by a one-way
ANOVA (F(1,1202) = .151, p = .697).
Identifying whether status or non-status plays a role in pension amount received
Descriptives
Pension Income Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Non-Status
536
2510.8824
5673.94274
245.07700
2029.4512
2992.3136
.00
35563.29
Status
668
880.1802
3236.79563
125.23538
634.2771
1126.0832
.00
25643.94
Total
1204
1606.1407
4558.88248
131.38481
1348.3718
1863.9095
.00
35563.29
ANOVA
Pension Income Amount
Sum of Squares
Between Groups
df
Mean Square
F
39.259
790795329.839
1
790795329.839
Within Groups
24211646268.453
1202
20142800.556
Total
25002441598.293
1203
Sig.
.000
There was a statistically significant difference between the amount of pension income
claimed as determined by one-way ANOVA (F(1,1202) = 39.259, p = .000). Non-status
individuals claimed more than status individuals.
Identifying whether status or non-status plays a role in amount of tax returned
Descriptives
Amount of Return
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Non-Status
536
159.99
537.256
23.206
114.40
205.57
-1369
5778
Status
668
82.31
351.961
13.618
55.57
109.04
-388
3673
1204
116.89
445.579
12.841
91.69
142.08
-1369
5778
Total
ANOVA
Amount of Return
Sum of Squares
Between Groups
df
Mean Square
1794503.735
1
1794503.735
Within Groups
237050250.088
1202
197213.186
Total
238844753.823
1203
10
F
9.099
Sig.
.003
There was a statistically significant difference between status and non-status
individuals in the amount of income tax returned as determined by one-way ANOVA
(F(1,1202) = 9.099, p = .003). Non-status individuals had more money returned than
status individuals.
Identifying the relationship between gender and WSIB claim amounts
Descriptives
WSIB Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Female
769
47.70
640.015
23.080
2.39
93.00
0
10849
Male
435
207.87
1642.764
78.765
53.06
362.68
0
20791
Total
1204
105.57
1114.008
32.105
42.58
168.55
0
20791
ANOVA
WSIB Amount
Sum of Squares
Between Groups
df
Mean Square
7127954.296
1
7127954.296
Within Groups
1485811379.249
1202
1236115.956
Total
1492939333.545
1203
F
Sig.
5.766
.016
Males receive more WSIB and is statistically significant p=.016
2.3 Housing
Identifying whether status or non-status plays a role in amount spent on housing
Descriptives
Housing Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Non-Status
536
3733.38
3189.627
137.771
3462.75
4004.02
0
15600
Status
668
3130.68
3350.918
129.651
2876.11
3385.26
0
19200
Total
1204
3398.99
3292.410
94.886
11
3212.83
3585.15
0
19200
ANOVA
Housing Amount
Sum of Squares
Between Groups
df
Mean Square
F
10.040
108023877.994
1
108023877.994
Within Groups
12932451148.989
1202
10759110.773
Total
13040475026.983
1203
Sig.
.002
There was a statistically significant difference the amount spent on housing by status
and non-status individuals as determined by one-way ANOVA (F(1,1202) = 10.04, p =
.002). Non-status individuals spent more on housing than status individuals.
Gender and Amount Spent on Housing
Descriptives
Housing Amount
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean
Lower Bound
Minimum
Maximum
Upper Bound
Female
769
3561.75
3380.930
121.919
3322.41
3801.08
0
19200
Male
435
3111.28
3112.827
149.249
2817.94
3404.62
0
14400
Total
1204
3398.99
3292.410
94.886
3212.83
3585.15
0
19200
ANOVA
Housing Amount
Sum of Squares
Between Groups
df
Mean Square
F
5.219
56380011.134
1
56380011.134
Within Groups
12984095015.849
1202
10802075.720
Total
13040475026.983
1203
Sig.
.023
Females pay more for housing, p=.023
2.4 Other results
•
No statistical significance between employment income amount and marital
status
•
Widows receive less than other groups (single, p=.000; common law, p=.000;
married, p=0.001; divorced, p=.009; separated, p=.000) in social assistance. No
other significance between groups (no groups earn more or less than other
groups).
12
•
No statistical significance between marital status and bus passes expense
•
No statistical significance between marital status and amount spent on housing
•
No statistical significance between marital status and tax amount returned
•
Widows donate more than any other group by a large margin
•
Singles receive less WSIB than married people (p=.004); all other groups
showed no significant differences.
•
Number of dependents had no statistical effect on amount claimed for
transportation expenditures
•
Number of dependents had no statistical effect on amount claimed for medical
expenditures
•
Individuals with five dependants received statistically significantly more in social
assistance compared to individuals with 2 (p=.006), 1 (p=.011) and 0 (p=.004)
dependents
•
Individuals with zero dependents received statistically significantly more in
pension assistance compared to individuals with 2 (p=.014) and 1 (p=.001)
dependent
•
Number of dependants had no statistical effect on employment income earned
•
Those born between 1940-60 claimed statistically significantly more in medical
expenses (all p=.000)
•
Gender and employment income were not significant
•
Gender and pension income were not significant
•
Gender and social assistance were not significant
13
2.5 Volunteer Income Tax Program Users from outside of the Thunder Bay
Census Metropolitan Area
The map below shows the distribution Volunteer Income Tax Program users from
outside of the Thunder Bay Census Metropolitan Area. It is unlikely that these
individuals travelled to Thunder Bay solely for the purpose of filing their tax returns. As
such, the map demonstrates that the city acts as an important hub for services for
individuals throughout the District and region.
14
3. Wage Calculation Comparison
As previously mentioned, although Canada has no officially defined measure of poverty,
most countries around the world are using the Low Income Measure (LIM) as a starting
point. This is beneficial for international comparisons. The 2014 Low Income Measure
for Canada adjusted for inflation is $24,992 before-tax.
Displaying a low income measure can sometimes make it difficult to conceptualize what
it means for those working or utilizing government programs. What does $24,992 look
like as an hourly wage assuming a 35 hour workweek (the standard measure for a fulltime work week) for 52 weeks a year, with only statutory holidays off? What are people
on Ontario Works receiving as an hourly wage? The following chart uses annual figures
to be paid out through specific programs or measures and parses them down into an
hourly wage.
Assessing the annual incomes of various programs and measures as an hourly rate
reveals a harsh reality. Ontario Works recipients are only receiving the equivalent of
$4.50/hr; in an eight hour workday they’d make $36 before-tax.
To display the most accurate, comparative figures of annual income and wages, it is
important to use before-tax measures of income as wages are almost exclusively
discussed in terms of the before-tax amount. An individual who is working full-time for
minimum wage in Ontario will make $11.00/hr, more than $2 below the $13.02/hr
necessary to reach the unofficial poverty line. This has a number of implications for the
working class.
Income Source
Annual
Hourly
2014 CPI Adjusted Low Income
Measure
$24,992
$13.02
Minimum Wage
$20,020
$11.00
Ontario Works
$7,872
$4.32
Ontario Disability Support Program
$13,032
$7.16
Employment Insurance Maximum
(45 week maximum)
$23,130
$14.68
Canadian Pension Plan (CPP)
Maximum
$12,460
$6.84
$7,960,000
$4,373.62
Average Canadian CEO (top 100)
15
4. Canadian Centre for Policy Alternatives Living Wage Calculation
Currently, the minimum wage in Ontario is $11.00 before-tax per hour. This amount
translates to an annual income of $20,020 assuming the individual has worked 35 hours
a week and only received statutory vacation time. Working full-time and earning
minimum wage is not enough to meet the Low Income Measure.
The living wage is one approach that many communities across Canada are advocating
as a way to ensure that earned income is sufficient to meet basic needs. A living wage
calculation differs from the minimum wage. The minimum wage is the legal minimum
that an employer must pay to employees, as determined by the province. A living wage
is defined as the hourly rate that a worker in a family would need to earn to meet the
basics of quality of life based on the cost of living specific to a community.
The methodology adopted for the Thunder Bay living wage calculation is from the
Canadian Centre for Policy Alternatives model. According to the Canadian Centre for
Policy Alternatives template, the requirement for a living wage in Thunder Bay is
$30,030 annually and $16.50/hr before-tax. A living wage intends to assess the wage
necessary for living a fulfilling and healthy lifestyle. While the living wage is debatable in
regards to what should be included in the calculations, there is broad consensus that a
living wage should include the costs of: a home phone, internet usage, healthy foods,
utilities, clothing, transportation, health coverage, tuition and daycare, two weeks of pay
in savings and recreation. Presently, the hourly gap between the minimum wage and a
living wage in Thunder Bay is $5.50/hr. This is an alarming statistic as each year the
minimum wage stays stagnant, the gap between the working poor and a healthy,
prosperous lifestyle will grow.
5. Comparable Cities to Thunder Bay
To contextualize the impact of poverty on a specific community, Thunder Bay was
compared to a number of other similar cities to form a comparison. The cities were
selected based upon: isolation, demographics, labour market conditions, population,
health region peer groups and transit peer groups. As a result of the size of Canada,
comparable cities were identified in regards to their geographic location. The
comparable cities are:
Northern Ontario
Sault Ste. Marie
Sudbury
Western Canada
Kamloops
Prince George
Lethbridge
Southern Ontario
Peterborough
Kingston
Brantford
Eastern Canada
Saint John
St. John’s
Moncton
16
Key benchmarks of poverty were identified in the research process and used to
compare Thunder Bay to other cities. The results are as follows:
Health & Social Inclusion
1. Access to a Regular Medical Doctor by Health Region
StatsCan Table 105-0501
Health Indicators - Regular Medical Doctor (2013)
Zone 1 (Moncton Area), NB
Peterborough County-City, ON
Kingston, Frontenac and Lennox and Addington, ON
Brant County, ON
Zone 2 (Saint John area), NB
Eastern Regional Integrated Health Authority, NL
District of Algoma, ON
South Zone, AB
Interior Health Authority, BC
Thunder Bay District, ON
Northern Health Authority, BC
Sudbury and District, ON
Ontario
Canada
City
Moncton
Peterborough
Kingston
Brantford
Saint John
St. John’s
Sault Ste. Marie
Lethbridge
Kamloops
Thunder Bay
Prince George
Sudbury
Percentage
94.3
94.1
92.9
92.8
91.8
90.5
90.1
88.6
85.9
82.3
82.3
81.6
91.2
84.5
Thunder Bay ranks low in regards to the number of individuals over 12 years old who
have access to a regular medical doctor and is far below the provincial average.
2. Heavy Drinking by Health Region
StatsCan Table 105-0501
Health Indicators - Heavy Drinking (2013)
Thunder Bay District, ON
Eastern Regional Integrated Health Authority, NL
Zone 1 (Moncton Area), NB
District of Algoma, ON
Peterborough County-City, ON
Sudbury And District, ON
Interior Health Authority, BC
Northern Health Authority, BC
Kingston, Frontenac and Lennox and Addington, ON
South Zone, AB
Brant County, ON
Zone 2 (Saint John area), NB
Ontario
Canada
17
City
Thunder Bay
St. John’s
Moncton
Sault Ste. Marie
Peterborough
Sudbury
Kamloops
Prince George
Kingston
Lethbridge
Brantford
Saint John
Percentage
26.6
26.1
26.0
23.8
22.0
21.4
21.1
20.3
19.6
19.2
16.1
16.1
17.1
18.9
Thunder Bay ranks the highest in heavy drinking, which refers to males who reported
having 5 or more drinks, or women who reported having 4 or more drinks, on one
occasion, at least once a month in the past year. Thunder Bay is well above the
provincial average for heavy drinking.
3. Sense of Belonging to Community
StatsCan Table 105-0501
Health Indicators - Sense of Belonging to Community
(strong/very strong) (2013)
Zone 2 (Saint John area), NB
South Zone, AB
Eastern Regional Integrated Health Authority, NL
Thunder Bay District, ON
Northern Health Authority, BC
Interior Health Authority, BC
Kingston, Frontenac and Lennox and Addington, ON
Zone 1 (Moncton Area), NB
Sudbury And District, ON
Peterborough County-City, ON
District of Algoma, ON
Brant County, ON
Ontario
Canada
City
Saint John
Lethbridge
St. John’s
Thunder Bay
Prince George
Kamloops
Kingston
Moncton
Sudbury
Peterborough
Sault Ste. Marie
Brantford
Percentage
77.1
75.0
74.5
74.4
71.3
70.4
69.8
66.3
65.9
65.5
65.0
63.1
67.7
65.9
Thunder Bay ranks high in citizens having a strong or very strong sense of belonging to
the community in comparison to similar cities. Thunder Bay ranks much higher than the
provincial average.
4. Internet Use from Any Location
StatsCan Table 358-0152
Canadian Internet Use
Survey (2012)
Peterborough, ON
Moncton, NB
Kingston, ON
Brantford, ON
Greater Sudbury, ON
Saint John, NB
St. John's, NL
Thunder Bay, ON
Ontario
Canada
Rate
86.5
84.6
81.6
81.3
82.6
84.0
88.0
88.1
84.4
83.4
Bottom 20% of Income
73.9
69.2
68.5
65.9
65.9
61.6
58.9
57.7
65.5
62.5
Thunder Bay maintains the highest rate of internet usage amongst the comparable
cities at a rate much higher than the Canadian and Ontarian averages. Looking
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specifically at the bottom 20% of incomes in each city, Thunder Bay’s usage is the
lowest of the comparable cities and significantly less than the Canadian and Ontarian
averages.
Other Key Findings
o Thunder Bay ranks 7th largest in population size in the peer group. St. John’s
was ranked the largest, while Sault Ste. Marie was ranked the smallest within the
peer group.
o Thunder Bay was the only city in the peer group with no, or negative population
growth.
o Thunder Bay ranks only higher than two cities (Moncton and Kamloops) in
average discretionary income. By way of comparison, one Northern Ontario city
in the peer group (Sudbury) has the second highest average discretionary
income with a difference of over $4,000 per annum.
o In terms of labour market characteristics, two results were noteworthy. Thunder
Bay had the 4th lowest unemployment rate and only Sault Ste. Marie, Prince
George and Lethbridge had lower unemployment rates. However, Thunder Bay
has the 11th lowest rate (of 12) of participation in the labour market. One city that
shared this trend was Sault Ste. Marie. It is unclear if this suggests an increase in
absolute poverty or if other factors are at play.
o Thunder Bay ranks 6th highest in the percentage of people who use public
transportation to travel to work with 2.93%. Greater Sudbury ranked the highest
with 4.89% using public transportation to travel to work.
o Thunder Bay ranks 7th highest percentage of doctors per 1,000 people with 2.39,
while Kingston has the highest number of doctors per 1,000 people with 4.36.
o Thunder Bay ranks 5th in the percentage of individuals living in low income aftertax with 12.9%. Greater Sudbury had the lowest percentage of individuals in low
income with 11.1%, while Saint John had the highest percentage with 14.7%.
o Thunder Bay had the lowest vacancy rate within the peer group at 2.4% and the
5th highest median rent for a two-bedroom ($862).
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6. Housing
One of the biggest areas of concern in Thunder Bay is the lack of affordable housing.
The chart below shows the correlation between declining vacancy rates and increasing
social housing waitlists.
Also alarming are the dramatic increases in emergency shelter usage. Between 2004
and 2011, total bed usage at Shelter House increased by 35%. Below are the
emergency shelter average occupancy rates between the years 2011 and 2013.
Homeless Shelter Average
Occupancy Rate 2013
Shelter House: 138%
Salvation Army: 108%
Shelter Average: 123%
Homeless Shelter Average
Occupancy Rate 2012
Shelter House: 98%
Salvation Army: 75%
Shelter Average: 86.5%
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Homeless Shelter Average
Occupancy Rate 2011
Shelter House: 97.8%
Salvation Army: 79.9%
Shelter Average: 88.85%
7. Conclusions and Recommendations
Based on the evidence gathered in this report, some general conclusions may be
drawn. While there are fluctuations over time, it can be observed that both the
percentage of people living in low income and the unemployment rate are very slightly
and gradually trending downward. However, other key social indicators, especially with
regards to housing, show dramatic increases in the number of individuals who are
struggling to meet basic needs. This suggests that while levels of relative poverty are
trending downward slightly, levels of absolute poverty are rising. In other words, the
“bottom is falling out” and the position of the lowest income groups is getting worse.
Building on the recommendations contained in the Poverty Reduction Strategy’s
“Building a Better Thunder Bay for All” report, there are three key commitments that
could help to make a difference in the lives of low income individuals. These are as
follows:
Commitment 3.1: Develop partnerships for the purpose of determining the number of
homeless people in Thunder Bay to be updated on an annual basis
Recommendation: The City of Thunder Bay fund an annual homeless point-intime count.
Rationale: Although indications suggest relative poverty is trending downward
slightly over a ten year period, absolute poverty is dramatically increasing, as
evidenced by shelter occupancy rates. More information is necessary to better
understand the increase in absolute poverty, and in order to inform policy.
Commitment 11.2 – Assist in the development of the expansion and affordability of
internet access and education
Recommendation: The City of Thunder Bay examine its e-waste policies and the
feasibility of distributing old computer hardware to low-income individuals
through partnerships with local charities, and that to these ends a corporate
report be drafted.
Rationale: The necessity for internet access is increasingly critical in order to
address multiple areas of everyday life. Although Thunder Bay ranks highest in our
peer group for internet usage by the general population (88.1%), it ranks lowest
(57.7%) in internet usage among individuals in the bottom 20% of income.
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Commitment 12.0: Improve transportation affordability and accessibility
Recommendation: The City of Thunder Bay endorse the Thunder Bay Transit
Master Plan recommendation that subsidized bus fare be implemented for low
income individuals and families. Furthermore, implement the Thunder Bay Transit
Master Plan recommended route network and terminal concept.
Rationale: Volunteer income tax data demonstrates that there is no discernable
difference between low income group demographics and the purchase of multi-ride
passes. Multi-ride passes provide the cheapest per-ride fare rate, yet only 3% of the
sample size purchased a multi-ride pass suggesting affordability may be an issue.
Transportation is a barrier that prevents low income individuals from escaping
poverty. Enhancing public transportation affordability can have a number of
anticipated and unanticipated benefits.
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