Labour Force Status of Aboriginal Canadians: Do human and social capital make a difference?
Belayet Hossain & Laura Lamb
Department of Economics
Thompson Rivers University
Kamloops, British Columbia, Canada
Abstract
The labour force status of Aboriginal Canadians is analysed with a focus on determining the role
of an expanded definition of capital to include the human capital component of health status
and social capital. The dataset of the 2006 Aboriginal Peoples Survey is examined using
multivariate analysis to study labour force status. The results suggest that social capital and
human capital, measured by education and health status, among other socio-demographic
factors, are significant determinants of labour force status among Aboriginal Canadians. The
findings have implications for public policy.
JEL: J21, J48, O12
JEL: J15 J21 J18 O15 ??
Introduction
Aboriginal Canadians continue to live at a lower level of economic development than nonAboriginal Canadians. While many socio-economic and institutional factors are believed to be
determinants of the economic plight of Aboriginal Canadians, labour force status is consistently
considered to be one of the critical factors. The objective of the current research is to identify
the socio-economic and demographic determinants of the labour force status of Aboriginal
Canadians. Most past studies have compared employment and labour force participation
between Aborginal and non-Aboriginal Canadians (Sharpe, Arsenault, and Lapointe, 2007;
Walters, White& Maxim, 2004; White, Maxim, Gyimah, 2003; Kuhn and Sweetman, 2002), with
the exception of Drost (1994), while the current study focuses only on Aboriginal Canadians.
The most common socio-economic factors examined in past labour market research include
marital status, the presence of children, age, region of residence, and educational attainment.
All of the past studies analysed gender differences (Walters, White& Maxim, 2004; Kuhn and
Sweetman, 2002; Drost, 1994) except for White, Maxim, Gyimah (2003) who restricted their
analysis to females. Some other variables considered in past studies on Aboriginal labour force
participation include language, parental education, intermarriage, social assistance
dependency, and other household income.
In addition to many of the variables analysed in past studies on the labour force status of
Aboriginal Canadians, this research considers an expanded definition of human capital to
include the human capital component of health status. This research examines human capital
with both educational attainment and health status, as has been done in some past labour
supply research (Cai, 2010; Stephens, 2010; Latif, 2006), but not of the Canadian Aboriginal
labour supply. Walters, White, & Maxim (2004) speculate on the importance of health status as
a significant explanatory variable but are not able to include it in their analysis due to lack of
available data. Within a human capital framework, health status is pertinent in that it affects
potential labour market productivity and thus labour force participation (Mankiw & Scarth,
2011; Grossman, 1972).
The role of social capital has also not been empirically anlaysed in past research on the labour
force status of the Aboriginal Canadian population. White, Maxim and Gyimah (2003) mention
a possible link between labour force activity and social capital in the community but do not
include it in their empirical model. Social capital has been identified as a significant factor for
employment status among all Canadians (Matthews, Pendakur, and Young, 2009) and Canadian
immigrants (Grenier & Xue, 2009). Conceptually, the networks and social relations indicative of
social capital are viewed as instrumental for labour market success (Woolcock, 2001). Human
and social capital can be viewed as complements, although human capital is described as
existing in individuals while social capital exists in relationships. It is argued that human capital
1
and social capital work together to increase the probability of successful employment outcomes
(Woolcock, 2001). It is hypothesized that Aboriginal Canadians with higher levels of human
capital, as indicated by educational attainment and health status, and a high level of social
capital are more likely to have full-time employment and less likely to not be in the labour
force. Data from the 2006 Aboriginal Peoples Survey is used to study the determinants of
Aboriginal Canadian employment status with use of a multinomial probit model.
This research is expected to make a contribution to the literature on Aboriginal economic
development and Canadian economic growth. The relevance to the study of Aboriginal
economic development is grounded in the connection between labour force status and
economic development and well-being. Employment is seen as one of the most fundamental
ways people participate in society, and the basis of self-respect and autonomy (Mendelson,
2004). In addition, measures of well-being, such as the Community Well-Being (CWB) index
often incorporate labour force indicators. The CWB index, developed by Beavon and Cooke
(2003) in order to analyze the disparity between Aboriginal and non-Aboriginal Canadians, is
based on five indicators of socio-economic well-being which include measures of labour force
activity such as participation and employment rates, among other variablesi (McHardy and
O’Sullivan, 2004).
The current research is relevant to the economic growth of Canada because of the persistent
gap in employment experiences between Aboriginal and non-Aboriginal Canadians. While the
overall employment rate fell in Canada, as it did for most countries, after the onset of recent
global economic downturn that began in 2008, the employment rate fell further and for a
longer duration for the Aboriginal population than it did for the non-Aboriginal population.
From 2008 to 2010, the gap between the two labour force populations broadened in regard to
participation rates, employment rates, and unemployment rates (Statistics Canada, 2011). This
increasing gap is of concern given the relationship between employment and economic
development and well-being. The identification and resolution of the barriers surrounding the
employment gap is important in that it may impact Canada’s future economic growth, given the
anticipated labour shortage due to demographic changes in the non-Aboriginal population. The
Aboriginal population is younger with population growth projections greater than the total
Canadian population. The Aboriginal population accounted for 3.9% of the Canadian population
in 2006, and is expected to represent between 4.0% and 5.3% by 2031 (Statistics Canada, 2011).
Closing the employment gap is a potential solution to current and expected skills shortages in
some areas of the labour force.
The remainder of this paper is organized as follows. The methodology section includes a
description of the model, econometric techniques for analysis, the data and variables. Then the
2
results of the empirical analysis are explained, followed by a discussion of the results, policy
implications and the conclusion.
Methodology
Multivariate analysis is used to test the hypothesis and examine the determinants of labour
force status among Aboriginal Canadians. The dependent variable, labour force status, is an
unordered categorical variable with four mutually exclusive groups, listed as follows: not in the
labour force (NLF), unemployment (UEM), part-time employment (PTE) and full-time
employmentii (FTE). While both the multinomial logit and multinomial probit models are
suitable for analysis, the multinomial probit model is chosen because it allows for relaxation of
the independence of irrelevant alternatives (IIA) assumption, which may be too restrictive for
the unordered choice model of four employment status outcomes iii(Cameron & Trivedi, 2009;
Greene, 2000).
The structural equations of the multinomial probit model are as follows:
(1) 𝑌𝑖𝑗∗ = 𝑋𝑖/ 𝛽𝑗 + 𝜀𝑖𝑗 ;
Where 𝑌𝑖𝑗∗ is the latent value of the response variable, which is employment status;
𝑋𝑖 is the vector of explanatory variables;
𝛽𝑗 is the vector of parameters to be estimated;
j = 1, 2, 3 ….k categories; i = 1, 2,… N sample. 𝜀𝑖 ’s follows a multivariate normal distribution
with covariance matrix Σ, where Σ is not restricted to be a diagonal matrix, allowing errors to be
correlated to each other.
The category j is chosen if 𝑌𝑖𝑗∗ is the highest, that is if
𝑌𝑖 = 𝑗 𝑖𝑓
𝑌𝑖𝑗∗
𝑗 𝑖𝑓 𝑌𝑖𝑗∗ = max (𝑌𝑖1∗ , 𝑌𝑖2∗ , … . 𝑌𝑖𝑘∗ )
= { 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
The probability that category j is chosen is stated as
∗
𝑃(𝑌𝑖 = 𝑗|𝑋𝑖 ) = 𝑃(𝑌𝑖𝑗∗ > 𝑌𝑖1∗ … … . . ; 𝑌𝑖𝑗∗ > 𝑌𝑖𝑗−1
; 𝑌𝑖𝑗∗ > 𝑌𝑖𝑘∗ )
= 𝑃(𝜀𝑖𝑗 − 𝜀𝑖1 ) > 𝑋 / (𝛽1 − 𝛽𝑗 ) … … . (𝜀𝑖𝑗 − 𝜀𝑖𝑘 ) > 𝑋 / (𝛽𝑘 − 𝛽𝑗 ))
′
It is noted that only the differences between the 𝑌𝑖𝑗∗ 𝑠 are identified and hence a reference
category must be assigned.
3
The likelihood function for multinomial probit is
𝑛
𝑘
𝐿 = ∏∗ ∏ 𝑃(𝑌𝑖∗ = 𝑗)𝜆𝑖𝑗
𝑖=1
𝑗=1
1 𝑖𝑓 𝑌𝑖∗ = 𝑗
Where 𝜆𝑖𝑗 = {
0 𝑖𝑓 𝑌𝑖∗ ± 𝑗
Taking the log of both sides of the above function, we have the following log likelihood
function:
𝑛
𝑘
𝐿(𝛽, Σ) = ∑ ∑ 𝜆𝑖𝑗 𝑙𝑜𝑔𝑃(𝜆𝑖𝑗 = 1|𝑋𝑖 , 𝛽, 𝛴)
𝑖=1 𝑗=1
The above log likelihood function is to be maximized with respect to the coefficients, variances
and co-variances for the multinomial probit model.
Data and variables
This study uses data from the 2006 Aboriginal Peoples Survey (APS), conducted by Statistics
Canada (2006) in the fall of 2006 through the spring of 2007. The Aboriginal Peoples Survey
provides data on the social and economic conditions of First Nations people, over the age of six
years, living on reserve as well as off reserve, Métis and Inuitiv. Only the data for those in the
age range of 20 and older are used for the current analysis in order to omit the group most
likely to be in school full-time. After the variables for the model are identified, 18,165
observations are used in the analysis.
Labour force status is the dependent variable with four categories (NLF, UEM, PTE and FTE), as
outlined above. The focus of the study is to examine the role of human and social capital on the
employment status of Aboriginal Canadians. As previously mentioned, human capital consists of
educational attainment and health status. Five categorical variables are specified to measure
the impact of education on employment status (Educ1, Educ2, Educ3, Educ4 and Educ5). The
specification of each of these five variables is described in Table 1. Following Stephens (2010)
and Deschryvere (2005), the status of health is specified using a self-reported index. If a
respondent reports good or excellent health, the health variable (HEALTH) is coded 1, if fair or
poor health, the variable is coded 0.
An indicator variable is developed to measure social capital (Sock) based on responses to three
questions about networks and social relations on the APS 2006 survey. They are as follows:
How often is this available to you? (1) Someone you can count on when you need advice, (2)
4
Someone to confide in or talk to about yourself or your problems, and (3) Someone you can
count on to listen to you when you need to talk. If the sample member answers either all of the
time or most of the time to all three questions, social capital (Sock) is considered to be strong
and is coded 1; if the sample member answers either some of the time or almost none of the
time to all three questions, then social capital is considered to be weak and is coded 0.
Other socio-economic and demographic factors included which may influence employment
status are gender, age, marital status, number of child, geographical location, registered Indian
status, and presence of income support. All these factors, except for income support, have
been examined in previous research. Income support indicates if there is at least one other
person living in the household who contributes toward paying the household expenses. Income
support can be considered a proxy for spousal income or employment, which has been included
in labour supply research (Latif, 2006). The measurement and description of these variables are
presented in Table 1.
5
Table 1 Description and specification of all explanatory variables used in the analysis
Variable Name
Measurement and description of variable
Educ1
If respondent has completed high school then 1; otherwise 0.(default
is less than high school)
Educ2
If respondent has some non-university post-secondary then 1;
otherwise 0.
Educ3
If respondent has completed non-university post-secondary then 1;
otherwise 0.
Educ4
If respondent has some university then 1; otherwise 0.
Educ5
If respondent has completed university then 1; otherwise 0.
Health
If respondent self-reports good or excellent health then 1; otherwise
0.
Sock
If respondent has a strong indicator then 1; otherwise 0.
Gender
If the respondent is male then 1; if female then zero.
Age2
If respondent’s age is between 25 years and 34 years then 1;
otherwise 0. (default is age 20 – 24)
Age3
If respondent’s age is between 35 years and 44 years then 1;
otherwise 0.
Age4
If respondent’s age is between 45 years and 54 years then 1;
otherwise 0.
Age5
If respondent’s age is over 54 years then 1; otherwise 0.
Marital
If respondent is married then 1; otherwise 0.
Child1
If respondent has either one or two children then 1; otherwise
zero.(default is no children)
Child2
If respondent has more than two children then 1; otherwise zero.
Geo2
If respondent resides in a rural area then 1; otherwise zero. (default
is urban residence)
Geo3
If respondent resides in the arctic then 1; otherwise zero.
Status
If respondent is has registered Indian status then 1; otherwise zero.
Isupport
If respondent lives with another person who contributes toward
paying the household bills then 1; otherwise zero.
6
Table 2 Percentage distribution of frequencies for labour force outcomes (%)(n = 18,165)
NILF Unemployed Part-time Full-time
27.42
5.69
11.30
55.59
Total sample
Total
100
Education
< High school
High school
Some p.s. (non-univ)
Complete p.s. (non-univ)
Some university
Complete university
46.58
24.03
28.02
18.55
26.40
14.65
7.26
6.26
5.92
5.84
4.37
1.96
9.99
11.65
11.94
11.12
18.40
10.48
36.17
15.12
54.13
64.49
50.83
72.91
100
100
100
100
100
100
Health
Good/excellent
Fair/poor
Strong
Weak
Male
Female
20-24
25-34
35-44
45-54
55+
Married
Non-married
1-2 children
>2 children
Urban
Rural
Arctic
Registered
Not registered
Other maintainers
None
21.82
55.17
24.34
37.09
21.85
31.77
24.94
20.94
15.57
20.18
58.97
24.81
31.23
22.83
25.04
24.95
32.00
30.95
29.90
26.12
19.79
33.51
5.90
4.66
4.93
8.10
6.72
4.89
10.14
6.53
6.28
4.96
2.28
4.81
6.99
5.68
6.36
4.54
6.60
11.50
6.47
5.29
5.07
6.19
11.50
10.31
11.15
11.78
8.05
13.84
17.37
11.83
11.19
10.60
8.15
10.75
12.11
11.07
13.09
11.44
11.23
10.43
11.17
11.37
11.28
11.32
60.78
29.87
59.58
43.03
63.38
49.50
47.55
60.71
66.95
64.26
30.60
59.63
49.66
60.42
55.50
59.21
50.17
47.13
52.46
57.23
63.86
48.98
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Social Capital
Gender
Age
Marital
Children
Location
Status
Support
Source: Statistics Canada (2009) Aboriginal Peoples Survey, 2006. Public use microdata file (adults).
7
Results
Descriptive statistics of the frequency distributions of all variables by employment status are
illustrated in Table 2. The labour force consists of 73% of the sample members with 56% being
employed full-time, 11% part-time and close to 6% unemployed. Full-time employment
increases to 64% and 73% for those who completed postsecondary non-university and
university, respectively. Almost two-thirds (61%) of sample members reporting good health are
employed full-time compared to 30% reporting fair to poor health. The majority (61%) of those
with strong social capital work full-time while less than half (43%) with weak social capital work
full-time, and 37% with weak social capital are not in the labour force. The frequency
distributions indicate that 63% of males are employed full-time compared to almost 50% of
females, while 14% of females work part-time compared to only 8% of males. Over 60% of
sample members between the ages of 25 and 55 are employed full-time and almost 59% of
those over age 55 are not in the labour force. The proportion of married sample members
employed full-time (60%) is greater than the proportion of non-married (50%) sample members
employed full-time. Close to 60% of sample members living in urban areas are employed fulltime compared to 50% in rural areas and 47% in the arctic. Over half of those with registered
Indian status (52%) are employed full-time and 30% are not in the labour force compared to
57% without status being employed full-time and 26% not being in the labour force. Close to
two-thirds (64%) of sample members with income support are employed full-time compared to
49% of those without. One-third (33%) of those without income support are not in the labour
force while only 20% of those with household maintainers are not in the labour force.
Overall, the specifications of the multinomial probit model are found to be robust, as evidenced
by the Wald statistic. The Wald test of joint significance reveals that all explanatory variables
are jointly significant at the 1% level across the four categories of labour force status with the
exception of being married (Marital) and having one or two children (Child1). The marginal
effects of the independent variables of the multinomial probit estimation are presented in
Table 3 and the predicted probabilities in Table 4v.
The results of the base suggest that a single-female respondent, between the ages of 20 and 24,
with a weak level of social capital, living in an urban area without registered status, having less
than high school education, and fair to poor health is predicted to have 59% probability of
being employed full-time. Her expected probability of not being in the labour force is 23%, of
being unemployed is 4.7%, and of being employed part-time is 13%, as illustrated in Table 4.
These are considered to be the base probabilities without any special characteristics.
The probability of having full-time employment is expected to be 71% for a respondent who has
completed high school education when all other characteristics remain constant. In other
8
words, the probability of attaining full-time employment increased by 12% for a person with a
high school diploma compared to one without high school completion. Similarly, for those who
complete non-university and university post-secondary education, their probability of attaining
full-time employment further increases to 77% and 79%, respectively. On the other hand, for
those who have some non-university and university post-secondary education, but not
completion, their probability of being employed full-time increases less, to 68% and 63%,
respectively. Thus, the completion of post-secondary education, regardless of university or
non-university, appears to be important for attaining full-time employment for Aboriginal
Canadians.
The results suggest that health is the most important determinant of full-time employment. A
person with excellent or good health has 84% probability of being employed full-time. That is, a
healthy person has a 25% higher probability of being employed full-time compared to one with
only fair to poor health. Social capital measured in terms of access to networks and social
relations, is also found to be a significant factor affecting the probability of full-time
employment. The probability of full-time employment increases from 59% to 68% (a rise of 9%)
for a person with strong social capital compared to one with weak social capital.
Among the socio-demographic variables affecting full-time employment, gender, age, the
presence of more than two children, place of residence, status, and income support are found
to be statistically significant. Males and those between the ages of 25 and 54 have a higher
probability of being employed full-time than females and those between the ages of 20 and 24.
Those who have more than two children are predicted to have a lower probability than those
who do not have children. And a respondent living in a rural area has a lower probability of
being employed full-time than those living in an urban area.
9
Table 3 Multinomial probit estimates (marginal effects) of employment status
Variables
Human Capital
Educ1
Educ2
Educ3
Educ4
Educ5
Health
Soc Capital
Socio-demographic
Gender
Age2
Age3
Age4
Age5
Marital
Child1
Child2
Geo2
Geo3
Status
Isupport
Wald Statistics
Log Pseudo-Likelihood
NLF
UEM
PTE
FTE
-0.101***
(0.013)
-0.07***
(0.014)
-0.171***
(0.011)
-0.081***
(0.017)
-0.16***
(0.012)
-0.259***
(0.017)
-0.056***
(0.013)
-0.004
(0.006)
-0.008
(0.007)
-0.007
(0.005)
-0.022***
(0.006)
-0.03***
(0.006)
0.014***
(0.005)
-0.015***
(0.005)
-0.009
(0.013)
-0.008
(0.014)
-0.006
(0.011)
0.066***
(0.021)
-0.006
(0.014)
-0.003
(0.011)
-0.02**
(0.01)
0.114***
(0.017)
0.086***
(0.02)
0.183***
(0.015)
0.036
(0.024)
0.196***
(0.018)
0.245***
(0.017)
0.091***
(0.014)
-0.127***
(0.009)
-0.046***
(0.017)
-0.117***
(0.016)
-0.088***
(0.016)
0.242***
(0.023)
-0.011
(0.011)
-0.003
(0.011)
0.066***
(0.02)
0.04***
(0.01)
-0.012
(0.015)
0.028***
(0.01)
-0.091***
(0.01)
2284.02
-734073.07
0.008*
(0.004)
-0.018***
(0.006)
-0.024***
(0.006)
-0.032***
(0.005)
-0.041***
(0.005)
-0.008
(0.005)
0.011**
(0.005)
-0.002
(0.007)
0.02***
(0.005)
0.062***
(0.011)
0.016***
(0.005)
-0.009**
(0.005)
-0.071***
(0.007)
-0.056***
(0.012)
-0.065***
(0.012)
-0.069***
(0.011)
-0.064***
(0.012)
0.004
(0.009)
-0.004
(0.009)
0.019
(0.015)
0.001
(0.008)
-0.029***
(0.011)
-0.013*
(0.008)
-0.005
(0.008)
0.189***
(0.011)
0.12***
(0.02)
0.206***
(0.019)
0.189***
(0.019)
-0.136***
(0.024)
0.015
(0.013)
-0.003
(0.013)
-0.083***
(0.022)
-0.061***
(0.011)
-0.021
(0.019)
-0.03***
(0.012)
0.105***
(0.012)
Note 1: ***, ** and * indicate the level of significance at 1%, 5% and 10% respectively
Note2: Wald test of joint significance indicates all variables to be jointly significant at the .001 level,
except for marital (p=.296) and child1 (p=.230).
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Table 4 Predicted probabilities of employment status with selected
characteristics
Variables
NLF
UEM
PTE
FTE
Base
0.231
0.047
0.130
0.591
Human Capital
Educ1
0.130
0.044
0.121
0.705
Educ2
0.161
0.040
0.122
0.677
Educ3
0.060
0.041
0.124
0.774
Educ4
0.150
0.026
0.196
0.627
Educ5
0.071
0.018
0.124
0.787
Health
-0.028
0.062
0.127
0.836
Total
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Soc Capital
0.175
0.033
0.110
0.682
1.00
Sociodemographic
Gender
Age2
Age3
Age4
Age5
Marital
Child1
Child2
Geo2
Geo3
Status
Isupport
0.104
0.185
0.114
0.143
0.473
0.220
0.228
0.297
0.271
0.219
0.259
0.140
0.056
0.030
0.024
0.016
0.007
0.039
0.059
0.046
0.068
0.110
0.064
0.039
0.059
0.074
0.065
0.061
0.066
0.134
0.126
0.149
0.131
0.101
0.117
0.125
0.780
0.711
0.797
0.780
0.455
0.606
0.588
0.508
0.530
0.570
0.561
0.696
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Note: Predicted probabilities are estimated using base values and the marginal effects in Table 3
Note: All variables are jointly significant except for Marital and Child1.
The results suggest that an Aboriginal person with registered Indian status has a lower
probability of attaining full-time employment than one without status. The presence of other
household members who contribute to paying household expenses increases the probability of
full-time employment.
The likelihood of being ‘not in the labour force’ significantly decreases with an increase in the
level of education. The base level suggests those without high school diplomas have a 23%
probability of not being in the labour force, while those with high school diplomas experience a
lower probability of 13%. The probability decreases further to 6% and 7%, respectively, for
those, who completed either non-university post-secondary or university. Similarly, access to
11
social capital also helps to reduce the probability of being ‘not in the labour force’. The results
suggest that the possession of strong social capital reduces the probability of not being in the
labour force from 23% to 18%. Thus, both human and social capital may increase labour force
participation substantially.
Labour force participation is higher for males than for females. The probability of not being in
the labour force is higher for those, who have two or more children (30%) compared to those,
who do not have children. It is also higher for those, who live in rural areas (27%) compared to
those in urban areas. Likewise, it is higher for those, who hold registered Indian status (26%)
compared to those, who do not. The presence of other earning members in the household
decreases the likelihood of being ‘not in the labour force’ to 14% as opposed to the base
probability of 23%.
The likelihood of being unemployed decreases with higher levels of education as well as with
the possession of strong social capital. The completion of university reduces the probability of
being unemployed from 4% to 2%. Likewise, the possession of strong social capital reduces the
expected probability of being unemployed from 4.7% to 3% when all else remains constant.
Social capital also reduces the expected probability of being employed part-time when all other
factors are held constant.
Discussion, policy implications, and conclusion
The results provide support for the hypothesis that Aboriginal Canadians with higher levels of
human capital, as indicated by educational attainment and good health status, and a higher
level of social capital are more likely to have full-time employment and less likely to not be in
the labour force. The significant role of educational attainment in Aboriginal labour force
outcomes is not surprising given the evidence in not only the Aboriginal Canadian literature
(Walters, White, Maxim, 2004; White, Maxim, Gyimah, 2003; Kuhn and Sweetman, 2002; Drost
,1994), but in most all labour force literature. Although neither health status nor social capital
has been included in past empirical models of the Aborginal Canadian labour force, the
significance of both are supported by the broader literature. Labour force research including a
measure of health status found it to be significant (Cai, 2010; Stephens, 2010; Latif, 2006).
Past research on employment status in Canada found a significant relationship between social
capital and labour force participation (Grenier & Xue, 2009; Matthews, Pendakur, and Young,
2009), lending support to the current findings. Grenier & Xue (2009) examined the role of social
capital with multiple variables such as the number of sources of meeting friends, the frequency
of contact with friends, and participation in organizations, while Matthews, Pendacur, and
Young (2009) assessed the role of social capital in employment outcomes with several
12
measures of individual networks, trust, and involvement in organizations. It is acknowledged
that the measurement of social capital is challenging in the current research with the APS 2006
survey data and the limited number of variables available to measure networks and social
relations.
The findings of the socio-demographic variables are generally supported by past research on
Aboriginal Canadian labour force outcomes (Walters, White& Maxim, 2004; White, Maxim,
Gyimah, 2003; Kuhn and Sweetman, 2002; Drost, 1994). In regard to variables specific to the
Aboriginal population, past studies confirm the negative impact of registered status on
employment (Hull, 2005; Walters, White, Maxim, 2004; White, Maxim, Gyimah, 2003). It has
been argued that status Indians are more likely to live on reserve than non-status Indians, and
subsequently experience labour market barriers due to remoteness (Walters, White, Maxim,
2004), although recent findings show similar employment outcomes for status Indians living in
urban areas, including urban reserves (Pendakur & Pendakur, 2011). The geographic variable of
residence in the arctic has not been included in many Aboriginal Canadian studies because the
Canadian Arctic is comprised of many very small communities and unique labour market
conditions compared to the rest of Canadian Aborginal people (Pendakur & Pendakur, 2011).
The significance of the variable implies that those in the arctic have a lower predicted
probability of being employed, a higher probability of being unemployed, and a higher
probability of not being in the labour force than those who live in urban areas.
The results of income support (Isupport) are somewhat unexpected as it was thought that
having other household members contribute to paying the household bills may be a
disincentive for employment. In fact, the results imply that having other household maintainers
may be an incentive for employment, perhaps indicating some type of employment culture
within the household.
Future research in this area might consider other variables identified in past studies on the
Aboriginal Canadian labour force but not included in this study due to lack of available data,
such as intermarriage, province of residence, and parental education (Walters, White,
Maxim,2004; Kuhn and Sweetman, 2002; Drost ,1994). Additional measures of social capital are
recommended in order to substantiate the current findings.
Policy implications of the results suggest a greater recognition for the role of good health and
social capital in policies developed to improve labour force participation of the Aboriginal
Canadian population. While it is widely recognized that poor health is pervasive in many
Aboriginal Canadian communities and needs to be addressed, the link to labour force
participation is often not emphasized. A greater degree of inclusion of the Aboriginal
population in Canadian society may lead to higher levels of social capital leading to the creation
of networks and social relations necessary to improve labour force participation. In addition,
13
future policy development for Aboriginal labour force participation addressing all the relevant
determinants is expected to have a positive impact on the economic growth of the Canadian
economy by providing needed labour and by alleviating some pressure on social safety nets for
those not in the labour force.
This research makes a contribution to the literature on Aboriginal labour force participation and
economic development by identifying the importance of including measures of health status
and social capital in labour supply research.
14
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16
i
The other four indicators are education, income, housing, and life expectancy.
Full-time employment is defined as working 30 hours or more per week.
iii
The Hausman specification test is typically used to test for independence of irrelevant alternatives (IIA),
however, it cannot be used to test the estimates of the current model due to the robust estimates of the VCE
(Cameron & Trivedi, 2009). Thus, we estimated the model with both the multinomial logit and multinomial probit
model and found that the results are close to identical.
iv
In Canada, Aboriginal people consist of First Nations, Inuit, and Métis.
v
Predicted probabilities are calculated using the marginal effects in Table 3 and the values of the base in the first
row of Table 4. The marginal effects show the change in probability of being in the respective labour force category
associated with the respective explanatory variable.
ii
17