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The relation between education and labour force participation of Aboriginal peoples: A simulation analysis using the Demosim population projection model

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Canadian Studies in Population 41, no. 1–2 (spring/summer 2014): 144–164.
The relation between education and labour
force participation of Aboriginal peoples:
A simulation analysis using the Demosim
population projection model
Martin Spielauer1
Abstract
This study aims at quantifying the impact of educational attainments on the future labour force
participation of Aboriginal peoples. Using Statistics Canada’s Demosim population projection
model, we are able to simulate alternative scenarios of educational change and resulting effects
on the future labour force until 2056. About half of the observed difference in labour force par-
ticipation rates between Aboriginal peoples and the Canadian-born population belonging neither
to an Aboriginal nor to a visible minority group can be attributed to educational differences.
While the impact of educational improvements on the future labour force is signicant, the
change is found to be a slow and gradual process, as successive young school-age cohorts have
yet to enter the labour market and renew the workforce.
Keywords: aboriginal peoples, education, labour force participation, microsimulation, projection.
Résumé
Cette étude vise à quantier l’effet du niveau d’instruction sur la participation future des pe-
uples autochtones à la population active. Grâce au modèle de projections démographiques
Demosim de Statistique Canada, nous avons pu simuler des scénarios à partir de différents
niveaux d’éducation et d’en constater l’effet sur la population active future jusqu’en 2056. Près
de la moitié des différences observées dans les taux de participation à la population active entre
les peuples autochtones et la population née au Canada, n’appartenant ni aux peuples autoch-
tones ni à une minorité visible, est attribuable aux différences sur le plan de l’éducation. Bien
que l’effet des améliorations sur le plan de l’éducation soit considérable auprès de la popula-
tion active future, le changement aurait lieu lentement et progressivement au fur et à mesure
que les cohortes d’âges scolaires entrent sur le marché du travail et que la population active se
renouvelle.
Mots-clés : peuples autochtones, éducations, participation à la population active, microsimula-
tion, projections.
1. Martin Spielauer, Statistics Canada Modeling Division, R.H. Coats Building, 24-O, Ottawa K1A 0T6. Email:
martin.spielauer@statcan.gc.ca
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
145
Introduction
With the development of the Demosim microsimulation model, Statistics Canada has created a
population projection tool capable of capturing Canada’s diversity by visible minority status, Ab-
original identity, immigration status, and a number of other variables, either closely linked to
demographic behaviours, like education, or dependent on socio-demographic characteristics, like
labour force participation (Statistics Canada 2011). Demosim explicitly models the educational dif-
ferentials between the main groups of Aboriginal Peoples, the Canadian-born White population,
and various visible minorities. Both ethno-cultural and educational background feed into its labour
force models. In this study, we capitalize on Demosim’s ability to single out the effect of education
on labour force participation, and create scenarios allowing assessment of the extent and timeline
of hypothetical improvements in education on the future labour force participation of Aboriginal
peoples.
The educational attainment gap between the Aboriginal peoples and the non-Aboriginal popula-
tion is the subject of a growing body of literature, including historical studies (e.g., Kirkness 1999;
Carr-Stewart 2001, 2006), attempts to explain underlying causes (Frenette 2011; Richards and Scott
2009; Wotherspoon and Schissel 1998), and policy analysis and recommendations (Richards 2006;
Paquette and Fallon 2010; Richards and Scott 2009). Low educational attainments are determined
to be partially responsible for the relatively poor labour force participation of Aboriginal peoples
in Canada (e.g., Walters et al. 2004) and the income gap between Aboriginal peoples and the rest of
Canada (Wilson and Macdonald 2010).
There is broad agreement that improvements in education attainments would benet Aboriginal
youth and society as whole, facilitating greater participation in the Canadian economy and improv-
ing Aboriginal peoples’ community well-being and social cohesion. Or as Richards and Scott put it,
while “many of the Aboriginal/non-Aboriginal gaps have complex origins [...] improving education
outcomes is probably the most important dimension of social policy to tackle” (2009: 6). Concern-
ing the gap in labour force participation rates, we nd that about half of the gap can be attributed
to education, with the remaining gap decreasing with education level. In fact, Hull (2000, 2005) has
found that the gap virtually disappears for university graduates. However, Pendakur and Pendakur
(2011) showed that, controlling for similar characteristics, Aboriginal people will still earn less than
their non-Aboriginal Canadian majority counterparts even with the same education credentials.
From a demographic perspective, the Aboriginal population of Canada can be described as hav-
ing a strong natural rate of increase, resulting from a young age structure and high fertility. In the
context of an ageing society, this makes Aboriginal workers a growing segment of the native labour
force, especially among younger ages.2 Both the future size and human capital of this group will heav-
ily depend on current educational investments. For an economic perspective on investing in Aborig-
inal education, and the potential contribution of Aboriginal Canadians to labour force, employment,
productivity, and output growth, see, e.g., Sharpe and Arsenault (2010) and Sharp et al. (2009). The
2. Demosim projects a 35-per-cent increase of the Aboriginal population aged 25–44 until 2056, while, e.g., the
Canadian-born White population group is projected to decrease by 16 per cent in absolute size. The growth
of the Aboriginal population is close to the projected total growth for the whole population—including
immigration (34 per cent), which keeps the share of Aboriginal peoples in the total population of this age
group roughly constant. In terms of the labour force, in the base scenario described below, the proportion of
the aboriginal labour force aged 25–44 would stay virtually constant at around 3.3 per cent between 2012 and
2056, the natural growth of this group balancing out immigration. In the alternative convergence scenarios,
the relative share increases up to 3.7 per cent, due to increased labour force participation.
Canadian Studies in Population 41, no. 1–2 (2014)
146
literature agrees on high rates of return on investments in education, and single them out as “one of
the rare public policies with no equity-efciency trade-off ” (Sharpe and Arsenault 2010: 27). Sharpe
and Arsenault (2010) also calculate labour force outcomes, variations in GDP, and scal effects
resulting from a hypothesized full or partial convergence of educational attainments of Aboriginal
people towards the education distribution of the non-Aboriginal population. In contrast to such styl-
ized macro studies, Demosim accounts for the changing population composition, size, and age struc-
ture, and models education longitudinally. When creating convergence scenarios, we do not change
the education of the whole Aboriginal population, but rather model improvements in the education
attainments of current and future school-age cohorts, simulating the process of population renewal.
Therefore, our approach also allows an estimate of the time needed for this process to take place.
The contribution of this study is twofold. First, we aim to answer the question of how an in-
crease in educational attainment of Aboriginal peoples would impact their labour force participation.
By distinguishing four Aboriginal groups (see next section), we are able to detect a variety of patterns
in how education is linked to labour force participation. The second question concerns the timeline
of how improvements in educational attainments would impact future labour force participation.
This study is organized as follows: The rst part addresses data and modelling issues, capturing
how our analysis was performed. We start this discussion by depicting current education and labour
force differences between the studied Aboriginal groups and the Canadian-born White population
as captured in the 2006 Census. In the remainder of the rst part, we introduce the Demosim model
and the methodologies behind its labour force and educational projections—the latter captured in
more depth to provide a foundation for the education scenarios. The second part of this study then
describes selected education scenarios, resulting projection results, and their interpretation.
Data and methods
Variable categories and conventions
In this study, we distinguish four groups of Aboriginal peoples that are identiable in the 2006
Census and used in Demosim projections:
Registered North American Indian (NAI);
Non-registered NAI;
Métis;
Inuit.
We contrast these groups to the Canadian-born (CB) White population, which is used as a refer-
ence category. Concerning education, CB Whites currently closely represent the Canadian popula-
tion average. Canada’s visible minority population and immigrants generally have higher educational
attainments. By excluding them from the reference category, this study isolates educational scenarios
from immigration effects and other composition effects caused by different growth rates of visible
minorities.3 We distinguish three levels of education:
below high-school;
3. Demosim models the relative differences in educational attainments between Canadian-born (CB) Whites,
eleven visible minority groups, and four Aboriginal groups. These relative differences, expressed by odds
ratios, have been remarkably stable over time and are kept constant in the base projection scenario. Given
the different growth rates of the distinguished groups, selecting CB Whites as reference category prevents
composition effects in the modelling of relative differences.
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
147
high-school diploma only;
post-secondary diploma.
The considered age range throughout this study is 25–64 years, sometimes broken up into 25–44
and 45–64 age groups. We have selected an age cutoff of 25 years, as most people have left school
at this age.
All illustrations are based on Demosim projections which start from the 2006 Census (Demosim
is described below). Data are adjusted for Census net under-coverage, and concerning the average
labour force participation, they also take into account more recent observations from the Labour
Force Survey.
The demographic assumptions for the future used in this study are identical with the Scenario-1
published in “Population Projections by Aboriginal Identity in Canada” (Statistics Canada 2011)
and assumes constant Aboriginal fertility (for given individual level variables like education) and no
intra-generational ethnic mobility.4 Concerning labour force participation, we have chosen the recent
trend scenario (Scenario C) published in Martel et al. (2011), which is based on identical demographic
assumptions and produces population and labour force outcomes lying in the centre of the ve
scenarios published. Concerning educational projections, the baseline scenario of this study is identi-
cal with the assumptions in both above studies, and will be contrasted with two alternative scenarios
added in this study. Note that this study does not provide any forecasts, and is strictly to be seen as
a what-if projection. This is an important nuance, because we expect forecasts to tell us what the
future will most likely be, whereas projections instead tell us what would happen if the assumptions
and scenarios chosen were to be proven correct. Thus, this is a prospective exercise, whose purpose
is to support the planning of public policies rather than to predict the future.
Current labour force participation and education
As shown in Figure 1, Aboriginal peoples on average have lower educational attainments than CB
Whites, the gaps being largest for Inuit and Registered NAI. Currently, high-school non completion
rates are around 50 per cent for registered NAI and over 60 per cent for Inuit, compared to 16 per
cent of the CB White population, aged 25–64.
Education attainments are closely linked to labour force participation (Figure 2). While Aborig-
inal peoples’ labour force participation in most cases is lower also within education categories, the
differences are very pronounced when comparing education groups within all population groups. Of
the compared groups, registered NAI have the lowest overall labour force participation, with the gap
to CB Whites being around 20 percentage points. On the other end, for a given educational attain-
ment, labour force participation of Métis is almost indistinguishable from the rates of CB Whites—
and even slightly higher for high-school graduates.
4. Ethnic mobility is “the phenomenon by which individuals and families change their ethnic afliation”
(Guimond 2003). Ethnic mobility has two components: intragenerational and intergenerational (Boucher et
al. 2009). Intragenerational ethnic mobility results from a change in an individual’s ethnic afliation over time.
For example, a person who reports no Aboriginal identity in one census but a Métis identity in the following
census is deemed to have experienced intragenerational ethnic mobility (Boucher et al. 2009; Guimond 2003).
Intergenerational ethnic mobility results from a change in ethnic afliation between parents and their children,
with the parent(s) not having the same ethnic afliation as the child(ren). This mobility does not imply any
change in ethnic group for an individual, and is based on comparing the ethnic identity of an individual with
that of his/her parents.
Canadian Studies in Population 41, no. 1–2 (2014)
148
A simple way of quantifying the contribution of educational differences to the gaps in labour
force participation consists in standardizing the Aboriginal groups to resemble the education distri-
bution of CB Whites. Following this approach, one calculates the labour force participation rate that
would be obtained if the Aboriginal population had the same education composition as the reference
group of CB Whites. The remaining differences cannot be attributed to education, but stem from
differences in labour force participation within each education group.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Inuit Registered NAI Non-registered NAI Metis CB White
Below high-school High-school diploma Post-secondary
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Below high-school
High-school
Post-secondary
All
Below high-school
High-school
Post-secondary
All
Below high-school
High-school
Post-secondary
All
Below high-school
High-school
Post-secondary
All
Below high-school
High-school
Post-secondary
All
Inuit NAI registered NAI non-
registered
Metis Canadian-born
White
ACTIVE INACTIVE
Figure 2. Labour force participation by population group and education, ages
25–64, 2012 (Demosim projection).
Figure 1. Education composition by population group, ages 25–64, 2012
(Demosim projection).
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
149
Table 1. Labour force participation gaps between Aboriginal peoples and CB Whites, ages 25–64, 2012
(Demosim projection).
Labour force
participation rate
“Total gap
(compared to CB
White)”
Gap attributable
to educational
differences
Remaining gap
Proportion of
gap attributable
to educational
differences
Inuit 69.2% 12.5% 8.9% 3.6% 71.2%
NAI registered 62.9% 18.8% 9.5% 9.2% 50.8%
NAI non-registered 74.3% 7.4% 3.4% 4.0% 45.7%
Metis 78.9% 2.8% 2.5% 0.3% 89.3%
CB White 81.7%
As displayed in Table 1, the relative contribution of education differences to the gap in labour
force participation is highest for Métis, followed by Inuit, registered NAI, and unregistered NAI. In
absolute terms, closing the educational gaps would have the biggest effect for registered NAI, theor-
etically moving up labour force participation by 9.5 percentage points.
The Demosim Population Projection model5
Demosim is a microsimulation model designed for detailed population projections. It was de-
veloped at Statistics Canada in partnership with Human Resources and Skills Development Canada
(HRSDC), Aboriginal Affairs and Northern Development Canada (AANDC), Canadian Heritage
(PCH), and Citizenship and Immigration Canada (CIC). Using the micro-data le from the Canadian
Census of Population (20 per cent sample6) as its starting point, Demosim produces dynamic popu-
lation projections at the level of the provinces, territories, census metropolitan areas, and selected
smaller geographies.
Demosim includes a number of individual characteristics going beyond the typical age-sex clas-
sication of classic population projections: visible minority group, place of birth, generation status,
Aboriginal identity, highest level of educational attainment, and labour force participation, among
others. It does so by simulating events such as births, deaths, migrations and changes in level of edu-
cation, according to various population growth scenarios. Initially created in 2004 and on-going, the
model has been used over time to generate projections of the Canadian population’s ethno-cultural
composition (Statistics Canada 2010), the Aboriginal population of Canada (Statistics Canada 2011)
and the Canadian labour force (Martel et al. 2011).
Microsimulation removes the technical restrictions of more traditional population projections
produced by cohort-component models. Compared to such cell-based approaches, microsimulation
does not model population groups (e.g., people of the same age and sex) and changes in the sizes
of those groups, but rather simulates a large sample of individuals which together represent society.
5. The rst paragraph of this description is heavily based on the presentation of Demosim on Statistics
Canada’s website http://www.statcan.gc.ca/microsimulation/demosim/demosim-eng.htm. For a more
detailed description of the model, including methods and assumptions, please consult both Statistics Canada
2010 and Statistics Canada 2011.
6. The initial population size in Demosim equals the Canadian population. This was done by cloning the
persons in the 20 per cent sample according to their sampling weight. As a consequence, due to the large
scale of the simulation, Monte Carlo variability can be ignored at the aggregation level of this study, as
presented results are virtually identical when repeating the simulation (with a different random number
stream).
Canadian Studies in Population 41, no. 1–2 (2014)
150
As a consequence of simulating individuals, microsimulation allows for any level of detail, and for
modelling of people in their family context—the latter used in Demosim to transmit characteristics
like ethnicity and language over generations. (For a more detailed introduction of microsimulation
in the social sciences and population projections, see, e.g., Spielauer 2010 and Imhoff and Post
1998.) Like for all projection models, there exists a trade-off between the additional randomness
introduced by additional variables (potentially compromising the prediction power of a model) and
miss-specication errors, caused by models that are too simplied.7 In the context of most micro-
simulation models, the list of variables in Demosim is kept short, leading to aggregate projections
that are generally similar to those obtained by traditional population projections, while adding valu-
able detail. Within Statistics Canada, this ability makes Demosim a valuable tool for survey weighting
and survey validation.
Simulating labour force participation in Demosim8
Labour force participation is simulated by annually imputing an activity status to each individual
living in a Canadian province. The imputation is based on participation rates constructed in two
stages. First, a participation rate is selected according to the simulated individual’s age, sex, highest
level of education, and province of residence. These participation rates are drawn from the Labour
Force Survey (LFS), and assumptions are made about their future evolution.
Second, this rate is increased or decreased using a ratio to take into account other characteris-
tics—namely, immigrant status, period of immigration, membership in a visible minority group, and
Aboriginal identity. The ratios are calculated using participation data from the 2006 Census, and vary
for each combination of age, sex, and education level. Ratios are calculated for Canada as a whole,
and then applied to each province, under the assumption that the gap between persons belonging to
a visible minority group and the rest of the population, for example, does not vary from one province
to another. (For a more detailed discussion of assumptions underlying the labour force module of
Demosim, see Martel et al. 2011.)
The scenario selected for this study is based on a recent trends in participation rates assumption. It
takes the changes observed at the national level over the 10 years between 1999 and 2008, and ex-
trapolates them for the next 10 years. Thus, for all age groups between 15 and 79 years, a linear ex-
trapolation of trends, mostly upward, was applied until 2018. After 2018, the rates are held constant
to the end of the projection period.
While these overall trend scenarios affect all simulated individuals regardless of their ethnicity,
resulting in a universal upward trend of participation rates, relative differences between ethnicities,
as observed from the 2006 Census, are assumed to persist over time. In the context of the present
study, this means that we can isolate the effect of changes in education on labour force participation.
7. For a discussion of this point, see Imhoff and Post (1998: 109). One of the main reasons why the
prediction power of microsimulation models can diminish with complexity is the fact that microsimulation
models generate their own explanatory variables when run into the future, each additional explanatory
variable requiring an extra set of Monte Carlo experiments, with a corresponding increase in Monte Carlo
randomness. Thus “...extending a correctly specied model will increase the specication randomness: the
model will continue to be unbiased, in the sense that it will on average produce the expected value of the
future population, but its random variation around this expected value will increase. An extremely elaborate
model may be perfectly specied, but its specication bias may be so large that the projection results give no
information whatsoever about the future course of the population” (Imhoff and Post 1998: 111).
8. This chapter is heavily based on Martel et al. (2011).
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
151
While we account for composition effects resulting from altered education, we do not alter relative
differences in labour force participation between ethnic groups for given education levels.
Modelling education in Demosim
Like in the labour force model, the modelling of education follows the general idea of applying
a proportional model, separating general trends from relative inter-group differences. Differing from
the labour force model, which reassigns a labour force status to each individual each year, the educa-
tion module explicitly simulates education progressions using a longitudinal model.
Due to data limitations, we are not able to estimate models from one single data source, but
rather have to nd indirect ways to combine the detailed cross-sectional information of the Census
with longitudinal information from a separate source. For the (longitudinal) modelling of education
biographies, we have identied the General Social Survey (GSS) of 2001 as the best available data
source.9
The following gives an overview on the modelling approach; a more detailed description is avail-
able in Spielauer (2011). In spite of the technical and computational challenges, the resulting model
is highly transparent from a user’s perspective, with all parameters having an easy interpretation and
supporting scenario building.
As a rst step, we estimate six discrete time logistic event history models from GSS, which con-
stitute the basis for the modelling of education progression. These six models break down into:
Three models for high-school graduation. Due to differences in cohort trends, we distin-
guish between rst-chance graduations (i.e., those attained between ages 16 and 20), second-
chance graduations (attained between ages 21 and 25), and adult graduations;
Two simultaneous models for the rst post-secondary diploma—either a non-university
post-secondary graduation or a university certicate, diploma, or degree at bachelor’s level
(or above); and
One model for obtaining a BA diploma after a non-university post-secondary graduation.
These models serve as a standard surface of education progression probabilities by birth cohort
and age (respectively, time since last graduation in the case of post-secondary studies) and are estimated
separately by sex and place of birth. For high-school graduation, we found that cohort factors can
be closely approximated by a logarithmic trend; for the other models, we use piecewise-linear trends
(allowing to model the generally steeper trends in post-secondary graduations for birth cohorts after
1960; see the section “Overall trends” below).
Our models so far do not contain factors for the separate ethno-cultural groups, as such param-
eters cannot be estimated directly from GSS. To solve this problem we follow an indirect alignment
approach. The idea is to nd relative factors (log odds) for each birth cohort and ethno-cultural
group which, when added to our models, result in an exact match of cross-sectional educational at-
tainment, calculated from our longitudinal models and the Census targets.
The procedure we follow in order to obtain relative factors and the necessary assumptions of this
approach can be summarized as follows.
We assume that cohort trends estimated from 2001 GSS data continue until the Census year
2006 and project the (unaligned) educational composition in 2006 by birth cohort, sex, and
place of birth in each of our 6 longitudinal models;
9. Unfortunately, the 2006 Family Transitions cycle of the GSS, which otherwise collected almost equivalent
data, did not collect dates of graduation other than for the highest education attained, and thus could not be
used for modelling education transitions.
Canadian Studies in Population 41, no. 1–2 (2014)
152
We then compare the projected educational composition of each population group with the
composition found in the Census, and search for alignment factors which, when introduced
as additional factors (log-odds) into the logistic regression models, lead to an exact match
of simulated and observed data. This rst-round alignment is an overall alignment, not yet
distinguishing the different ethno-cultural groups. For each birth cohort, sex, and place of
birth, we have to nd a set of three alignment factors: one for high-school graduation, the
second for non-university post-secondary graduation, and a third for obtaining a BA. To
obtain these factors we use numerical simulation techniques.10 This alignment round can also
be interpreted as a test of consistency between the two data sources: in the optimal case,
alignment factors would be very close to zero and reect random sampling variations in the
survey rather than systematic differences. In fact, the factors found were satisfactory in this
sense (Spielauer 2011);
In a second alignment round, we then search for an additional set of factors for each ethno-
cultural group (again by birth cohort, sex, and place of birth). This additional alignment
leads to an exact match of the modeled educational composition with Census data for each
ethno-cultural group. A necessary assumption of this step is that for a given birth cohort,
the relative differences between groups remain constant over the cohort’s lifetime, e.g., the
same group-specic log-odds apply at each year of age. A second assumption is that the age
baselines are xed over all birth cohorts and ethno-cultural groups (but can vary between sex
and place of birth). The latter is a strong assumption, as Aboriginal peoples have different
timing patterns of educational attainment. We address this problem by limiting our analysis
to age groups older than 24.
In a third round, we search for factors of inter-provincial differences for all Canadian-born
people. The necessary underlying assumption for this step is that there are no interaction
effects between province and ethno-cultural group, i.e., that the relative differences between
groups found in the second alignment round are constant over provinces. This is an assump-
tion found similarly in the models for labour force participation.
The logic of this approach is most easily displayed for high school graduation. For a person of
given sex, place of birth, and birth cohort, the probability of obtaining a high-school diploma at
a given age can be expressed as a function of the log-odds estimated by logistic regression f(age,
cohort):
),(
1
1
cohortagef
e
p
+
=
The probability of having a high-school diploma in 2006 can be expressed as:
First-round alignment forces this equation to produce a target probability TARGET2006 by nding
a correction term c:
Second-round alignment forces the equation to produce a target probability VISMINTARGET2006
for a specic ethno-cultural group by adding an additional alignment factor v:
10. The simulation algorithm can be thought of as trying (and narrowing down) possible alignment factors that
reect the odds ratios within a feasible range set between 1:100 and 100:1 until a virtually exact solution is
found.
12
In a third round we search for factors of inter-provincial differences for all Canadian-born
people. The necessary underlying assumption for this step is that there are no interaction
effects between province and ethno-cultural group, i.e. that the relative differences between
groups found in the second alignment round are constant over provinces. This is an
assumption found similarly in the models for labour force participation.
The logic of this approach is most easily displayed for high school graduation. For a person of
given sex, place of birth, and birth cohort the probability of obtaining a high-school diploma at a
given age can be expressed as a function of the log-odds estimated by logistic regression f(age,
cohort):
),(
1
1
cohortagef
e
p
The probability of having a high-school diploma in 2006 can be expressed as:
2006
16
),(
2006 1
1
11
inage
age
cohortagef
e
PROB
First-round alignment forces this equation to produce a target probability TARGET2006 by
finding a correction term c:
2006
16
),(
2006 1
1
11
inage
age
ccohortagef
e
TARGET
Second round alignment forces the equation to produce a target probability
VISMINTARGET2006 for a specific ethno-cultural group by adding an additional alignment
factor v:
2006
16
),(
1
1
11
inage
age
vccohortagef
2006 e
ETVISMINTARG
The search for alignment factors follows the same idea for all types of graduation, but is
technically more challenging for post-secondary studies as we have to deal with simultaneous
processes. (For a more detailed discussion see Spielauer 2011).
The proportional model type is very convenient for the development of scenarios, as it allows
distinguishing between assumptions concerning future overall trends for each of the
distinguished graduation types and assumptions on the future evolvement of inter-group
differences.
12
In a third round we search for factors of inter-provincial differences for all Canadian-born
people. The necessary underlying assumption for this step is that there are no interaction
effects between province and ethno-cultural group, i.e. that the relative differences between
groups found in the second alignment round are constant over provinces. This is an
assumption found similarly in the models for labour force participation.
The logic of this approach is most easily displayed for high school graduation. For a person of
given sex, place of birth, and birth cohort the probability of obtaining a high-school diploma at a
given age can be expressed as a function of the log-odds estimated by logistic regression f(age,
cohort):
),(
1
1
cohortagef
e
p
The probability of having a high-school diploma in 2006 can be expressed as:
2006
16
),(
2006 1
1
11
inage
age
cohortagef
e
PROB
First-round alignment forces this equation to produce a target probability TARGET2006 by
finding a correction term c:
2006
16
),(
2006 1
1
11
inage
age
ccohortagef
e
TARGET
Second round alignment forces the equation to produce a target probability
VISMINTARGET2006 for a specific ethno-cultural group by adding an additional alignment
factor v:
2006
16
),(
1
1
11
inage
age
vccohortagef
2006 e
ETVISMINTARG
The search for alignment factors follows the same idea for all types of graduation, but is
technically more challenging for post-secondary studies as we have to deal with simultaneous
processes. (For a more detailed discussion see Spielauer 2011).
The proportional model type is very convenient for the development of scenarios, as it allows
distinguishing between assumptions concerning future overall trends for each of the
distinguished graduation types and assumptions on the future evolvement of inter-group
differences.
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
153
The search for alignment factors follows the same idea for all types of graduation, but is technic-
ally more challenging for post-secondary studies, as we have to deal with simultaneous processes (for
a more detailed discussion, see Spielauer 2011).
The proportional model type is very convenient for the development of scenarios, as it allows
distinguishing between assumptions concerning future overall trends for each of the distinguished
graduation types and assumptions on the future evolvement of inter-group differences.
Scenarios
Overall trends
Similar to the labour force model, future educational trends in Demosim’s baseline projection
scenario are an extrapolation of past trends for a period before levelling them off. Being based on six
separate graduation types, assumptions had to be made for each of them (separately by sex and Can-
adian versus foreign-born). Concerning high-school graduation, observed values11 are used for birth
cohorts up to 1986, continued by a (logarithmic) trend for another 5 years, and continued by half of
this trend until levelling off for birth cohorts 1995 and later. For all post-secondary graduation types,
projections start for birth cohorts 1982 and later, following a linear trend until 1985, half this trend
until 1990, and one-fourth of the initial trend until 1995, where rates level off. These assumptions were
chosen in order to level off average post-secondary graduation rates at levels already observed today
for the visible minorities with the highest educational attainments (i.e., most Canadian-born Asian min-
ority groups), thereby preventing the model from converging towards universal university graduation.
Figure 3 illustrates the trends for three selected graduation types. Recent trends have become
very at already for rst-chance (i.e., age 16–21) high-school graduation, while they remained almost linear
and steep for post-secondary graduations of birth cohorts 1960 and onwards. For allowing compari-
son with the following scenarios for Aboriginal peoples, trends are displayed in log odds,12 with the
1996+ birth cohorts of CB Whites used as a reference category.
Past and projected differences in educational attainment
One of the central ndings of the analysis underlying Demosim’s education module is the pro-
nounced and remarkably persistent relative differences in graduation probabilities between most of the
16 ethnicities distinguished in the model. This means that most ethnicities followed the same cohort
trends while maintaining relative differences between each other (expressed as odds ratios). This high
persistence in relative differences is a very convenient observation when designing projection scenarios,
and provided the rationale for Demosim’s baseline education scenario, which keeps relative differences
constant in the future. Exceptions to this general pattern are the Black population (where the gap nar-
11. Technically, “observed” data points consist of the estimated value plus the rst-round alignment term.
12. Odds ratios can be interpreted like betting quotes. For example, if the cohort trend moved from −1 for the
1940 birth cohort to 0 for the 2000 birth cohort, as observed for high-school graduations in Figure 4, you
would bet exp(−1) : exp(0)—equivalent to 1:2.72—that it is the younger person who graduated from high
school, if you know that only one of both did. As displayed in Figure 5, the relative differences in high-school
graduation rates between CB Whites and Aboriginal groups are in about the same 3:1 range.
12
In a third round we search for factors of inter-provincial differences for all Canadian-born
people. The necessary underlying assumption for this step is that there are no interaction
effects between province and ethno-cultural group, i.e. that the relative differences between
groups found in the second alignment round are constant over provinces. This is an
assumption found similarly in the models for labour force participation.
The logic of this approach is most easily displayed for high school graduation. For a person of
given sex, place of birth, and birth cohort the probability of obtaining a high-school diploma at a
given age can be expressed as a function of the log-odds estimated by logistic regression f(age,
cohort):
),(
1
1
cohortagef
e
p
The probability of having a high-school diploma in 2006 can be expressed as:
2006
16
),(
2006 1
1
11
inage
age
cohortagef
e
PROB
First-round alignment forces this equation to produce a target probability TARGET2006 by
finding a correction term c:
2006
16
),(
2006 1
1
11
inage
age
ccohortagef
e
TARGET
Second round alignment forces the equation to produce a target probability
VISMINTARGET2006 for a specific ethno-cultural group by adding an additional alignment
factor v:
2006
16
),(
1
1
11
inage
age
vccohortagef
2006 e
ETVISMINTARG
The search for alignment factors follows the same idea for all types of graduation, but is
technically more challenging for post-secondary studies as we have to deal with simultaneous
processes. (For a more detailed discussion see Spielauer 2011).
The proportional model type is very convenient for the development of scenarios, as it allows
distinguishing between assumptions concerning future overall trends for each of the
distinguished graduation types and assumptions on the future evolvement of inter-group
differences.
Canadian Studies in Population 41, no. 1–2 (2014)
154
rowed considerably) and both non-registered and—to a larger extent—registered NAI, for whom the gap
increased, partly counterbalancing the educational expansion experienced by the rest of the population
for birth cohorts 1960 and later. In contrast, the Métis and Inuit populations follow the typical pattern.
Technically, this analysis comprises the second-round alignment, as outlined above: we searched
for cohort series of relative factors which, when added to the rst-round alignment, make the longi-
tudinal models match the group-specic education composition of the 2006 Census. We obtain a set
of three alignment factors (high-school, post-secondary below BA, BA) per birth cohort for each of
the separate ethno-cultural groups, with further breakdowns by sex. The full result of this exercise
is available in the form of 168 cohort-series of alignment factors in Spielauer (2009); in this section,
we limit ourselves to highlighting some of the main patterns of relevance for Aboriginal peoples.
The following graphs on high-school graduation of Aboriginal groups emphasize the central ndings
of this analysis. All four Aboriginal groups have negative log-odds compared to the reference group of
Canadian-born Whites, i.e., lower graduation rates. The range of differences between groups shows no
differences due to gender. A negative cohort trend can be found for registered NAI; in size, this group-
specic trend almost exactly outbalances the general upward trend in the Non-Aboriginal population.
Figure 4 displays the log-odds as observed in the past, and the three scenarios designed for the
future; the scenarios will be discussed in more detail below.
14
Figure 3. Cohort trends for selected graduation types (log-odds, reference 1996+, CB Whites).
Past and projected differences in educational attainment
One of the central findings of the analysis underlying Demosim’s education module is the
pronounced and remarkably persistent relative differences in graduation probabilities between
most of the 16 ethnicities distinguished in the model. This means that most ethnicities followed
the same cohort trends while maintaining relative differences between each other (expressed as
odds ratios). This high persistence in relative differences is a very convenient observation when
designing projection scenarios, and provided the rationale for Demosim’s baseline education
scenario, which keeps relative differences constant in the future. Exceptions to this general
pattern are the Black population (where the gap narrowed considerably) and both non-registered
and—to a larger extent—registered NAI, for whom the gap increased, partly counterbalancing
the educational expansion experienced by the rest of the population for birth cohorts 1960 and
later. In contrast, the Métis and Inuit populations follow the typical pattern.
Technically, this analysis comprises the second-round alignment, as outlined above: we searched
for cohort series of relative factors which, when added to the first-round alignment, make the
2
1.5
1
0.5
0
0.5
OBSERVEDCOHORTTREND PROJECTEDCOHORTTREND
HighSchoolGraduation
(atage1621)
2
1.5
1
0.5
0
0.5
PostSecondary Graduation
below/otherthanBA
2
1.5
1
0.5
0
0.5
1940
1946
1952
1958
1964
1970
1976
1982
1988
1994
2000
2006
2012
2018
1943
1949
1955
1961
1967
1973
1979
1985
1991
1997
2003
2009
2015
Male Female
BAGraduation
("directgraduation"afterHighSchool)
Figure 3. Cohort trends for selected graduation types (log-odds, reference 1996+, CB Whites).
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
155
Demosim differentiates two distinct post-secondary education levels, while underlying models
distinguish between three simultaneous respectively linked processes. Graduation from a non-uni-
versity post-secondary program and ‘direct’ graduation from university are modeled as simultaneous
processes, meaning that the probability of one event inuences the probability of the other. After a
non-university diploma, we start a third process of ‘indirect’ university graduation, i.e., obtaining a
BA after having obtained another, non-university diploma.
Relative factors for non-university post-secondary graduations were found to follow similar pat-
terns but with less variation between the Aboriginal groups (not shown). In contrast, we found very
16
Figure 4: Relative differences (log-odds) in high-school graduation compared with CB White.12
Demosim differentiates two distinct post-secondary education levels; underlying models
distinguish between three simultaneous respectively linked processes. Graduation from a non-
university post-secondary program and ‘direct’ graduation from university are modeled as
simultaneous processes, meaning that the probability of one event influences the probability of
the other. After a non-university diploma, we start a third process of ‘indirect’ university
graduation, i.e. obtaining a BA after having obtained another non-university diploma.
Relative factors for non-university post-secondary graduations were found to follow similar
patterns but with less variation between the Aboriginal groups (not shown). In contrast, we found
12 See Footnote 11 on Page 12 for interpretation.
2
1.5
1
0.5
0
0.5
Observed BaseScenario 100%ImmediateConvergence 50%PhasedConvergence
RegisteredNAI
2
1.5
1
0.5
0
0.5
NonregisteredNAI
2
1.5
1
0.5
0
0.5
Metis
2
1.5
1
0.5
0
0.5
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
Male Female
Inuit
Figure 4. Relative differences (log-odds) in high-school graduation compared with CB Whites.
See footnote 11 for interpretation.
Canadian Studies in Population 41, no. 1–2 (2014)
156
pronounced negative log-odds for university graduations (Figure 5). Again, there are pronounced
negative trends for registered NAI, and to a lesser extent non-registered NAI. The negative trend
is steepest for females, counterweighing the equally steep general trend of females in university
graduations.
Future scenarios assume constant relative differences between groups, averaging the observa-
tions of the ten most recent birth cohorts for birth cohorts up to 1995. For cohorts born 1996 and
later, we have created three scenarios:
17
very pronounced negative log-odds for university graduations. Again, there are pronounced
negative trends for registered NAI, and to a lesser extent non-registered NAI. The negative trend
is steepest for females, counterweighing the equally steep general trend in female in university
graduations.
Figure 5: Relative differences (log-odds) in university graduations
Future scenarios assume constant relative differences between groups averaging the observations
of the ten most recent birth cohorts for birth cohorts up to 1995. For cohorts born 1996 and later,
we have created three scenarios:
2
1.5
1
0.5
0
0.5
Observed BaseScenario 100%ImmediateConvergence 50%PhasedConv ergence
RegisteredNAI
2
1.5
1
0.5
0
0.5
NonregisteredNAI
2
1.5
1
0.5
0
0.5
Metis
2.5
2
1.5
1
0.5
0
0.5
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2015
Male Female
Inuit
Figure 5. Relative differences (log-odds) in university graduations.
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
157
− Baseline scenario: Education progression follows the projected trends of Demosim. The relative
differences between Aboriginal groups and the reference group (Canadian-born Whites) stay
constant. This is the scenario used in all previous published projections. Note that these assump-
tions do break the downward trend where such a trend was observed. This leads to increases
in educational attainment following the general trends for birth cohorts 1986 (1982 for post-
secondary graduations) up to the 1995 birth cohorts, where all processes level off.
− Immediate 100% convergence scenario: All Aboriginal people born in 1996 and after have the same
education progression rates as Canadian-born Whites. This is an extreme scenario to highlight
the theoretically fastest and complete closure of the education gap.
− Phased 50% convergence scenario: Starting with people born in 1996, over 10 years, the gap between
the Aboriginal and CB White populations is gradually reduced to 50 per cent. Note that the slope
of these upward trends is in range of the historic upward trends for the majority of the popula-
tion. This scenario models a delayed educational expansion, at the pace observed in other groups
earlier.
None of the three scenarios is intended to produce a forecast of future education trends of Ab-
original peoples, but are stylized what-if settings for the study of how alternative assumptions affect
the educational composition and labour force participation in the future.
Results
In this section, we study the effect of the alternative assumptions on education trends on the
evolution of future labour market participation rates of Aboriginal peoples, as well as the size and
education composition of their workforce. For this analysis, we have chosen a period view, drawing
the timelines of change for the calendar years 2011 up to 2056. The analysis is limited to persons
aged 25–64, the 25-year cutoff selected to isolate from the effects of increased school attendance on
labour force participation.
Changes in education
As depicted in Figure 6, the proportion of the population aged 25–44 graduated from high
school will stay at current levels in the baseline scenario, while there is still an upward trend for the
45–64-year age group, as younger, higher-educated cohorts renew the population. The latter effect
is not present for registered NAI who did not participate in the educational expansions for cohorts
born after 1960.
For both convergence scenarios, assumed increases in education would not become visible
immediately, as the school-age population affected by the change still has to reach age 25 (and,
respectively, 45). In the immediate convergence scenario, for the younger age group, the transition
process is nished by 2041, and for the older age group, accordingly, 20 years later. (The phased
convergence scenario takes yet another 10 years to fully unfold.) Note that education in Demosim
operates on a provincial level. This leaves the education rates of Inuit peoples staying below the
average value of CB Whites, as their population is concentrated in Territories with generally lower
education attainments.
Figure 7 shows the proportion of the population with a post-secondary education. In contrast to
high-school graduation, upward trends are also present in the baseline scenario.
Canadian Studies in Population 41, no. 1–2 (2014)
158
Changes in labour force participation rates
Labour force participation of CB Whites is projected to stay stable at around 90 per cent for the
younger age group, and to increase for 45–64-year-olds, pushing up labour force participation for
the full 25–64 age range from 80 to 85 per cent (Figure 8). In the base scenario, this trend is only fol-
lowed by non-registered NAI and Métis, while it stays at, or even slightly decreases, for registered
NAI and Inuit peoples. The latter mostly results from the distinct demographic patterns for these
groups, including faster population growth in provinces with generally lower education and labour
force participation, and faster relative growth of age groups with lower labour force participation
(below 30 and above 60 years of age).
The pace of increases in labour force participation rates resulting from the two alternative scen-
arios of educational improvements is slow, with rst signicant increases not to be expected within
20 years in the case of the phased convergence. Immediate full convergence obviously leads to faster
and more pronounced effects.
19
3 Results
In this section we study the effect of the alternative assumptions on education trends on the
evolution of future labour market participation rates of Aboriginal peoples, as well as the size
and education composition of their workforce. For this analysis we have chosen a period view,
drawing the timelines of change for the calendar years 2011 up to 2056. The analysis is limited
to persons 25–64, the 25 year cut off selected to isolate from effects of increased school
attendance on labour force participation.
3.1 Changes in education
As depicted in Figure 6, the proportion of the population 25–44 graduated from high school will
stay at current levels in the baseline scenario, while there is still an upward trend for the 45–64
years age group as younger higher educated cohorts renew the population. The latter effect is not
present for registered NAI who did not participate in the educational expansions for cohorts born
after 1960.
Figure 6: Proportion of population graduated from high school
30%
40%
50%
60%
70%
80%
90%
100%
AGE2544
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
Base Scenario Phased 50% Convegence
Scenario
Immediate 100% Convergence
Scenario
Registered NAI Canadian-born White Non-registered NAI Inuit Metis
AGE4564
Figure 6. Proportion of population graduated from high school.
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
159
Changes in size and educational composition of the Aboriginal peoples’ workforce
The high population growth observed for Aboriginal peoples makes Aboriginal workers an
over-proportionally growing sector of the labour force. In this sense, all changes in labour force
participation rates are magnied by population growth in absolute terms. Education-induced in-
creases in labour force participation also dramatically alter the education composition within the
Aboriginal work force; increases therefore not only concern the number of workers but also their
human capital.
In the baseline scenario, the number of Aboriginal people aged 25–44 active in the labour mar-
ket would increase from 269,000 to 356,000 (+32%) in the next four decades. In the phased 50%
convergence scenario, this increase would be 41 per cent; and in the immediate full-convergence scenario 46
per cent, which means an education-induced increase of the size of the labour force by 24,000
and 37,000 workers aged 25–44, respectively (Figure 9). In both convergence scenarios, all in-
creases in the size of the labour force are driven by people with post-secondary education, the
number of people with lower education diminishing both relatively but also in absolute numbers.
In other words, while projected improvements lead to up to 37,000 additional people aged 25–44
Figure 7. Proportion of population with postsecondary education.
20
For both convergence scenarios, assumed increases in education would not become visible
immediately, as the school age population affected by the change still has to reach age 25
respectively 45. In the immediate convergence scenario, for the younger age group, the transition
process is finished by 2041, for the older age group accordingly 20 years later. (The phased
convergence scenario takes another 10 years to fully unfold.) Note that education in Demosim
operates on a provincial level. This leaves the education rates of Inuit peoples staying below the
average value of CB Whites, as their population is concentrated in Territories with generally
lower education attainments.
Figure 7 shows the proportion of the population with post-secondary education. In contrast to
high-school graduation, upward trends are also present in the baseline scenario.
Figure 7: Proportion of population with postsecondary education
20%
30%
40%
50%
60%
70%
80%
90%
100%
AGE2544
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
Base Scenario Phased 50% Convegence
Scenario
Immediate 100% Convergence
Scenario
Registered NAI Canadian-born White Non-registered NAI Inuit Metis
AGE4564
Canadian Studies in Population 41, no. 1–2 (2014)
160
in the workforce, the number of workers with a post-secondary education would increase by up
to 132,000.
Given the slow pace of population renewal, the education-induced increase in the Aboriginal
labour force of people aged 45–64 would start later but follow similar patterns (Figure 10).
For the whole age range of 25–64 years old, the baseline scenario projects an increase of the Ab-
original labour force from currently 450,000 to 657,000 people (+46 per cent) over the next four
decades (Figure 11). In the phased 50 per cent convergence scenario, an additional 39,000 persons would be
in the workforce (+55 per cent). In the case of immediate 100% convergence, the number of additional
workers would be 80,000 (+64 per cent).
While the Aboriginal population will increase substantially over the next decades, increases in
educational attainment would lead to a decrease in the size of the Aboriginal population without
post-secondary education. Comparing the Aboriginal workforce as projected by the three scen-
Figure 8. Labour force participation rates.
21
3.2 Changes in labour force participation rates
Labour force participation of CB White is projected to stay stable at around 90 per cent for the
younger age group and to increase for 45–64 year old pushing up labour force participation for
the full 25–64 age range from 80 to 85 per cent. In the base scenario, this trend is only followed
by non-registered NAI and Métis while it stays flat or even slightly decreases for registered NAI
and Inuit peoples. The latter mostly results from distinct demographic patterns of these groups
including faster population growth in provinces with generally lower education and labour force
participation and faster relative growth of age groups with lower labour force participation
(below 30 and above 60 years of age).
Figure 8: Labour force participation rates
The pace of increases in labour force participation rates resulting from the two alternative
scenarios of educational improvements is slow with first significant increases not to be expected
50%
60%
70%
80%
90%
100%
AGE2544
50%
60%
70%
80%
90%
100%
AGE4564
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
Base Scenario Phased 50% Convegence
Scenario
Immediate 100% Convergence
Scenario
Registered NAI Canadian-born White Non-registered NAI Inuit Metis
AGE2564
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
161
22
within 20 years in the case of the phased convergence. Immediate full convergence obviously
leads to faster and more pronounced effects.
3.3 Changes of the size and educational composition of the Aboriginal peoples’ workforce
The high population growth observed for Aboriginal peoples makes Aboriginal workers an over-
proportionally growing sector of the labour force. In this sense, all changes in labour force
participation rates are magnified by population growth in absolute terms. Education-induced
increases in labour force participation also dramatically alter the education composition within
the Aboriginal work force; increases therefore not only concern the number of workers but also
their human capital.
In the baseline scenario, the number of Aboriginal people 25–44 active in the labour market
would increase from 269,000 to 356,000 (+32%) in the next four decades. In the phased 50%
convergence scenario, this increase would be 41 per cent; in the immediate full convergence
scenario 46 per cent, which means an education-induced increase of the size of the labour force
by 24,000 and 37,000 workers age 25–44 respectively. In both convergence scenarios, all
increases in the size of the labour force are driven by people with post-secondary education, the
number of people with lower education diminishing both relatively but also in absolute numbers.
In other words: while projected improvements lead to up to 37,000 additional people age 25–44
in the workforce, the number of workers with post-secondary education would increase by up to
132,000.
Figure 9: Aboriginal peoples’ labour force by education, age 25–44
0
50000
100000
150000
200000
250000
300000
350000
400000
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
BaseScenario Phased50%Convegence
Scenar io
Immediate100%Convergence
Scenario
Belowhighschool Highschooldiploma Postsecondary
Figure 9. Aboriginal peoples’ labour force by education, ages 25–44.
Figure 10. Aboriginal peoples’ labour force by education, ages 45–64.
23
Figure 10: Aboriginal peoples’ labour force by education, age 45–64
Given the slow pace of population renewal, the education-induced increase in the Aboriginal
labour force of people 45–64 would start later but follows similar patterns.
For the whole age range of 25–64 year old, the baseline scenario projects an increase of the
Aboriginal labour force from currently 450,000 to 657,000 people over the next four decades
(+46%). In the phased 50 per cent convergence scenario, an additional 39,000 persons would be
in the workforce (+55%). In the case of immediate 100% convergence, this number of additional
workers would be 80,000 (+64%).
0
50000
100000
150000
200000
250000
300000
350000
400000
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
BaseScenario Phased50%Convegence
Scenar io
Immediate100%Convergence
Scenario
Belowhighschool Highschooldiploma Postsecondary
Canadian Studies in Population 41, no. 1–2 (2014)
162
arios for 2056, we nd considerable differences concerning its education composition. While
a phased 50% convergence would increase the overall size of the Aboriginal workforce by 39,000
people, the number of participants with post-secondary education would increase by 107,000.
In the immediate 100% convergence scenario, the number of Aboriginal workers with post-secondary
education in the 2056 labour force would be 600,000, which is 234,000 more people than in the
base scenario.
Summary and conclusions
This study aimed at quantifying the impact of educational attainments on the future labour
force participation of Aboriginal peoples. Using Statistics Canada’s Demosim population projection
model, we were able to simulate alternative scenarios of educational change and the resulting effects
on the future labour force until 2056.
While about half of the observed difference in labour force participation rates between Aborig-
inal and Canadian-born White peoples can be attributed to educational differences, the patterns are
very different for different groups. The contribution of education differences to the gap in labour
force participation is highest for Métis, followed by Inuit, registered NAI, and unregistered NAI. In
absolute terms, closing the educational gaps would have the biggest effect for registered NAI, theor-
etically moving up labour force participation by 9.5 percentage points.
Following a medium growth–recent trend scenario, over the next four decades, population growth of
Aboriginal peoples would result in a 46-per-cent increase in size of its labour force, if relative edu-
Figure 11. Aboriginal peoples’ labour force by education, ages 25–64.
24
Figure 11: Aboriginal peoples’ labour force by education, age 25–64
While the Aboriginal population will increase substantially over the next decades, increases in
educational attainments would lead to a decrease in the size of the Aboriginal population without
post-secondary education. Comparing the Aboriginal workforce as projected by the three
scenarios for 2056, we find considerable differences concerning its education composition.
While a phased 50% convergence would increase the overall size of the Aboriginal workforce by
39,000 people, the number of participants with post-secondary education would increase by
107,000. In the immediate 100% convergence scenario, the number of Aboriginal workers with
post-secondary education in the 2056 labour force would be 600,000, which are 234,000 more
people than in the base scenario.
4 Summary and Conclusions
This study aimed at quantifying the impact of educational attainments on the future labour force
participation of Aboriginal peoples. Using Statistics Canada’s Demosim population projection
model, we were able to simulate alternative scenarios of educational change and resulting effects
on the future labour force until 2056.
While about half of the observed difference in labour force participation rates between
Aboriginal and Canadian-born White peoples can be attributed to educational differences,
patterns are very different for different groups. The contribution of education differences to the
gap in labour force participation is highest for Métis, followed by Inuit, registered NAI and
0
100000
200000
300000
400000
500000
600000
700000
800000
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
2011
2016
2021
2026
2031
2036
2041
2046
2051
2056
BaseScenario Phased50%Convegence
Scenar io
Immediate100%Convergence
Scenario
Belowhighschool Highschooldiploma Postsecondary
Spielauer: The relation between education and labour force participation of aboriginal peoples—using Demosim
163
cational differences persist. In education scenarios that close the educational gap, this number would
increase to almost 64 per cent. In absolute terms, this means up to 80,000 additional workers.
At the same time, composition of future Aboriginal peoples’ labour force would be dramatically
different, with up to 234,000 additional people in the Aboriginal labour force having a post-second-
ary education.
Besides this huge impact of potential educational improvements on the future labour force, the
changes are a slow gradual process, as successive young school-age cohorts yet have to enter the
labour market and renew the workforce.
Acknowledgements
I am thankful for the reviews, many comments, and opportunities for discussion provided by the
Demosim team in Statistics Canada’s Demography Division, as well as the Department of Aboriginal
Affairs and Northern Development Canada, especially Eric Caron Malenfant, Eric Guimond, and
Andrea Street.
I am also thankful for excellent support in the literature search provided by Paul Slater of Statis-
tics Canada’s library.
This study was nanced by the Department of Aboriginal Affairs and Northern Development Can-
ada which has also supported the development of the Demosim population microsimulation model.
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... Still, applications so far were limited to the developed world. The most prominent example is Demosim, the microsimulation population projection model developed and used by Statistics Canada to project the Canadian population by visible minority group (Caron-Malenfant et al., 2010) and Aboriginal identity (Morency et al., 2015), to project its labor force (Martel et al., 2011), and to study the effect of educational improvements on the future size and composition of the Aboriginal labor force (Spielauer, 2014). Variants of Demosim were recently developed as well for several European countries and Australia (Marois et al., 2017). ...
... Increase in the education maximizes the opportunity for the people to take part in the labor force of the country. Finding of the study is consistent with the previous studies of ( Rauf et al., 2018;Zohaib Ali, 2017;Mushtaq, Mohsin, & Zaman, 2013;Chaudhary, Iqbal, & Gillani, 2009;PATRINOS, 2016;Faridi, Malik, & Ahmad, 2010;Spielauer, 2014;Hedley, 2003;Heath & Jayachandran, 2016;SANUSI, 2016;Bowen & Finegan, 2015). Education is one of the prevalent factors in determining the human capital formation and also in relation to income and labor force participation. ...
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