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Demosim: An Overview of Methods and Data Sources

Demosim's main projected components

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Events with waiting times
Characteristics imputed annually
Creation of individuals during the simulation

Text begins

This section, which aims to document the main components projected by Demosim, is subdivided into three main parts. The first is concerned with events that are modelled using waiting times, the second discusses characteristics that are imputed annually, and the third gives an overview of how individuals are created during the simulation.

It should be noted that from this point onward, the document will at times refer to the concept of “module” because Demosim is built in a modular way, with each of its components corresponding to a module. A module includes the computer code specifying the dimensions and functioning of the modelled event, including its relation to other parts of the model and its associated parameters. Table 1 presents the different components of the projection model and summarizes the methods and data sources used in modelling these components.^Note 1

Table 1
Methods and data sources of components used to calculate the Demosim parameters
Table summary
This table displays the results of Methods and data sources of components used to calculate the parameters specific to the Aboriginal population projections in Demosim. The information is grouped by Components (appearing as row headers), Data sources and Main methods (appearing as column headers).
Components	Data sources	Main methods
1) Events with waiting times
Fertility	-2011 National Household Survey (NHS); -Vital Statistics.	-Own-children method; -Rates; -Complementary log-log regressions.
Mortality	-Vital Statistics; -1991-to-2006 Census Mortality Follow-up Study; -First Nation Client File.	-Li-Lee projection; -Proportional hazards regressions.
Internal migration	-2001 and 2006 censuses; -2011 NHS.	-Complementary log-log regressions; -Matrices; -Rates.
Emigration	-Demographic estimates; -Longitudinal Administrative Database linked with immigration data.	-Rates; -Proportional hazards regressions.
Registration on the Indian Register and reclassification of registration categories over an individual's lifetime	-Indian Register; -Linkage between the Indian Register and the 2011 Census/NHS.	-Registrations/reclassifications rates with predetermined targets.
Intragenerational ethnic mobility of Aboriginal people	-1996 to 2006 censuses; -2011 NHS.	-Residual method.
Intragenerational religious mobility	-2001 Census; -2011 NHS.	-Residual method.
Intragenerational linguistic mobility	-Linkages between the 2001 and 2006 censuses and the 2006 and 2011 censuses.	-Multinomial regressions; -Matrices; -Distributions.
Change in level of education	-2001 General Social Survey; -2011 NHS.	-Logistic regressions; -Alignment methods.
2) Characteristics imputed annually
Marital status	-2001 and 2006 censuses; -2011 NHS; -Linkage between the Indian Register and the 2011 Census/NHS.	-Logistic regressions; -Rates.
Head of household and head of family	-2011 NHS.	-Headship rates.
Labour force participation	-Labour Force Survey; -2001 and 2006 censuses; -2011 NHS.	-Participation rates; -Logistic regressions.
3) Creation of individuals during the simulation
Creation of newborns	-2011 NHS; -Linkage between the Indian Register and the 2011 Census/NHS.	-Deterministic imputation; -Matrices; -Distributions; -Multinomial regressions.
Immigration	-2011 NHS; -Data from Citizenship and Immigration Canada.	-Imputation; -Distributions.
Non-permanent residents	-2011 NHS; -Data from Citizenship and Immigration Canada.	-Imputation; -Distributions.
Source: Statistics Canada, Demography Division.

Events with waiting times

The first category of events contains those events that are modelled using waiting times (see Box 1). These events make it possible to create a dynamic and distinct life course for each simulated individual. Events in this category are fertility, mortality, internal migration, emigration, registration on the Indian Register and reclassification of registration categories over a lifetime, intragenerational ethnic mobility of Aboriginal people, intragenerational religious mobility, intragenerational linguistic mobility and changes in education level.

Fertility

The fertility module has been designed to obtain a projection of births that reflects the differences in fertility between the various groups projected—for example, Aboriginal people and immigrants. The module contains, on one side, the ‘base probabilities’ of an individual having given birth to one or more children during the year preceding the 2011 NHS. These base probabilities were calculated by age, number of children in the home, and having or not having an Aboriginal identity, using the own-children method^Note 2 applied to the NHS data.^Note 3 They include adjustments for children not living with their mother, and for mortality. They are also adjusted to reflect what is observed in Vital Statistics. The base probabilities are then combined with the results of complementary log-log regressions (computed using the same NHS data to which the own-children method was applied). The regressions aim to estimate the probability—for various combinations of age group, number of children in the home and Aboriginal identity^Note 4—of having given birth to one or more children during the same period according to other variables. These variables are marital status, education, Aboriginal group, registered Indian status, immigrant status, time elapsed since immigration, generation status, immigrant admission category, place of birth, visible minority group, religion, mother tongue and detailed place of residence (on or off Indian reserves, Inuit Nunangat, CMA, and province or territory).^Note 5 For the sake of consistency between the base probabilities and the regressions results, both estimate the number of women having given birth to one or more children, and not the total number of births (since multiple births are possible). To obtain the total number of births, an adjustment consisting of ratios between the number of births during the period and the number of women who have given birth, by Aboriginal identity or visible minority group, is applied..

Mortality

The mortality module has a similar structure to the fertility module, as it also uses base rates combined with regressions results. The mortality module aims to simulate the future number of deaths, taking into account the differences between the groups projected. Because of the fragmented nature of the available data, mortality is modelled separately for Inuit and non-Inuit populations, and among non-Inuit, separately for the population aged 25 and older and that aged 24 and younger.^Note 6

For non-Inuit aged 25 or older, the base rates consist of mortality rates projected by age and sex at the national level, consistent with the methods documented in the technical report of the most recent national projections (Dion et al. 2014).^Note 7 The base rates are then combined with the results of proportional hazards regressions (Cox models), stratified by sex and broad age groups. These regressions estimate the risk of dying by Aboriginal ancestry group, visible minority group, time elapsed since immigration, education, living on an Indian reserve, and province or territory of residence. The models were applied to the data from the 1991-to-2006 Canadian Census Mortality Follow-up Study,^Note 8 which linked 1991 Census data for the population aged 25 and older to Vital Statistics data up to 2006.
For the non-Inuit population aged 24 and younger, mortality is modelled differently for the non-Aboriginal population, the Aboriginal group of First Nations people and the Aboriginal group of Métis. For the non-Aboriginal population, Demosim uses the Li-Lee model to project mortality rates by age, sex and province or territory of residence. For the Aboriginal group of First Nations people, mortality rates come from mortality tables for Registered Indians,^Note 9 and are then projected under the assumption that the difference between these rates and the rates for non-Aboriginal people remain constant.^Note 10 For the Aboriginal group of Métis, as there are no data on this particular population, the rates are obtained by multiplying the rates for Registered Indians by a factor derived from the difference between the mortality of Métis and that of Registered Indians in the population aged 25 to 64, according to the 1991-to-2006 Canadian Census Mortality Follow-up Study.
For the Inuit population, mortality tables were calculated using death data for Inuit living in Nunavut from special Vital Statistics data extractions for the years 2000 to 2002, 2005 to 2007, and 2010 and 2011. Among Inuit, the risks of dying are projected while keeping constant the relative difference observed during these periods between the Inuit population and the overall Canadian population.^Note 11

Internal migration

The purpose of the internal migration module is to simulate movements among the 84 geographic regions in Demosim, taking into account the main characteristics included in the projection. Two types of migration are modelled—interregional and intraregional. Interregional migration refers to migration between the model’s 50 main regions (CMAs and non-CMAs^Note 12). Intraregional migration pertains to migration on and off reserve, or within and outside Inuit Nunangat, within each of the main regions where there are Indian reserves or Inuit Nunangat regions.

Modelling internal migration includes several steps. The first steps aim to estimate interregional and intraregional migration based on the relationship between place of residence one year earlier and current place of residence (mobility one year) contained in a database consisting of the 2001 and 2006 censuses and the 2011 NHS, to which a constant geography has been applied.^Note 13

Interregional migration is modelled in two separate stages. First, complementary log-log regressions models are used to estimate the probabilities of an individual leaving each region based on age, Aboriginal group, registered Indian status, immigrant status, time elapsed since immigration, marital status, place of birth, generation status, visible minority group, religion, number of children at home, age of the youngest child at home, mother tongue, knowledge of official languages and living on an Indian reserve or in an Inuit Nunangat region.^Note 14 Migrants are then assigned a destination region using matrices that take into account region of origin, place of birth, mother tongue, Aboriginal group, registered Indian status, visible minority group and age. If the destination region includes Indian reserves or an Inuit Nunangat region, additional models which take into account registered Indian status or having an Inuit identity determine whether or not the individual will live on a reserve or in an Inuit Nunangat community in the destination region.^Note 15 Intraregional migration is simulated using migration rates specific to the origin and destination that take into account age and, as the case may be, Aboriginal identity and education.

The next steps are a series of adjustments to the out-migration rates, origin-destination matrices and additional vectors. They are performed using the same database as the previous steps, but they correlate information on mobility over the past year with information on mobility over the last five years. These adjustments were intended to allow the Demosim migration parameters to reproduce (at the start of a projection) the contribution of net internal migration to the population growth of each region, as observed on average from 1996 to 2001, 2001 to 2006, and 2006 to 2011. One advantage of basing regional migration schemas on a longer period is reducing the weight of exceptional short-term phenomena that could substantially change net migration counts in a given year. As adjustments affect total in-migration and out-migration rates regardless of the other variables considered, they preserve the related differentials obtained in earlier steps.

Emigration

The purpose of the emigration module is to project net emigration, which is defined according to the components of the Statistics Canada Demographic Estimates Program (DEP) as the sum of emigration, plus net temporary emigration, minus return emigration. The emigration module has a similar structure to the fertility and mortality modules, as it combines base rates and regression results for the population aged 18 and older, making it possible to take into account a large number of characteristics, in particular immigrant status, which is known to be a predisposing factor for emigration.^Note 16 For the population aged 18 and older, the base emigration rates were calculated by age and sex at the national level by dividing the net number of emigrants, as estimated by the Statistics Canada DEP from 2002/2003 to 2011/2012, by the population (excluding non-permanent residents^Note 17) from the same source for the same period.^Note 18 They are combined with the results of a proportional hazards regression model (Cox model) that uses a linkage between the Longitudinal Administrative Database and immigration data from 1995 to 2010 to estimate the propensity of the adult population to emigrate, by country of birth, period of immigration, province or territory of residence, age and sex.

For the population aged 17 and younger, net emigration rates were calculated by age, sex, and province or territory using population estimates for 2002/2003 to 2011/2012.

Registration on the Indian Register and reclassification of registration category over an individual's lifetime

The modules involving registration on the Indian Register have three separate purposes: 1) to model registrations that may occur during an individual’s lifetime^Note 19 as a result of legislative amendments or the agreement recognising the Qalipu Mi’kmaq First Nation;^Note 20 2) to model the reclassifications of registration category from 6(2) to 6(1) that may occur during an individual’s lifetime;^Note 21 and 3) to model the late registration of individuals who were entitled to registration at birth.

The legislative amendments that could cause individuals to register during their lifetime are the 1985 amendments of the Indian Act (Bill C-31) and the Gender Equity in Indian Registration Act (Bill C-3) adopted in January 2011. For these legislative amendments, target numbers of registrations were estimated from the number of registrations by year. For registrations under Bill C-31 from May 2011 to August 2014, target numbers were calculated from the actual registrations recorded on the Indian Register. For subsequent periods, target numbers were calculated by continuing the average downward trend in registrations observed on the Indian Register data from 2007 to 2014. For Bill C-3, the initial targets were again the number of registrations from May 2011 to August 2014 according to the Indian Register. For subsequent years, target numbers of this type of registration were obtained from projections by Aboriginal Affairs and Northern Development Canada. For registrations resulting from the agreement recognising the Qalipu Mi’kmaq First Nation, the target numbers were calculated from the registrations that occurred between the order-in-council coming into force on September 22, 2011 (the date of the band’s creation) and the Supplemental Agreement of June 2013, whose impact on the number of Qalipu registrations is currently unknown. Once the targets had been determined, the individuals in the base population who were likely to register under these components were identified from among those who did not have registered Indian status (according to distributions specific to each component), and then their time of registration was determined in advance.^Note 22 The vast majority of individuals selected in this way were initially Non-Status Indians.
Reclassification from registration category 6(2) to category 6(1) may result from the application of Bill C-3, or for various other reasons.^Note 23 The changes resulting from Bill C-3 were modelled using a method similar to the one described above for registration because of legislative amendments. Target numbers were first determined by age, sex and year of change. From May 2011 to August 2014, the numbers were determined using reclassifications that occurred during that period according to the Indian Register. For subsequent years, target numbers of reclassifications vary at the same rate as registrations under Bill C-3. For reclassifications not resulting from Bill C-3, annual reclassification rates were calculated by age and sex based on 2010 data from the Indian Register. They are assumed to be constant throughout the projection period, except from 2011 to 2014 where they were adjusted to reflect the reclassifications observed on the Indian Register.
The late registration of individuals entitled to registration on the Indian Register at birth is modelled separately for children and adults. For children born during the simulation, the modelling is done in two steps. First, children entitled to Indian registration are identified from among the simulated births.^Note 24 Entitlement depends on the mother’s registration category, whether or not she was in a mixed union at the time the child was born, and the inheritance rules for registered Indian status. Those entitled to registration but not having a registered Indian status at birth are assigned a probability of registering on the Indian Register that depends on their age and whether or not they live on an Indian reserve. The probabilities were derived so that they can reproduce the progression by age observed in the NHS of the proportion of children with a registered Indian status among children who are in principle entitled to register—namely the children with two parents who are Registered Indians, or with one parent who is in registration category 6(1).^Note 25 Late registration rates derived from the same data are also applied to some children in the base population. Among adults aged 19 years and older, populations at risk are applied late registration rates by age and sex, calculated by dividing the average annual number of registrations of this type from 2008 to 2014, according to the Indian Register, by the 2011 estimated population of Non-Status Indians living off reserve that will not become registered as Qalipu or for legislative reasons.

Intragenerational ethnic mobility of Aboriginal people

The purpose of the Aboriginal intragenerational ethnic mobility module is to simulate changes in reporting of Aboriginal group from one census to the next, a phenomenon that is behind a significant part of the increase in the number of Métis and First Nations people observed at least since 1986 in Canada.^Note 26 The parameters of the intragenerational ethnic mobility of Aboriginal people were calculated using a residual method applied to the 1996, 2001 and 2006 censuses and the 2011 NHS, adjusted for net undercoverage. It involves calculating the share of the growth of a given Aboriginal group that remains unexplained after fertility, mortality and net migration have been taken into account, for each five-year period. This unexplained share is interpreted as resulting from changes in the Aboriginal group reported in the censuses (or the NHS). The net gains in Métis and First Nations people obtained this way were divided by the population that was non-Aboriginal, non-immigrant and not belonging to a visible minority group at the start of the period to obtain probabilities of an individual joining the First Nations group and Métis group over five years, taking into account age and region of residence. The probabilities were averaged for the three periods considered (1996 to 2001, 2001 to 2006 and 2006 to 2011).^Note 27

Intragenerational religious mobility

Intragenerational religious mobility refers to changes in religion occurring over an individual’s lifetime. The probability of changing religions was estimated by religion, age, immigrant status and place of birth by applying (similar to intragenerational ethnic mobility) a residual method to the 2001 Census and the 2011 NHS, adjusted for net undercoverage. Net losses for a given religion were divided by the religion’s population at the start of the period to yield ‘exit’ rates over 10 years. Individuals who have left a given religion are then distributed among the ‘gaining’ religions in proportion to the net gains recorded over the same period by these religions.

Intragenerational linguistic mobility

The intragenerational linguistic mobility module models changes that may occur over an individual’s lifetime regarding mother tongue,^Note 28 language spoken most often at home and knowledge of official languages. It includes two distinct sets of parameters:

Changes in mother tongue and language spoken most often at home were estimated using data from a linkage between the 2001 and 2006 censuses.^Note 29 First, a variable combining the categories of the two variables in question was created^Note 30 for each census to estimate the changes, separately for Quebec and the rest of Canada. The most frequent changes were identified and then models using multinomial logistic regressions were developed (specific to the initial linguistic profile) to estimate the probability of an individual having made one of these changes by age, sex, place of birth, age at immigration, generation status, education and place of residence, over the five years considered. For the other, less frequent changes, matrices crossing the linguistic profiles in 2001 and 2006 are used.^Note 31
The changes in knowledge of official languages are modelled by estimating multinomial logistic models, specific to the initial official language, that consider age, sex, immigrant status, generation status, place of birth and education. The models are estimated separately for Quebec and the rest of Canada based on data from a linkage between the 2006 and 2011 censuses.^Note 32

Change in level of education

The last event projected using waiting times is change in education level. The probabilities associated with this event were derived by combining 2001 General Social Survey (GSS) and 2011 NHS data adjusted for net undercoverage which, together, include the information required for the projection. First, probabilities of change in education level by year of birth, age, sex and immigrant status were obtained by applying logistic regression models to historical data from the 2001 GSS. The population of the 2001 GSS was then projected to 2011 using the calculated probabilities, which were calibrated in three separate steps. An initial calibration was done to specifically reproduce the NHS distributions by education level, year of birth, age, sex and immigrant status. A second calibration added differential probabilities by visible minority group, Aboriginal identity and registered Indian status to reproduce the distributions of these groups in the NHS. The third calibration added differentials by province and territory of birth.

Characteristics imputed annually

Some components of Demosim are not meant to project events but rather to impute certain characteristics to individuals, including marital status, head-of-family and head-of-household status as well as labour force participation. These characteristics are assigned once a year on a fixed date.

Marital status

Marital status is a variable that is projected mainly for its use in determining other events during the simulation, particularly fertility, to which it is closely related. The marital status module is derived from logistic regression models that are estimated using data from the adjusted 2011 NHS for the population aged 15 and older. The initial models determine whether or not the individual is in a union. If the individual is in a union, other models determine the type of union (married or common law). The models are stratified by sex and by having or not having an Aboriginal identity. They take into account age, number of children at home, immigrant status, generation status, time elapsed since immigration, place of birth, mother tongue, visible minority group, religion, Aboriginal group, registered Indian status and place of residence. The probabilities derived from these models evolve during the projection based on trends observed in the 2001 and 2006 censuses and the 2011 NHS (adjusted), which showed an increasing propensity for couples to live in a common-law union. The marital status module also includes mixed union parameters that are used to assign characteristics such as registered Indian status to newborns (see the “Creation of individuals during the simulation” section).^Note 33

Head of household

A head-of-household status^Note 34 is assigned to individuals annually to obtain a projection of the number of households by certain characteristics, including a household’s Aboriginal composition. The headship rates method^Note 35 is used to establish a relationship between the number of heads of household and the population, by certain characteristics of the projected population. This rate is then multiplied by the projected population to obtain a future number of households. For the purposes of these projections, different types of households were identified in the data from the 2011 NHS according to a combination of household characteristics (Aboriginal composition, household size and the presence of individuals younger than 19 years of age^Note 36). The number of heads of household for each type by age, Aboriginal group, registered Indian status, marital status and place of residence was determined and then divided by the total population with the same characteristics to obtain headship rates for use in the annual imputation of head-of-household status during the simulation.^Note 37. Head-of-household status is used strictly to derive a number of households. It is not used as a determining factor for other events during simulation. The same holds true for labour force participation.

Labour force participation

The purpose of the labour force participation module is to impute a status to individuals aged 15 and older regarding their labour force participation. The module has been designed to take into account differences in labour force participation among the various groups projected (for example, Aboriginal people, visible minority groups and immigrants). It includes two sets of parameters. The first is composed of labour force participation rates by sex and age group taken from Labour Force Survey (LFS) data. The parameters are then adjusted to take into account populations excluded from the survey, in particular Indian reserves. The second parameter is composed of the results of logistic regressions that estimate (separately by sex and age group) the probability of being in the labour force by the following variables: Aboriginal group, registered Indian status, visible minority group, immigrant status, time elapsed since immigration, generation status, place of birth, marital status, presence of children and age of youngest child, education, knowledge of official languages, and place of residence. The logistic regressions use data from a file that combines data from the 2001 and 2006 censuses and the 2011 NHS (adjusted). These two sets of parameters are combined each January to determine the labour force participation for the upcoming year.

Creation of individuals during the simulation

Aside from individuals in the Demosim base population, individuals may be added to the population during the simulation as a result of births, immigration and the arrival of non-permanent residents. New individuals are added by creating complete records, that is, records having all the characteristics required for them to be projected by Demosim. The process for assigning characteristics to new individuals is described below.

Creation of newborns

The creation of newborns from births occurring after the start of the simulation requires the use of methods that differ according to the characteristic to be assigned to the new individuals. First, a number of characteristics assigned to newborns do not require any parameters to be calculated and can be assigned automatically, for example marital status (not in a union), education (less than high school) and immigrant status (non-immigrant). Other characteristics are assigned probabilistically. The sex of the child is determined by applying a sex ratio of 105 boys born for every 100 girls, as has been observed in Canada for several decades. Religion, the three linguistic variables, visible minority group and Aboriginal group are assigned based on parameters derived by applying the own-children method to data from the adjusted 2011 NHS. Linking the youngest children in this data source to the woman most likely to be the mother makes it possible to calculate the probability that the child has a given set of characteristics, depending on the characteristics of the mother (see the characteristics considered in Table 2). Transition matrices and vectors were created for assigning religion, visible minority group and Aboriginal group. The probabilities for the linguistic variables are obtained from multinomial logistic regression models. To ensure consistency among linguistic variables, the models are applied sequentially,^Note 38 with each taking into account the results of the previous models in addition to the characteristics of the mother.

Table 2
Variables considered in the probabilistic attribution of characteristics to the newborns
Table summary
This table displays the results of Variables considered in the probabilistic attribution of characteristics to the newborns . The information is grouped by Characteristics attributed (appearing as row headers), Variables considered (appearing as column headers).
Characteristics attributed	Variables considered
Sex	N/A (application of a fixed sex ratio at birth).
Religion	Characteristics of mother:	-Religion; -Place of birth.
Mother tongue	Characteristic of the child:	-Sex.
Mother tongue	Characteristics of the mother:	-Language spoken most often at home; -Mother tongue; -Place of birth; -Age group; -Education; -Generation status; -Place of residence.
Language spoken most often at home	Characteristics of the child:	-Sex; -Mother tongue.
Language spoken most often at home	Characteristics of the mother:	-Language spoken most often at home; -Mother tongue; -Place of birth; -Age group; -Education; -Generation status; -Place of residence.
Knowledge of official languages	Characteristics of the child:	-Sex; -Mother tongue; -Language spoken most often at home.
Knowledge of official languages	Characteristics of the mother:	-Mother tongue; -Place of birth; -Age group; -Education; -Generation status; -Place of residence.
Generation status	Characteristics of the mother:	-Immigrant status; -Mixed union status.
Visible minority group	Characteristics of the mother:	-Visible minority group; -Immigrant status; -Age at immigration; -Place of residence.
Aboriginal group	Characteristics of the mother:	-Aboriginal group; -Registered Indian status; -Visible minority group; -Immigrant status; -Age at immigration; -Place of residence.
Registered Indian status and registration category (6(1) or 6(2))	Characteristics of the child:	-Aboriginal group; -Visible minority status.
	Characteristics of the mother:	-Registered Indian status; -Registration category; -Marital status; -Mixed union status; -Place of residence.
Source: Statistics Canada, Demography Division.

The methods for assigning registered Indian status, registration category and generation status differ from the methods above by indirectly taking into account information about the child’s father. Births in Demosim are generated by women, and women are not associated with a spouse. Therefore, it is not possible to directly know the father’s characteristics at the time of birth. However, spousal characteristics may be associated with mothers through mixed unions. This is done in Demosim when a child is born.

An initial mixed-union module determines whether the mother is in a union with a category 6(1) Registered Indian, a category 6(2) Registered Indian or an individual not having registered Indian status. The probability that the mother is in one of these types of unions is estimated using a file derived from adjusted 2011 NHS microdata that—by using information on the relationship among members of the same census family—links women in a union who gave birth during the previous year to their spouse and their children. The probabilities are calculated based on the mother’s marital status, registered Indian status combined with registration category, area of residence (on reserve or off reserve), and the child’s Aboriginal group and visible minority status. The same probabilities were also calculated using the 2001 Census to establish trends related to mixed unions. Registered Indian status (including registration category) is then probabilistically assigned to newborns using transition matrices that consider the mother’s type of mixed union and other characteristics of the mother and the child (Table 2).^Note 39

A second mixed-union module uses the same data source to calculate the probability that the woman is in a union with a spouse whose immigrant status is identical or different at the time the child is born in order to determine the child’s generation status. The module consists of logistic regression models that take into account age, religion, visible minority group, Aboriginal group^Note 40, time elapsed since immigration, mother tongue, language spoken most often at home, presence of young children at home and place of residence.^Note 41 Generation status is then assigned to the newborn as follows: the newborn is considered second generation if the mother is an immigrant not in a mixed union, 2.5 generation if the mother is in a mixed union, and third generation or higher if the mother is not an immigrant and not in a mixed union.

Immigration

Immigration also involves the creation of individuals possessing all the characteristics required for their simulation following their arrival in Canada. This module includes two main dimensions. First, the number of new immigrants is projected annually. Second, the characteristics of the new immigrants are determined using a donor imputation method, with donors being selected from among the immigrants in the Demosim base population. The result is a projected immigrant population whose composition is representative of the immigrant population of the donor pool (which itself may be a subset of the immigrant population, for example recently-admitted immigrants). Adjustments are also made to some of the characteristics that are likely to have changed between the time of immigration and the time of the 2011 NHS—the survey on which Demosim is based—so that they will be as close as possible to what they were at the time of arrival. For example, when a new immigrant is created, the age assigned to the new immigrant is the donor’s age at immigration (and not the donor’s current age); the marital status is imputed on arrival using Demosim’s annual marital status imputation parameters; and education on arrival is imputed using the Demosim education module.

Non-permanent residents

The final Demosim component that requires the creation of individuals is the arrival of new non-permanent residents. This component functions similarly to immigration. Like immigrants, non-permanent residents are projected in two steps: 1) determining an annual net gain in non-permanent residents; and 2) imputing the characteristics of the new non-permanent residents using donors who are selected from among the non-permanent residents in the Demosim base population.^Note 42

Notes

Note 1.

Demosim also includes a component for the departure of children from the parental home that is not discussed here because, unlike all the other components, it has not changed since the release of Projections of the Diversity of the Canadian Population, 2006 to 2031 (Statistics Canada 2010). Readers interested in the contents and functioning of the module are invited to read this publication.

Return to note 1 referrer

Note 2.

For a description of this method, which is derived from the relationship between the youngest children and the women likely to be their mother, see Grabill and Cho (1965) and Desplanques (1993). For examples of the application of the method to Canadian data see Bélanger and Gilbert (2003), Caron-Malenfant and Bélanger (2006), Morency and Caron-Malenfant (2014), Ram (2004), or previous releases of microsimulation projections (Statistics Canada 2011, Statistics Canada 2010, Statistics Canada 2005).

Return to note 2 referrer

Note 3.

The database includes all the adjustments made to the Demosim base population except for the imputation of incompletely enumerated reserves.

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Note 4.

In this case, having or not having an Aboriginal identity.

Return to note 4 referrer

Note 5.

The models also include some interactions among variables.

Return to note 5 referrer

Note 6.

No data source contains all the information required to model the mortality of each of the projected groups, which is why multiple sources are used.

Return to note 6 referrer

Note 7.

The projection is performed using a variant of the Li-Lee model (2005).

Return to note 7 referrer

Note 8.

See Wilkins et al. (2008) and Tjepkema and Wilkins (2011).

Return to note 8 referrer

Note 9.

The mortality tables used for Registered Indians aged 10 to 24 are those published in Amorevieta-Gentil et al. (2014). These tables underestimate the mortality of Registered Indians younger than age 10 because a significant number of parents do not register their children on the Indian Register until the children are older. For this age group, the mortality rates from Amorevieta-Gentil et al. (2014) were adjusted using ratios derived from estimates from British Columbia for the population entitled to Indian registration (British Columbia Provincial Health Officer 2012).

Return to note 9 referrer

Note 10.

More specifically, the ratios between the mortality rates of both groups are kept constant.

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Note 11.

Here also, the ratios between the mortality rates of both groups are kept constant.

Return to note 11 referrer

Note 12.

The main regions include all 33 Canadian CMAs and the parts of each province that are not included in a CMA. Some of these regions are further divided into sub-regions that are also part of the main regions: the Montréal CMA is divided into Montréal Island and the rest of the CMA; the Ottawa–Gatineau CMA is divided into the Ontario part and the Quebec part; and the non-CMA parts of New Brunswick and of Ontario are each divided into a part with a concentration of francophones and a non-francophone part (see Caron-Malenfant [forthcoming] for a map of these regions in New Brunswick and Ontario).

Return to note 12 referrer

Note 13.

The database is also adjusted to account for net undercoverage in each of the censuses.

Return to note 13 referrer

Note 14.

The models are specified independently from one region to another. Therefore, there are as many models as there are regions, and the variables selected may differ from one model to another.

Return to note 14 referrer

Note 15.

The parameters in this step are obtained from complementary log-log regression models that also take into account age group, immigrant status and time elapsed since immigration, marital status, place of birth, mother tongue, education, visible minority status and the presence of children in the home.

Return to note 15 referrer

Note 16.

This greater propensity is documented in Caron-Malenfant et al. (2011) and in Aydemir and Robinson (2006).

Return to note 16 referrer

Note 17.

The departures of non-permanent residents are not included in Statistics Canada’s emigration data.

Return to note 17 referrer

Note 18.

This period is the same period as that selected in Population Projections for Canada (2013 to 2063), Provinces and Territories (2013 to 2038) (Statistics Canada 2014). In addition, as in this series of projections, an adjustment is made to increase the number of emigrants from Ontario and British Columbia on the basis of estimates obtained from the Reverse Record Check (Bohnert et al. 2014).

Return to note 18 referrer

Note 19.

Registrations resulting from births are modelled during the creation of newborns. See the “Creation of individuals created during the simulation” section.

Return to note 19 referrer

Note 20.

To learn more about the legislative changes and the agreement recognising the Qalipu Mi’kmaq First Nation, see Box 2 of the Projections of the Aboriginal Population and Households in Canada, 2011 to 2036.

Return to note 20 referrer

Note 21.

Only changes from 6(2) to 6(1) are modelled in Demosim, since there are not enough changes in the other direction. For more information on the concepts, see “Registration categories 6(1) and 6(2) on the Indian Register” and “Reclassification of registration categories from 6(2) to 6(1)” in the glossary. Additional information on these topics is also found in the “Concepts” section of Projections of the Aboriginal Population and Households in Canada, 2011 to 2036.

Return to note 21 referrer

Note 22.

When individuals identified as future Registered Indians give birth to a child, the methods for assigning registered Indian status and the registration category to the newborns are applied as described in the “Creation of individuals created during the simulation” section. However, if the birth occurs before the mother’s predetermined time of registration or reclassification, intergenerational transmission rules are used to determine whether or not the registered Indian status and registration category of the child determined at birth should be changed when the mother becomes registered or is reclassified.

Return to note 22 referrer

Note 23.

For example, a person whose mother is in registration category 6(1) will be registered in category 6(2) if information on the father is unavailable at the time of registration, and then may be reclassified in category 6(1) if it is later determined that the father also had registered Indian status.

Return to note 23 referrer

Note 24.

The concept of registration that is referred to here is “self-reported registration.” This is not the same as “entitlement to registration,” which in principle refers to a larger population, since a certain number of individuals entitled to registration do not register.

Return to note 24 referrer

Note 25.

These proportions increase gradually up to the age of 3, reaching 98% on Indian reserves and 92% off reserve, and then fluctuate in no clear direction for subsequent ages. For this reason, it is assumed that all late registrations occur before age 3.

Return to note 25 referrer

Note 26.

For example, see Guimond (1999), Guimond et al. (2007) and Caron-Malenfant et al. (2014) for estimates of ethnic mobility in Canada between 1986 and 2006.

Return to note 26 referrer

Note 27.

In three regions (Thunder Bay, Regina and Saskatoon), the First Nations Aboriginal group experienced net losses as a result of intragenerational ethnic mobility on average from 1996 to 2011. In these cases, exit rates to non-Aboriginal people were calculated by taking the non-registered First Nations population as the population ‘at risk’ of changing. As well, ethnic mobility was assumed to be nil on Indian reserves and in the territories, given the homogeneous identity of those populations. Intragenerational ethnic mobility among the Inuit population is also assumed to be nil. Lastly, because it is possible that the declaration of Aboriginal identity in Newfoundland and Labrador between 2006 and 2011 was affected by the recognition of the Qalipu Mi’kmaq First Nation, and it would be inadvisable to make long-term projections based on the effect of an event that is unlikely to occur again, the parameters of ethnic mobility for this province are based only on the 1996 to 2006 period.

Return to note 27 referrer

Note 28.

Mother tongue may change over an individual’s lifetime because it is defined in Canadian censuses as the first language learned in childhood that is still understood. A person may no longer understand the first language learned. An analysis of this phenomenon was conducted by Lepage (2011).

Return to note 28 referrer

Note 29.

Because 2006 and 2011 censuses data on mother tongue and the language spoken most often at home have comparability issues (see Houle and Corbeil 2013), mobility relative to these variables was modelled on the period from 2001 to 2006. However, the 2006 and 2011 data on knowledge of official languages are comparable . This is why, for the latter, the linkage of the 2006 and 2011 censuses was chosen instead of the linkage of the 2001 and 2006 censuses.

Return to note 29 referrer

Note 30.

Since each of these two variables has four categories—1) French or French and at least one non-official language, 2) English or English and at least one non-official language, 3) English and French, or English and French and at least one non-official language, and 4) non-official language(s) only — the variable that combines them has 16 categories.

Return to note 30 referrer

Note 31.

In some cases, a knowledge of official language is reassigned immediately (through distributions) after a change is made to the mother tongue and/or the language spoken most often at home. This helps to avoid inconsistent individual linguistic profiles, such as a person who speaks French most often at home but does not know French well enough to conduct a conversation.

Return to note 31 referrer

Note 32.

In some cases, a language spoken most often at home is reassigned immediately (through distributions) after a change is made to knowledge of official languages. This happens when an individual ‘forgets’ one or both official languages, but speaks one or both of them most often at home. It goes without saying that, to avoid transfers being modelled twice, changes in the language spoken most often at home resulting from changes in knowledge of official languages are excluded from the modelling of changes in the language spoken most often at home described in (1). The reverse is also true: changes in knowledge of official languages resulting directly from changes in the language spoken at home are excluded from the modelling of changes in knowledge of official languages described in (2). The transfers that are modelled are therefore mutually exclusive.

Return to note 32 referrer

Note 33.

Unlike marital status, mixed unions are not assigned automatically each year to the whole population aged 15 years and older, but only to women when they give birth to a child. For that reason, the methods used to assign mixed unions are described in the section on creating newborns (to which they relate).

Return to note 33 referrer

Note 34.

Note that Demosim also has a module for imputing head-of-family status. It uses the same methods as the head-of-household status module. To avoid redundancy, only household projection methods are described here. For more information on methods for projecting families and households in Demosim, see Morency (forthcoming).

Return to note 34 referrer

Note 35.

This method, widely used to project numbers of households, is documented in United Nations (1973).

Return to note 35 referrer

Note 36.

Household’s Aboriginal composition refers to the presence of at least one Aboriginal person in the household, or at least one person belonging to a specific Aboriginal group. Household size includes the following categories: one person, two to five persons, and six persons or more. Household type is a combination of household’s Aboriginal composition, household size and the presence of individuals younger than 19 years of age. For example, there are Aboriginal households two to five persons in size with no individuals younger than 19, and Aboriginal households two to five persons in size with at least one individual younger than 19.

Return to note 36 referrer

Note 37.

Heads of household were selected from among the household maintainers identified by the 2011 NHS. In households with more than one maintainer, the head was selected randomly from among the maintainers. The number of households projected is the number of private households. Since the projected population, to which the headship rates are applied, includes an adjustment for collective dwellings, headship rates were created by dividing the number of heads without the adjustment for collective dwellings by the population including the adjustment for collective dwellings. Lastly, the headship rates account for net undercoverage of households by province or territory and by household size (one person or two or more persons). For Inuit Nunangat regions, Nunavut household undercoverage rates were applied. Information on the household net undercoverage rates used is available in Statistics Canada (forthcoming).

Return to note 37 referrer

Note 38.

The models are applied in the following order: mother tongue, language spoken most often at home and knowledge of official languages.

Return to note 38 referrer

Note 39.

For women not in a union when their child is born, the transmission matrices do not take into account the type of union (mixed or non-mixed).

Return to note 39 referrer

Note 40.

In this case, self-identifying or not self-identifying with one or more Aboriginal groups.

Return to note 40 referrer

Note 41.

Here, the mixed union parameters for married women are applied to women not in a union when their child is born, since the concordance of the status of the mother and the child for women not in a union resembles that of married mothers.

Return to note 41 referrer

Note 42.

Because of its special characteristics, the non-permanent resident population is processed separately in Demosim. Individuals are non-permanent residents for only a short time and there is almost no information available on the propensity of non-permanent residents to experience the events simulated. Therefore, and to maintain consistency with Statistics Canada’s population estimates data on non-permanent residents (annual net change), it is assumed that composition of the non-permanent resident population is perfectly stable. To achieve this, members of this population do not experience any of the events simulated except for fertility, since children born in Canada of non-permanent residents become Canadian citizens at birth. Thus, the assumption is made that each departing non-permanent resident is immediately offset by the arrival of a new non-permanent resident with the same characteristics as the one who just left. The resulting stability is echoed by the data, at least regarding the composition of this population by age, sex and place of residence, in spite of slight variations from one period to the next.

Return to note 42 referrer

Date modified:: 2015-11-30

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