Annual Demographic Estimates: Canada, Provinces and Territories, 2022
Data quality, concepts and methodology

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Methodology

This document describes the concepts, data sources and methodology used to produce the population estimates. Population estimates are produced to measure the population counts according to various characteristics and geographies between two censuses. The demographic estimates are the official population estimates at the national, provincial and territorial levels.

Postcensal estimates are based on the 2016 Census.

Specific information regarding age and sex distributions is provided in boxes.

Population Estimates

Estimates of the total population

Types of estimates

Population estimates can be either intercensal or postcensal. Intercensal estimates are produced using the counts from two consecutive censuses adjusted for census net undercoverage (CNU)Note 1 and postcensal estimates. The production of intercensal estimates involves updating the postcensal estimates using the counts from a new census adjusted for CNU.Note 1

Postcensal estimates are produced using data from the most recent census adjusted for CNUNote 1 and the components of demographic growth. In terms of timeliness, postcensal estimates are more up-to-date than data from the most recent census adjusted for CNU,Note 1 but as they get farther from the date of that census, they become more variable.

Levels of estimates

The production of the population estimates between censuses entails the use of data from administrative files or surveys. The quality of population estimates therefore depends on the availability of a number of administrative data files that are provided to Statistics Canada by Canadian and foreign government departments. Since some components are not available until several months after the reference date, three kinds of postcensal estimates are produced preliminary postcensal (PP), updated postcensal (PR) and final postcensal (PD). The time lag between the reference date and the release date is three months for preliminary estimates and two to three years for final estimates. Though it requires more vigilance on the part of users, the production of three successive series of postcensal estimates is the strategy that best satisfies the need for both timeliness and accuracy of the estimates. All tables indicate the level of the estimates they contain.

Calculation of postcensal population estimates

Population estimates – preliminary, updated and final – are produced by the component method. This method consists of taking the population figures from the most recent census, adjusted for the CNUNote 1 (census undercoverage minus census overcoverage), and adding or subtracting the number of births, deaths, and components of international and interprovincial migration.

A. Provincial / territorial estimates of total population

Population estimates are produced for the provinces and territories first; then they are summed to obtain an estimate of the population of Canada.

The component-method formula for estimating the total provincial / territorial populations is as follows:

P ( t+i ) = P ( t ) + B ( t,t+i ) D ( t,t+i ) + I ( t,t+i ) [ E ( t,t+i ) +ΔT E ( t,t+i ) ]+R E ( t,t+i ) +ΔNP R ( t,t+i ) +ΔN inter ( t,t+i ) Resi d ( t,t+i ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaBa aaleaadaqadaqaaiaadshacqGHRaWkcaWGPbaacaGLOaGaayzkaaaa beaakiabg2da9iaadcfadaWgaaWcbaWaaeWaaeaacaWG0baacaGLOa GaayzkaaaabeaakiabgUcaRiaadkeadaWgaaWcbaWaaeWaaeaacaWG 0bGaaiilaiaadshacqGHRaWkcaWGPbaacaGLOaGaayzkaaaabeaaki abgkHiTiaadseadaWgaaWcbaWaaeWaaeaacaWG0bGaaiilaiaadsha cqGHRaWkcaWGPbaacaGLOaGaayzkaaaabeaakiabgUcaRiaadMeada WgaaWcbaWaaeWaaeaacaWG0bGaaiilaiaadshacqGHRaWkcaWGPbaa caGLOaGaayzkaaaabeaakiabgkHiTmaadmaabaGaamyramaaBaaale aadaqadaqaaiaadshacaGGSaGaamiDaiabgUcaRiaadMgaaiaawIca caGLPaaaaeqaaOGaey4kaSIaeuiLdqKaamivaiaadweadaWgaaWcba WaaeWaaeaacaWG0bGaaiilaiaadshacqGHRaWkcaWGPbaacaGLOaGa ayzkaaaabeaaaOGaay5waiaaw2faaiabgUcaRiaadkfacaWGfbWaaS baaSqaamaabmaabaGaamiDaiaacYcacaWG0bGaey4kaSIaamyAaaGa ayjkaiaawMcaaaqabaGccqGHRaWkcqqHuoarcaWGobGaamiuaiaadk fadaWgaaWcbaWaaeWaaeaacaWG0bGaaiilaiaadshacqGHRaWkcaWG PbaacaGLOaGaayzkaaaabeaakiabgUcaRiabfs5aejaad6eaciGGPb GaaiOBaiaacshacaGGLbGaaiOCamaaBaaaleaadaqadaqaaiaadsha caGGSaGaamiDaiabgUcaRiaadMgaaiaawIcacaGLPaaaaeqaaOGaey OeI0IaamOuaiaacwgacaGGZbGaaiyAaiaacsgadaWgaaWcbaWaaeWa aeaacaWG0bGaaiilaiaadshacqGHRaWkcaWGPbaacaGLOaGaayzkaa aabeaaaaa@98CE@

where, for each province and territory:

(t,t+i)
interval between times t and t+i;
P(t+i)
estimate of the population at time t+i;
P(t)
base population at time t (census adjusted for (CNU)Note 1 or most recent estimate);
B
number of births;
D
number of deaths;
I
number of immigrants;
E
number of emigrants;
ΔTE
net temporary emigration;
RE
number of returning emigrants;
ΔNPR
net non-permanent residents;
ΔNinter
net interprovincial migration;
Resid
residual deviation (for intercensal estimates).

B. Provincial / territorial estimates by age and sex

Population estimates by age and sex are produced by applying the component method to each age-sex cohort in the base population.

At age 0:

P (t+1) 0 = B (t,t+1) D (t,t+1) 1 + I (t,t+1) 1 [ E (t,t+1) 1 +ΔT E (t,t+1) 1 ]+R E (t,t+1) 1 +ΔNP R (t,t+1) 1 +ΔNinte r (t,t+1) 1 Resi d (t,t+1) 1 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4HqaqFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbqaqaiaadcfada qhaaqcbawaaiaacIcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacaaI WaaaaOGaaGPaVlabg2da9iaaykW7caWGcbWaaSbaaKqaGfaacaGGOa GaaiiDaiaacYcacaWG0bGaey4kaSIaaGymaiaacMcaaSqabaGccaaM c8UaeyOeI0IaaGPaVlaadseadaqhaaqcbawaaiaacIcacaGG0bGaai ilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiabgkHiTiaaigdaaaGc caaMc8Uaey4kaSIaaGPaVlaadMeadaqhaaqcbawaaiaacIcacaGG0b GaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiabgkHiTiaaigda aaGccaaMc8UaeyOeI0IaaGPaVpaadmaabaGaamyramaaDaaajeayba GaaiikaiaacshacaGGSaGaamiDaiabgUcaRiaaigdacaGGPaaabaGa eyOeI0IaaGymaaaakiaaykW7cqGHRaWkcaaMc8UaeyiLdqKaamivai aadweadaqhaaqcbawaaiaacIcacaGG0bGaaiilaiaadshacqGHRaWk caaIXaGaaiykaaqaaiabgkHiTiaaigdaaaaakiaawUfacaGLDbaaca aMc8UaaGPaVlabgUcaRiaaykW7caWGsbGaamyramaaDaaajeaybaGa aiikaiaacshacaGGSaGaamiDaiabgUcaRiaaigdacaGGPaaabaGaey OeI0IaaGymaaaakiaaykW7cqGHRaWkcaaMc8UaeyiLdqKaamOtaiaa dcfacaWGsbWaa0baaKqaGfaacaGGOaGaaiiDaiaacYcacaWG0bGaey 4kaSIaaGymaiaacMcaaeaacqGHsislcaaIXaaaaOGaaGPaVlabgUca RiaaykW7cqGHuoarcaWGobGaamyAaiaad6gacaWG0bGaamyzaiaadk hadaqhaaqcbawaaiaacIcacaGG0bGaaiilaiaadshacqGHRaWkcaaI XaGaaiykaaqaaiabgkHiTiaaigdaaaGccaaMc8UaeyOeI0IaaGPaVl aadkfacaWGLbGaam4CaiaadMgacaWGKbWaa0baaKqaGfaacaGGOaGa aiiDaiaacYcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacqGHsislca aIXaaaaaaa@BF8D@

From 1 to 99 years:

P (t+1) a+1 = P (t) a D (t,t+1) a + I (t,t+1) a [ E (t,t+1) a +ΔT E (t,t+1) a ]+R E (t,t+1) a +ΔNP R (t,t+1) a +ΔNinte r (t,t+1) a Resi d (t,t+1) a MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4HqaqFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbqaqaiaadcfada qhaaqcbawaaiaacIcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacaWG HbGaey4kaSIaaGymaaaakiaaykW7cqGH9aqpcaaMc8UaamiuamaaDa aaleaadaWgaaadbaGaaiikaiaacshacaGGPaaabeaaaSqaaiaadgga aaGccaaMc8UaeyOeI0IaaGPaVlaadseadaqhaaqcbawaaiaacIcaca GG0bGaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiaadggaaaGc caaMc8Uaey4kaSIaaGPaVlaadMeadaqhaaqcbawaaiaacIcacaGG0b GaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiaadggaaaGccaaM c8UaeyOeI0IaaGPaVpaadmaabaGaamyramaaDaaajeaybaGaaiikai aacshacaGGSaGaamiDaiabgUcaRiaaigdacaGGPaaabaGaamyyaaaa kiaaykW7cqGHRaWkcaaMc8UaeyiLdqKaamivaiaadweadaqhaaqcba waaiaacIcacaGG0bGaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqa aiaadggaaaaakiaawUfacaGLDbaacaaMc8UaaGPaVlabgUcaRiaayk W7caWGsbGaamyramaaDaaajeaybaGaaiikaiaacshacaGGSaGaamiD aiabgUcaRiaaigdacaGGPaaabaGaamyyaaaakiaaykW7cqGHRaWkca aMc8UaeyiLdqKaamOtaiaadcfacaWGsbWaa0baaKqaGfaacaGGOaGa aiiDaiaacYcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacaWGHbaaaO GaaGPaVlabgUcaRiaaykW7cqGHuoarcaWGobGaamyAaiaad6gacaWG 0bGaamyzaiaadkhadaqhaaqcbawaaiaacIcacaGG0bGaaiilaiaads hacqGHRaWkcaaIXaGaaiykaaqaaiaadggaaaGccaaMc8UaeyOeI0Ia aGPaVlaadkfacaWGLbGaam4CaiaadMgacaWGKbWaa0baaKqaGfaaca GGOaGaaiiDaiaacYcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacaWG Hbaaaaaa@B8C3@

For 100 years and older:

P (t+1) 100+ = P (t) 99+ D (t,t+1) 99+ + I (t,t+1) 99+ [ E (t,t+1) 99+ +ΔT E (t,t+1) 99+ ]+R E (t,t+1) 99+ +ΔNP R (t,t+1) 99+ +ΔNinte r (t,t+1) 99+ Resi d (t,t+1) 99+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4HqaqFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbqaqaiaadcfada qhaaqcbawaaiaacIcacaWG0bGaey4kaSIaaGymaiaacMcaaeaacaaI XaGaaGimaiaaicdacqGHRaWkaaGccaaMc8Uaeyypa0JaaGPaVlaadc fadaqhaaWcbaGaaiikaiaadshacaGGPaaabaGaaGyoaiaaiMdacqGH RaWkaaGccaaMc8UaeyOeI0IaaGPaVlaadseadaqhaaqcbawaaiaacI cacaGG0bGaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiaaiMda caaI5aGaey4kaScaaOGaaGPaVlabgUcaRiaaykW7caWGjbWaa0baaK qaGfaacaGGOaGaaiiDaiaacYcacaWG0bGaey4kaSIaaGymaiaacMca aeaacaaI5aGaaGyoaiabgUcaRaaakiaaykW7cqGHsislcaaMc8+aam WaaeaacaWGfbWaa0baaKqaGfaacaGGOaGaaiiDaiaacYcacaWG0bGa ey4kaSIaaGymaiaacMcaaeaacaaI5aGaaGyoaiabgUcaRaaakiaayk W7cqGHRaWkcaaMc8UaeyiLdqKaamivaiaadweadaqhaaqcbawaaiaa cIcacaGG0bGaaiilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiaaiM dacaaI5aGaey4kaScaaaGccaGLBbGaayzxaaGaaGPaVlaaykW7cqGH RaWkcaaMc8UaamOuaiaadweadaqhaaqcbawaaiaacIcacaGG0bGaai ilaiaadshacqGHRaWkcaaIXaGaaiykaaqaaiaaiMdacaaI5aGaey4k aScaaOGaaGPaVlabgUcaRiaaykW7cqGHuoarcaWGobGaamiuaiaadk fadaqhaaqcbawaaiaacIcacaGG0bGaaiilaiaadshacqGHRaWkcaaI XaGaaiykaaqaaiaaiMdacaaI5aGaey4kaScaaOGaaGPaVlabgUcaRi aaykW7cqGHuoarcaWGobGaamyAaiaad6gacaWG0bGaamyzaiaadkha daqhaaqcbawaaiaacIcacaGG0bGaaiilaiaadshacqGHRaWkcaaIXa GaaiykaaqaaiaaiMdacaaI5aGaey4kaScaaOGaaGPaVlabgkHiTiaa ykW7caWGsbGaamyzaiaadohacaWGPbGaamizamaaDaaajeaybaGaai ikaiaacshacaGGSaGaamiDaiabgUcaRiaaigdacaGGPaaabaGaaGyo aiaaiMdacqGHRaWkaaaaaa@C6AC@

where, for each province and territory:

(t,t+1)
interval between times t and t+1;
a
age;
P(t+1)
estimate of the population at time t+1;
P(t)
base population at time t (census adjusted for (CNU),Note 1 or most recent estimate);
B
number of births;
D
number of deaths;
I
number of imigrants;
E
number of emigrants;
ΔTE
net temporary emigration;
RE
number of returning emigrants;
ΔNPR
net non-permanent residents;
ΔNinter
net interprovincial migration;
Resid
residual deviation (for intercensal estimates).

C. Levels of estimates

The difference between preliminaryNote 2 and final postcensal population estimates lies in the timeliness of the components. When all the components are preliminary, the population estimate is described as preliminary postcensal (PP). When they are all final, the estimate is referred to as final postcensal (PD). Any other combination of levels is referred to as updated postcensal (PR).

Base population and components of demographic growth

A. Base population

The base populations are derived from the quinquennial censuses between 1971 and 2016. The population universe of the 2016Note 3 Census includes the following groups:

For census purposes, the last three groups are referred to as non-permanent residents (NPR). They have been included in the census universe since 1991 but foreign residents are not included. Foreign residents are persons who belong to the following groups:

These base populations are adjusted as follows:

Adjustment for the census net undercoverage (CNU)

The adjustment for CNU is important. CNU is the difference between the number of persons who should have been enumerated but were missed (undercoverage) and the number of persons who were enumerated but should not have been or who were counted more than once (overcoverage).

Coverage studies provide undercoverage estimates for the 1991, 1996, 2001, 2006, 2011 and 2016 censuses at the provincial and territorial levels, and for the 1971, 1976, 1981 and 1986 censuses at the provincial level only. Estimates of overcoverage at the provincial and territorial levels are available only for the last six censuses (1991 to 2016). Overcoverage for previous censuses was estimated by assuming that the overcoverage-to-undercoverage ratio for each census between 1971 and 1986 was the same as in 1991. The CNU for the Yukon and the Northwest Territories prior to 1991 was estimated by assuming that the ratio between the CNU for each territory and the 10 provinces for each census between 1971 and 1986 was the same as in 1991.

For consistency, the 1991 Census undercoverage and overcoverage were revised in 1998 to take into account the methodological improvements made in the 1996 Census coverage studies. This revision altered CNU in all censuses between 1971 and 1986. Similarly, the 1996 Census undercoverage and overcoverage were revised in 2003.

Various methods were used to produce the estimates of CNU by age and sex for 1991, 1996, 2001, 2006, 2011 and 2016. First, the national estimates of CNU based on the coverage studies by age and sex were smoothed. Then an Empirical Bayes regression model was used to generate the provincial and territorial estimates of CNU by broad age groups, and a synthetic model produced estimates by single year of age. Lastly, two-way rakingNote 4 was used to ensure that CNU estimates were consistent with the provincial and territorial CNU totals and the national estimates by age and sex.

For the 1971 to 1986 period, CNU estimates by age and sex were simply prorated to the revised CNU estimates for the total population.

Demographic adjustment at age 0

To minimize inconsistencies with vital statistics information, it was decided to adjust the censal population estimates at age 0 to the postcensal estimates at the same age.

Demographic adjustment for very elderly populations

An analysis of the age and sex structure of recent census counts and postcensal population estimates reveals that the very elderly population, particularly people aged 95 and older, can be affected by overestimation or underestimation that coverage studies do not manage to correct. For very elderly populations, the types of errors and their magnitude can vary from one census to another, from misreporting errors (voluntary and involuntary) to data capture and/or process errors.

On 2016 Census Day, postcensal estimates of the number of centenarians, still based on the 2011 Census, were significantly lower than the 2016 Census counts, translating into significant errors of closure. Specifically, among women, the postcensal estimates of the number of centenarians corresponded to only 59% of the 2016 Census counts and, among men, to only 4%. Historically, the enumerated centenarian population has often been overestimated; however, gaps of this size between census counts and postcensal estimates are symptomatic of a defect. This could indicate that the downward adjustment to the 2011 Census counts was too aggressive for the population aged 95 and older, the group that made up the centenarian population in 2016.

When the 2011 Census cycle was rebased, Statistics Canada’s Population Estimates Program reviewed its demographic adjustment method for very elderly populations using the extinct cohort method and the survival ratio method. The resulting observations revealed that these approaches, although tested in the scientific literature, are highly sensitive to the choice of certain parameters, such as assumptions on the future evolution of survival rates. This could partially account for the unsatisfactory results recently observed following a comparison of the number of centenarians between the postcensal estimates and the 2016 Census counts.

In light of these findings, the demographic adjustment for very elderly populations for rebasing the 2016 Census cycle used a more holistic strategy to make use of a vast range of available data sources. First, administrative data from the Office of the Chief Actuary of Canada (OCA) as well as from the T1 Family File (T1FF) were considered to compare them with the census counts. Next, we also used the most recent life tables published by Statistics Canada. Using the mortality rates in these tables and deaths, as measured in vital statistics, enabled us to calculate a theoretical population centred on the date of the four most recent censuses. The very elderly populations were also calculated using the extinct cohort method and the survival ratio method, as a point of comparison.

For the entire period from 2001 to 2016, we simulated different scenarios, using the data sources and methods identified in the previous paragraph on their own or combined with others. Next, the age and sex structures produced by each scenario chosen were examined in detail, particularly to detect possible inconsistencies. Special attention was given to evaluating the ratios between men and women, given that the adjustments were calculated independently for each sex. A similar analysis was done on the basis of the probabilities of death calculated for each scenario chosen. Finally, a detailed analysis of errors of closure rounded out the comparative analysis of the scenarios being studied.

For the two most heavily populated provinces in Canada, Ontario and Quebec, the method that performed the best was the one based on the calculation of a theoretical population using data from the life tables and vital statistics. In the other provinces and territories, this method did not perform optimally, likely because the number of observations for deaths in very elderly populations drawn from vital statistics was too limited. The administrative data from OCA helped to produce a more consistent portrait of very elderly populations in terms of their age and sex structure and their death probabilities and generated the biggest error of closure decreases. The universe of these administrative data is more or less the universe of Old Age Security (OAS) program beneficiaries. For Quebec and Ontario, the administrative data from OCA were also used to revise the calculation of potential outliers. The adjusted censal estimate was therefore systematically capped to correspond to the value obtained using administrative data from OCA. This approach is based on the assumption that OCA has very complete data, which are more likely characterized by a very slight overestimation than by underestimation. Similarly, the adjusted censal estimate was systematically replaced by administrative data from the T1FF if the latter were higher than the former. This approach is based on the assumption that the T1FF data are characterized by slightly incomplete coverage, and therefore, constitute a lower limit.

To ensure the best possible consistency of estimates by cohort, the demographic adjustment for very elderly populations was carried out on the 2001, 2006, 2011 and 2016 census populations, by age and sex for each province.Note 5 These adjustments were performed from age 90 on. The surplus populations were redistributed among the population aged 5 to 74 years, by their relative weight per province or territory and by sex.

The robustness of this new adjustment method will be monitored throughout the 2016 cycle and research to improve its accuracy and coherence will continue.

B. Births and deaths

The numbers of births and deaths are derived directly from the vital statistics database of Statistics Canada’s Centre for Population Health Data. Although Statistics Canada manages the National system of vital statistics, the central vital statistics registries of the provinces and territories are responsible for collecting and processing the information from those administrative files. Under provincial / territorial vital statistics statutes (or similar legislation), all live births and all deaths must be registered, and all provinces and territories provide this information to Statistics Canada.

The vital statistics universe applied to the population estimates includes births and deaths occurring in Canada, in which the usual place of residence of either the birth mother or the deceased is Canada. Any death or birth occurring outside of Canada, even if the mother or the deceased is Canadian, is excluded from the vital statistics population.

Vital statistics by province or territory of residence are used to produce our final estimates of births and deaths. However, before 2011, the final estimates may differ from the data released by the Centre for Population Health Data due to the imputation of certain unknown values. In addition, for estimates of deaths, the age represents age at the beginning of the period (July 1st) and not the age at the time of occurrence, as with the Centre for Population Health Data. The Centre for Population Health Data releases preliminary data that the Centre for Demography will use. However, this data will not be final.

When there are no vital statistics, the number of births is estimated using quarterly fertility rates by the mother’s age group. The number of deaths is estimated by using quarterly mortality rates by age group and sex. These methods are used to calculate preliminaryNote 2 estimates.

Special treatment for preliminaryNote 2 estimates for Quebec, British Columbia and Yukon

Quebec, British Columbia and Yukon provide their most recent estimates of births and deaths. The figures are used to produce preliminaryNote 2 estimates. For the final estimates, births and deaths for Quebec and British Columbia are derived from the vital statistics compiled by the Centre for Population Health Data. As of 2017, the total number of births and deaths for Yukon come from their statistical agency.

With regard to the preliminaryNote 2 estimates, the number of births by sex is derived by applying an average proportion by sex for each province and territory to the total births. These proportions are calculated using the births from vital statistics from the past 10 years.

With regard to the preliminaryNote 2 estimates, the number of deaths by age and sex is derived by applying mortality rates by age and sex for each province and territory to the total deaths. These mortality rates are calculated using the deaths from vital statistics from the past 2 final years.

Quebec provides its most recent estimates of births by sex and deaths by age and sex. They are used for the preliminaryNote 2 estimates.

In the absence of births and deaths from vital statistics for Yukon, the 2016 distribution is used to generate births by sex and deaths by age and sex.

Levels of estimates

For information on the differences between preliminaryNote 2 and final estimates, see sections B. Births and Deaths, above.

C. Immigration

Like the numbers of births and deaths, Canadian immigration statistics must be kept by law. In Canada, immigration is regulated by the Immigration and Refugee Protection Act (IRPA) of 2002. This statute superseded the Immigration Act, which was passed in 1976 and amended more than 30 times in the years thereafter. Immigration, Refugees and Citizenship Canada (IRCC) collects and processes immigrants’ administrative files. It then provides Statistics Canada with information from Global Case Management System (GCMS) files (until December 2010, data come from the Field Operational Support System files (FOSS)). The information is used to estimate the number and characteristics of people granted permanent resident status by the federal government on a given date. For the Centre for Demography, the terms immigrant and permanent resident are equivalent.

An immigrant is a person who is not a Canadian citizen by birth, but has been granted the right to live in Canada permanently by Canadian immigration authorities. The number of immigrants does not include persons born abroad to Canadian parents who are only temporarily outside the country.

Immigrants are usually counted on or after the date on which they are granted permanent resident status or the right to live in Canada.

The estimates of immigrants by age and sex are derived from the Global Case Management System (GCMS).

Levels of estimates

The difference between preliminaryNote 2 and final postcensal estimates lies in the timeliness of the source used to estimate this component. Since the GCMS files are continually being updated, new calculations are carried out each year to update the immigration estimates. Immigration estimates are preliminary the first year and final the second year.

D. Net non-permanent residents

Like the numbers of births and deaths, Canadian immigration statistics must be kept by law. In Canada, the non-permanent residents (NPR) are regulated by the Immigration and Refugee Protection Act (IRPA) of 2002. This statute superseded the Immigration Act, which was passed in 1976 and amended more than 30 times in the years thereafter. Immigration, Refugees and Citizenship Canada (IRCC) collects and processes the administrative files of immigrants and NPRs in Canada. It then provides Statistics Canada with information from Global Case Management System (GCMS) files (until June 2011, data come from the Field Operational Support System files (FOSS)). The information is used to estimate the number and characteristics of people granted non-permanent resident status by the federal government.

NPRs are persons who are lawfully in Canada on a temporary basis under the authority of a temporary resident permit, along with members of their family living with them. NPRs include foreign workers, foreign students, the humanitarian population and other temporary residents. The humanitarian population includes refugee claimants and temporary residents who are allowed to remain in Canada on humanitarian grounds and are not categorized as either foreign workers or foreign students. For the Centre for Demography, the terms non-permanent resident and temporary resident are equivalent.

The number of people in IRCC’s administrative system is estimated on a specific date in each period of observation. First, the end-of-period number of NPR is estimated, and then the start-of-period number of NPR is subtracted from that estimate. That yields the net NPRs used in the calculation of the population estimates.

Anyone who received non-permanent resident status prior to the observation date is counted. For refugee claimants, the date of their application is used. Permit holders and refugee claimants are excluded from the population if their permit has expired, if they receive permanent resident status, or if they are deported. In addition, refugee claimants are excluded if their file has been inactive for two years.

Since GCMS files are continually being updated, the figures are recalculated each year until the estimates of net NPR are final.

The estimates of net non-permanent residents by age and sex are derived from the Global Case Management System (GCMS).

Levels of estimates

The difference between preliminaryNote 2 and final estimates lies in the timeliness of the source used to estimate this component. Since the GCMS files are continually being updated, the figures are recalculated each year to update the estimates of the net number of NPRs. Non-permanent resident (NPR) estimates are preliminary the first year and updated the following year. They become final two to three years after the reference year, when all other components are also final.

E. Emigration

The number of emigrants is estimated using data from the Office of Immigration Statistics, U.S. Department of Homeland Security, data collected by the Canada child benefit (CCB) program and data from the T1 Family File (T1FF).Note 6 The first source is used to estimate emigration to the United States. CCB data are used to estimate emigration to other countries. The estimates of the number of child emigrants have to be adjusted because the CCB is not universal and does not provide direct information on the number of adult emigrants. As a result, four adjustment factors are taken into account:

The adult emigration rate is applied to the adult population. Adult emigration is distributed by province and territory using data from the T1FFNote 6 file. We calculate a ratio of the number of emigrant adults to the number of emigrant children from the T1FFNote 6 file. We then apply this ratio to the number of emigrant children from the CCB by province, which yields the number of adult emigrants whose provincial distribution will differ from that of the children.

The number of adult emigrants combined with the number of child emigrants (once adjusted for the coverage and differential emigration factors) generate the number of emigrants for the entire population.

Emigration is disaggregated by province and territory based on the number of child emigrants adjusted for coverage and differential emigration.

Please note that the estimates for the most recent periods are expected to be very similar. In the absence of more up-to-date data sources, the emigration rate of the last available year is applied to the beginning of the year population estimate to be estimated.

The estimates of the emigrants by age and sex are obtained by using the data by five-year age group, sex, province and territory from T1FFNote 6 files adjusted for the coverage. We distribute these estimates by single year of age using Sprague coefficients.

Levels of estimates

For information on the differences between preliminaryNote 2 and final estimates, see section E. Emigration, above.

F. Net temporary emigration

Some people leave Canada to live temporarily in another country while others who were temporarily outside of Canada return. The net result of those departures and returns is the component known as “net temporary emigration”. Estimates of the number of departures are derived from the Reverse Record Check (RRC), the most important census coverage study. The RRC provides an estimate of the number of people who left Canada temporarily during an intercensal period and are still out of the country at the end of the period. Estimates of the number of returns are based on two sources: the census and the Centre for Demography estimates of returning emigrants. The census provides the number of people who were outside Canada at the time of the previous census and returned during the intercensal period. That number includes all returning emigrants. Then the Centre for Demography’s estimate of the returning emigrants component is subtracted to produce the number of returning temporary emigrants. The estimated numbers of departures (RRC) and returns (census and Centre for Demography) yield an estimate of net temporary emigration.

The five-year net temporary emigration is calculated first at the national level. It is then disaggregated by province or group of provinces based on RRC estimates of temporary emigration. For the Atlantic provinces and the territories, the estimate for the group is disaggregated on the basis of each province / territory’s proportion of the group’s total population.

This estimate is for the whole intercensal period; it is disaggregated into estimates for each of the five years in the period and then into monthly estimates using a seasonal adjustment that is an average between zero seasonality and the seasonality of emigration.

Net temporary emigration can be estimated only for the intercensal period preceding the most recent census. For the postcensal period, the rate of the last available year (2015/2016) is applied to the beginning of the year population estimate to be estimated.

The age and sex distribution of the net temporary emigration is derived from the emigration age and sex distribution.

Levels of estimates

The difference between preliminaryNote 2 and final estimates lies in the timeliness of the emigration estimate used to calculate the seasonal adjustment for the net temporary emigration. The same estimation method is used.

G. Returning emigrants

A returning emigrant is a person who returns to Canada after having been classified as an emigrant. In a manner similar to the procedure used to calculate the number of emigrants, data from the Canada child benefit (CCB) file from Canada Revenue Agency (CRA) and T1FFNote 6 file are used to estimate the number of returning emigrants. Adjustment factors are applied to compensate for the fact that the CCB program is not universal, and an adult/child ratio is used to estimate the number of adult returning emigrants. As a result, four adjustment factors are used to take into account:

Please note that the estimates for the most recent periods are expected to be identical or very similar. In the absence of more up-to-date data sources, the assumption is made that levels remain similar.

The age and sex distribution of returning emigrants is based on the census at the national level. Characteristics of returning emigrants are derived from the census question on location of residence one year ago, after excluding non-permanent residents and immigrants. From 2016/2017, the distribution by age and sex derived from the 2016 Census is used.

Levels of estimates

For information on the differences between preliminaryNote 2 and final estimates, see section G. Returning emigrants, above.

H. Interprovincial migration

Interprovincial migration represents movements from one province or territory to another, involving a change in usual place of residence. As is the case for emigration, there is no provision for recording interprovincial migration in Canada. Consequently, such movements have to be estimated using data from the Canada child benefit (CCB) of Canada Revenue Agency (CRA) and T1FF.Note 6

Final estimates of interprovincial migration are obtained by comparing addresses indicated on personal income tax returns over two consecutive tax years. However, the migration status of tax filers’ dependants has to be imputed. An adjustment is also required to take into account migrants who do not file income tax returns. From 2001/2002 to 2005/2006, the adjustment was slightly modified (for further information, see Wilkinson, 2004). From 2006/2007, this adjustment has been slightly modified (Cyr, 2008 – Internal document).

Since income tax returns are not available at the time preliminaryNote 2 estimates are produced, the estimation of preliminaryNote 2 interprovincial migration is based on CCB administrative files, which provide counts of child migrants (aged 0 to 17) registered to the program. The estimates have to be adjusted later for children who are not registered to the CCB program. Finally, the number of adult migrants is calculated using the number of child migrants and factors derived from the T1FF.Note 6 As a result, three adjustment factors are used to take into account:

The adult migration rate is then applied to the estimated adult population. The number of adult migrants is then added to the number of child migrants to produce the number of interprovincial migrants for the entire population.

Since 2015, the method to estimate the interprovincial migration has been modified. This new method is applied from July 2011 onward. In order to reduce the differences between the preliminary annual series (which was derived from the sum of 12 monthly migration matrices) and the final annual series, CCB microdata have been used. Using microdata is allowing estimating migration for various periods (monthly, quarterly and annually). It also allows improving the comparability between preliminary and final estimates. Final annual estimates (T1FF)Note 6 are now distributed by quarter on the basis of preliminaryNote 2 quarterly estimates derived from CCB microdata. It is important to note that, as a result of using CCB microdata, it is not possible to add the quarterly interprovincial in-migrants and out-migrants estimates to get the annual estimates. It is however possible to add the quarterly net interprovincial migration estimates to get the annual estimates.

Interprovincial migration by age and sex is derived from T1FFNote 6 data and counts from the last available census (question on location of residence one year ago). From 2016/2017, the 2016 Census age and sex distribution is used to split the broad age groups of the T1FFNote 6 file.

Levels of estimates

For information on the differences between preliminaryNote 2 and final estimates of total interprovincial migration, see section H. Interprovincial migration above.

Intercensal population estimates

Intercensal estimates – population estimates for reference dates between two censuses – are produced following each census. They reconcile previous postcensal estimates with the new census counts adjusted for the CNU.Note 1

There are two main steps in the production of intercensal estimates:

The error of closure is defined as the difference between the postcensal population estimates on Census Day and the population enumerated in that census adjusted for CNU.Note 1

The error of closure is spread uniformly over the intercensal period of days within each month.

Intercensal estimates by age and sex are adjusted in the same way, i.e., by distributing the error of closure uniformly across the age-sex cohorts.

Quality of demographic data

The estimates contain certain inaccuracies stemming from two types of errors:

Census data

A. Coverage, response and imputation errors

The errors attributable to census data can be divided into two groups: response and processing errors, and coverage errors. The first group implies non-response error, misinterpretation by respondents, incorrect coding and non-response imputation. Errors in the second group primarily result from undercoverage and, to a lesser extent, overcoverage. It should be noted that both types of errors are intrinsic to any survey data.

The coverage errors occur when dwellings and/or individuals are missed, incorrectly included (except for the 2006, 2011 and 2016 censuses, where people incorrectly included were not considered in the Census Overcoverage Study) or counted more than once. Following each census, Statistics Canada undertakes coverage studies to measure these errors. The main studies are the Reverse Record Check Survey (RRC) and the Census Overcoverage Study (COS). Based on these studies, estimates of census undercoverage and overcoverage are produced. The Centre for Demography adjusts the population enumerated in the census by province and territory using these estimates.

When creating base populations, the Demographic Estimates Program (DEP) corrects the census populations only for coverage errors. This correction, which is based on the findings of coverage studies, is primarily subject to sampling errors, and to a lesser extent, processing errors. Statistical tests indicate that coverage adjustments improve the quality of census data. The DEP uses the estimates from coverage studies for the provinces and territories. However, given the size of the samples in these studies, estimates by age and sex are modelled. Furthermore, it is assumed that the coverage rates estimated for a province or territory apply to the regions within that geographic area. Prior to 1993,Note 7 the DEP used census data that was unadjusted for coverage errors. Coverage studies had been done to measure undercoverage, but none measured overcoverage. Following the decision to integrate a correction for the coverage to the enumerated population in 1991, the DEP had to revise the population estimates for the period from 1971 to 1992. The correction is based on the findings of the coverage studies conducted during this period and on hypotheses regarding the ratio between the overcoverage and undercoverage levels based on the findings of subsequent coverage studies.

The corrections to the census data due to CNU improved, in general, the quality of the estimates by compensating for the differential undercoverage by age, sex and by province/territory across censuses.

Text table 1
Estimated census net undercoverage, Canada, provinces and territories, 2001 to 2016 censuses
Table summary
This table displays the results of Estimated census net undercoverage. The information is grouped by Geography (appearing as row headers), Census population, Census net undercoverage, Incompletely enumerated Indian reserves, Adjusted population, Rate, A, B, C, D=A+B+C and (B+C)/D*100, calculated using number and percent units of measure (appearing as column headers).
Geography Census population Census net undercoverage Incompletely enumerated reserves Adjusted population Rate
A B C D=A+B+C (B+C)/D*100
number percent
2016Text table Note 1
Canada 35,151,728 849,727 27,790 36,029,245 2.44
Newfoundland and Labrador 519,716 9,774 0 529,490 1.85
Prince Edward Island 142,907 3,464 0 146,371 2.37
Nova Scotia 923,598 17,809 0 941,407 1.89
New Brunswick 747,101 15,735 0 762,836 2.06
Quebec 8,164,361 35,191 11,985 8,211,537 0.57
Ontario 13,448,494 381,542 11,640 13,841,676 2.84
Manitoba 1,278,365 31,895 0 1,310,260 2.43
Saskatchewan 1,098,352 34,844 0 1,133,196 3.07
Alberta 4,067,175 115,968 4,043 4,187,186 2.87
British Columbia 4,648,055 197,267 122 4,845,444 4.07
Yukon 35,874 2,370 0 38,244 6.20
Northwest Territories 41,786 2,939 0 44,725 6.57
Nunavut 35,944 929 0 36,873 2.52
2011Text table Note 1
Canada 33,476,688 759,125 37,392 34,273,205 2.32
Newfoundland and Labrador 514,536 10,192 0 524,728 1.94
Prince Edward Island 140,204 3,386 0 143,590 2.36
Nova Scotia 921,727 21,911 0 943,638 2.32
New Brunswick 751,171 3,930 0 755,101 0.52
Quebec 7,903,001 73,240 16,882 7,993,123 1.13
Ontario 12,851,821 369,874 14,926 13,236,621 2.91
Manitoba 1,208,268 21,698 608 1,230,574 1.81
Saskatchewan 1,033,381 29,580 768 1,063,729 2.85
Alberta 3,645,257 128,584 4,094 3,777,935 3.51
British Columbia 4,400,057 91,280 114 4,491,451 2.03
Yukon 33,897 1,356 0 35,253 3.85
Northwest Territories 41,462 1,977 0 43,439 4.55
Nunavut 31,906 2,117 0 34,023 6.22
2006Text table Note 1
Canada 31,612,897 868,658 40,115 32,521,670 2.79
Newfoundland and Labrador 505,469 5,046 0 510,515 0.99
Prince Edward Island 135,851 1,903 0 137,754 1.38
Nova Scotia 913,462 24,558 0 938,020 2.62
New Brunswick 729,997 16,059 0 746,056 2.15
Quebec 7,546,131 60,751 16,600 7,623,482 1.01
Ontario 12,160,282 465,824 15,391 12,641,497 3.81
Manitoba 1,148,401 34,330 0 1,182,731 2.90
Saskatchewan 968,157 22,594 739 991,490 2.35
Alberta 3,290,350 111,353 7,272 3,408,975 3.48
British Columbia 4,113,487 121,551 113 4,235,151 2.87
Yukon 30,372 1,805 0 32,177 5.61
Northwest Territories 41,464 1,620 0 43,084 3.76
Nunavut 29,474 1,264 0 30,738 4.11
2001Text table Note 1
Canada 30,007,094 924,430 34,539 30,966,063 3.10
Newfoundland and Labrador 512,930 9,401 0 522,331 1.80
Prince Edward Island 135,294 1,325 0 136,619 0.97
Nova Scotia 908,007 24,521 0 932,528 2.63
New Brunswick 729,498 20,095 0 749,593 2.68
Quebec 7,237,479 140,232 12,648 7,390,359 2.07
Ontario 11,410,046 436,349 15,960 11,862,355 3.81
Manitoba 1,119,583 30,903 110 1,150,596 2.70
Saskatchewan 978,933 21,231 581 1,000,745 2.18
Alberta 2,974,807 69,857 4,977 3,049,641 2.45
British Columbia 3,907,738 164,542 263 4,072,543 4.05
Yukon 28,674 1,423 0 30,097 4.73
Northwest Territories 37,360 3,295 0 40,655 8.10
Nunavut 26,745 1,256 0 28,001 4.49

The adjustment also incorporates the results of a study on the estimates of the number of people living on incompletely enumerated reserves to complete the corrections for coverage errors in the census. The results of the coverage studies contain mainly sampling errors.

These adjustments have a direct impact on:

Text table 2
Census adjustment rates by age group, 2001 to 2016 censuses, Canada
Table summary
This table displays the results of Census adjustment rates by age group 2001, 2006, 2011 and 2016 (appearing as column headers).
2001 2006 2011 2016
All ages 3,10 2,79 2,32 2,44
0 to 4 years 3,38 1,91 0,95 2,14
5 to 9 years 2,18 0,96 -0,25 -0,94
10 to 14 years 1,07 0,95 0,08 -0,36
15 to 19 years 2,93 3,14 2,90 2,90
20 to 24 years 7,09 7,56 6,76 5,98
25 to 29 years 8,26 8,88 8,26 6,97
30 to 34 years 6,38 6,83 6,70 6,09
35 to 39 years 4,62 4,95 4,12 4,66
40 to 44 years 2,70 4,14 2,51 3,55
45 to 49 years 1,49 1,73 1,91 2,93
50 to 54 years 1,33 0,66 0,98 2,36
55 to 59 years 1,14 0,00 0,03 1,53
60 to 64 years 0,69 -0,08 -0,27 0,51
65 to 69 years 0,75 -0,48 -0,41 -0,35
70 to 74 years 0,83 -0,73 -0,52 -0,99
75 to 79 years 0,48 -0,48 -0,51 -1,36
80 to 84 years 0,54 -0,70 -0,51 -1,15
85 to 89 years 0,38 -0,33 -0,49 -0,89
90 to 94 years -0,14 -3,67 1,48 -0,76
95 to 99 years -1,99 -7,66 0,91 2,55
100 years and older -8,27 -6,07 1,42 3,40

For further information regarding the main coverage studies, please see the following document on Statistics Canada’s web site: 1996, 2001, 2006, 2011 and 2016 Census Technical Report on Coverage.

Components

Errors due to estimation methodologies and data sources other than the census can also be significant.

A. Births and deaths

Since the law requires the recording of vital statistics, the final estimates for births and deaths data meet very high standards. Nevertheless, since preliminaryNote 2 estimates are derived, they can be slightly different from final estimates.

B. Immigration and non-permanent residents

With respect to immigrants and non-permanent residents, Immigration, Refugees and Citizenship Canada (IRCC) administers special data files on both of these components. Since immigration is controlled by law, data on immigrants and NPRs are compiled upon arrival in Canada. These data represent only “legal” immigration and exclude illegal immigrants. Thus, for the “legal” part of international movement into Canada, the data are considered to be of high quality. However, some biases such as the difference between the stated province of intended residence at the time of arrival and the actual province of residence, may persist. Finally, since information provided by the Visitor Data System (VDS) from IRCC is not complete (age and sex of dependents, province of residence for certain groups of permit holders), estimates of NPRs are more prone to error than data on immigrants.

C. Emigration, returning emigration and net temporary emigration

Of all the demographic components that are used by the DEP, the emigration, returning emigration and net temporary emigration are the most difficult to estimate with precision. Canada does not have a complete border registration system. While immigration and non-permanent residents (NPRs) are well documented by the federal government, Statistics Canada has always used indirect techniques for the estimation of the number of persons leaving the country. For this reason, available statistics regarding these three components have historically been of a lower quality than other components.

Estimates of the number of emigrants and returning emigrants are both derived using Canada child benefit (CCB) data provided by Canada Revenue Agency (CRA). Estimates must be adjusted to take into account the incomplete coverage of the program and to derive the emigration and returning emigration of adults.

These adjustments and the delay in obtaining the data are the two main sources of errors. As current information on the number of persons living temporarily abroad does not exist, estimates are based on the Reverse Record Check (RRC) and the census. Estimates for the intercensal period are distributed equally among the five years. Moreover, assumptions were made to allow for the distribution of national estimates by province and territory and of annual estimates to a quarterly level. Assumptions must also be made to establish the variation for the postcensal period. Any geographical or quarterly variation may introduce error in the estimation of these components.

D. Interprovincial migration

Since July 1993, preliminaryNote 2 interprovincial migration estimates have been based on Canada child benefit (CCB) files. As this program covers only children, various adjustments must be done in order to derive the migration of adults. Consequently, preliminaryNote 2 CCB based estimates are subject to larger error than final estimates derived from Canada Revenue Agency (CRA) tax files.

E. Level of detail of components

As a more detailed breakdown of the data introduces a greater risk of inaccuracy into the estimates, the possibility of error in the components is augmented by the method used to distribute the estimates by age and sex. It seems that, in general, the initial errors should be minimal where the distribution of annual estimates of births, deaths and immigrants is concerned, and more significant with regard to the distribution of other components (non-permanent residents, emigrants, returning emigrants, net temporary emigrants and interprovincial migrants). Finally, the size of error due to the age and sex distribution may vary by period and errors in some components may have a greater impact on a given age group or sex.

Quality assessment

In order to assess the quality of our estimates, two evaluation measures are used: precocity errors and errors of closure.

A. Precocity error

The quality of preliminary estimates of components is evaluated using precocity errors. Precocity error is defined as the difference between preliminary and final estimates of a particular component in terms of its relative proportion of the total population for the relevant geographical area. The precocity error can be calculated for both population and component estimates. The precocity error measures the impact of the trade-off of accuracy in favour of timeliness on the estimated population. The annual precocity error of a component is calculated as:

P E (t1,t) = ( N (t1,t) preliminary N (t1,t) final ) P (t1) postcensal ×1,000 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuaiaadw eadaWgaaWcbaGaaiikaiaadshacqGHsislcaWGPbGaaiilaiaadsha caGGPaaabeaakiabg2da9maalaaabaGaaiikaiaad6eadaqhaaWcba GaaiikaiaadshacqGHsislcaWGPbGaaiilaiaadshacaGGPaaabaae aaaaaaaaa8qacaWGWbGaamOCaiaadwgacaWGSbGaamyAaiaad2gaca WGPbGaamOBaiaadggacaWGYbGaamyEaaaak8aacqGHsislcaWGobWa a0baaSqaaiaacIcacaWG0bGaeyOeI0IaamyAaiaacYcacaWG0bGaai ykaaqaaiaadAgacaWGPbGaamOBaiaadggacaWGSbaaaOGaaiykaaqa aiaadcfadaqhaaWcbaGaaiikaiaadshacqGHsislcaWGPbGaaiykaa qaaiaadchacaWGVbGaam4CaiaadshacaWGJbGaamyzaiaad6gacaWG ZbGaamyyaiaadYgaaaaaaOGaey41aqRaaGymaiaacYcacaaIWaGaaG imaiaaicdaaaa@7249@

where:

P E (t1,t) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuaiaadw eadaWgaaWcbaGaaiikaiaadshacqGHsislcaWGPbGaaiilaiaadsha caGGPaaabeaaaaa@3D97@
= the precocity error for the period from t-1 to t;

 

N (t1,t) preliminary MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOtamaaDa aaleaacaGGOaGaamiDaiabgkHiTiaadMgacaGGSaGaamiDaiaacMca aeaaqaaaaaaaaaWdbiaadchacaWGYbGaamyzaiaadYgacaWGPbGaam yBaiaadMgacaWGUbGaamyyaiaadkhacaWG5baaaaaa@474F@
= the preliminary estimate of a component of demographic change;

 

N (t1,t) final MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamOtamaaDa aaleaacaGGOaGaamiDaiabgkHiTiaadMgacaGGSaGaamiDaiaacMca aeaacaWGMbGaamyAaiaad6gacaWGHbGaamiBaaaaaaa@416F@
= the final estimate of a component of demographic change;

 

P (t1) postcensal MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaamiuamaaDa aaleaacaGGOaGaamiDaiabgkHiTiaadMgacaGGPaaabaGaamiCaiaa d+gacaWGZbGaamiDaiaadogacaWGLbGaamOBaiaadohacaWGHbGaam iBaaaaaaa@4493@
= postcensal estimates of population for the relevant geographical area at time t-1.

Precocity error allows for useful comparisons between components, as well as between provinces and territories having different population size. Precocity error can either be positive or negative. A positive precocity error denotes that the preliminary estimate is larger than the final estimate while a negative precocity error indicates the opposite. As precocity errors measure differences between preliminary and final estimates, small precocity errors refer to those that are close to zero per thousand.

Precocity error by component for Canada

At the national level, the immigration component yielded the smallest precocity errors in absolute values, with error values close to zero per thousand throughout the years under consideration. On the other hand, interprovincial in-migrants and out-migrantsNote 8 yielded the largest precocity errors in absolute values, ranging between 0.17 per thousand and 0.98 per thousand during the period 2017/2018 to 2020/2021 (see Table 3).


Text table 3
Most up-to-date annual precocity errors for components, Canada, provinces and territories
Table summary
This table displays the results of Most up-to-date annual precocity errors for components. The information is grouped by Year/Component (appearing as row headers), Canada, N.L., P.E.I., N.S., N.B., Que., Ont., Man., Sask., Alta., B.C., Y.T., N.W.T. and Nvt., calculated using per thousand units of measure (appearing as column headers).
Year/Component Canada N.L. P.E.I. N.S. N.B. Que. Ont. Man. Sask. Alta. B.C. Y.T. N.W.T. Nvt.
per thousand
Births
2016/2017   0.28   0.05   0.42   0.26   0.11   0.00   0.44   0.52   0.24   0.62   -0.05   -0.70   1.48   1.11
2017/2018   0.25   0.68   0.96   0.39   0.20   0.00   0.30   0.05   0.40   0.79   -0.03   0.33   0.22   -0.08
2018/2019   0.26   0.15   0.59   0.53   0.25   0.00   0.39   0.50   0.65   0.42   -0.01   0.62   -0.09   0.58
2019/2020   0.10   0.04   0.06   0.22   0.11   -0.02   0.16   0.09   -0.02   0.28   -0.02   1.91   0.07   -0.54
Deaths
2016/2017   0.12   0.25   0.68   0.28   -0.21   -0.04   0.26   -0.18   0.24   0.19   -0.05   -0.10   -0.49   1.22
2017/2018   -0.10   0.10   -1.23   -0.39   -0.21   -0.04   -0.16   -0.05   0.06   -0.08   -0.05   0.28   0.27   0.35
2018/2019   0.13   0.32   0.47   0.19   0.25   -0.04   0.22   0.32   0.13   0.28   -0.05   -0.20   0.56   -1.18
2019/2020   0.09   -0.11   -0.24   -0.11   0.47   0.02   0.23   -0.03   0.08   0.02   -0.05   -0.34   -0.13   0.10
Immigration
2017/2018   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00
2018/2019   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00
2019/2020   0.01   0.00   0.02   0.01   0.00   0.00   0.01   0.01   0.00   0.01   0.02   0.00   0.00   0.00
2020/2021   0.00   0.00   0.01   0.00   0.00   0.00   0.00   0.00   -0.01   0.00   0.00   0.00   0.00   0.00
Emigration
2016/2017   0.24   -0.01   0.32   0.21   0.29   0.19   0.31   0.29   0.04   0.16   0.28   -1.53   -0.56   0.00
2017/2018   0.52   0.28   0.36   0.07   -0.14   0.39   0.69   0.26   0.33   0.63   0.51   0.66   -0.25   0.05
2018/2019   0.39   0.23   0.04   0.39   0.04   0.35   0.40   0.36   0.04   0.39   0.60   0.39   0.42   0.34
2019/2020   0.11   -0.02   -0.01   0.31   0.26   0.10   0.07   0.08   -0.14   0.08   0.33   -0.05   0.22   -0.03
Returning emigration
2016/2017   -0.02   -0.02   -0.23   -0.20   -0.04   -0.07   -0.03   0.00   -0.15   0.19   0.01   -0.26   -0.22   0.00
2017/2018   0.04   -0.08   -0.13   0.08   -0.11   0.07   -0.01   0.13   -0.03   0.05   0.17   0.08   -0.42   0.00
2018/2019   0.05   0.11   -0.04   0.21   -0.01   0.03   0.05   0.08   0.11   0.01   0.09   -0.07   -0.18   0.39
2019/2020   0.10   0.01   0.01   0.03   0.10   0.02   0.22   0.17   0.03   0.11   -0.11   -0.27   0.11   0.00
Net temporary emigration
2016/2017   -0.24   -0.03   -0.03   -0.02   -0.03   -0.29   -0.46   0.05   -0.25   0.01   0.10   -0.26   -0.20   -0.24
2017/2018   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   -0.03
2018/2019   0.00   -0.01   -0.01   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   -0.02   0.02   0.00
2019/2020   -0.17   -0.06   -0.06   -0.06   -0.06   -0.12   -0.21   -0.06   -0.10   -0.12   -0.24   -0.15   -0.18   -0.18
Net non-permanent residents
2016/2017   0.08   0.58   0.76   0.55   0.36   -0.23   0.42   0.50   0.69   0.57   -1.28   -0.91   0.25   0.11
2017/2018   0.09   0.55   0.34   0.64   0.38   -0.41   0.58   0.61   0.41   0.00   -0.78   -0.30   0.16   -0.03
2018/2019   0.08   0.25   -0.87   0.23   0.23   -0.39   0.66   0.70   0.28   -0.05   -0.91   -1.18   -0.73   -0.03
2019/2020   0.00   0.04   0.16   -0.30   -0.05   -0.41   0.41   -0.13   0.03   -0.06   -0.37   -1.11   -0.33   -0.10
In-migrants
2017/2018   0.67   0.92   2.01   1.16   0.92   0.21   0.71   0.36   1.67   1.73   -0.09   7.30   1.31   11.91
2018/2019   0.98   0.55   2.35   1.64   1.46   0.37   0.72   0.68   2.43   3.02   0.43   -0.07   2.73   14.13
2019/2020   -0.17   0.51   -7.65   -1.37   -0.35   0.03   -0.36   -0.33   0.44   0.83   -0.56   -6.46   0.40   12.75
2020/2021   0.94   1.45   2.11   1.46   1.76   0.43   0.61   -2.33   1.39   1.98   2.24   -1.00   4.39   9.45
Out-migrants
2017/2018   0.67   2.67   6.15   1.57   1.61   0.34   0.15   1.90   2.20   0.63   1.17   8.00   12.05   4.85
2018/2019   0.98   4.18   5.82   1.98   2.84   0.25   0.36   2.08   2.64   1.26   1.87   13.94   5.60   10.88
2019/2020   -0.17   -0.53   2.59   0.35   0.76   -0.34   -0.20   -0.04   0.30   -0.21   -0.30   0.07   3.51   7.70
2020/2021   0.94   0.95   3.53   0.40   2.66   0.26   0.53   2.66   3.29   2.51   0.51   4.86   6.24   16.29
Net interprovincial migration
2017/2018   Note ...: not applicable   -1.75   -4.14   -0.41   -0.69   -0.13   0.56   -1.54   -0.53   1.10   -1.26   -0.71   -10.74   7.06
2018/2019   Note ...: not applicable   -3.62   -3.47   -0.34   -1.38   0.13   0.36   -1.40   -0.21   1.76   -1.44   -14.02   -2.87   3.25
2019/2020   Note ...: not applicable   1.05   -10.24   -1.72   -1.11   0.38   -0.17   -0.29   0.14   1.05   -0.25   -6.53   -3.11   5.05
2020/2021   Note ...: not applicable   0.49   -1.43   1.07   -0.90   0.17   0.09   -4.99   -1.90   -0.54   1.73   -5.86   -1.85   -6.84

Precocity errors for births were positive for the entire period under consideration, ranging from 0.10 per thousand in 2019/2020 to 0.28 per thousand in 2016/2017. Precocity errors for deaths were positive for three of the past four years under consideration. Only the year 2017/2018 had a negative precocity error at -0.10 per thousand, the other years ranging from 0.09 per thousand (2019/2020) to 0.13 per thousand (2018/2019).

Precocity errors for emigration and returning emigration were mostly positive. During the years under consideration, precocity error in absolute values for emigration was lowest in 2019/2020 (0.11 per thousand) and largest in 2017/2018 (0.52 per thousand). For returning emigration, the absolute value of the error ranged from 0.02 per thousand in 2016/2017 to 0.10 per thousand in 2019/2020. In 2016/2017, the precocity error for net temporary emigration was -0.24 per thousand and it was -0.17 per thousand in 2019/2020. In 2017/2018 and 2018/2019 it was close to zero per thousand.

Over the period 2016/2017 to 2019/2020, precocity error of the net non-permanent residents ranged from close to zero per thousand in 2019/2020 to 0.09 per thousand in 2017/2018.

Precocity error by component for provinces and territories

In general, precocity error is typically more prone to higher volatility for smaller provinces or territories as it is an error measurement relative to the population size. At the provincial and territorial level, precocity errors in absolute values for births ranged from close to zero per thousand (Quebec from 2016/2017 to 2018/2019)Note 9 to 1.91 per thousand (in Yukon for 2019/2020).

Overall, precocity errors for deaths were slightly more often positive than negative. For the years under consideration, the largest precocity error in absolute value for deaths was 1.23 per thousand (Prince Edward Island in 2017/2018).

Compared to the other demographic components, precocity errors for immigration were low among the provinces and territories. The largest absolute error value was 0.02 per thousand in Prince Edward Island and in British Columbia in 2019/2020. Otherwise, the precocity error values for all provinces and all territories for the years from 2017/2018 and 2018/2019 was close to zero per thousand. For 2020/2021, the precocity errors for most provinces and territories were also about zero per thousand, with the exception of Prince Edward Island (0.01 per thousand) and Saskatchewan (-0.1 per thousand).

Precocity errors in absolute values for the net change in the number of non-permanent residents were less than or equal to 1.28 per thousand across the provinces and territories, during the years 2016/2017 to 2019/2020.

Precocity errors in absolute values for emigration ranged from close to zero per thousand (in Nunavut for 2016/2017) to 1.53 per thousand (in Yukon for 2016/2017). The absolute values of precocity errors for returning emigration ranged from close to zero per thousand for some years in Manitoba and Nunavut to 0.42 per thousand for the Northwest Territories in 2017/2018. Precocity errors for net temporary emigration were negative during the year 2016/2017 (with the exception of Manitoba, Alberta and British Columbia) and the year 2019/2020. The precocity errors were close to zero per thousand for all provinces and all territories in 2017/2018, except for Nunavut (-0.03 per thousand) and for the year 2018/2019, except for Newfoundland and Labrador (-0.01 per thousand), Prince Edward Island (-0.01 per thousand), Yukon (-0.02 per thousand), and the Northwest Territories (0.02 per thousand).

Most of the time, precocity errors for interprovincial in-migrants and out-migrants were positive during the years under consideration, meaning that final estimates were mostly lower than preliminary estimates. Precocity errors for these two components were comparatively larger at the territorial level than for the provinces mainly due to the smaller population size of the territories.

At the provincial level, the largest absolute precocity error value for net interprovincial migration was 10.24 per thousand (Prince Edward Island in 2019/2020), while the smallest was 0.09 per thousand (Ontario in 2020/2021). At the territorial level, precocity errors for net interprovincial migration were comparatively higher, the smallest absolute precocity error was 0.71 per thousand for Yukon in 2017/2018 and the largest was 14.02 per thousand also in Yukon but for the year 2018/2019.

Contribution of components to the sum of precocity errors

When looking at aggregated estimates of precocity errors, there is the potential for a “netting-out” effect, referring to negative precocity errors in one component canceling out positive errors in another component. The analysis of the contribution of each component to the sum of precocity errors without the netting-out effect can be done by using absolute values of the precocity errors. A mean absolute percentage precocity error by component is calculated by dividing the mean absolute precocity error by component by its sum and expressed in percentage. In this case, the mean absolute precocity error by component is the mean of the absolute precocity errors for the 2015/2016 to 2019/2020 period, the latest 5-year period that annual precocity errors by all components are available.

At the national level, the mean absolute precocity error for the total emigrationNote 10 component contributed the most to the sum of mean absolute precocity errors (50.92%), followed by the errors related to births (25.79%) and deaths (14.37%) between 2015/2016 and 2019/2020. Immigration (1.64%) and net non-permanent residents (7.28%) accounted the least to the sum of mean absolute precocity errors (refer to Table 4).


Text table 4
Mean absolute percentage precocity error by componentsText table 4 Note 1, 2015/2016 to 2019/2020, Canada, provinces and territories
Table summary
This table displays the results of Mean absolute percentage precocity error by components Births, Deaths, Immigration, Total emigration, Net non-permanent residents and Net interprovincial migration, calculated using percent units of measure (appearing as column headers).
Births Deaths Immigration Total emigrationText table 4 Note 2 Net non-permanent residents Net interprovincial migration
percent
Canada 25.79 14.37 1.64 50.92 7.28 0.00
Newfoundland and Labrador 10.42 7.13 0.25 8.30 13.19 60.71
Prince Edward Island 6.66 10.16 0.19 4.46 5.44 73.10
Nova Scotia 13.01 7.96 0.48 12.60 15.47 50.49
New Brunswick 7.13 11.53 0.33 11.00 11.00 59.02
Quebec 0.69 3.62 0.77 42.00 30.51 22.42
Ontario 17.04 11.85 0.59 29.16 20.95 20.42
Manitoba 13.94 6.53 1.21 14.49 18.26 45.56
Saskatchewan 23.29 9.38 1.92 24.15 20.90 20.36
Alberta 17.91 6.18 0.95 16.36 6.23 52.38
British Columbia 1.34 2.22 1.02 24.97 32.87 37.59
Yukon 5.20 2.02 0.00 7.31 4.88 80.59
Northwest Territories 6.03 3.67 0.06 8.10 3.72 78.42
Nunavut 9.67 10.64 0.00 5.43 0.98 73.28

At the provincial and territorial level, the contribution of the individual components to the sum of mean absolute precocity errors was not uniform across the country. Net interprovincial migration accounted for the largest share of the sum of mean absolute precocity errors in ten out of the thirteen provinces and territories, ranging for those ten provinces between 37.59% in British Columbia to 80.59% in Yukon. In Quebec (42.00%), Ontario (29.16%) and Saskatchewan (24.15%), it is total emigration that explains the largest share of the mean absolute precocity errors (refer to Table 4).

On the other hand, immigration accounted for the smallest share of the sum of mean absolute precocity errors in almost all provinces and territories, ranging from close to zero per thousand in Nunavut to 1.92% in Saskatchewan. The province of Quebec was the sole exception, where births (0.69%) were the smallest share, followed by immigration (0.77%).

Precocity errors by age and sex are not currently available.

B. Error of closure

The error of closure measures the accuracy of the final postcensal estimates. It is defined as the difference between the final postcensal population estimates on Census Day and the enumerated population of the most recent census adjusted for census net undercoverage (CNUNote 1). A positive error of closure means that the postcensal population estimates have overestimated the population.

The error of closure comes from three sources: errors primarily due to sampling when measuring the starting (2011) and end of period (2016) censuses coverage and errors related to the components of population growth over the intercensal period. For each five-year intercensal period, the error of closure can only be calculated following the release of census data and estimates of CNU.Note 1 The error of closure can be calculated for the total population of each province and territory as well as by age and sex. For the moment, the error is only available for total population by province and territory.

Text table 5 shows postcensal population estimates on May 10, 2016 and census counts adjusted for CNUNote 1 and the errors of closure for Canada, provinces and territories from 2001 to 2016.

For Canada as a whole, the error of closure was estimated at 110,310 or 0.31% in 2016. This is a decrease over the error for 2011 (0.42%).

The population estimates overestimated the population of eight provinces, one territory and Canada as a whole. Five provinces posted errors of closure greater than 1% or less than -1%. Of these jurisdictions, only British Columbia’s estimated population differed from the adjusted census population by more than 2% (-2.07%). In 2011, four provinces and two territories posted errors of closure greater than 1% or less than -1%.

By considering the variance in CNU, it is possible to identify errors of closure that are statistically significant. Text table 5 shows the results of this analysis.

The error of closure is statistically significant for Canada and seven provinces. This means that the population estimates significantly overestimated or underestimated the adjusted census population in these jurisdictions. As noted above, these results are due to both the sampling for census coverage studies and errors in the components of population growth over the intercensal period. Among these components, interprovincial migration and emigration are mostly associated with large errors of closure.

Text table 5
Error of closure of the population estimates, Canada, provinces and territories, 2001 to 2016
Table summary
This table displays the results of Error of closure of the population estimates. The information is grouped by Geography (appearing as row headers), Postcensal estimate on Census Day, Census adjusted for CNU, Error of closure, CNU standard error, t value, A, B, C=A-B, D=C/B*100, E and F=C/E, calculated using number and % units of measure (appearing as column headers).
Geography Postcensal estimate on Census Day Census adjusted for CNUText table Note 1 Error of closure CNU standard errorText table Note 2 t valueText table Note 3
A B C=A-B D=C/B*100 E F=C/E
number % number
2016
Canada 36,139,555 36,029,245 110,310 0.31 43,844 2.52
Newfoundland and Labrador 530,465 529,490 975 0.18 2,015 0.48
Prince Edward Island 149,116 146,371 2,745 1.88 870 3.16
Nova Scotia 948,080 941,407 6,673 0.71 3,042 2.19
New Brunswick 756,736 762,836 -6,100 -0.80 2,777 -2.20
Quebec 8,297,802 8,211,537 86,265 1.05 20,613 4.18
Ontario 13,902,359 13,841,676 60,683 0.44 33,316 1.82
Manitoba 1,313,904 1,310,260 3,644 0.28 4,829 0.75
Saskatchewan 1,145,156 1,133,196 11,960 1.06 4,651 2.57
Alberta 4,231,285 4,187,186 44,099 1.05 13,530 3.26
British Columbia 4,745,041 4,845,444 -100,403 -2.07 16,561 -6.06
Yukon 37,927 38,244 -317 -0.83 191 -1.66
Northwest Territories 44,667 44,725 -58 -0.13 257 -0.23
Nunavut 37,017 36,873 144 0.39 229 0.63
2011
Canada 34,417,759 34,273,205 144,554 0.42 57,546 2.51
Newfoundland and Labrador 513,622 524,728 -11,106 -2.12 2,912 -3.81
Prince Edward Island 145,759 143,590 2,169 1.51 923 2.35
Nova Scotia 948,457 943,638 4,819 0.51 5,346 0.90
New Brunswick 756,547 755,101 1,446 0.19 3,335 0.43
Quebec 7,968,651 7,993,123 -24,472 -0.31 23,660 -1.03
Ontario 13,345,467 13,236,621 108,846 0.82 44,121 2.47
Manitoba 1,251,999 1,230,574 21,425 1.74 6,104 3.51
Saskatchewan 1,055,858 1,063,729 -7,871 -0.74 6,306 -1.25
Alberta 3,774,557 3,777,935 -3,378 -0.09 18,046 -0.19
British Columbia 4,543,807 4,491,451 52,356 1.17 19,494 2.69
Yukon 35,356 35,253 103 0.29 303 0.34
Northwest Territories 44,139 43,439 700 1.61 323 2.17
Nunavut 33,540 34,023 -483 -1.42 608 -0.79
2006
Canada 32,553,799 32,521,670 32,129 0.10 53,926 0.60
Newfoundland and Labrador 508,874 510,515 -1,641 -0.32 2,710 -0.61
Prince Edward Island 137,746 137,754 -8 -0.01 701 -0.01
Nova Scotia 933,692 938,020 -4,328 -0.46 4,885 -0.89
New Brunswick 748,737 746,056 2,681 0.36 3,105 0.86
Quebec 7,644,701 7,623,482 21,219 0.28 24,077 0.88
Ontario 12,657,808 12,641,497 16,311 0.13 41,363 0.39
Manitoba 1,176,744 1,182,731 -5,987 -0.51 6,469 -0.93
Saskatchewan 987,706 991,490 -3,784 -0.38 4,805 -0.79
Alberta 3,357,637 3,408,975 -51,338 -1.51 16,091 -3.19
British Columbia 4,296,518 4,235,151 61,367 1.45 16,591 3.70
Yukon 31,146 32,177 -1,031 -3.20 194 -5.31
Northwest Territories 42,160 43,084 -924 -2.14 236 -3.92
Nunavut 30,330 30,738 -408 -1.33 176 -2.32
2001
Canada 31,016,011 30,966,063 49,948 0.16 44,749 1.12
Newfoundland and Labrador 533,712 522,331 11,381 2.18 1,782 6.39
Prince Edward Island 138,102 136,619 1,483 1.09 775 1.91
Nova Scotia 941,533 932,528 9,005 0.97 4,170 2.16
New Brunswick 754,180 749,593 4,587 0.61 3,555 1.29
Quebec 7,390,137 7,390,359 -222 0.00 21,033 -0.01
Ontario 11,873,643 11,862,355 11,288 0.10 33,472 0.34
Manitoba 1,149,561 1,150,596 -1,035 -0.09 5,423 -0.19
Saskatchewan 1,016,762 1,000,745 16,017 1.60 4,333 3.70
Alberta 3,051,245 3,049,641 1,604 0.05 11,308 0.14
British Columbia 4,068,196 4,072,543 -4,347 -0.11 15,598 -0.28
Yukon 29,737 30,097 -360 -1.20 372 -0.97
Northwest Territories 41,152 40,655 497 1.22 362 1.37
Nunavut 28,051 28,001 50 0.18 411 0.12

The error of closure can be calculated for total population estimates and for age and sex.

Text table 6
Error of closure of the estimates of population by age and sex, 2016, Canada
Table summary
This table displays the results of Error of closure of the estimates of population by age and sex Both sexes, Male and Female, calculated using number and percent units of measure (appearing as column headers).
Both sexes Male Female
  number   percent   number   percent   number   percent
All ages 110,310 0.31 46,349 0.26 63,961 0.35
0 to 4 years -6,932 -0.36 -955 -0.10 -5,977 -0.63
5 to 9 years -22,391 -1.12 -5,447 -0.54 -16,944 -1.73
10 to 14 years -34,237 -1.79 -11,105 -1.14 -23,132 -2.46
15 to 19 years -13,941 -0.67 -9,851 -0.91 -4,090 -0.41
20 to 24 years 75,634 3.17 21,255 1.71 54,379 4.75
25 to 29 years 43,111 1.75 -2,018 -0.16 45,129 3.77
30 to 34 years 32,547 1.31 7,727 0.62 24,820 2.01
35 to 39 years 36,817 1.53 27,234 2.29 9,583 0.79
40 to 44 years -409 -0.02 8,378 0.72 -8,787 -0.74
45 to 49 years -19,783 -0.81 -3,663 -0.30 -16,120 -1.32
50 to 54 years -29,205 -1.06 -9,376 -0.68 -19,829 -1.45
55 to 59 years -18,258 -0.69 -3,759 -0.28 -14,499 -1.08
60 to 64 years -15,130 -0.66 -394 -0.03 -14,736 -1.26
65 to 69 years -1,060 -0.05 2,821 0.30 -3,881 -0.38
70 to 74 years 21,606 1.54 6,827 1.01 14,779 2.02
75 to 79 years 22,059 2.19 6,915 1.49 15,144 2.79
80 to 84 years 12,374 1.67 2,968 0.92 9,406 2.25
85 to 89 years 13,578 2.84 4,376 2.38 9,202 3.13
90 to 94 years 7,159 3.23 2,226 3.26 4,933 3.21
95 to 99 years 5,908 10.19 1,905 14.13 4,003 8.99
100 years and older 863 10.13 285 20.85 578 8.08
 
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