Longitudinal Immigration Database (IMDB) Technical Report, 2019
7 Data evaluation and quality indicators

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7.1 Error sources

Because the IMDB is the product of several record linkages, it is subject to different sources of errors, including record linkage errors, measurement errors, and coverage errors. In this section, the sources of errors are explained and the prevalence of some of these errors is presented.

It is to be noted that, given that it is a census of immigrant taxfilers who were admitted in 1980 or thereafter, no weights are created in the IMDB. No adjustments are made for the missing tax years of filers or for linkage errors; no sampling is performed; and every linked taxfilers is kept in the final dataset. However, the linkage itself presents a form of sampling error when links are missed.

7.1.1 Record linkage errors

Datasets produced from the results of record linkages are subject to record linkage errors. Two types of errors are possible—false positives (false matches) and false negatives (false non-matches). A link is considered a false positive when two records not belonging to the same person are deemed a match. A link is considered a false negative when two records belonging to the same person are deemed a non-match.

It is possible to miss part of an immigrant’s fiscal history since some immigrants have more than one social insurance number (SIN) through time (a temporary SIN assigned at arrival to the individual as a non-permanent resident, and later a permanent SIN assigned after admission). Both SINs are required in order to have a complete fiscal history from arrival in Canada. The SDLE (described in Sections 2.3) allows for identification of these SINs. It is possible that, in a few instances, some SIN connections are missed or false connections are made.

7.1.2 Measurement errors

Measurement error is the difference between a variable’s measured value and its true value. This type of error can be attributed to a number of factors, including data capture (e.g., typos) and respondent error (e.g., misinterpretation of the question asked). This type of error was taken into account in the creation of the Integrated Permanent and Non-permanent Resident File (PNRF) to avoid conflicting information for any individual. For example, when a person has a record on both the ILF and the NRF, and the sociodemographic variables have inconsistent values, the values at admission (in the ILF) are kept. See sections 7.2 and section 7.5 for some counts.

7.1.3 Coverage errors

Coverage errors are the result of omissions, erroneous additions, duplicates, and errors of classification of records in the database. Coverage errors can result from inadequate coverage of the population. They can create biased estimates, and the impact can vary for different sub-groups of the population. These errors often result in undercoverage. Undercoverage in the IMDB is in part the result of the exclusion of tax files of immigrant taxfilers from the database. Immigrants who do not file taxes for a given year or who file late would not have an IMDB_T1FF record although linked to tax and part of the population of interest. If, for any reason, an immigrant record was not included in the Immigrant Landing File (ILF), it would not be part of the IMDB. Overcoverage is the result of the addition to the database of records excluded from the target population. An immigrant could have more than one ILF record as a result of multiple admissions not identified as such, for example. Please refer to Section 7.4 and Appendix B for more information on IMDB coverage.

7.2 Data accuracy

This section will discuss the accuracy of the immigration data. For details on the accuracy of the T1 Family File (T1FF), please refer to the T1FF entry (record number 4105).

The accuracy of the IMDB is dependent on the representativeness of the population included in it. A study conducted in the first years of the IMDB concluded that the IMDB “appears to be representative of the population most likely to file tax returns. Therefore, the results obtained from the IMDB should not be inferred to the immigrant population as a whole, but rather to the universe of tax-filing immigrants” (Carpentier and Pinsonneault 1994).

The reasons for the differences between taxfilers and the entire foreign-born population are explained in an article by Badets and Langlois (2000) describing the challenges of using the IMDB:

The characteristics of the immigrant taxfiler population will differ from those of the entire foreign-born population because the tendency or requirement to file a tax return will vary in relation to a person’s age, family status, and other factors. One would expect a higher percentage of males to file a tax return, for example, because males have higher labour force participation rates than females. The extent to which immigrants are “captured” in the IMDB will also be influenced by changes to the income tax. For example, the introduction of federal and provincial non-refundable tax credit programs encourage individuals with no taxable income to file a return to qualify for certain tax credits. (Badets and Langlois 2000)

7.2.1 2019 IMDB: Linkage rates

This section is based on the 2019 IMDB. The overall linkage rate between IRCC immigration file and the SDLE Derived Record Depository was 97.1% (see Section 4). A link does not necessarily mean that a tax file is available since it is possible to link dependents of taxfilers or immigrants who have yet to file taxes. This SDLE theoretical linkage rate mostly informs on how well IRCC files could be associated within a larger repository environment.

Of the immigrants who landed in any year from 1980 to 2018, 84.6% were linked to at least one T1FF record. This rate represents the effective coverage of immigrant linkage to tax files. As presented in the following statistics, this coverage rate may change according to gender and age.

The proportion of linked taxfilers by age group at admission and sex is shown in Table 5. The lower rates for the 0-to-14 age group are expected since those in this age group are not of working age. See Appendix B for rates by sex, age group and admission cohort.


Table 5
Proportion of linked taxfilers by age group at landing and sex
Table summary
This table displays the results of Proportion of linked taxfilers by age group at landing and sex Age at landing, 0 to 14, 15 to 24 , 25 to 34, 35 to 49, 50 to 64, 65 and older and Total, calculated using percent units of measure (appearing as column headers).
Age at landing
0 to 14 15 to 24 25 to 34 35 to 49 50 to 64 65 and older Total
percent
Male 57.6 92.3 93.3 93.0 89.9 78.4 84.5
Female 56.8 91.5 93.3 93.4 88.2 76.6 84.8
Total 57.2 91.9 93.3 93.2 89.0 77.4 84.6

As immigrants become older, they start filing taxes and are included in the IMDB. Chart 1 shows that, among immigrants who landed at any age from birth to age 14, the proportion of linked taxfilers is higher for immigrants who landed prior to 2000 than for immigrants who have landed since 2000. Recent immigrants also have lower linkage rates. See Appendix B for table showing the proportion of linked taxfilers by age group at admission, sex and admission decade.

Chart 1 Proportion of linked taxfilers by age groups at admission and admission decade

Data table for Chart 1 
Data table for Chart 1
Table summary
This table displays the results of Data table for Chart 1. The information is grouped by Cohorts (appearing as row headers), Age groups, 0 to 14 years, 15 to 24 years, 25 to 34 years, 35 to 49 years, 50 to 64 years and 65 years and older, calculated using proportion units of measure (appearing as column headers).
Cohorts Age groups
0 to 14 years 15 to 24 years 25 to 34 years 35 to 49 years 50 to 64 years 65 years and older
proportion
1980 to 1989 cohorts 0.82 0.93 0.94 0.93 0.83 0.61
1990 to 1999 cohorts 0.81 0.92 0.93 0.93 0.89 0.76
2000 to 2009 cohorts 0.64 0.93 0.92 0.93 0.93 0.88
2010 to 2018 cohorts 0.14 0.90 0.94 0.94 0.90 0.83

Chart 2 illustrates the proportion of filers, and the number of filers and non-filers by landing year, where the term “non-filer” means that no T1FF records are available. For the 2019 IMDB, the filing rate varies by landing year, ranging from 68.1% for those who landed in 2018 to 91.2% for those who landed in 1989. The filing rates increase with the number of years that immigrants stay in Canada; this may explain why the linkage rates are higher for those who landed in the 1990s and early 2000’s. See Appendix B, tables 14 and 15, for detailed distribution numbers by landing year.

Chart 2 	Distribution of taxfilers compared to non-taxfilers, by landing year

Data table for Chart 2 
Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2. The information is grouped by Landing Year (appearing as row headers), Taxfilers, Non-taxfilers and Rates, calculated using number of immigrants and percent units of measure (appearing as column headers).
Landing Year Taxfilers Non-taxfilers Rates
number of immigrants percent
1980 120,410 22,710 84.1
1981 107,670 20,910 83.7
1982 103,400 17,680 85.4
1983 77,090 11,950 86.6
1984 77,510 10,520 88.0
1985 75,020 8,920 89.4
1986 88,980 9,790 90.1
1987 137,490 13,680 91.0
1988 146,210 14,550 90.9
1989 173,880 16,780 91.2
1990 192,170 23,260 89.2
1991 208,540 23,280 90.0
1992 228,700 25,230 90.1
1993 230,790 24,880 90.3
1994 198,810 24,780 88.9
1995 188,810 23,340 89.0
1996 198,630 26,730 88.1
1997 189,420 26,040 87.9
1998 155,460 18,230 89.5
1999 168,810 20,550 89.1
2000 203,400 23,350 89.7
2001 224,190 25,580 89.8
2002 202,760 25,450 88.8
2003 194,110 26,410 88.0
2004 205,190 30,160 87.2
2005 224,680 37,100 85.8
2006 214,560 36,550 85.4
2007 199,770 36,410 84.6
2008 205,380 41,230 83.3
2009 208,910 42,680 83.0
2010 225,880 54,190 80.7
2011 197,780 50,350 79.7
2012 205,620 51,620 79.9
2013 205,970 52,540 79.7
2014 207,230 52,280 79.9
2015 211,020 59,980 77.9
2016 216,730 78,660 73.4
2017 213,860 71,460 75.0
2018 217,650 102,140 68.1

7.2.2 Availability of date of death

The year and month of death, as well as a death flag, are included in the PNRF. In the 2019 IMDB, these variables were linked by using the Canadian Mortality Database (CMDB). In the past, these variables were based on Statistics Canada’s Amalgamated Mortality Database (AMDB), which is a retired dataset that combined records between CMDB and vital statistics and tax files. The CMDB is an administrative database that collects information on death dates and cause of death from all provincial and territorial vital statistics registries in Canada. Some undercoverage, while minimal, exists in the database as it does not include deaths of Canadians (1) who died outside of Canada, with the exception of United States; (2) who served as members of the Canadian military, or (3) whose bodies were unidentified. Note that the CMDB does not include deaths which were reported in the tax files.

Chart 3 describes the general trend in the number of deaths per year since 1974 for immigrants admitted since 1952. The availability of data for pre-1980 admission was recently added to the IMDB. The value “9999” represents the records of deceased immigrants for which the year of death is not available.

Chart 3 	Permanent residents, by year of death

Data table for Chart 3 
Data table for Chart 3
Table summary
This table displays the results of Data table for Chart 3. The information is grouped by Year of death (appearing as row headers), Permanent residents from 1952 to 1979 and Permanent residents since 1980, calculated using number of deaths units of measure (appearing as column headers).
Year of death Permanent residents from 1952 to 1979 Permanent residents since 1980
number of deaths
1974 4,840 0
1975 5,270 0
1976 5,580 0
1977 6,220 0
1978 6,520 0
1979 7,090 0
1980 7,690 80
1981 7,620 300
1982 7,960 510
1983 8,530 730
1984 8,940 940
1985 9,370 1,110
1986 10,090 1,330
1987 10,470 1,630
1988 11,080 1,900
1989 11,560 2,210
1990 11,860 2,440
1991 12,660 2,910
1992 13,200 3,200
1993 13,980 3,750
1994 14,480 4,280
1995 15,270 4,710
1996 15,690 5,110
1997 16,160 5,450
1998 16,730 5,760
1999 17,490 6,180
2000 17,520 6,380
2001 17,970 6,930
2002 18,690 7,310
2003 19,140 8,070
2004 19,390 8,270
2005 20,120 8,650
2006 20,360 9,100
2007 21,300 9,860
2008 21,820 10,280
2009 22,320 10,770
2010 22,700 11,150
2011 22,980 11,940
2012 23,500 12,180
2013 24,790 13,190
2014 25,340 14,160
2015 26,230 15,010
2016 26,860 15,870
2017 27,620 17,140
2018 27,800 17,990
9999 30 490

7.2.3 Prefilers compared to records on the Non-permanent Resident File (NRF)

The results included in this section are drawn from a study based on the 2014 IMDB. Prefilers are immigrants who filed taxes prior to their landing year. It is sometimes assumed that all prefilers are immigrants who were non-permanent residents prior to admission. This section discusses why it is not the case. A total of 1.26 million individuals filed taxes before officially admission in 1980 or a subsequent year—of these, 212,500 are not linked to a non-permanent resident record as may otherwise be expected. Upon further investigation, it has been discovered that most permanent resident prefilers not linked to a non-permanent resident record are likely immigrants who have filed taxes when not required: 96% of these prefilers filed taxes only for the year prior to admission, and 75% reported no income (96% had no wages). As shown in Chart 4, most of these prefilers landed in the first months of the year, prior to the deadline to file taxes for the previous year. It appears some immigrants who landed prior to the month of May filed taxes for the year prior to their landing year, for which they were not required to file.

Given these findings, whether it is appropriate to remove records with Prefiler_ind=1 and FIRST_EFFECTIVE_YEAR=. from studies on immigrants with pre-admission experience depends on the analysis since FIRST_EFFECTIVE_YEAR=. means no record is available on the non-permanent permit file.

Chart 4 	Distribution of prefilers without a non-permanent resident permit, by admission month

Data table for Chart 4 
Data table for Chart 4
Table summary
This table displays the results of Data table for Chart 4. The information is grouped by Landing month (appearing as row headers), Number of immigrants (appearing as column headers).
Landing month Number of immigrants
January 32,300
February 36,100
March 35,500
April 24,100
May 20,500
June 18,200
July 16,100
August 11,200
September 9,800
October 5,500
November 2,000
December 1,200

Not all immigrants with pre-admission experience are identified as prefilers: 478,100 immigrants have non-permanent resident records with Prefiler_ind=0. Depending on the subject of interest, using the FIRST_EFFECTIVE_YEAR<>. or the number of temporary resident permits (variable NUMBER_ALL_PERMITS) is more appropriate to study immigrants with pre-admission experience. Prefiler_ind=0 indicates that no tax records have been filed prior to admission, but this does not mean that the individual had no pre-admission Canadian experience.

7.2.4 Spouse indicator

The IMDB contains variables that enable data users to obtain information on marital status and spouses. The following section contains results of a study done on the 2012 IMDB. No major changes have occurred since then in the marital status codes or family flag.

The spouse identification number (SP__IDI) is derived from tax files. This information can be derived only when the respondent claims his or her spouse or common-law partner while filing taxes; this causes an underestimation of couples as compared to the marital status declared in the tax files. From the T1FF, it is also possible to obtain the marital status at time of filing.

Prior to 1991, the “single” category was not available as marital status (MSTCO). The “common-law” status was made available as of 1992 for all datasets (1982 to 2012). Since 1992, the proportion of IMDB records indicating marital status as “single” has ranged from 20% to 30%. The proportion of “separated” has declined from 30% prior to 1992 to 4% after. The other marital status categories have not been affected by pattern changes.

Analysis done on the distribution of marital status (MSTCO from tax files) and the spouse ID (SP__IDI) shows differences between the two variables. This is because values for marital status are missing for some records. In a perfect situation, the records of all married persons would have spousal information, and the records of all single persons would have no spousal information. This analysis shows data quality to be better after 1992, when separate statuses for “common-law” and “single” were introduced.

Presence of spouse reporting gaps

Further to a review of the longitudinal history of immigrants on the 2012 IMDB, some cases where the spouse or common-law partner is missing (or different) for a given year and the same spouse is declared two or three years later have been found. The Chart 5 gives a summary of these gaps.

Chart 5 Proportion of cases with inconsistent spouse identification number, (SP__IDI) by landing year

Data table for Chart 5 
Data table for Chart 5
Table summary
This table displays the results of Data table for Chart 5. The information is grouped by Tax year (appearing as row headers), Percent (appearing as column headers).
Tax year Percent
1980 16.8
1981 16.6
1982 17.1
1983 17.7
1984 17.4
1985 17.7
1986 17.7
1987 16.9
1988 14.4
1989 13.9
1990 13.4
1991 14.4
1992 13.5
1993 12.7
1994 9.8
1995 9.4
1996 8.7
1997 8.1
1998 7.8
1999 7.1
2000 6.3
2001 5.6
2002 5.0
2003 4.6
2004 4.0
2005 3.1
2006 2.9
2007 2.4
2008 1.8
2009 1.1
2010 0.7
2011 0.5

Most immigrants on the file have one or no spouse in the years from 1980 to 2012 according to the IMDB_T1FF files. It is to be noted that no marital status (and no spouse info) is available for 1.2 million immigrants out of approximately 6 million immigrants.

7.3 Imputed variables

7.3.1 Imputation of education variables

A data quality issue regarding the variables for education qualifications and years of schooling was identified. A non-negligible proportion of individuals who did not state their education qualifications or years of schooling were coded as “0” or “None” instead of “Missing” on EDUCATION_QUALIFICATIONS and YEARS_OF_SCHOOLING. This problem was prevalent from 2011 to 2014. In 2011, 35% of immigrants stated that they had no education qualifications, compared to roughly 10% in the 1990s.

This issue was resolved by imputing the education variables by means of values for education variables from 2008 to 2010 to model the most recent year’s education variables. For the imputation, variables such as admission age, immigration_category_rollup2, intended occupation, gender and country of last permanent residence were used. The nearest-neighbour imputation method was employed. The variable Education_imputation_ind (0: no; 1: yes), available in the PNRF, was created to identify records with imputed education variables.

For immigrants admitted in 2016, the number of cases where a non-stated education was coded to “0” or “None” instead of “Missing” was reduced. However, a non-negligible number of records had a missing education qualification with a valid number of years of schooling. For these records, years of schooling was used to impute a value for education qualifications.

For principal applicants admitted since 2015, under the express entry, the year of schooling in most cases are underestimated.

For the 2019 IMDB, those who were admitted between 2015 and 2018 and who were connected to an Express Entry record had their education imputed using values found in the Express Entry file. A more comprehensive education variable called Education_Derived was created, combining data from Education_Qualification, for those who were not found in the Express Entry file, and the new values from Express Entry. Users should note that the values from the Express Entry file are based on the immigration officer’s assessment of the educational qualifications of the applicant in the context of a Canadian equivalency, whereas the values from Education_Qualification are self-declared by the applicant, and do not necessarily reflect a Canadian equivalency.

7.3.2 Imputation of language variables

For the 2019 IMDB, two new language variables were added, English_IND and French_IND, reporting the first official language known at admission. For those who were admitted in 2018 or earlier, they are defined as permanent residents having English (French) as their mother tongue or having a mother tongue other than English or French while declaring English only (French only) as their knowledge of official language at admission.

For those admitted in 2019 or later, they are defined as permanent residents having declared English only (French only) as their knowledge of official language at admission or having declared English and French as their knowledge of official language at admission and reporting English (French) as the language in which they are most at ease.

7.4 Coverage

7.4.1 Coverage of the Integrated Permanent and Non-permanent Resident File (PNRF)

The 2019 Integrated Permanent and Non-permanent Resident File (PNRF) contains over 8 million records (Table 6); of these, over 7.1 million records (84.6%) are linked to at least one tax file. It is to be noted that immigration data belonging to non-taxfilers and taxfilers alike are included in a file named PNRF_ 1980_2019. The following table shows the distribution of records depending on their presence in the different immigration and tax files. About 1.9million records belong to immigrants who were temporary residents prior to becoming permanent residents; over 1.8 million of these records are linked to at least one tax file.

See Appendix B for detailed distribution numbers by landing year.


Table 6
Coverage of permanent residents since 1980
Table summary
This table displays the results of Coverage of permanent residents since 1980 Permanent resident, Permanent resident with non-permanent resident permit and Total, calculated using number and x units of measure (appearing as column headers).
Permanent resident Permanent resident with non-permanent resident permit Total
number
Total filers 5,223,060 1,829,450 7,052,510
Total non-filers 1,188,730 93,200 1,281,940
Total 6,411,780 1,922,650 8,334,450
percent
Percent taxfilers 81.5 95.2 84.6

Data on immigrants with non-permanent resident permits are available. The proportion of immigrants with pre-admission experience varies by landing year (Chart 6); it ranges from 3.8% in 1980 to 37.8% in 2017. As a result, the proportion of immigrants with pre-admission experience in the early 1980s is underrepresented. The proportion of immigrant filers with pre-admission experience (solid line) is higher than the overall proportion of immigrants with pre-admission experience (dotted line) because the linkage rate for these immigrants is higher than that for immigrants without pre-admission experience.

Chart 6 	Proportion of immigrants with non-permanent resident permits, by landing year

Data table for Chart 6 
Data table for Chart 6
Table summary
This table displays the results of Data table for Chart 6. The information is grouped by Landing Year (appearing as row headers), All Immigrants, Taxfilers and Non-taxfilers, calculated using percent units of measure (appearing as column headers).
Landing Year All Immigrants Taxfilers Non-taxfilers
percent
1980 3.8 4.1 2.6
1981 11.3 12.4 5.8
1982 14.2 15.5 6.8
1983 17.3 18.8 7.7
1984 19.8 21.3 8.8
1985 20.0 21.3 9.0
1986 24.7 26.3 10.4
1987 23.4 24.8 9.2
1988 11.5 12.0 6.3
1989 13.6 14.3 7.1
1990 16.6 17.6 8.0
1991 32.0 34.1 13.0
1992 34.7 36.9 14.9
1993 26.8 28.4 12.3
1994 18.1 19.5 7.6
1995 19.9 21.4 7.4
1996 19.6 21.4 6.5
1997 17.4 19.0 5.7
1998 19.3 20.8 7.0
1999 19.0 20.6 6.4
2000 18.2 19.6 6.2
2001 16.3 17.5 5.6
2002 15.2 16.5 4.7
2003 15.7 17.3 4.2
2004 19.2 21.3 4.7
2005 20.0 22.5 5.0
2006 22.7 25.7 5.3
2007 23.3 26.6 5.4
2008 23.2 26.9 4.8
2009 24.2 28.1 5.1
2010 23.0 27.3 5.2
2011 23.6 28.0 6.2
2012 25.4 30.2 6.3
2013 26.9 32.0 6.8
2014 34.1 40.7 8.0
2015 33.2 40.4 7.9
2016 30.0 38.5 6.7
2017 37.8 47.2 9.5
2018 37.2 49.2 11.6

7.4.1.2 Coverage of non-permanent residents

This section describes the coverage of individuals who only had non-permanent residents permits since 1980, overall tax records are available for 27.9% of them. Among individuals who have not become permanent residents, asylum seekers have the highest coverage rate, tax records are available for 40.3% of them (see table 6.B). There is a wide variety of non-permanent resident permits; some permits are as short as one day.


Table 6b
Coverage of permanent residents since 1980
Table summary
This table displays the results of Coverage of permanent residents since 1980 With work permit, With study permits, Asylum claimants and Total, calculated using number and percent units of measure (appearing as column headers).
With work permit With study permits Asylum claimants Total
number
Total filers 1,289,110 603,370 177,310 1,529,870
Total non-filers 2,243,580 1,617,640 262,420 3,951,330
Total 3,532,690 2,221,020 439,740 5,481,190
percent
Percent taxfilers 36.5 27.2 40.3 27.9

7.4.2 T1 Family File (T1FF) size and coverage by year

Tax files for 1982 and subsequent years are available for linked non-permanent and permanent residents. Some permanent residents were non-permanent residents prior to admission. Table 7 gives details on the distribution of linked permanent residents with and without non-permanent permits prior to admission, by tax year. At least one tax file is available for the 81.5% of permanent residents without a non-permanent permit prior to admission and for the 95.2% of permanent residents who were non-permanent residents prior to admission. The fact that permanent residents with pre-admission temporary permits have a higher rate of filing taxes than permanent residents without pre-admission permits can be explained by a requirement in the permanent resident application process with respect to non-permanent residents. Non-permanent residents who apply for permanent residency are required to fulfil their obligation to file tax in Canada. The number of taxfilers on the IMDB_T1FF increases as the years pass since the size of the in-scope population increases.


Table 7
Permanent and non-permanent residents by tax year
Table summary
This table displays the results of Permanent and non-permanent residents by tax year Permanent resident admitted prior to 1980, Permanent resident since 1980, Permanent resident with non-permanent resident permit, Non-permanent resident only and Number of taxfilers, calculated using number and percent units of measure (appearing as column headers).
Permanent resident admitted prior to 1980 Permanent resident since 1980 Permanent resident with non-permanent resident permit Non-permanent resident only Number of taxfilers
number
1982 1,633,720 188,314 55,159 24,786 1,897,209
1983 1,619,691 224,600 65,464 23,287 1,928,252
1984 1,615,316 263,818 79,829 23,660 1,977,623
1985 1,596,466 298,078 94,716 22,369 2,006,421
1986 1,651,911 356,204 124,690 26,369 2,153,218
1987 1,632,055 415,832 157,775 26,544 2,225,655
1988 1,652,439 508,166 199,260 35,594 2,387,891
1989 1,679,711 622,051 262,191 47,959 2,603,301
1990 1,688,818 743,453 309,456 51,308 2,783,542
1991 1,685,953 841,101 358,279 51,066 2,926,389
1992 1,692,359 947,226 402,212 50,563 3,081,877
1993 1,724,716 1,091,259 441,584 50,531 3,296,813
1994 1,709,448 1,212,686 467,118 49,688 3,427,451
1995 1,693,426 1,324,852 492,413 52,055 3,551,103
1996 1,674,466 1,432,375 513,221 53,990 3,662,356
1997 1,650,008 1,546,778 533,705 56,016 3,774,847
1998 1,626,034 1,646,612 553,795 54,928 3,869,742
1999 1,619,564 1,770,924 589,608 59,573 4,028,037
2000 1,601,817 1,914,429 630,090 67,486 4,202,285
2001 1,588,358 2,071,271 679,075 77,642 4,404,761
2002 1,558,415 2,201,293 716,810 83,288 4,548,334
2003 1,538,449 2,323,548 753,950 86,423 4,690,962
2004 1,522,399 2,454,757 795,865 87,952 4,849,642
2005 1,497,256 2,570,898 829,651 93,311 4,979,948
2006 1,479,491 2,720,371 886,942 98,304 5,173,997
2007 1,460,643 2,843,981 955,855 110,396 5,359,880
2008 1,440,365 2,969,054 1,034,081 132,946 5,565,587
2009 1,419,939 3,084,511 1,101,097 143,213 5,738,023
2010 1,395,149 3,210,533 1,158,235 152,724 5,906,038
2011 1,376,067 3,341,223 1,224,011 161,377 6,092,164
2012 1,349,423 3,459,663 1,291,914 174,266 6,264,904
2013 1,330,990 3,592,703 1,366,659 197,181 6,477,281
2014 1,309,274 3,712,175 1,442,937 224,352 6,678,651
2015 1,281,934 3,839,521 1,497,356 253,948 6,862,904
2016 1,253,715 3,961,074 1,544,192 311,662 7,060,970
2017 1,227,744 4,064,283 1,575,544 449,172 7,307,287
2018 1,204,382 4,199,974 1,592,911 641,093 7,629,133
Total taxfilers 2,054,432 5,223,052 1,829,450 1,522,358
Total non-taxfilers 2,059,247 1,188,735 93,206 3,473,903
percent
Percent taxfilers         49.9     81.5     95.2     30.5     

Chart 7 shows that the proportion of permanent residents who were non-permanent residents prior to admission varies by tax year from a low of 22.6% for the 1983 tax year to a high of 29.9% for the 1991 tax year. Since the 2000s, this proportion has been stable at about 26.2%.

Chart 7 	Percentage of permanent residents who were non-permanent residents prior to admission, by tax year

Data table for Chart 7 
Data table for Chart 7
Table summary
This table displays the results of Data table for Chart 7. The information is grouped by Tax year (appearing as row headers), Percent (appearing as column headers).
Tax year Percent
1982 22.7
1983 22.6
1984 23.2
1985 24.1
1986 25.9
1987 27.5
1988 28.2
1989 29.7
1990 29.4
1991 29.9
1992 29.8
1993 28.8
1994 27.8
1995 27.1
1996 26.4
1997 25.7
1998 25.2
1999 25.0
2000 24.8
2001 24.7
2002 24.6
2003 24.5
2004 24.5
2005 24.4
2006 24.6
2007 25.2
2008 25.8
2009 26.3
2010 26.5
2011 26.8
2012 27.2
2013 27.6
2014 28.0
2015 28.1
2016 28.0
2017 27.9
2018 27.5

An immigrant who filed taxes for a given year will not necessarily file taxes the next year. For example, if Person A landed in 1983, this individual might be found on tax files from 1984 to 1999, but not be found on the 2000 file, and again be found on the 2001 to 2013 files. For example, 35.6% of filers from the 1980 cohort had tax files available for all years. Out-migration, death and late filing are some of the reasons immigrant filers might stop filing permanently or for some years.

Most immigrants file taxes for the first time in the year they land or one year before or after. For example, of the
251,100 immigrants who landed in 2006, 100,880 (40.2%) filed taxes for the first time in 2006, while 15,560 (6.2%) did so in 2007 and 3,220 (1.3%) did so in 2015.

7.5 Quality assessment of the Integrated Permanent and Non-permanent Resident File (PNRF)

A validation of the content of the PNRF_1980_2019 was done. While admission and tax data are collected mandatorily from those in scope, some fields may not have been completed. They could be left empty because the response was unknown, or for other reasons unbeknownst to database users (e.g., refusal) (McLeish 2011). Item non-response can present issues when one is considering the IMDB for statistical purposes, including the following:

  1. If the database user is interested in producing a sample based on characteristics for which there are missing records, there will be coverage error (i.e., those being included in the sampling frame may not be representative of the target population).
  2. If the non-response is non-ignorable (i.e., the fact that information is missing is not a random occurrence; the fact that there is no response is indicative of what the response would have been), any analysis using those variables would be biased.

The presence of missing variables and invalid values was assessed. The numbers presented in this section are rounded. Invalid values are either inconsistent or not listed in the metadata tables available to users (see the immigration component of the data dictionary appendix). Most of the quality issues listed in Table 8 are for data collected in the 1980s and 1990s. It should be noted that some seemingly valid values may be erroneous as well.

The variable Case Identification Number (CASE_ID) has item response rates generally in the high 90% range (usually over 99%). However, for some landing years, the response rate drops significantly (to as low as 80% in 1991 and 1992). Therefore, any analysis using this variable for all landing years will under-represent those years where the item non-response is higher (e.g., 1986, 1987, 1990, 1991, 1992, 1993). No detection of invalid values was performed for the variable Case Identification Number (CASE_ID).

The variable Landing_age was defined as invalid when it was greater than 99, although it is possible in some instances that these values are accurate. It should be noted that, according to the values for this variable, the number of immigrants who landed after age 99 was much higher between 1986 and 1994 than the other landing years. This could be the result of a data capture issue.

In the 2019 PNRF, 25 records had a birth year prior to 1880, with 18 records having birth year 1753 with corresponding landing years that are post 1985, with one specific record having a landing year of 2012.

The variables related to country have quality issues as well. The country of birth is missing for some records in almost all landing years. For example, values are missing for over 100 records in each of the years from 1985 to 1993. The country of citizenship is missing for fewer than 10 records per landing year for most years (with the exception of 2004, 2005 and 2006, where over 40 records were missing per landing year). The country of residence is missing for many admission records from 2013 (this value is missing for 980 records, or 0.5% of admissions taking place that year) and 2014, (this value is missing for 4000 records, or 2% of admissions taking place that year) and 2015 (missing for 5000 records, or 3% of admissions in that year).

The education variables after imputation (see Section 6.3), have over 150 missing values per landing year from 1980 to 1984; this translates as a rate of missing values per landing year of less than
0.5%. A new derive variable using Express Entry data was used to help fill some missing data for the education variables for those admitted from 2015 to 2019.

The percentage of valid responses for the occupation variables is above 99% for all landing years.

The variables Family_Status, Mother_Tongue, Official_Language, CSQ_IND and Destination_Province have most of their missing values for records with a landing year prior to 1999.

Mother_Tongue is missing for 550 records from the 2011 admissions.

The variable Official language has an increasing number of missing values;from 2016 to 2018, over 7500 per cohort have a missing value.

The year and month of death was missing for some individuals identified as deceased (Death_Indicator=1). The value “9999” was assigned to Death_Year and the value “99” was assigned to Death_Month in cases where the year and month of death were unknown.

The variables Destination_CD, Destination_CMA, and Destination_CSD have fewer missing values in the 2019 IMDB than in previous years since the Standard Geographical Classification (SGC) updated the geographical region and code in 2016.


Table 8
Quality assessment of the Integrated Permanent and Non-permanent Resident File since 1980
Table summary
This table displays the results of Quality assessment of the Integrated Permanent and Non-permanent Resident File since 1980. The information is grouped by PNRF variables (appearing as row headers), Valid responses, Blanks and Invalid responses, calculated using number and percent units of measure (appearing as column headers).
PNRF variables Valid responses Blanks Invalid responses
number percent number percent number percent
Case_ID 8,673,960 100.00% 0 0.00% 0 0.00%
Landing_age 8,668,980 99.94% 720 0.01% 4,260 0.05%
Birth_Year 8,673,905 100.00% 30 0.00% 25 0.00%
Gender 8,673,960 100.00% 0 0.00% 0 0.00%
Country_Birth 8,670,850 99.96% 3,110 0.04% 0 0.00%
Country_Citizenship 8,673,020 99.99% 940 0.01% 0 0.00%
Country_Residence 8,657,590 99.81% 16,370 0.19% 0 0.00%
Education_Qualification 8,353,750 96.31% 320,210 3.69% 0 0.00%
Level_of_Education 8,673,960 100.00% 0 0.00% 0 0.00%
Years_of_Schooling 8,624,460 99.43% 49,500 0.57% 0 0.00%
Education_Derived 8,217,210 94.73% 456,750 5.27% 0 0.00%
Landing_age_6_groups 8,673,240 99.99% 720 0.01% 0 0.00%
Landing_age_9_groups 8,673,240 99.99% 720 0.01% 0 0.00%
Occupation_CD 8,667,840 99.93% 6,120 0.07% 0 0.00%
NOC5-NOC2 8,667,840 99.93% 6,120 0.07% 0 0.00%
Skill_level_CD11 8,667,390 99.92% 6,570 0.08% 0 0.00%
Family_Status 8,671,370 99.97% 2,590 0.03% 0 0.00%
Family_Status_rollup 8,671,370 99.97% 2,590 0.03% 0 0.00%
Marital_status 8,671,200 99.97% 2,760 0.03% 0 0.00%
Marital_status_rollup 8,669,360 99.95% 4,600 0.05% 0 0.00%
Mother_Tongue 8,671,200 99.97% 2,760 0.03% 0 0.00%
Official_Language 8,634,480 99.54% 39,480 0.46% 0 0.00%
Special_Program 1,385,170 15.97% 7,288,790 84.03% 0 0.00%
CSQ_ind 8,673,730 100.00% 230 0.00% 0 0.00%
Destination_CD 8,673,580 100.00% 380 0.00% 0 0.00%
Destination_CMA 8,673,580 100.00% 380 0.00% 0 0.00%
Destination_CSD 8,673,580 100.00% 380 0.00% 0 0.00%
Destination_Province 8,673,580 100.00% 380 0.00% 0 0.00%
Permits and NPR-specific variables 2,047,724 100.00% 0 0.00% 0 0.00%
Death_Year 8,673,480 99.99% 480 0.01% 0 0.00%
Death_Month 8,673,480 99.99% 480 0.01% 0 0.00%

7.6 Quality Assessment of the Province of Residence Variable (PRCO_)

A validation of the geography variables included in the IMDB tax files was done. This section discusses how the variable Province of Residence (PRCO_) was derived and its quality.

The Province of residence (PRCO_) is based on information from tax filers when available. Missing information from the province of residence is replaced by information collected on the postal code of the mailing address either from the individual (PSCO_I), if available, otherwise from the family (PSCO_F).


Table 9
Concordance between PRCO and PSCO_
Table summary
This table displays the results of Concordance between PRCO and PSCO_. The information is grouped by PRCO (appearing as row headers), Province and Territories and First character of the postal code (PSCO) (appearing as column headers).
PRCO Province and Territories First character of the postal code (PSCO)
0 Newfoundland and Labrador A
2 Prince Edward Island B
1 Nova Scotia C
3 New Brunswick E
4 Quebec G, H, J
5 Ontario K, L, M, N ,P
6 Manitoba R
7 Saskatchewan S
8 Alberta T
9 British Columbia V
10 Northwest Territories X
11 Yukon Territories Y
12 Non-residents missing
14 Nunavut X

While the Province of residence (PRCO_) is more reliable than the Taxing province (TXPCO_), some abnormalities were observed mostly on the non-resident code in the reporting for taxation years 1989, 1993, and 1998. These may impact specific provinces.

For the 1993 tax year, IMDB_T1FF includes anomalies for the province of Manitoba with an unusual number of residents (48,130 in 1993, compared to 33,650 the tax year before, and 37,365 the tax year after). Similar changes are observed for the Northwest Territories. Additionally, 740 individuals are coded as residing in Nunavut while Nunavut was created in 1998. 725 individuals are coded as residing in multiple jurisdictions. Users can use the information from the variable PSCO_F to diminish the effect of the anomalies on their analyses that include province of residence. However, as stated above, the time are different between PSCO_ (based residence at time of filling) and PRCO_ (residence on December 31st).

Non-resident (PRCO_=12) records appear to be overestimated in the 1989 IMDB_T1FF. It includes 79,210 non-residents of Canada, with many of them having a non-permanent residency status. Users can decide to use the postal code of the mailing address (PSCO_ at the individual or family level) to derive the value of PRCO_ or remove the non-residents from their analysis.

In the 1998 IMDB_T1FF, a higher than expected number of records are assigned to Newfoundland and Labrador (PRCO_). In these cases the place of residence of the family at the time of filing is also Newfoundland based on variable PSCO_F.


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