Economic and Social Reports
Immigrant credit visibility: Access to credit over time in Canada

Release date: September 27, 2023

DOI: https://doi.org/10.25318/36280001202300900001-eng

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Abstract

Using data from the 2016 and 2019 Survey of Financial Security, this paper studies the extent to which immigrants to Canada have access to credit and whether they are credit invisible. A person is credit invisible if they do not have a credit file or sufficient credit information for a credit reporting agency to be able to calculate a credit score. A person can also be classified as having a "thin" credit file, meaning a score can be calculated but it is based on a small number of credit accounts or limited credit history. Typically, newcomers to Canada do not have a credit history in the country, and their history from their home country is not available. The study uses logistic regression to estimate the factors associated with credit invisibility, including demographic and financial characteristics and the number of years in Canada. This study found that newly landed immigrants who had been in Canada for less than two years were less visible (14.8%) than Canadian-born families (7.5%). The difference in visibility disappeared after the first two years; that is, immigrants quickly became visible, and, over time, immigrant families that had been in Canada for two to four years were actually 3.15 percentage points more visible than comparable Canadian-born families. Notably, the difference in visibility in the first two years also disappeared once financial and demographic characteristics were taken into account. However, much of new immigrants’ visibility was due to access to credit cards and not higher credit limit instruments such as mortgages, vehicle loans and student loans.

Immigrants are generally eager to build credit and to obtain credit visibility, but they are often not able to access all credit products in a timely manner. New immigrants tend to quickly obtain a cell phone account and acquire a low-limit or secured credit card. This creates a credit file yet provides insufficient credit history. Thus, their ability to be approved for larger credit amounts for products such as an automobile loan or a mortgage remains impeded, as would be the case for a Canadian-born individual new to credit with only a cell phone or low-limit secured credit card. These larger credit products can have a significant impact on an immigrant’s daily life and ability to create wealth. To minimize credit invisibility and improve a newly landed immigrant’s ability to access credit, credit bureaus could capture data from new, non-traditional sources, such as rent, phone and utility payments, on these individuals that inform the Canadian credit scores of newly arrived immigrants and inform them earlier.

Keywords: credit invisibility, Survey of Financial Security, credit history, credit score, thin consumer file, logistic regression

Authors

Jesse Tweedle and Amélie Lafrance-Cooke are with the Economic Analysis Division at Statistics Canada. Rebecca Oakes and Attila Imecs are with Equifax.

Acknowledgments

This study was prepared in collaboration with Equifax Canada. The authors would like to thank Danny Leung, René Morissette and Haozhen Zhang for their very helpful comments on the paper.

Introduction

Credit is a key element to modern economic life in Canada. If a person wants to rent a car, a credit card is typically required. If a person wants to attend postsecondary school, they may require a student loan. If a person wants to buy a house, they may need a mortgage. Credit provides investment opportunities such as building home equity with a mortgage, increasing human capital by investing in an education with student loans, and accessing transportation to places of employment by buying a vehicle with an automobile loan. Credit also provides consumption-smoothing opportunities for significant purchases, such as a washing machine or a computer for a child’s schooling, where the buyer can pay off the purchase over time. A person requires credit from a financial institution or lender to access these opportunities, and financial institutions and lenders tend to use a person’s credit history as a major factor when evaluating their credit worthiness. According to Equifax Canada, a person who does not have a credit file (no file) or for whom the credit information on file is insufficient to generate credit scores (also known as a thin file) is defined as credit invisible (Equifax 2022).

Millions of Canadians are in this situation (Equifax 2022). Immigrants may be at greater risk of lacking access to credit markets since credit information is, for the most part, not shared across international borders.Note  Using data from Statistics Canada’s 2016 and 2019 Survey of Financial SecurityNote  (SFS), this paper studies the extent to which immigrants are credit invisible, what characteristics mitigate or exacerbate credit invisibility, and how those relationships change for different credit products as immigrants build credit histories in Canada. It contributes to the literature by providing a comprehensive picture of credit invisibility based on the number of years since arrival in Canada.

Individuals are defined as credit invisible in this paper if they do not use any credit products at the time of the survey. That is, it is assumed that individuals with no credit products do not have enough information to generate a credit history, which is a proxy for the no file industry definition.Note  Because financial institutions and lenders use credit histories to inform their credit decisions, newly landed immigrants (defined here as those who have lived in Canada for less than two years) are likely to be disadvantaged when applying for credit and thus more likely to be credit invisible than people born in Canada. However, financial institutions and lenders may also use information on employment, income and assets to inform credit decisions,Note  which may be different between recent immigrants and people born in Canada. As a result, the SFS, which includes the necessary data on credit usage, such as the money owing on mortgages, vehicles, credit cards, student loans and other debts, as well as the value of all major financial and non-financial assets, and demographic characteristics, provides the best platform from which to study the relationship between immigrant status and credit invisibility, and how a lack of traditional credit history may impact this.

After these characteristics are accounted for, immigrants have similar credit visibility as individuals born in Canada do. However, immigrants do differ in the mix of credit products they use—recent immigrants are more likely to have lower credit amount products (such as credit cards) than Canadian-born individuals, and less likely to have larger credit amount products (such as student loans or mortgages). This credit mix changes over time, with immigrants who have been in Canada for 10 to 39 years being more likely to have mortgages than people born in Canada. This suggests that credit invisibility has an effect on immigrants’ lives, because recent immigrants have less access to larger credit amount products and therefore the housing market and longer-term equity investment opportunities.

Research on immigrant credit access overlaps with two main areas of focus. First, credit access is a part of the larger issue of financial exclusion, which is defined as the exclusion of an individual or individuals from the formal banking system. For example, payday loan usage is an indicator of financial exclusion and is one method of income smoothing used by credit-invisible households (Islam and Simpson 2017; Melzer 2011). Financial exclusion may have far-reaching effects on households: Claessens (2006) and Gloukoviezoff (2007) have shown a tight relationship between financial and social exclusion. Second, financial exclusion makes it harder for an immigrant to access higher education, such as college or university (Solis 2013), and this may lead to diverging paths in income, career and social status.

Studies have found that certain groups are more likely to be financially excluded. For example, see PERC Canada (2019) for evidence of credit invisibility among immigrants and Indigenous people in Canada, Lamb (2015) for a study of Indigenous financial statistics in Canada, Sanchez-Moyano and Shrimali (2021) for racialized groups in the United States, and Deku et al. (2016) for evidence in the United Kingdom and a broad review of the literature. One possible improvement in providing access to financially excluded groups is capturing data outside the formal credit system that are considered non-traditional, such as including rent, phone and utility payments (Equifax 2022; Brevoort et al. 2015).

Another important aspect to consider is that individuals with no credit history may not always benefit from high-interest access to build their credit. Islam and Simpson (2017) found that both payday lending in Canada and microcredit in Bangladesh are frequently used by financially excluded consumers who have common demographic characteristics (e.g., low income, low education, low financial literacy). In other words, providing high-interest loans outside the formal financial system does not necessarily lead to credit visibility. Low income, low wealth and financial exclusion are all co-determined, and credit invisibility is an important facet of this relationship.

Immigrants face barriers when integrating into their new home country, and barriers to accessing the financial system can be considerable. For example, immigrants have lower returns to education.Note  This is partly due to a lack of credential recognition (see Brücker et al. [2021] for a summary and recent results). In addition, recent immigrants’ difficulties in the labour market are also related to the fact that Canadian employers do not appear to value foreign work experience (Picot and Sweetman 2005). The formal credit system can also be difficult to access for immigrants since it relies on a local credit history.Note  However, existing evidence based on the SFS in Canada (Morissette 2019) suggests wealth and debt characteristics are not different for most immigrant families over time compared with their Canadian-born counterparts after accounting for age and education. For instance, Morissette (2019) found that immigrant families do not use payday loans more than Canadian-born families and are not refused credit cards more than Canadian-born families, although they do pay a lower percentage of their credit card debt each billing cycle. However, Morissette (2019) focused on immigrant wealth over time and not exclusively on the first or second year after landing in Canada, or specifically on credit invisibility or financial exclusion.

The results in this study are in line with these findings. Although immigrants have different credit behaviours, their overall credit visibility is not different from their Canadian-born peers over time, once demographic characteristics are taken into account.

The paper is organized as follows: Section 2 presents the data and definitions. Section 3 presents the model and results. Section 4 concludes.

Data sources and definitions

The data source for this study is the SFS of 2016 and 2019. The purpose of the survey is to collect information from a sample of Canadian households on their assets, debts, employment, income and education. The SFS provides a comprehensive picture of the financial health of Canadians. Information is collected on the value of all major financial and non-financial assets and on the money owing on mortgages, vehicles, credit cards, student loans and other debts.

Credit and debt data on the SFS are reported at the economic family level, while demographic characteristics are reported at the individual level. An economic family is defined in this paper as “credit invisible” if (1) they answer “No” to all of the questions that ask “Do you have [credit product X]?” or (2) they provide a zero value as a response to all of the questions that ask “What is the outstanding balance [on credit product X]?”Note  The first condition is critical to define credit invisibility since many renters and prime-aged homeowners who always pay their credit cards will not have an outstanding balance on this credit product.

Since the demographic characteristics are reported for individuals, the characteristics of the major income earner (MIE)Note  represent the characteristics of the economic family. This includes employment, education, age and immigrant status (reported as the year they became a landed immigrant in Canada). Approximately 29% of economic families in Canada had an immigrant as the MIE (Table 1). For the purposes of the paper, immigrants who have been in Canada for less than two years will be referred to as “newly landed immigrants,” while any immigrant in Canada for less than five years will be referred to as a “recent immigrant.” This approach is consistent with the literature.

Based on data from the 2016 and 2019 SFS, about 1.1 million economic families, or 7.2%, were credit invisible (Table 1). Approximately 26% of credit-invisible economic families were immigrants. Among immigrants, 6.4% were credit invisible, slightly lower than the share among non-immigrant families (7.5%). However, immigrant families that had been in Canada for less than two years were disproportionally invisible: 14.8% compared with the 7.5% invisibility rate of Canadian-born people. The rate of invisibility quickly decreased with the number of years the family had been in Canada, finally increasing again once the family had been in Canada for 60 years or more. Since the last survey was in 2019, this means these families had been in Canada since 1959 at the latest. This result may be driven by age, whereby families that have been in Canada for 60 years or more tend to be older and may not need certain credit products.Note  It is important to note that immigrants who have been in Canada for less than 10 years will skew to younger ages, while the Canadian-born population includes all ages. In other words, it is not a completely equal comparison.


Table 1
Distribution of credit-visible and credit-invisible economic families based on years in Canada, 2016 and 2019 pooled
Table summary
This table displays the results of Distribution of credit-visible and credit-invisible economic families based on years in Canada. The information is grouped by Years in Canada (appearing as row headers), Credit status, Visible and Invisible, calculated using number and percent units of measure (appearing as column headers).
Years in Canada Credit status
Visible Invisible Visible Invisible
number percent
Canadian-born 10,204,567 827,944 92.5 7.5
Less than 2 years 236,710 41,156 85.2 14.8
2 to 4 years 405,747 26,330 93.9 6.1
5 to 9 years 569,357 27,941 95.3 4.7
10 to 19 years 912,758 32,620 96.5 3.5
20 to 39 years 1,191,342 68,709 94.5 5.5
40 to 59 years 744,607 59,386 92.6 7.4
60 years or more 229,119 37,501 85.9 14.1
Total 14,494,205 1,121,586 92.8 7.2

Although newly landed immigrants were much more likely to be credit invisible, they were also different in many other financial and economic dimensions, which are factors that may affect credit visibility. Tables 2 and 3 display the rate of employment, median assets, median income and median age for immigrants of different tenures, with all families represented in Table 2 and families with MIEs aged 25 to 44 years in Table 3. This discussion will focus on Table 3 to better compare families of similar ages.


Table 2
Median characteristics of all major income earners, selected variables, based on years in Canada, pooled across years
Table summary
This table displays the results of Median characteristics of all major income earners. The information is grouped by Years in Canada (appearing as row headers), Employment rate, Age of major income earner, Total assets and Total income of major income earner, calculated using percent, years and dollars units of measure (appearing as column headers).
Years in Canada Employment rate Age of major income earner Total assets Total income of major income earner
percent years dollars
Canadian-born 57.80 53 339,000 58,400
Less than 2 years 58.20 30 9,000 18,200
2 to 4 years 61.00 30 20,500 29,900
5 to 9 years 74.80 38 117,100 56,200
10 to 19 years 77.70 43 416,200 66,400
20 to 39 years 67.40 53 614,500 69,300
40 to 59 years 35.50 68 707,000 56,700
60 years or more 12.70 76 622,000 43,900
Total 58.60 51 351,500 57,700

Table 3
Median characteristics of all major income earners aged 25 to 44, selected variables, based on years in Canada, pooled across years
Table summary
This table displays the results of Median characteristics of all major income earners aged 25 to 44. The information is grouped by Years in Canada (appearing as row headers), Employment rate, Age of major income earner, Total assets and Total income of major income earner, calculated using percent, years and dollars units of measure (appearing as column headers).
Years in Canada Employment rate Age of major income earner Total assets Total income of major income earner
percent years dollars
Canadian-born 81.0 45 400,600 79,200
Less than 2 years 54.7 41 46,200 26,600
2 to 4 years 72.5 41 59,500 49,000
5 to 9 years 84.5 41 171,000 60,500
10 to 19 years 81.3 44 462,000 74,600
20 to 39 years 82.8 47 708,000 78,700
40 to 59 years 86.2 51 715,900 96,200
Total 81.0 45 414,000 76,400

Immigrant MIEs aged 25 to 44 were just as likely to be employed as Canadian-born people unless the immigrant family had been in Canada for four years or less. Immigrant MIEs aged 25 to 44 who had been in Canada for less than two years had an employment rate of 54.7%, almost 27 percentage points lower than their Canadian-born counterparts. Note that immigrants in Canada for four years or less have a higher chance of currently being students and enrolled in higher-level education, limiting their ability to seek employment.

The median assets and income of recent immigrant MIEs aged 25 to 44 were substantially lower than those of the average Canadian-born person. Nonetheless, immigrant families had median assets that surpassed those of similar Canadian-born families after they had been in Canada for 10 to 19 years. Similarly, this occurred for median income after 20 to 39 years in Canada.


Table 4
Distribution of education of all major income earners across years in Canada categories, pooled across years
Table summary
This table displays the results of Distribution of education of all major income earners across years in Canada categories. The information is grouped by Years in Canada (appearing as row headers), High school diploma or lower, College or trade diploma and University degree or diploma, calculated using percent units of measure (appearing as column headers).
Years in Canada High school diploma or lower College or trade diploma University degree or diplomaTable 4 Note 1
percent
Canadian-born 40.1 31.5 28.4
Less than 2 years 26.1 12.9 61.0
2 to 4 years 28.4 17.0 54.6
5 to 9 years 18.3 23.8 57.9
10 to 19 years 18.8 21.7 59.5
20 to 39 years 35.2 25.3 39.6
40 to 59 years 41.7 25.0 33.3
60 years or more 57.6 22.1 20.3
Total 37.4 28.9 33.7

Table 5
Distribution of education of all major income earners aged 25 to 44 across years in Canada categories, pooled across years
Table summary
This table displays the results of Distribution of education of all major income earners aged 25 to 44 across years in Canada categories. The information is grouped by Years in Canada (appearing as row headers), High school diploma or lower, College or trade diploma and University degree or diploma, calculated using percent units of measure (appearing as column headers).
Years in Canada High school diploma or lower College or trade diploma University degree or diplomaTable 5 Note 1
percent
Canadian-born 29.6 37.9 32.5
Less than 2 years 16.6 21.6 61.8
2 to 4 years 15.1 20.2 64.6
5 to 9 years 12.7 26.4 61.0
10 to 19 years 14.7 22.2 63.1
20 to 39 years 33.8 26.0 40.2
40 to 59 years 25.4 36.1 38.5
Total 26.8 33.6 39.6

Tables 4 and 5 compare education levels for immigrants of different tenures for all families (Table 4) and families with MIEs aged 25 to 44 (Table 5). With the exception of immigrants who had been in Canada for 60 years or more, immigrants were more likely to have university degrees or diplomas than Canadian-born people. The difference was very large for immigrants who had been in Canada for zero to nine years; between 61% and 65% of immigrant MIEs aged 25 to 44 had a university education versus 32.5% of Canadian-born people. Solis (2013) found that education is correlated with credit visibility, suggesting that highly educated immigrants may be more visible than Canadian-born people. This is explored further in the next section with multivariate analysis.


Table 6
Use of credit or debt products by economic families based on years in Canada, all years pooled
Table summary
This table displays the results of Use of credit or debt products by economic families based on years in Canada. The information is grouped by Years in Canada (appearing as row headers), Mortgages, Credit cards, Student loans, Vehicle loans, Home equity
lines of credit and Other lines
of credit, calculated using percent units of measure (appearing as column headers).
Years in Canada Mortgages Credit cards Student loans Vehicle loans Home equity
lines of credit
Other lines
of credit
percent
Canadian-born 35.1 87.4 11.3 32.4 10.3 51.8
Less than 2 years 4.7 84.8 7.3 9.4 0.2 7.8
2 to 4 years 13.5 93.2 12.2 18.3 0.8 17.6
5 to 9 years 37.1 93.7 23.5 33.5 3.9 41.5
10 to 19 years 50.6 94.1 24.8 32.6 8.4 51.9
20 to 39 years 46.3 91.3 16.7 25.0 13.1 57.1
40 to 59 years 26.6 90.5 4.8 15.3 12.3 51.9
60 years or more 9.7 84.3 0.9 11.1 8.8 43.5
Total 35.0 88.6 12.4 29.8 9.8 50.0

Table 7
Use of credit or debt products by economic families with major income earner aged 25 to 44 based on years in Canada, all years pooled
Table summary
This table displays the results of Use of credit or debt products by economic families with major income earner aged 25 to 44 based on years in Canada. The information is grouped by Years in Canada (appearing as row headers), Mortgages, Credit cards, Student loans, Vehicle loans, Home equity
lines of credit and Other lines
of credit, calculated using percent units of measure (appearing as column headers).
Years in Canada Mortgages Credit cards Student loans Vehicle loans Home equity
lines of credit
Other lines
of credit
percent
Canadian-born 54.6 88.1 11.5 42.9 14.4 59.2
Less than 2 years 12.3 93.3 8.0 14.5 0.4 12.0
2 to 4 years 24.3 90.8 15.1 25.7 1.8 23.6
5 to 9 years 41.1 94.0 26.4 37.0 4.5 46.2
10 to 19 years 58.0 95.9 20.4 34.9 9.5 57.4
20 to 39 years 59.4 92.9 17.0 30.4 15.1 62.3
40 to 59 years 52.0 91.7 12.3 29.5 25.8 62.2
Total 53.3 90.0 14.0 39.3 13.1 57.1

Although immigrants may be more credit visible (after being in Canada for a few years) or less credit visible (after either just landing in Canada or having been in Canada for 60 years or more) relative to the Canadian-born population, they may not have access to the same credit products that Canadian-born families do. In tables 6 and 7, the overall credit access pattern is decomposed into several credit products: mortgages, credit cards, student loans, vehicles, home equity lines of credit (HELOCs) and other lines of credit. This shows the proportion of economic families that reported using the credit product. Table 6 displays statistics for all families, while Table 7 displays statistics for families with an MIE aged 25 to 44. For instance, 35.0% of all economic families had a mortgage during the survey years 2016 and 2019, while 53.3% of economic families with an MIE aged 25 to 44 had a mortgage.

Few newly landed immigrants with an MIE aged 25 to 44 had mortgages; 12.3% of immigrants who had been in Canada for two years or less had a mortgage (Table 7). However, immigrants in this age group who had been in Canada for 10 to 39 years seem more likely to have mortgages than Canadian-born families. For instance, 58.0% of immigrants who had been in Canada for 10 to 19 years had a mortgage, compared with 54.6% of Canadian-born families.

Regardless of the number of years in Canada, immigrants were more likely to report using credit cards than any other debt product. Among immigrants with an MIE aged 25 to 44, the rate of credit card usage was consistently higher than for Canadian-born families, suggesting that immigrants either come to Canada with foreign credit cards or get access to credit cards quickly. Newly landed immigrant families had a particularly high credit card usage of 93.3%.

The student loan rate was lower for immigrants than Canadian-born families but rose above that after two to four years living in Canada before dipping down again. This may be due to immigration policies or economic conditions at the time of immigration. For instance, immigrants are eligible for student loans from Canadian financial institutions without a guarantor after receiving permanent residency; otherwise, an international student is likely to have either family financing or student loans from their home country. As for economic conditions, a family that immigrated to Canada 40 years ago or more was less likely to have children young enough to have student loans since their children either went to university during a period when tuition was lower than in recent decades or grew up when university education was less common. In addition, immigrant families that had been in Canada for 60 years or more may have no longer had children living in the home.

Immigrant families with an MIE aged 25 to 44 were less likely than Canadian-born families to have vehicle loans; the rate of vehicle loan usage peaked at 37.0% after five to nine years in Canada, while 42.9% of Canadian-born families had vehicle loans. HELOCs were much less common among immigrants until they had lived in Canada for 20 to 59 years. Similarly, immigrants were less likely to have lines of credit than Canadian-born families until they had lived in Canada at least 10 years.

The raw patterns of credit product usage are certainly different for immigrant families than Canadian-born families, and these could be caused by different demographic, social or economic characteristics compounded by limited credit history, rather than their immigrant status alone, as shown in tables 2, 3, 4 and 5. The next section uses a regression to account for these characteristics.

Model and results

A logistic regression model is used to study the relationship between credit invisibility and demographic characteristics:

log[ p( Y i )/( 1p( Y i ) ) ]= X i β+ε MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGSbGaam4BaiaadEgadaWadaWdaeaapeGaamiCamaabmaapaqa a8qacaWGzbWdamaaBaaaleaapeGaamyAaaWdaeqaaaGcpeGaayjkai aawMcaaiaac+cadaqadaWdaeaapeGaaGymaiabgkHiTiaadchadaqa daWdaeaapeGaamywa8aadaWgaaWcbaWdbiaadMgaa8aabeaaaOWdbi aawIcacaGLPaaaaiaawIcacaGLPaaaaiaawUfacaGLDbaacqGH9aqp caWGybWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabek7aIjabgU caRiabew7aLbaa@5025@

Where Yi is the indicator of credit invisibility (1 if the economic family is credit invisible; 0 otherwise), Xi is a vector of demographic characteristics and ε MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaH1oqzaaa@37BE@ is an error term. The coefficients β MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHYoGyaaa@37B7@ are estimated via maximum likelihood.

The demographic characteristics used for X are variables expected to be (inversely) correlated with immigrant status and (inversely) correlated with credit invisibility to tease out the underlying relationship between immigration and credit invisibility. For instance, assets of the economic family were included because having assets is beneficial to access credit and the median recent immigrant family had much lower assets ($9,000) than the median Canadian-born family ($339,000). Therefore, recent immigrants may have had less access to credit because of a lack of assets and not necessarily because of their immigrant status.

The variables used in the model are survey year, province, language spoken well enough to conduct a conversation, household size, age, education, employment status, income, assets and years in Canada. Survey year, province, language, education and employment status are categorical variables. Household size is binned into categories one, two and three or more. Age is binned into the following categories: 16 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65 and older. Income and assets are binned into quintiles.

Finally, years in Canada is the difference between the survey year and the year the economic family landed in Canada, then binned into Canadian-born, less than 2 years, 2 to 4 years, 5 to 9 years, 10 to 19 years, 20 to 39 years, 40 to 59 years, and 60 years or more.

Main results for overall credit visibility

Three logistic regressions were estimated. The first specification aimed to determine whether differences in credit invisibility between recent immigrant families and Canadian-born families of similar size are simply caused by an age effect. In other words, it aimed to determine whether higher credit invisibility among recent immigrant families reflects the fact that they are younger and thus have had less time to develop or have experience with credit history. In the second specification, education was added to recognize that recent immigrant families are more highly educated than Canadian-born families of similar age. The third specification includes additional indicators related to credit invisibility, such as income, assets, employment, language, and province of residence.


Table 8
Main results for overall credit visibility
Table summary
This table displays the results of Main results for overall credit visibility. The information is grouped by Variables (appearing as row headers), Model 1
Age only, Model 2
Age and education and Model 3
Full model, calculated using coefficient, standard
error and number units of measure (appearing as column headers).
Variables Model 1
Age only
Model 2
Age and education
Model 3
Full model
coefficient coefficient standard
error
coefficient standard
error
Survey year (reference: 2016)
2019 -0.247Note ** -0.21 (0.0794)Note ** -0.129 (0.0868)
Language (reference: English, French or both)
Neither English nor French Note ...: not applicable .... Note ...: not applicable 1.064Note ** (0.265)
Household size (reference: one person)
Two people -1.495Note ** -1.453Note ** (0.0928) -0.358Note ** (0.110)
Three or more people -1.958Note ** -1.939Note ** (0.128) -0.441Note ** (0.156)
Age of major income earner (reference: 16 to 24 years)
25 to 34 years -0.814Note ** -0.239 (0.230) 0.0728 (0.213)
35 to 44 years -0.316 0.244 (0.234) 0.852Note ** (0.224)
45 to 54 years -0.249 0.156 (0.223) 0.873Note ** (0.209)
55 to 64 years -0.172 0.121 (0.222) 0.872Note ** (0.205)
65 years and older 0.0136 0.197 (0.213) 0.665Note ** (0.202)
Education of major income earner (reference: college or trade diploma)
High school diploma or lower Note ...: not applicable 0.957Note ** (0.100) 0.571Note ** (0.107)
University degree or diploma Note ...: not applicable -0.838Note ** (0.161) -0.491Note ** (0.173)
Years in Canada (reference: Canadian-born)
Less than 2 years 0.73Note * 1.159Note ** (0.338) 0.316 (0.307)
2 to 4 years -0.240 0.0262 (0.344) -0.733Note * (0.331)
5 to 9 years -0.0538 0.368 (0.330) -0.479 (0.325)
10 to 19 years -0.310 0.0763 (0.243) -0.436Table 8 Note  (0.261)
20 to 39 years 0.0211 0.165 (0.156) -0.0764 (0.187)
40 to 59 years -0.133 -0.0123 (0.170) 0.147 (0.201)
60 years or more 0.299 0.262 (0.216) 0.844Note ** (0.269)
Employed (reference: not employed)
Employed Note ...: not applicable Note ...: not applicable Note ...: not applicable -0.744Note ** (0.110)
Family income quintile (reference: bottom quintile)
Second Note ...: not applicable Note ...: not applicable Note ...: not applicable -0.827Note ** (0.118)
Third Note ...: not applicable Note ...: not applicable Note ...: not applicable -1.384Note ** (0.178)
Fourth Note ...: not applicable Note ...: not applicable Note ...: not applicable -2.039Note ** (0.269)
Fifth Note ...: not applicable Note ...: not applicable Note ...: not applicable -1.842Note ** (0.411)
Assets quintile (reference: bottom quintile)
Second Note ...: not applicable Note ...: not applicable Note ...: not applicable -1.197Note ** (0.118)
Third Note ...: not applicable Note ...: not applicable Note ...: not applicable -2.235Note ** (0.163)
Fourth Note ...: not applicable Note ...: not applicable Note ...: not applicable -2.499Note ** (0.228)
Fifth Note ...: not applicable Note ...: not applicable Note ...: not applicable -2.526Note ** (0.284)
number
Observations 22,821 22,821 Note ...: not applicable 22,821 Note ...: not applicable
Province fixed effects No No Note ...: not applicable Yes Note ...: not applicable

The size of the household was negatively correlated with credit invisibility, and this finding occurred in all model specifications. Households that had three or more people were associated with a lower likelihood of being credit invisible.

In the simple model (1), age was negatively associated with credit invisibility, but this relationship changed when more covariates were added to the model. For instance, in the full model (3), a family with an older MIE was more likely to be credit invisible. This result may be due to the inclusion of income, assets and employment. Age, by itself, was negatively correlated with credit invisibility, but holding income and other economic variables constant in model (3), age was revealed to be positively correlated with credit invisibility.

More education was negatively correlated with credit invisibility. Postsecondary degrees may require individuals to take out student loans, and this may explain the negative coefficient on having a university degree or diploma. In contrast, a person with a high school diploma or less education was more likely to be credit invisible than someone with a college or trade diploma, indicating that education does positively affect credit access.

Income and assets were negatively correlated with credit invisibility; a family with income or assets in a higher quintile had a lower likelihood of being credit invisible. In other words, the coefficient on the third quintile of income was more negative than the coefficient on the second quintile of income, meaning that moving from the second to the third income quintile was associated with a lower likelihood of the family being credit invisible. Both higher income and assets make it easier for a family to acquire credit, and having more assets may be a result of using credit products, since it can be difficult to acquire high-value assets such as vehicles and housing without credit. For similar reasons, being employed was negatively correlated with credit invisibility. Indeed, being employed makes it more likely to acquire credit from financial institutions.

The fact that immigrant families had lower income and lower assets than Canadian-born families, combined with the positive correlation between credit visibility and both income and assets, can help explain the apparent credit invisibility of newly landed immigrant families. However, this shows a clear correlation and not causation.

Two important demographic characteristics for immigrants are language and the number of years the family has lived in Canada. Notably, families that do not speak English or French were much more likely to be credit invisible than families that speak English, French or both languages.

All years in Canada coefficients in the regression were compared with the base case of Canadian-born families. In models (1) and (2), newly landed immigrants showed a positive and statistically significant likelihood of being more invisible; this is consistent with the idea that it takes time to start up life in a new country and acquire credit. Moreover, accounting for the age and education of the MIE did not reduce the probability of recent immigrants being credit invisible. However, in model (3), while the coefficient for newly landed immigrants remained positive, it was no longer statistically significant. In other words, language, income and assets are likely also important factors when it comes to credit invisibility.

Immigrants who have lived in Canada for two to four years had a negative and statistically significant likelihood of being credit invisible; that is, they were more visible than Canadian-born families with similar characteristics (Table 8, full model). Between 5 and 39 years of living in Canada, immigrants had a statistically insignificant likelihood of being more credit invisible. Immigrant families that had been in Canada for 40 years or more were more likely than similar Canadian-born families to be credit invisible, particularly those that had been in Canada for 60 years or more, since the coefficient for this group was statistically significant. As mentioned above, this means the family had immigrated before 1959 at least (60 years before the last survey year of 2019), so there is likely a generational effect, even though the age of the MIE and the income and assets of the economic family were also taken into account. Potentially, they may not have had a need for credit and thus not had any credit products.


Table 9
Average marginal effects of credit invisibility based on years in Canada
Table summary
This table displays the results of Average marginal effects of credit invisibility based on years in Canada. The information is grouped by Variables (appearing as row headers), Marginal effect and Standard error, calculated using number units of measure (appearing as column headers).
Variables Marginal effect Standard error
Years in Canada (reference: Canadian-born)
Less than 2 years 0.0181 (0.0188)
2 to 4 years -0.0315Note ** (0.0115)
5 to 9 years -0.0221Table 9 Note  (0.0132)
10 to 19 years -0.0204Table 9 Note  (0.0109)
20 to 39 years -0.00395 (0.00950)
40 to 59 years 0.00803 (0.0114)
60 years or more 0.0547Note ** (0.0206)
number
Observations 22,821 Note ...: not applicable

To further quantify the extent to which recent immigrant families are more likely to be credit invisible than Canadian-born families, marginal effects of the impact of years in Canada on the probability of being credit invisible are presented in Table 9, based on model (3) from Table 8. Although not statistically significant, all else being equal, being in Canada for less than two years led to a probability of being credit invisible that was 1.8 percentage points higher than for Canadian-born families. In contrast, an immigrant family that had been in Canada for two to four years was 3.15 percentage points less likely to be credit invisible.

In summary, newly landed immigrants were more likely to be credit invisible, but other factors, such as language, income and assets, were also important in explaining credit invisibility. For instance, recent immigrants who do not speak English or French may face unique difficulties in requesting credit products. However, although they may have had difficulty acquiring credit when arriving in Canada, immigrants seemed to be able to quickly acquire credit (relative to Canadian-born families). After two years in Canada, they had lower rates of credit invisibility compared with Canadian-born families.

Results for different credit products

Although a person may have access to credit, that does not necessarily mean they have access to the credit products they need or the amount of credit they need. In this section, immigrants’ access to the following individual credit products is investigated: credit cards, lines of credit, mortgages, HELOCs and student loans.

Large loan amounts and long loan terms increase the potential risk to the financial institution that extends the loan. Thus, financial institutions naturally require more information to provide these types of loans. Because of the information required for large loans, an immigrant may be able to acquire credit through low-limit, high interest rate credit cards, but may have difficulty accessing auto loans, student loans, HELOCs or mortgages. Although this immigrant would be visible because of their credit card and would be able to build credit slowly, they may not have access to the most effective credit instruments that exist or at the best terms, which may hold back their financial or social development (Claessens 2006; Gloukoviezoff 2007).

These patterns are studied via the same methods, using the full model of overall credit invisibility, but with the indicator of credit invisibility replaced by an indicator of usage of each individual credit product. Table 10 shows the results from the logistic regressions across all products, and these regressions include all control variables from the main model (3) in Table 8. For the full set of results, see the Appendix Table A.1.

In general, newly landed immigrants and immigrants who had been in Canada for 60 years or more were less likely to have any credit products than Canadian-born households (Table 10). Newly landed immigrant households used mortgages at a significantly lower rate than Canadian-born households. Indeed, immigrants who had been in Canada for less than two years were 20.9 percentage points less likely than Canadian-born families to have a mortgage (Table 10). In contrast, immigrant households that arrived 10 to 39 years ago (relative to the survey year) were more likely to have mortgages than Canadian-born people. Immigrants that have been in Canada for 60 years or more may no longer require a mortgage.

Immigrants who had been in Canada for 2 to 19 years used credit cards at a higher rate than Canadian-born people, and then at similar rates after the household had been in Canada for 20 to 59 years. Immigrants who had been in Canada for two to four years were 7.8 percentage points more likely to have a credit card (Table 10). Recent immigrant households were less likely to have student loans after being in Canada for 0 to 4 years, but more likely to after being in Canada for 10 to 39 years. The latter result may be due to first-generation immigrant households that had children at home who were in college or university.

All immigrants, regardless of years in Canada, were less likely than Canadian-born families to have lines of credit (other than HELOCs). This effect was particularly negative for newly landed immigrants, who were 30.6 percentage points less likely to have such credit.

Taken as a whole, it appears that immigrants start by acquiring credit cards at a higher rate than Canadian-born people (while not acquiring mortgages, student loans, vehicles, credit lines or HELOCs), then slowly build credit through credit cards to acquire mortgages and student loans at greater rates than Canadian-born people with similar characteristics.


Table 10
Average marginal effects of credit products based on years in Canada
Table summary
This table displays the results of Average marginal effects of credit products based on years in Canada. The information is grouped by Variables (appearing as row headers), Model 1
Mortgages, Model 2
Credit cards, Model 3
Student loans, Model 4
Vehicle loans, Model 5
Home equity lines of credit and Model 6
Other lines of credit, calculated using number units of measure (appearing as column headers).
Variables Model 1
Mortgages
Model 2
Credit cards
Model 3
Student loans
Model 4
Vehicle loans
Model 5
Home equity lines of credit
Model 6
Other lines of credit
Years in Canada (reference: Canadian-born)
Less than 2 years
Coefficient -0.209Note ** 0.0286 -0.0997Note ** -0.144Note ** -0.094Note ** -0.306Note **
Standard error (0.0318) (0.0193) (0.00912) (0.0340) (0.00806) (0.0495)
2 to 4 years
Coefficient -0.104Note ** 0.0775Note ** -0.0745Note ** -0.0589Note * -0.081Note ** -0.192Note **
Standard error (0.0238) (0.0114) (0.0102) (0.0279) (0.0109) (0.0307)
5 to 9 years
Coefficient -0.0130 0.0597Note ** 0.00730 -0.00506 -0.0554Note ** -0.0572Note *
Standard error (0.0187) (0.0127) (0.0132) (0.0228) (0.0114) (0.0270)
10 to 19 years
Coefficient 0.0541Note ** 0.0432Note ** 0.0356Note ** -0.0285Table 10 Note  -0.0343Note ** -0.0351Table 10 Note 
Standard error (0.0155) (0.0117) (0.0124) (0.0169) (0.00948) (0.0197)
20 to 39 years
Coefficient 0.0334Note ** 0.00596 0.0472Note ** -0.0808Note ** -0.0122 -0.0118
Standard error (0.0116) (0.0122) (0.0127) (0.0140) (0.00840) (0.0160)
40 to 59 years
Coefficient 0.00909 -0.00632 0.00163 -0.0997Note ** -0.000521 -0.0417Note *
Standard error (0.0133) (0.0143) (0.0188) (0.0159) (0.0110) (0.0174)
60 years or more
Coefficient -0.0768Note ** -0.0572Note * -0.0434 -0.0752Note ** -0.000737 -0.0645Note *
Standard error (0.0246) (0.0246) (0.0489) (0.0292) (0.0173) (0.0258)
number
Observations 22,821 22,821 22,821 22,821 22,821 22,821

Conclusion

Using data from the 2016 and 2019 SFS, this paper examined the extent to which immigrants to Canada are credit invisible. Newly landed immigrant families were less visible than Canadian-born families, but this difference can be accounted for by characteristics such as language, education, income and wealth. Furthermore, immigrants quickly became visible, and, over time, immigrant families that had been in Canada for two to four years were actually 3.15 percentage points more visible than comparable Canadian-born families. However, much of new immigrants’ visibility and their greater visibility than Canadian-born families two to four years after landing was due to greater credit card usage and not access to mortgages, student loans and vehicle loans that could speed their integration into Canada and improve their well-being.Note 

Future work could delve deeper into immigrants’ access to credit by studying not only the types of products being used, but the value of debt being carried. This would provide supplementary information on the extent of immigrants’ credit disadvantage. It could also examine how it may be possible to improve the access to credit of immigrants by using data outside the formal credit system, such as rent, phone, utility payments (Equifax 2022; Brevoort et al. 2015), and asset and income information, to inform their credit scores and inform them earlier.

Appendix


Appendix Table A.1
Logistic regression results for having various credit products
Table summary
This table displays the results of Logistic regression results for having various credit products. The information is grouped by Variables (appearing as row headers), Model 1 - mortgages, Model 2 - credit cards and Model 3 - student loans, calculated using coefficient, standard error and number units of measure (appearing as column headers).
Variables Model 1 - mortgages Model 2 - credit cards Model 3 - student loans
coefficient standard error coefficient standard error coefficient standard error
Survey year (reference: 2016)
2019 -0.081Appendix Table A.1 Note  (0.0447) 0.205Note ** (0.0686) 0.0331 (0.0620)
Language (reference: English, French or both)
Neither English nor French 0.376Appendix Table A.1 Note  (0.226) -0.786Note ** (0.260) 0.917Note ** (0.338)
Household size (reference: one person)
Two people 0.0754 (0.0661) 0.296Note ** (0.0869) 0.663Note ** (0.0923)
Three or more people 0.585Note ** (0.0744) 0.120 (0.110) 1.397Note ** (0.0956)
Age of major income earner (reference: 16 to 24 years)
25 to 34 years 0.558Note ** (0.190) -0.559Note ** (0.169) -0.299Appendix Table A.1 Note  (0.156)
35 to 44 years 0.775Note ** (0.189) -1.139Note ** (0.177) -1.336Note ** (0.168)
45 to 54 years 0.159 (0.188) -1.085Note ** (0.173) -1.196Note ** (0.165)
55 to 64 years -0.564Note ** (0.188) -0.836Note ** (0.171) -1.365Note ** (0.165)
65 years and older -1.477Note ** (0.193) -0.464Note ** (0.170) -2.662Note ** (0.203)
Education of major income earner (reference: college or trade diploma)
High school diploma or lower -0.00563 (0.0584) -0.523Note ** (0.0804) -0.718Note ** (0.0885)
University degree or diploma -0.213Note ** (0.0570) 0.762Note ** (0.127) 0.365Note ** (0.0748)
Years in Canada (reference: Canadian-born)
Less than 2 years -1.756Note ** (0.340) 0.391 (0.288) -1.86Note ** (0.345)
2 to 4 years -0.8Note ** (0.192) 1.345Note ** (0.294) -1.104Note ** (0.215)
5 to 9 years -0.0977 (0.141) 0.931Note ** (0.253) 0.0761 (0.135)
10 to 19 years 0.408Note ** (0.118) 0.623Note ** (0.196) 0.345Note ** (0.113)
20 to 39 years 0.251Note ** (0.0871) 0.0757 (0.158) 0.446Note ** (0.110)
40 to 59 years 0.0683 (0.100) -0.0776 (0.174) 0.0173 (0.199)
60 years or more -0.584Note ** (0.191) -0.63Note ** (0.244) -0.540 (0.727)
Employed (reference: not employed)
Employed 0.469Note ** (0.0559) 0.579Note ** (0.0839) 0.216Note ** (0.0806)
Family income quintile (reference: bottom quintile)
Second 0.322Note ** (0.0947) 0.741Note ** (0.0897) -0.239Note * (0.112)
Third 0.436Note ** (0.0944) 1.172Note ** (0.120) -0.107 (0.116)
Fourth 0.610Note ** (0.0994) 1.598Note ** (0.158) -0.0304 (0.128)
Fifth 0.531Note ** (0.107) 1.832Note ** (0.265) 0.0144 (0.139)
Assets quintile (reference: bottom quintile)
Second 6.380Note ** (0.374) 0.964Note ** (0.0867) -0.676Note ** (0.0917)
Third 7.516Note ** (0.375) 1.972Note ** (0.122) -1.129Note ** (0.105)
Fourth 7.315Note ** (0.376) 2.555Note ** (0.174) -1.232Note ** (0.112)
Fifth 6.655Note ** (0.378) 2.660Note ** (0.219) -1.851Note ** (0.132)
Constant -7.908Note ** (0.426) 0.273 (0.209) -0.961Note ** (0.225)
number
Observations 22,821 Note ...: not applicable 22,821 Note ...: not applicable 22,821 Note ...: not applicable
Province fixed effects Yes Note ...: not applicable Yes Note ...: not applicable Yes Note ...: not applicable

Appendix Table A.1 - part 2
Logistic regression results for having various credit products (continued)
Table summary
This table displays the results of Logistic regression results for having various credit products (continued). The information is grouped by Variables (appearing as row headers), Model 4 - vehicle loans, Model 5 - home equity lines of credit and Model 6 - other lines of credit, calculated using coefficient, standard error and number units of measure (appearing as column headers).
Variables Model 4 - vehicle loans Model 5 - home equity lines of credit Model 6 - other lines of credit
coefficient standard error coefficient standard error coefficient standard error
Survey year (reference: 2016)
2019 0.0328 (0.0408) -0.239Note ** (0.0569) -0.0589 (0.0395)
Language (reference: English, French or both)
Neither English nor French -0.334 (0.271) -1.353Note ** (0.479) -0.855Note ** (0.218)
Household size (reference: one person)
Two people 0.409Note ** (0.0615) 0.219Note * (0.0907) 0.154Note ** (0.0544)
Three or more people 0.701Note ** (0.0675) 0.476Note ** (0.0982) 0.105 (0.0649)
Age of major income earner (reference: 16 to 24 years)
25 to 34 years 0.380Note ** (0.143) 0.512 (0.385) 0.246 (0.170)
35 to 44 years 0.320Note * (0.144) 1.097Note ** (0.377) 0.449Note ** (0.169)
45 to 54 years 0.374Note ** (0.142) 1.387Note ** (0.374) 0.554Note ** (0.168)
55 to 64 years 0.137 (0.143) 1.420Note ** (0.374) 0.659Note ** (0.168)
65 years and older -0.204 (0.147) 0.825Note * (0.378) 0.435Note * (0.169)
Education of major income earner (reference: college or trade diploma)
High school diploma or lower -0.0885Appendix Table A.1 Note  (0.0516) -0.121 (0.0751) -0.252Note ** (0.0486)
University degree or diploma -0.377Note ** (0.0526) -0.0487 (0.0683) 0.100Appendix Table A.1 Note  (0.0531)
Years in Canada (reference: Canadian-born)
Less than 2 years -0.917Note ** (0.265) -2.493Note ** (0.731) -1.668Note ** (0.336)
2 to 4 years -0.336Note * (0.168) -1.663Note ** (0.469) -0.981Note ** (0.165)
5 to 9 years -0.0276 (0.125) -0.871Note ** (0.249) -0.287Note * (0.135)
10 to 19 years -0.158Appendix Table A.1 Note  (0.0955) -0.472Note ** (0.151) -0.177Appendix Table A.1 Note  (0.0990)
20 to 39 years -0.472Note ** (0.0878) -0.151 (0.108) -0.0595 (0.0808)
40 to 59 years -0.596Note ** (0.105) -0.00614 (0.129) -0.210Note * (0.0873)
60 years or more -0.437Note * (0.182) -0.00868 (0.204) -0.324Note * (0.130)
Employed (reference: not employed)
Employed 0.436Note ** (0.0519) 0.190Note * (0.0750) 0.365Note ** (0.0484)
Family income quintile (reference: bottom quintile)
Second 0.549Note ** (0.0841) 0.382Note * (0.155) 0.489Note ** (0.0681)
Third 0.913Note ** (0.0865) 0.487Note ** (0.147) 0.663Note ** (0.0726)
Fourth 1.134Note ** (0.0932) 0.539Note ** (0.148) 0.780Note ** (0.0792)
Fifth 1.233Note ** (0.102) 0.544Note ** (0.154) 1.000Note ** (0.0905)
Assets quintile (reference: bottom quintile)
Second 0.561Note ** (0.0728) 4.462Note ** (0.715) 0.920Note ** (0.0668)
Third 0.442Note ** (0.0754) 5.081Note ** (0.714) 1.381Note ** (0.0702)
Fourth 0.173Note * (0.0792) 5.112Note ** (0.716) 1.576Note ** (0.0736)
Fifth -0.191Note * (0.0887) 5.135Note ** (0.717) 1.523Note ** (0.0796)
Constant -1.665Note ** (0.165) -8.95Note ** (0.798) -2.164Note ** (0.194)
number
Observations 22,821 Note ...: not applicable 22,821 Note ...: not applicable 22,821 Note ...: not applicable
Province fixed effects Yes Note ...: not applicable Yes Note ...: not applicable Yes Note ...: not applicable

References

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