# Analytical Studies Branch Research Paper Series Are the Career Prospects of Postsecondary Graduates Improving?

by Marc Frenette
11F0019M No. 415
Release date: January 23, 2019

## Abstract

Given the time and money invested in higher education by students, parents and governments, there is considerable interest in the economic outcomes of postsecondary graduates. Most assessments of recent graduates have focused on their short-term, early labour market results. As new entrants to the labour force, recent postsecondary graduates may be particularly vulnerable to the economic cycle. Consequently, comparisons of short-term outcomes across graduating cohorts may be highly dependent on prevailing economic conditions and may not reflect the longer-term returns on investments. This is the first study to compare the long-term labour market outcomes of two cohorts of young postsecondary graduates using linked census and tax data. Specifically, graduates who were 26 to 35 years old in 1991 were followed from 1991 to 2005 (when they were 40 to 49 years old) and compared with a similarly aged 2001 cohort, which was followed from 2001 to 2015.

The results suggest that median cumulative earnings were higher among members of the more recent cohort of male and female postsecondary graduates. Increases were observed across all postsecondary levels and across most major disciplines where sample sizes were large enough to permit analysis. Also, no discipline registered a decline in cumulative earnings. Although the economic conditions faced by the 2001 cohort over the 15-year study period were generally more favourable, this cohort also registered higher earnings than the 1991 cohort during the latter portion of the period (i.e., when the 2001 cohort was faced with an economic recession). Furthermore, the initial labour market conditions upon graduation (an important determinant of career earnings) were similar for both cohorts. The improvements in long-term earnings for postsecondary graduates are important in light of the significant increase in the number of graduates over the period. However, the results also indicate that the number of years of union membership declined or remained steady across cohorts of male and female postsecondary graduates. Furthermore, while women with postsecondary qualifications registered increases in the number of years of employer-sponsored pension plan coverage, their male counterparts experienced mixed results depending on their level of postsecondary studies.

## Executive summary

Although most studies find that postsecondary education yields significant economic and non-pecuniary benefits to its participants, several developments in recent years may have affected the supply and demand conditions in the market for postsecondary graduates. On the supply side, enrolment and graduation rates have increased steadily over the last three decades. Technological changes and globalization have been cited as two factors that may have increased the relative demand for postsecondary graduates. However, not all demand-side factors are likely to have benefited individuals with higher education. For example, the construction and oil resource boom experienced during the majority of this century may have benefited workers without a university degree. Also, the 2008/2009 recession could have exerted downward pressure on the wages of all workers. Thus, it is not clear how the labour market outcomes of postsecondary graduates are expected to have evolved in recent years.

When comparing outcomes of graduates over time, most studies focus on the short-term outcomes of recent graduates. Although important, this approach may miss the long-term outlook, which is clearly an important consideration when deciding to pursue higher education. Postsecondary students invest heavily in their education—by paying tuition and other fees, and by foregoing earnings during their studies—and it is unlikely that such costs can be recovered from additional earnings shortly after graduation. Students will be better equipped to make informed decisions based on their long-term labour market prospects.

Using linked census and tax data, this is the first study to compare the long-term labour market outcomes of two cohorts of young postsecondary graduates. Specifically, graduates who were 26 to 35 years old in 1991 were followed from 1991 to 2005 (when they were 40 to 49 years old) and compared with a similarly aged 2001 cohort, which was followed from 2001 to 2015. Thus, the study examines labour market outcomes during the early to mid-career portion of the working lives of postsecondary graduates.

The results suggest that median cumulative earnings were higher among members of the more recent cohort of male and female postsecondary graduates. Increases were observed across all postsecondary levels and across most major disciplines where sample sizes were large enough to permit analysis. Also, no discipline registered a decline in cumulative earnings. Although the economic conditions faced by the 2001 cohort over the 15-year study period were generally more favourable, this cohort also registered higher earnings than the 1991 cohort during the latter portion of the period (i.e., when the 2001 cohort was faced with an economic recession). Furthermore, the initial labour market conditions upon graduation (an important determinant of career earnings) were similar for both cohorts. The improvements in long-term earnings for postsecondary graduates are important in light of the significant increase in the number of graduates over the period. However, the results also indicate that the number of years of union membership declined or remained steady across cohorts of male and female postsecondary graduates. Furthermore, while women with postsecondary qualifications registered increases in the number of years of employer-sponsored pension plan coverage, their male counterparts experienced mixed results depending on their level of postsecondary studies.

## 1 Introduction

Postsecondary education involves considerable investment in both money and time from students, parents and governments. As a result, there is significant interest from all parties involved to understand the benefits of attending postsecondary. Research in this area has generally found that annual earnings increase by 7% to 15% for students who complete an additional year of postsecondary education, including those who just barely qualify for the program. Non-pecuniary benefits are also prevalent, including better health outcomes, marriage to a more educated spouse and lower divorce rates.Note

However, the world is rapidly changing, and various factors affect both the supply of and demand for postsecondary graduates. On the supply side, the gradual deregulation of tuition fees has coincided with an increase in the number of postsecondary-educated young entrants to the labour market over the last quarter-century. In 1990, 41.0% of 25-year-old men and 46.1% of 25-year-old women held a postsecondary credential; by 2016, 60.1% of 25-year-old men and 73.7% of 25-year-old women did so.Note An increased supply of well-educated young workers will put downward pressure on their wages unless there is an accompanying increase in demand. On that front, technological change has often been cited as one factor that favours workers with skills that are complementary to new technology. Globalization may also disproportionately favour higher-skilled workers since lower-skilled jobs are at greater risk of being offshored. More specific to Canada, the construction and oil resource boom during most of the current century may have benefited workers with only a high school diploma relative to university-educated workers (Frenette and Morissette 2014).

As a result of all these changes, it is not clear a priori in which direction the labour market outcomes of postsecondary graduates are headed. Canadian studies on the topic have generally focused on the outcomes of recent postsecondary graduates (e.g., Finnie and Frenette 2003; Walters 2004; Frank, Frenette and Morissette 2015). These studies generally found that recent graduates’ earnings tended to remain relatively stable over the economic cycle. However, graduating during an economic recession appears to be associated with lower entry earnings among university graduates in the Maritime Provinces (Galarneau, Hinchley and Ntwari 2017).Note

Although important, the focus on short-term outcomes sheds little light on how graduates fare over their entire career—clearly an important consideration when deciding to pursue higher education. Postsecondary students invest heavily in their education—by paying tuition and other fees, and by foregoing earnings during their studies—and it is unlikely that such costs can be recovered from additional earnings shortly after graduation. Students will be better equipped to make informed decisions based on their long-term labour market prospects.

Two recent studies examined the cumulative earnings of individuals over a 20-year period across completed levels of education and major fields of study. Frenette (2014) used linked census and taxation data and found that cumulative earnings varied considerably by level of education and that the gap in annual earnings across levels also varied throughout the careers of workers. Using a similar approach, Ostrovsky and Frenette (2014) highlighted the large variation in cumulative earnings by field of study and within fields of study.

This study is a natural follow-up to the studies by Frenette (2014) and Ostrovsky and Frenette (2014). Using two linked census and tax data files, this is the first national study to compare the long-term labour market outcomes of two cohorts of young postsecondary graduates. Specifically, graduates who were 26 to 35 years old in 1991 were followed from 1991 to 2005 (when they were 40 to 49 years old) and compared with a similarly aged 2001 cohort, which was followed from 2001 to 2015. Results were generated by sex, level of completed education and (when samples permitted) major field of study. Aside from earnings, the tax data also contained two other important labour market outcomes: participation in an employer-sponsored pension plan (through the pension adjustment field in tax data, which covers registered pension plans and deferred profit-sharing plans) and union membership. Both of these outcomes were also examined in the study.

The study is organized into four sections. Section 2 describes the data and methods used. The results are presented in Section 3. The study concludes in Section 4.

## 2 Data and methods

This study is based on Census of Population data linked to the T1 Personal Master File (T1PMF). Both the 1991 Census and 2001 Census files were linked to the 1983-to-2015 T1PMF using information available in census and tax data, such as date of birth, sex, postal code and marital status.Note The target groups for the linkages were individuals aged 25 and older in the 1991 Census and individuals aged 19 and older in the 2001 Census. Overall, 97.0% of the 1991 target group were linked to the 1991 tax data, and 97.5% of the 2001 target group were linked to the 2001 tax data. More information on the linkage processes is available in the work of Wilkins et al. (2008) and Pinault et al. (2016).

The objective of this study is to compare the long-term labour market outcomes of successive cohorts of Canadian postsecondary graduates. While census data do not indicate when an individual has graduated, it is still possible to select and compare similarly aged graduates across census cohorts. To this end, the analytical sample consists of all individuals who were between the ages of 26 and 35 on December 31, 1991 (i.e., they were born between 1956 and 1965) or similarly aged on December 31, 2001 (i.e., they were born between 1966 and 1975). These individuals were followed in tax data for 15 years (1991 to 2005 for the 1991 Census cohort and 2001 to 2015 for the 2001 Census cohort). In other words, they were followed from the time they were 26 to 35 years old until they were 40 to 49 years old. This period roughly corresponds to the early to mid-career for most workers.

Only individuals who appeared in the tax data for all 15 years were included in the analytical sample, whether they worked or not. The inclusion of non-workers in the sample was important in case employment conditions changed, even though hourly wage conditions might not have. In other words, any employment effects will be captured in the earnings measure under this approach. Three primary reasons why individuals stopped appearing on tax files included death, departure from the country or non-filing. However, someone may file taxes for recently deceased individuals to claim income or benefits, and recent emigrants may also file Canadian taxes for the same reason. The T1PMF has flags for deceased individuals and emigrants. This study used the flags to remove those individuals from the sample. Specifically, if an individual was listed as deceased or as an emigrant at any point during the study period, they were dropped entirely from the sample.

Additional sample selection criteria were also imposed, including limiting the sample to individuals born in Canada (to focus on individuals who were likely educated in Canada), to those who did not attend school in the nine months prior to the census (conducted in the spring), and to those who did not attend a postsecondary institution at any point in the following 15 years.Note Altogether, these sample criteria resulted in an analytical sample consisting of 133,393 men and 139,317 women in the 1991 Census cohort, and of 97,806 men and 104,330 women in the 2001 Census cohort.Note

Three labour market outcomes were examined in the study, all of which were taken from tax data. The main outcome of interest was median total earnings (the sum wages and salaries shown on the T4 Statement of Remuneration Paid; net self-employment income from business, commission, farming, fishing or professional sources; and other employment income). These were expressed in 2015 constant dollars. The focus was on the median cumulative sum of total earnings over the 15-year period, although some results show annual median total earnings over the period.Note Two additional outcomes were examined. The first was the mean number of years of participation in an employer-sponsored pension plan—identified through the pension adjustment field on line 206 in the tax data, which covers registered pension plans and deferred profit-sharing plans and is used to determine remaining Registered Retirement Savings Plan (RRSP) contributions. The second was the mean number of years of union membership—determined by the payment of union, professional or like dues on line 212 in the tax data, which are used as deductions.Note

These outcomes were broken down by sex, highest level of completed schooling (no educational credentials, high school diploma, trades certificate, college certificate, bachelor’s degree, master’s degree and doctorate) and field of study (postsecondary graduates only). These variables were all available from the census files. Only cells with a minimum of 200 observations were included in the analysis. Graduates of professional degree programs (medicine, dentistry, veterinary medicine or optometry) were excluded from the analysis since many of them choose to incorporate, which requires filling out a T2 Corporation Income Tax Return to report corporate earnings.

Nine major field of study groups were used in this study. They are listed below, along with the major field of study codes:

• educational, recreational and counselling services (1-46)
• fine and applied arts (47-79)
• humanities and related fields (80-124)
• social sciences and related fields (125-187)
• agricultural, biological, nutritional, and food sciences (221-266)
• engineering and applied sciences and technologies (267-369)
• health professions and related technologies (370-441)
• mathematics, computer and physical sciences (442-480).

Results are shown by field of study only for college and university levels. Although individuals who complete a trades certificate are also asked to describe their major field of study on the census, this classification is more conducive to analyzing college and university students. For trades students and registered apprentices, program choices are more concentrated and are better described by using an occupational classification, which is the approach adopted in the National Apprenticeship Survey and the Registered Apprenticeship Information System.

Comparisons of the outcomes for each of the cells described above were made across the two census cohorts in a simple quantile (median) regression framework for cumulative earnings, and in a Poisson regression model for the number of years with an employer-sponsored pension plan and the number of years of union membership. For any given group (e.g., women with a bachelor’s degree in social sciences), a pooled regression including individuals in both cohorts was estimated, with a dummy variable indicating the cohort, and covariates such as age and age squared (from tax data), and province or territory of birth (from census data).

In all cases, the analysis was weighted to account for the initial selection probability (from the census weights) and the probability of successful linkage to tax data (from an adjustment to the census weights created as part of the project to link the files). In addition, the first 100 bootstrap weights on the files were used to calculate standard errors. This was necessary to account for the fact that the final linked file was not a simple random sample of the underlying population of interest (i.e., the 1991 or 2001 Census population corresponding to the sample criteria outlined above).

## 3 Results

The sample characteristics are described in Table 1. Recall that the cohorts consist of 26 to 35 year-old men and women in 1991 or 2001 who were born in Canada, did not attend school in the 9 months prior to the Census, and appeared in the tax data and did not attend PSE during the 15 year follow-up period. The main differences across cohorts involve the highest level of completed education. Both men and women in the 2001 cohort were more highly educated than their counterparts from the 1991 cohort. Specifically, the proportions who held a college certificate, bachelor’s degree, master’s degree or doctorate all increased between the two cohorts. This was especially the case among women. For example, 17.4% of the 2001 cohort held a bachelor’s degree, compared with 7.6% of the 1991 cohort. While less common, the proportion of women who held a master’s degree was also more than twice as high among the 2001 cohort. Men also registered substantial gains in college and university credentials, albeit not quite as much as women.Note

The other sample differences were much smaller. Both men and women were slightly older in the 2001 cohort (by about 0.2 years). Members of the 2001 cohort were also slightly more likely to have been born in Ontario, Alberta or British Columbia, but slightly less likely to have been born in the Maritime Provinces, Quebec or Saskatchewan.

The median total annual earnings are shown by sex and education level in Charts 1 to 13. For men, annual earnings over the 15-year study period were higher for the 2001 cohort among all groups of postsecondary graduates, but particularly among masters and doctoral graduates. Men with only a high school diploma generally saw no change in median annual earnings, while those with no education credentials registered a decline in annual earnings across cohorts (particularly in the latter portion of the study period). Generally, increases were registered at all levels of educational attainment among women, even among those with no postsecondary credentials. Since the sample included non-workers, rising labour force participation over the last two decades could explain increases in earnings for women.

The median cumulative earnings over the 15-year study periods are shown in Table 2 by sex, highest level of completed education and major field of study. Many groups earned roughly $1,000,000 over the 15-year follow-up periods for both cohorts, with considerable variation by sex, education level and field of study. Men earned more than women across education levels and fields of study. Although many factors may be responsible for this earnings gap, it is important to note that individuals who did not work or who worked part-time were included in the sample—this approach will produce differences in earnings that are highly sensitive to differences in work hours. Similar to results shown in the work of Frenette (2014), cumulative earnings also increased with the level of completed educational attainment, with two exceptions. First, women with a trades certificate earned about the same as women with only a high school diploma. Similarly, male doctoral graduates earned about the same as men with a master’s degree. Low sample sizes prevented the release of results for female doctoral graduates. Cumulative earnings also varied considerably by field of study, as was demonstrated by Ostrovsky and Frenette (2014). Generally, graduates in the following fields of study earned the most: engineering and applied sciences and technologies; mathematics, computer and physical sciences; commerce, management and business administration; health professions and related technologies; and social sciences and related fields. Graduates in the following fields of study generally earned the least: fine and applied arts; humanities and related fields; agricultural, biological, nutritional, and food sciences; and educational, recreational and counselling services. The relevant analytical question in this study is whether or not cumulative earnings improved across cohorts. While Table 2 shows the median cumulative earnings for both cohorts, these do not account for any differences in the age structure of the two cohorts, or place of birth. Table 3 shows the results based on such adjustments through median regressions that are estimated separately by sex, education level and field of study. The coefficients shown represent the estimated differences in adjusted median cumulative earnings between the 2001 and 1991 cohorts. Among men, those with no postsecondary credentials (i.e., no educational credentials or only a high school diploma) registered no significant changes in median cumulative earnings. A small but statistically significant increase of$20,000 was registered among men with a college certificate. All other groups of men saw more substantial gains, including $71,000 among those with a trades certificate,$66,000 among bachelor’s degree holders, $161,000 among master’s degree holders and$210,000 among doctoral graduates. Expressed as annual earnings, the gains enjoyed by master’s and doctoral graduates ranged from $11,000 to$14,000. The largest increase registered within a specific field was among commerce, management and business administration graduates at the master’s degree level ($264,000). No substantial losses were registered by graduates of any field. Interestingly, some fields typically associated with lower earnings at the bachelor’s degree level (educational, recreational and counselling services; and humanities and related fields) witnessed gains in excess of$100,000.

Median cumulative earnings increased across all education levels for women, although the gains were smallest among those with no education credentials, followed by moderate gains registered by high school and college graduates. Women and men with a trades certificate saw similar gains (about $70,000 in both cases), while women with a bachelor’s degree registered larger gains ($85,000) than men with similar qualifications ($66,000). At the master’s degree level, the overall gains were substantially smaller among women ($56,000) than among men ($161,000). The same was true among the two fields that could be examined for men and women at the master’s degree level (social sciences and related fields; and commerce, management and business administration). Thus, the largest gains for women were among bachelor’s degree graduates, and this was true for graduates of many major fields as well. In fact, sizeable gains were registered across most fields among women with a bachelor’s degree. Moreover, three of the five fields at the bachelor’s degree level with gains of$100,000 or more are typically associated with lower earnings (educational, recreational and counselling services; fine and applied arts; and agricultural, biological, nutritional, and food services).

Despite rising educational attainment among men and women, the results described so far indicate that the long-term earnings outlook of postsecondary graduates has been on an upward trend for both sexes. This suggests that demand-side factors may be at play. But does it suggest the presence of a secular trend? Not necessarily, as the impact of the economic cycle has not been taken into account. Indeed, members of the 2001 cohort faced a more favourable labour market than their counterparts from the 1991 cohort. Chart 14 demonstrates this point by showing the unemployment rate for 25- to 54-year-olds across the study for both cohorts. During the first eight years of the follow-up (in the chart, between zero and seven years from the start of the cohort study), members of the 2001 cohort faced unemployment rates between 2 and 4 percentage points lower than members of the 1991 cohort. This can be explained by the fact that the early 1990s saw the beginning of an economic recession that was followed by what some termed the “jobless recovery” throughout much of the remaining part of the decade. In contrast, most of the 2000s were characterized by economic booms in the construction and resource sectors. Following the 2008/2009 recession, unemployment rates increased, which affected only the 2001 cohort in this study. As a result, the 2001 cohort faced a similar labour market as the 1991 cohort during the last seven years of the follow-up.

Thus, the 15-year study period could arguably be divided into two sub-periods: up to and including seven years after the start of the period—when the labour market was far more favourable to the 2001 cohort—and later, when both cohorts faced a similar market. If the superior outcomes of postsecondary graduates from the 2001 cohort noted so far were the result of better labour market conditions, most of the increase in cumulative earnings would be expected to have occurred during the first eight years of the period, as opposed to during the last seven years. However, this is generally not the case, as evidenced by Table 4, where the methodology was the same as in Table 3, except that the regressions were estimated separately for each sub-period. What Table 4 shows is that postsecondary graduates from the 2001 cohort outperformed their counterparts from the 1991 cohort in both sub-periods. These results are consistent with a positive secular trend in long-term labour market outcomes of postsecondary graduates.

One possible counterargument to this is the fact that previous research has suggested that initial labour market conditions upon graduation affect the long-term outcomes of postsecondary graduates (Oreopoulos, von Wachter and Heisz 2012). Census results do not indicate exactly when someone completes a program. In the first year of the study, the unemployment rate faced by the 2001 cohort was 3 percentage points lower than what was faced by the 1991 cohort (Chart 4). However, all individuals in the analytical sample were 26 to 35 years old in the first year of the study. Arguably, most of them would have entered the labour market well before the first year of the study. As the analysis moves into the pre-study period (and likely closer to the year of graduation for many individuals), the unemployment rate differential diminishes rapidly and actually levels out within only two years. Going 8 to 11 years back results in higher unemployment rates faced by the 2001 cohort (likely to affect the initial conditions faced by the oldest members of the cohorts). Thus, initial labour market conditions upon graduation were similar for the majority of members in both cohorts.

Graduates may also measure labour market success through the lens of non-pecuniary benefits. On this front, the tax data offer two possibilities: participation in an employer-sponsored pension plan and union membership. The average number of years of participation in an employer-sponsored pension plan or union membership is shown for the two cohorts by level of education in Table 5.

Generally speaking, individuals with higher levels of education spent more years in an employer-sponsored pension plan. For example, the average number of years of employer-sponsored pension plan coverage among women in the 2001 cohort was about twice as high among those with a bachelor’s degree (8.7 years), compared with those with a high school diploma (4.3 years).

A positive relationship was also evident between educational attainment and the number of years of union membership, although the association was generally weaker for men. Indeed, there was little to no difference in years of union membership among men with a trades or college certificate, or a bachelor’s or master’s degree. Among women, higher educational attainment was generally associated with more years of union membership.

Although differences in non-pecuniary benefits across the two cohorts can also be ascertained from the results in Table 5, those results do not account for any differences in age or province/territory of birth shown in Table 1. In Table 6, the differences are shown after adjusting for these factors in a series of Poisson regression models estimated separately by sex and education level.

All education groups registered a decline in union membership.Note The results were more mixed for employer-sponsored pension plan coverage. In fact, most groups of postsecondary-educated women registered increases, namely those with a trades or college certificate and those with a bachelor’s degree. An increase was also registered among women with a master’s degree, but this was not statistically significant. For postsecondary-educated men, those with a bachelor’s degree registered an increase, while a decline was evident for those with a college certificate or a doctorate.

## 4 Conclusion

This study examined the long-term labour market prospects of postsecondary graduates using linked census and taxation data. It focused on comparing outcomes of two groups of graduates: those aged 26 to 35 in 1991 and their counterparts in 2001. In both cases, individuals were followed for 15 years (until they were 40 to 49 years old), thus covering the early to mid-career portion of their working lives. Given changes in both supply-side and demand-side factors, it is not clear how the long-term labour market outcomes of graduates have evolved in recent years.

The results suggest that median cumulative earnings were greater among members of the more recent cohort of male and female postsecondary graduates. Increases were observed across all postsecondary levels and across most major disciplines where sample sizes were large enough to permit analysis. Also, no discipline registered a decline in cumulative earnings. Although the economic conditions faced by the 2001 cohort over the 15-year study period were generally more favourable, this cohort also registered higher earnings than the 1991 cohort during the latter portion of the period (i.e., when the 2001 cohort was faced with an economic recession). Furthermore, the initial labour market conditions upon graduation (an important determinant of career earnings) were similar for both cohorts. The improvements in long-term earnings for postsecondary graduates are important in light of the significant increase in the number of graduates over the period. However, the results also indicate that the number of years of union membership declined or remained steady across cohorts of male and female postsecondary graduates. Furthermore, while women with postsecondary qualifications registered increases in the number of years of employer-sponsored pension plan coverage, their male counterparts experienced mixed results depending on their level of postsecondary studies.

The focus of this study was on labour market outcomes of postsecondary graduates. However, previous research has suggested that higher education also confers non-pecuniary private and public benefits. To that end, the tax data contain various other outcomes, including marital status, childbearing and charitable donations. Follow-up research could examine the long-term relationship between postsecondary education and charitable donations or family formation decisions. In addition, the data have also been linked to the Canadian Mortality Database, so that examining the relationship between education level, field of study choice and mortality is also possible.

## 5 Tables and charts

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Men Women 1991 cohort 2001 cohort 30.8 31.0 30.7 30.9 33.2 21.5 33.0 19.4 25.0 24.9 30.6 23.1 19.4 17.8 11.3 11.9 12.7 19.1 16.8 25.9 8.2 14.3 7.6 17.4 1.2 2.1 0.8 2.2 0.2 0.3 0.1 0.2 3.6 3.4 3.9 3.9 0.7 0.6 0.7 0.6 4.5 3.7 4.9 4.2 3.9 3.6 4.1 4.0 33.9 33.0 33.6 31.1 29.9 31.8 29.7 31.7 4.9 4.7 4.6 4.8 5.4 4.7 5.3 4.9 7.2 7.4 6.9 7.6 6.0 6.8 6.2 6.9 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.2 133,393 97,806 139,317 104,330 Sources: Statistics Canada, Census of Population–T1 Personal Master File.
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Men Women 1991 cohort 2001 cohort 526,351 523,915 155,934 172,742 708,115 723,499 308,442 344,012 739,264 813,060 273,808 352,885 917,660 938,882 475,553 517,658 730,500 747,353 373,141 444,048 674,874 711,140 293,015 369,726 711,777 762,038 397,232 456,400 1,069,255 1,109,839 424,545 507,277 890,903 890,912 469,207 529,330 622,389 666,175 352,113 397,009 973,525 1,006,435 571,152 585,130 941,102 1,019,731 596,095 600,281 932,901 917,718 582,224 623,861 1,223,383 1,292,247 729,108 816,282 994,389 1,048,580 684,586 791,748 Note ...: not applicable Note ...: not applicable 397,467 547,256 837,632 987,408 628,257 696,297 1,177,109 1,296,924 727,480 768,290 1,293,111 1,388,192 846,022 924,355 1,059,783 1,020,731 579,265 762,800 1,474,215 1,538,106 955,925 1,081,357 1,248,697 1,400,673 819,776 950,769 1,349,803 1,357,569 958,523 978,069 1,417,774 1,576,957 979,918 1,028,857 1,370,091 1,510,829 928,135 1,036,291 1,721,181 1,940,623 1,354,749 1,395,535 1,550,411 1,632,579 Note F: too unreliable to be published Note F: too unreliable to be published 1,403,968 1,617,261 Note F: too unreliable to be published Note F: too unreliable to be published ... not applicable F too unreliable to be published Sources: Statistics Canada, Census of Population–T1 Personal Master File.
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Men Women coefficient bootstrap standard error -6,164 5,025 12,563Note *** 2,697 5,388 3,818 35,799Note *** 3,657 71,179Note *** 4,219 71,999Note *** 4,507 20,423Note ** 6,380 38,941Note *** 3,619 -10,956 27,355 71,113Note *** 10,848 368 27,134 77,437Note *** 15,971 32,510 28,115 64,500Note ** 23,004 43,717Table 3 Note † 22,798 75,996Note *** 14,344 -8,245 13,614 55,462Note *** 5,691 28,472 21,361 45,400Note * 19,817 31,285Note *** 7,938 16,179 18,798 77,874Note ** 26,425 12,202 11,385 -21,733 56,492 86,826Table 3 Note † 52,124 65,585Note *** 9,826 84,735Note *** 6,344 102,473Note *** 13,465 116,607Note *** 13,017 Note F: too unreliable to be published Note F: too unreliable to be published 138,968Note *** 33,945 127,153Note ** 39,175 75,929Note *** 22,445 115,026Note *** 22,608 43,531Note * 17,242 62,134Note * 24,395 70,166Note *** 20,768 -10,185 35,878 167,072Note *** 34,832 72,099Note *** 17,110 83,773 57,624 126,251Note ** 46,917 123,333Note *** 14,809 30,293 23,811 17,072 42,740 160,928Note *** 26,735 55,681Note ** 20,942 140,223Note * 58,839 56,991 48,674 263,708Note *** 73,613 84,705 82,978 99,216Table 3 Note † 52,770 Note F: too unreliable to be published Note F: too unreliable to be published 209,708Note *** 60,893 Note F: too unreliable to be published Note F: too unreliable to be published F too unreliable to be published Note * significantly different from reference category (p < 0.05) Return to note * referrer Note ** significantly different from reference category (p < 0.01) Return to note ** referrer Note *** significantly different from reference category (p < 0.001) Return to note *** referrer Note † significantly different from reference category (p < 0.10) Return to note † referrer Notes: Differences are adjusted for age, age squared, and province or territory of birth by median regression models that were estimated separately by sex, education level and field of study. Coefficients are expressed in 2015 constant dollars. Sources: Statistics Canada, Census of Population–T1 Personal Master File.
﻿
Year 1 to year 8 Year 9 to year 15 coefficient bootstrap standard error 2,118 2,525 -8,800Note *** 2,521 4,432Note * 1,869 -541 1,818 39,669Note *** 2,400 30,997Note *** 2,423 11,968Note *** 3,104 7,597Note * 2,975 43,120Note *** 4,260 17,343Note ** 5,793 85,135Note *** 10,587 61,223Note *** 18,445 100,802Note *** 30,202 90,677Note *** 26,987 13,549Note *** 1,415 -691 1,424 22,527Note *** 1,758 10,414Note *** 2,097 38,147Note *** 2,394 32,548Note *** 2,633 17,743Note *** 2,057 16,651Note *** 1,900 31,565Note *** 3,603 47,942Note *** 4,059 -2,674 11,327 46,211Note *** 12,679 Note F: too unreliable to be published Note F: too unreliable to be published Note F: too unreliable to be published Note F: too unreliable to be published F too unreliable to be published Note * significantly different from reference category (p < 0.05) Return to note * referrer Note ** significantly different from reference category (p < 0.01) Return to note ** referrer Note *** significantly different from reference category (p < 0.001) Return to note *** referrer † significantly different from reference category (p < 0.10) Notes: Differences are adjusted for age, age squared, and province or territory of birth by median regression models that were estimated separately by sex and education level. Coefficients are expressed in 2015 constant dollars. Sources: Statistics Canada, Census of Population–T1 Personal Master File.
﻿
Number of years with an employer-sponsored pension plan Number of years of union membership 1991 cohort 2001 cohort 4.5 3.7 4.8 3.6 6.4 5.7 5.6 4.4 6.4 6.2 6.2 5.5 7.7 7.4 5.7 5.0 7.8 7.9 5.5 4.9 8.7 8.6 5.9 5.5 11.3 9.5 10.0 8.5 2.5 2.4 2.6 2.2 4.5 4.3 3.4 2.9 4.1 4.3 3.9 3.7 6.1 6.2 5.3 4.9 7.7 8.7 6.5 6.7 9.4 9.6 7.6 7.3 Sources: Statistics Canada, Census of Population–T1 Personal Master File.
﻿
Number of years with an employer-sponsored pension plan Number of years of union membership coefficient bootstrap standard error -0.770Note *** 0.061 -1.294Note *** 0.065 -0.663Note *** 0.077 -1.245Note *** 0.070 -0.142 0.098 -0.901Note *** 0.094 -0.356Note ** 0.115 -0.702Note *** 0.092 0.280Note * 0.123 -0.913Note *** 0.096 0.134 0.351 -0.658Note ** 0.253 -3.219Note *** 0.951 -2.919Note ** 0.907 -0.169Note *** 0.040 -0.372Note *** 0.044 -0.298Note *** 0.063 -0.389Note *** 0.052 0.258Note ** 0.085 -0.271Note *** 0.079 0.215Note ** 0.080 -0.442Note *** 0.067 1.412Note *** 0.134 -0.076 0.110 0.272 0.348 -0.449 0.377 Note * significantly different from reference category (p < 0.05) Return to note * referrer Note ** significantly different from reference category (p < 0.01) Return to note ** referrer Note *** significantly different from reference category (p < 0.001) Return to note *** referrer Note: Differences are adjusted for age, age squared, and province or territory of birth by Poisson regression models that were estimated separately by sex and level of education. Sources: Statistics Canada, Census of Population–T1 Personal Master File.

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 30,470 29,811
1 30,515 30,688
2 31,215 31,501
3 33,095 32,707
4 33,552 33,031
5 33,931 34,134
6 35,312 34,732
7 36,180 35,227
8 37,105 34,190
9 37,940 35,261
10 38,011 36,134
11 38,026 36,841
12 37,931 37,357
13 38,641 37,414
14 38,756 37,362

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 41,885 41,643
1 42,954 42,891
2 43,446 43,907
3 45,137 45,315
4 45,748 46,409
5 46,141 47,440
6 47,111 48,532
7 48,625 49,264
8 49,261 48,225
9 50,162 49,308
10 50,135 50,054
11 50,694 50,854
12 50,561 51,712
13 51,251 52,124
14 51,498 52,076

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 44,169 46,912
1 44,877 48,200
2 44,973 48,826
3 46,838 50,484
4 47,716 51,682
5 48,373 52,957
6 49,655 54,257
7 50,847 55,362
8 51,785 54,469
9 52,885 55,443
10 52,763 56,496
11 53,033 57,418
12 53,202 58,711
13 53,969 59,376
14 54,695 59,411

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 52,388 52,785
1 54,079 54,467
2 54,747 55,911
3 56,543 57,677
4 57,756 59,469
5 58,524 61,470
6 60,117 62,959
7 61,936 64,325
8 63,273 64,630
9 64,658 65,350
10 65,310 66,143
11 65,910 67,130
12 65,788 68,784
13 67,071 69,355
14 67,887 69,833

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 64,023 67,710
1 67,164 71,145
2 68,698 73,512
3 71,692 77,406
4 73,705 80,456
5 75,786 83,279
6 78,262 85,846
7 81,437 88,410
8 84,438 89,948
9 87,329 90,704
10 89,008 91,902
11 90,192 93,020
12 90,562 95,110
13 92,008 95,518
14 93,783 96,213

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 73,693 80,858
1 78,322 84,674
2 80,786 87,669
3 83,942 91,226
4 85,381 96,571
5 87,619 100,902
6 89,361 104,271
7 94,506 107,529
8 98,980 110,677
9 101,017 110,404
10 102,208 110,366
11 105,802 112,362
12 105,647 118,424
13 106,911 114,458
14 109,195 114,773

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 Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 75,241 77,768
1 78,911 83,052
2 80,206 85,742
3 83,495 89,703
4 82,971 91,613
5 85,224 100,344
6 88,095 105,685
7 92,959 108,392
8 96,181 114,835
9 101,544 113,923
10 103,782 115,328
11 108,548 120,354
12 112,452 125,871
13 117,667 124,454
14 117,620 129,894

Data table for Chart 8
Data table for Chart 8
Table summary
This table displays the results of Data table for Chart 8. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 4,263 5,632
1 4,178 6,330
2 4,009 6,589
3 4,500 7,676
4 5,214 8,382
5 5,995 8,984
6 7,055 9,833
7 9,138 10,600
8 10,469 10,908
9 11,819 11,562
10 12,848 12,320
11 13,653 13,414
12 14,229 13,912
13 14,777 13,985
14 14,959 14,750

Data table for Chart 9
Data table for Chart 9
Table summary
This table displays the results of Data table for Chart 9. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 15,472 17,680
1 15,674 17,880
2 15,429 18,653
3 16,049 19,290
4 16,754 19,763
5 17,297 21,086
6 18,688 22,327
7 20,594 23,337
8 21,804 23,982
9 23,306 24,921
10 24,152 25,660
11 25,006 26,483
12 25,504 27,250
13 26,441 27,775
14 26,814 28,490

Data table for Chart 10
Data table for Chart 10
Table summary
This table displays the results of Data table for Chart 10. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 14,736 18,532
1 14,473 18,310
2 14,203 19,033
3 14,346 19,811
4 14,993 20,173
5 15,386 21,281
6 16,261 22,481
7 17,774 23,571
8 19,109 24,354
9 20,397 25,247
10 21,056 26,372
11 21,908 27,047
12 22,672 28,323
13 23,447 28,980
14 23,817 29,220

Data table for Chart 11
Data table for Chart 11
Table summary
This table displays the results of Data table for Chart 11. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 27,347 29,618
1 27,591 29,350
2 27,509 29,742
3 28,090 30,688
4 28,684 31,473
5 29,503 32,850
6 30,261 34,414
7 31,825 35,485
8 33,249 36,629
9 34,567 37,496
10 35,175 38,161
11 36,086 38,898
12 36,592 40,028
13 37,506 40,389
14 37,769 41,036

Data table for Chart 12
Data table for Chart 12
Table summary
This table displays the results of Data table for Chart 12. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 40,951 45,482
1 41,660 45,403
2 41,467 46,305
3 42,490 47,536
4 43,356 48,484
5 44,729 50,339
6 45,724 52,457
7 48,726 54,844
8 50,422 57,519
9 52,479 59,265
10 52,952 60,819
11 54,766 62,548
12 55,820 64,824
13 57,173 65,830
14 58,634 67,500

Data table for Chart 13
Data table for Chart 13
Table summary
This table displays the results of Data table for Chart 13. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using 2015 constant dollars units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
2015 constant dollars
0 57,681 57,879
1 61,449 58,898
2 61,130 60,044
3 63,222 61,506
4 61,823 62,384
5 62,981 65,645
6 63,891 67,400
7 65,275 69,556
8 67,917 74,704
9 69,264 75,534
10 69,269 77,154
11 71,911 78,911
12 74,511 81,244
13 75,376 81,971
14 75,753 82,682

Data table for Chart 14
Data table for Chart 14
Table summary
This table displays the results of Data table for Chart 14. The information is grouped by Years since start of cohort (appearing as row headers), 1991 cohort and 2001 cohort, calculated using percent units of measure (appearing as column headers).
Years since start of cohort 1991 cohort 2001 cohort
percent
-10 6.0 9.2
-9 8.9 10.0
-8 10.0 10.3
-7 9.8 9.4
-6 9.1 8.5
-5 8.3 8.6
-4 7.7 7.8
-3 6.9 7.1
-2 6.8 6.4
-1 7.3 5.7
0 9.2 6.2
1 10.0 6.6
2 10.3 6.4
3 9.4 6.0
4 8.5 5.8
5 8.6 5.3
6 7.8 5.1
7 7.1 5.1
8 6.4 7.1
9 5.7 6.9
10 6.2 6.3
11 6.6 6.0
12 6.4 5.9
13 6.0 5.8
14 5.8 5.8

## References

Finnie, R., and M. Frenette. 2003. “Earning differences by major field of study: Evidence from three cohorts of recent Canadian graduates.” Economics of Education Review 22 (2): 179–192.

Frank, K., M. Frenette, and R. Morissette. 2015. Labour Market Outcomes of Young Postsecondary Graduates, 2005 to 2012. Economic Insights, no. 50. Statistics Canada Catalogue no. 11-626-X. Ottawa: Statistics Canada.

Frenette, M. 2014. An Investment of a Lifetime? The Long-term Labour Market Premiums Associated with a Postsecondary Education. Analytical Studies Branch Research Paper Series, no. 359. Statistics Canada Catalogue no. 11F0019M. Ottawa: Statistics Canada.

Frenette, M. 2017. Postsecondary Enrolment by Parental Income: Recent National and Provincial Trends. Economic Insights, no. 70. Statistics Canada Catalogue no. 11-626-X. Ottawa: Statistics Canada.

Frenette, M., and R. Morissette. 2014. Wages and Full-time Employment Rates of Young High School Graduates and Bachelor’s Degree Holders, 1997 to 2012. Analytical Studies Branch Research Paper Series, no. 360. Statistics Canada Catalogue no. 11F0019M. Ottawa: Statistics Canada.

Galarneau, D., C. Hinchley, and A. Ntwari. 2017. “Labour market outcomes of graduates from universities in the Maritime provinces.” Insights on Canadian Society (April). Statistics Canada Catalogue no. 75-006-X. Ottawa: Statistics Canada.

Oreopoulos, P., and U. Petronijevic. 2013. “Making college worth it: A review of the returns to higher education.” The Future of Children 23 (1): 41–65.

Oreopoulos, P., T. von Wachter, and A. Heisz. 2012. “The short- and long-term career effects of graduating in a recession.” American Economic Journal: Applied Economics 4 (1): 1–29.

Ostrovsky, Y., and M. Frenette. 2014. The Cumulative Earnings of Postsecondary Graduates Over 20 Years: Results by Field of Study. Economic Insights, no. 40. Statistics Canada Catalogue no. 11-626-X. Ottawa: Statistics Canada.

Pinault, P., P. Finès, F. Labrecque-Synnott, A. Saidi, and M. Tjepkema. 2016. The 2001 Canadian Census–Tax–Mortality Cohort: A 10-Year Follow-up. Analytical Studies: Methods and References, no. 3. Statistics Canada Catalogue no. 11-633-X. Ottawa: Statistics Canada.

Statistics Canada, n.d. Table 17-10-0059-01 Estimates of the components of natural increase, quarterly. (accessed September 4, 2018).

Walters, D. 2004. “A comparison of labour market outcomes of postsecondary graduates of various levels and fields over a four-cohort period.” Canadian Journal of Sociology 29 (1): 1–27.

Wilkins, R., M. Tjepkema, C. Mustard, and R. Choinière. 2008. “The Canadian census mortality follow-up study, 1991 through 2001.” Health Reports 19 (3): 25–43. Statistics Canada Catalogue no. 82-003-X.

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