Economic and Social Reports
Fine tuning or re-skilling? Educational strategies of prime-aged displaced workers

Release date: January 26, 2022

DOI: https://doi.org/10.25318/36280001202200100005-eng

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Abstract

While a significant literature has documented the substantial and persistent earnings losses often experienced by displaced workers, relatively little is known about the educational strategies that prime-aged displaced workers use to cope with job loss. Specifically, the extent to which laid off Canadian workers undergo re-skilling—entering new fields of study after losing their jobs rather than simply upgrading their skills and remaining within their initial fields of study—is currently unknown. This study fills that information gap. It shows that, among all prime-aged postsecondary-educated workers who lost their jobs from 2009 to 2013, close to 10% entered postsecondary education (PSE) in the three years following job loss. Of those who entered PSE after job loss, almost 60% changed fields of study. This study also shows that displaced men and women who went back to school after job loss and who had similar initial fields of study tended to choose different fields of study after displacement. In particular, displaced men were less likely to move into health-related fields of study than displaced women.

Keywords: training; job displacement; lifelong learning; postsecondary education

Authors

Tomasz Handler and René Morissette are with the Social Analysis and Modelling Division, Analytical Studies and Modelling Branch at Statistics Canada.

Introduction

The COVID-19 pandemic has brought the issue of job loss in Canada and in many industrialized countries to the forefront. Previous research (Couch & Placzek, 2010; Hijzen et al., 2010; Jacobson et al., 1993; Kletzer & Fairlie, 2003; Morissette et al., 2013; Morissette & Qiu, 2020; Stevens, 1997;) shows that displaced workers often experience substantial and persistent earnings losses and that some of them enter or re-enter postsecondary education (PSE) to cope with job loss (Ci et al., 2016; Foote & Grosz, 2020; Frenette et al., 2011; Morissette & Qiu, 2021). However, relatively little is known about the fields of study displaced workers choose after job loss.Note  Specifically, the degree to which displaced workers choose fields that lead them to upgrade their skills (fine tuning) or imply a major career reorientation (re-skilling) remains unknown.

This distinction is important for a variety of reasons. Discussions of lifelong learning generally assume that workers often reorient their careers, but present no rigorous evidence on the frequency of such major career moves. Determining the relative importance of post-displacement educational transitions across different fields of study, compared with those that simply reflect a progression within similar fields of study, sheds light on this issue. Furthermore, it is unclear whether the first type of educational transitions are associated with greater earnings growth than the latter. Providing information on this question helps inform discussions about optimal assistance and training policies, if any, for displaced workers.

Using data from the Postsecondary Student Information System (PSIS) in conjunction with Statistics Canada’s Longitudinal Worker File (LWF) and the 2006 Census of Population, this study quantifies the frequency of post-displacement educational transitions that are made across different fields of study relative to those that reflect a progression within fairly similar fields of study. It also assesses whether the first type of educational transitions are associated with greater earnings growth than the latter.

This study answers the following questions:

This paper is structured as follows: The next section briefly reviews past research on educational transitions after job loss. Subsequent sections describe the datasets used in the study and quantify the degree to which displaced workers enter PSE after job loss, and whether they choose different fields of study or continue studying in their initial field. The focus is on postsecondary-educated workers displaced from 2009 to 2013, who were aged 30 to 54 at the time of displacement.Note  To provide context, estimates of enrollment in PSE are also produced for a comparison group of postsecondary-educated non-displaced workers. A fifth section features a matrix comparing pre- and post-job displacement fields of study. It compares the field of study chosen after job loss with the initial field of study (i.e. , the field of study reported previously in the 2006 Census of Population) for displaced workers who entered PSE after job loss. It also assesses whether there are gender differences in the field of study selected after displacement. A sixth section compares the earnings growth experienced after job loss for the following three groups of displaced workers: a) those who did not enter PSE after job loss, b) those who entered PSE and chose a different field of study and c) those who entered PSE and kept studying in their initial field. Concluding remarks follow.

Previous research

Although several studies have documented the negative impact of job loss on workers’ earnings (Couch & Placzek, 2010; Hijzen et al., 2010; Jacobson et al., 1993; Kletzer & Fairlie, 2003; Morissette et al., 2013; Morissette & Qiu, 2020; Stevens, 1997), the link between job loss and PSE has been analyzed to a lesser extent. 

Frenette et al. (2011) use Canadian administrative data and find that job displacement from firm closures and mass layoffs is associated with a one percentage point increase in postsecondary attendance (from a baseline rate of 10%). Since job losses from firm closures or mass layoffs account for less than half of all permanent layoffs in Canada (Morissette & Qiu, 2020), their analysis is restricted to a subset of job losses. Ci et al. (2016) consider all permanent layoffs experienced by workers aged 35 to 44 who were displaced from 2004 to 2011. They found that laid-off male and female Canadian workers are 2 to 4 percentage points more likely than other men and women to transition to PSE in the year of the layoff or in the following year (from a baseline rate of about 3%). Morissette and Qiu (2021) reached a similar conclusion, focusing on workers displaced in 2009. Because these studies did not use the PSIS, none assessed the degree to which Canadian displaced workers chose fields of study that led them to upgrade their skills or that implied a major career reorientation.

Foote and Grosz (2020) use U.S. data from the Integrated Postsecondary Education Data System (IPEDS), a dataset that allows results to be disaggregated by field of study. They found that for every 100 workers laid off, enrollment in community colleges increases by 3 students within the following three years. They also showed that for every 100 workers involved in a mass layoff, degree completion increases by 2 students; most of this effect is concentrated among short-term certificates, as opposed to associate’s degree programs.

In summary, job loss appears to trigger a modest increase in enrollment in PSE institutions, both in Canada and the United States. The degree to which this increase in PSE enrollment is caused by transitions to different or similar fields of study is currently unknown in Canada and will be investigated in this paper.

Data and samples

Data

The study integrates data from three different sources: Statistics Canada’s LWF, the 2006 Census of Population and the PSIS.

The LWF is a longitudinal administrative dataset that covers the 1989-to-2018 period and combines information from four different sources: T1 tax files, the T4 statement of paid remuneration, the Record of Employment (ROE) and the Longitudinal Employment Analysis Program (LEAP). T1 tax files provide individual-level information on variables such as worker age and sex, province of residence, annual wages, salaries and income from self-employment, coverage by registered pension plans or deferred profit-sharing plans, and—starting in 1999—deductions for part-time and full-time enrollment in PSE institutions. The T4 statement of paid remuneration provides job-level information on workers’ annual wages and salaries, union status and province of employment.Note  The ROE provides job-level information on the separation of employees from employers because of layoffs, quits, retirement or other reasons, as well as temporary work interruptions (related to parental leave, injury or illness, strike or lockout, etc. ) that employees experience while holding a given job. The LEAP is an enterprise-level file that follows firms over time and has information on firm size and industry of employment. It allows a distinction between permanent employee separations (from an employer) and temporary employee separations or work interruptions.Note  In this study, the LWF is used for two distinct purposes: a) to identify displaced workers, i.e., workers who are permanently laid off in a given year, and construct a comparison group of other, non-displaced workers, and b) to measure the earnings trajectories of both groups of workers from year t-1, the year prior to job loss, to year t+5, the fifth year after job loss.

The study also takes advantage of the 2006 Census of Population to measure workers’ fields of study prior to job loss. The 2006 Census is also used to classify displaced workers and non-displaced workers according to their immigration status, population group (Black, White, Chinese, Arab, etc.), and educational attainment. These variables are not available in the LWF.Note 

Lastly, the PSIS is used to determine the field of study and type of program selected by the subset of displaced workers who enter PSE after job loss.Note  By comparing the field of study selected after job loss—as measured from the PSIS—with the field of study observed in the 2006 Census, it can be determined whether displaced workers choose fields of study that lead to upgrading their skills (fine tuning) or that imply a major career reorientation (re-skilling).

Samples

This study focuses on workers who had PSENote  in 2006, were permanently laid off from 2009 to 2013, and were aged 30 to 54 in the year of job loss. Different samples are considered, depending on whether the analysis is about a) educational transitions after job loss or b) the earnings trajectories associated with these transitions.

To analyze educational transitions after job loss, five cohorts of displaced workers are pooled. Each cohort is identified by the year of displacement (t=2009 to t=2013) and satisfies the following conditions:

  1. Workers earned wages and salaries (as evidenced by the T4 statement of remuneration) and did not attend PSE institutions during the year prior to job loss (year t-1) or during the year of job loss (year t).
  2. Workers were not permanently laid off during the year prior to job loss(year t-1).
  3. An income tax form (T1 form) was found for workers from year t-1 to year t+4 and workers were still living by year t+4.
  4. Non-permanent residents and institutional residents are excluded.

Comparison groups are created for each cohort of displaced workers. Workers in the comparison groups also had PSE in 2006, were aged 30 to 54 in year t, and satisfy conditions 1, 3 and 4, but were not permanently laid off at any point from year t-1 to year t+4.Note 

Transitions into PSE after job loss are measured using the PSIS. Displaced workers and workers in the comparison groups are deemed to have entered PSE if they are observed in the PSIS at some point between year t+1 and year t+3. To simplify the analysis, a worker’s field of study after job loss is based on the spell of PSE that lasted the greatest number of years (where n = 1, 2, 3).Note  The resulting field of study is compared with that recorded in the 2006 Census to distinguish re-skilling from fine tuning.

When analyzing the earnings growth associated with educational transitions, condition 3 is replaced by the following conditions:

Condition 3a) ensures that workers are still living five years after job loss. Condition 3b) allows the earnings growth for workers involved in the labour market to be measured, both prior to job loss and after job loss. Condition 3c) ensures that the annual earnings measured in year t+5 are not reduced by time constraints resulting from PSE attendance, which would introduce noise into the analysis of earnings trajectories of various groups of displaced workers. In addition, the analysis of earnings growth is restricted to workers who earned no more than $500,000 (in 2018 dollars) in any year during the t-1 to t+5 period.

In sum, workers are tracked from year t-1 to year t+4 when the analysis is about educational transitions, and from year t-1 to year t+5 when the analysis is about the earnings growth associated with these transitions.Note 

Transitions into postsecondary education after job loss

Descriptive evidence

Table 1 compares the sample of displaced workers with the comparison group defined above. The numbers indicate that although both groups average roughly the same age, the displaced worker group tends to be less educated than the comparison group. For example, displaced workers have a bachelor’s degree less often (28.1%) than workers in the comparison group (33.6%).

Table 1 also shows that displaced workers are more likely than other workers to be recent immigrants, non-White, and work in construction, low-skilled services (e.g. , retail trade and accommodation and food services), small firms (firms with fewer than 20 employees) or non-unionized jobs. They are also more likely to have been with the same employer for less than 6 years and to come from the 2009 cohort. By contrast, displaced workers are less likely than other workers to have studied education or health and related fields.

Table 2 quantifies the degree to which displaced workers and other workers a) enrolled in PSE at some point during the three years following the reference year t, b) enrolled in PSE and chose a field of study different from that observed in 2006 and c) enrolled in PSE and chose the same field of study observed in 2006. Workers are deemed as having changed fields of study based on the 12 broad fields of study shown in Table 1.

Overall, 9.5% of the displaced workers selected in this study enrolled in PSE at some point during the three years following job loss. By contrast, 6.1% of workers in the comparison group enrolled in PSE at some point during the three years following the reference year t. These numbers suggest that job loss tends to increase PSE enrollment by 3.4 percentage points, and are in line with those obtained by Frenette et al. (2011), Ci et al. (2016) and Morissette and Qiu (2021).


Table 1
Descriptive statistics
Table summary
This table displays the results of Descriptive statistics Displaced workers and Comparison group, calculated using percent and number units of measure (appearing as column headers).
Displaced workers Comparison group
Average age (years) 42.0 42.7
percent
Women 52.2 54.8
Education in 2006 (Census, HCDD)
College 50.5 42.7
Certificate below a bachelor's degree 9.2 8.2
Bachelor's degree 28.1 33.6
Above a bachelor's degree 12.2 15.5
Field of study in 2006
Education 5.9 8.2
Visual and performing arts, and communications technologies 5.9 3.1
Humanities 6.4 5.4
Social and behavioural sciences and law 11.6 12.4
Business, management and public administration 24.1 25.2
Physical and life sciences and technologies 3.8 4.1
Mathematics, computer and information sciences 6.5 6.2
Architecture, engineering, and related technologies 20.8 16.1
Agriculture, natural resources and conservation 2.7 2.4
Health and related fields 8.8 13.9
Personal, protective and transportation services 3.4 3.0
Other 0.0 0.0
Immigration status
Canadian-born 71.1 77.0
Landed 10 years ago or less (as of year t) 10.3 5.7
Landed more than 10 years ago (as of year t) 18.6 17.3
Population group
South Asian 5.9 4.4
Chinese 5.6 4.9
Black 2.5 1.8
Filipino 2.8 2.4
Arab 1.3 0.8
Latin American 1.5 0.9
Southeast Asian 0.8 0.6
Other 4.1 3.0
White 75.6 81.2
Industry in year t
Mining, oil and gas 1.4 1.3
Construction 9.7 2.6
Manufacturing 11.0 9.3
Low-skilled services 32.5 26.2
Highly skilled services 9.7 8.1
Public services 21.4 41.9
Other 13.2 10.2
Unknown 1.2 0.4
Firm size in year t
Fewer than 20 employees 27.8 15.6
20 to 99 employees 19.5 11.4
100 to 499 employees 13.8 11.6
500 employees or more 38.9 61.5
Unionized in year t 15.4 36.6
Six or more years of tenure with employer 22.9 66.0
Province of employment in year t
Newfoundland and Labrador 2.0 1.4
Prince Edward Island 0.6 0.4
Nova Scotia 3.2 2.7
New Brunswick 2.7 2.3
Quebec 23.5 23.7
Ontario 39.4 41.4
Manitoba 2.5 3.1
Saskatchewan 1.8 2.6
Alberta 11.2 10.5
British Columbia 12.8 11.6
Cohort
2009 24.5 19.9
2010 18.8 20.0
2011 18.6 20.1
2012 18.9 20.1
2013 19.2 20.0
This is an empty cell number
Sample size 57,939 2,354,637

The 9.5% enrollment rate observed for displaced workers can be partitioned into three segments: 5.5% of displaced workers chose a field of study different from that observed in 2006, and 1.7% chose the same field of study observed in 2006. For the remaining 2.3%, fields of study selected prior to and after job loss cannot be compared.Note  The corresponding percentages for workers in the comparison group are 3.2%, 1.2% and 1.7%, respectively. Taken together, these numbers suggest that job loss increases the likelihood of re-skilling by about 2 percentage points (5.5% minus 3.2%). They also indicate that almost 60% (5.5% divided by 9.5%) of displaced workers who enroll in PSE after job loss do so in a new field of study, i.e., go through re-skilling.

Among displaced workers, the likelihood of re-skilling after job loss appears to be higher for a) women (6.0%) than for men (5.0%); b) workers aged 30 to 44 (6.6%) than for older workers (3.8%); c) workers coming from physical and life sciences and technologies (9.2%) than for those coming from other fields of study, including architecture, engineering, and related technologies (4.0%) and business, management and public administration (4.3%); d) Black people (7.9%) than for White (5.6%), South Asian (3.1%) or Filipino (2.4%) people. Differences in the propensity to change fields of study are also observed across industries—workers displaced from public services change fields of study more often (7.3%) than those displaced from low-skilled services (4.4%)—and provinces, where workers displaced in Newfoundland and Labrador, Prince Edward Island and British Columbia generally changed fields of study more often than their counterparts in other provinces. By contrast, no substantial differences in the likelihood of re-skilling are observed across firm sizes. All of the qualitative patterns described above are also found when fine tuning and re-skilling are based on 41 two-digit fields of study instead of the 12 broad groups shown in Table 2.


Table 2
Rates of entry into postsecondary education, workers displaced from 2009 to 2013 and other workers
Table summary
This table displays the results of Rates of entry into postsecondary education. The information is grouped by Rates of entry (appearing as row headers), Displaced workers, Other workers, PSE, New field, Same field, Unknown and New
field, calculated using percent and number units of measure (appearing as column headers).
Rates of entry Displaced workers Other workers
PSE New field Same field Unknown PSE New
field
Same field Unknown
percent
Overall 9.5 5.5 1.7 2.3 6.1 3.2 1.2 1.7
Gender
Men 8.8 5.0 1.7 2.1 5.2 2.9 0.9 1.4
Women 10.1 6.0 1.8 2.4 6.8 3.5 1.5 1.8
Age in year t
30 to 44 11.1 6.6 2.0 2.4 7.4 3.9 1.7 1.9
45 to 54 7.1 3.8 1.2 2.1 4.4 2.3 0.7 1.4
Education in 2006
College 8.7 5.0 1.5 2.2 5.9 3.1 1.0 1.8
Certificate below a bachelor's degree 9.5 5.7 1.5 2.2 6.7 3.6 1.5 1.6
Bachelor's degree 10.5 6.0 2.0 2.4 6.3 3.3 1.4 1.6
Above a bachelor's degree 10.2 5.9 2.0 2.3 5.9 2.9 1.4 1.6
Field of study in 2006
Education 10.8 5.8 3.1 1.9 7.7 2.9 3.7 1.1
Visual and performing arts, and communications technologies 10.3 6.7 0.8 2.7 6.3 4.1 0.3 1.9
Humanities 12.3 7.5 2.5 2.3 6.9 3.6 1.6 1.7
Social and behavioural sciences and law 10.5 6.6 1.2 2.7 6.7 4.1 0.7 1.8
Business, management and public administration 8.4 4.3 1.9 2.2 5.3 2.4 1.3 1.6
Physical and life sciences and technologies 12.0 9.2 0.5 2.3 6.4 4.6 0.2 1.7
Mathematics, computer and information sciences 8.6 5.5 0.9 2.2 5.2 3.1 0.5 1.7
Architecture, engineering, and related technologies 8.1 4.0 2.1 2.1 5.1 2.7 0.7 1.7
Agriculture, natural resources and conservation 12.4 8.8 1.2 2.3 7.7 5.2 0.6 1.9
Health and related fields 9.4 5.3 1.9 2.1 6.7 3.3 1.5 1.9
Personal, protective and transportation services 9.5 6.0 0.8 2.7 6.9 3.8 1.3 1.8
Other 24.2 3.0 9.1 12.1 6.0 4.9 0.0 1.2
Immigration status
Canadian-born 9.8 5.8 1.8 2.2 6.2 3.4 1.3 1.6
Landed 10 years ago or less (as of year t) 10.4 5.6 2.1 2.7 7.0 3.4 1.2 2.4
Landed more than 10 years ago (as of year t) 7.8 4.1 1.2 2.5 5.1 2.4 0.8 1.9
Population group
South Asian 7.2 3.1 1.6 2.5 5.5 2.4 0.9 2.2
Chinese 8.2 4.8 1.4 1.9 5.0 2.5 0.7 1.9
Black 14.6 7.9 2.6 4.1 9.0 4.1 2.1 2.8
Filipino 6.0 2.4 1.0 2.7 4.4 2.0 0.8 1.7
Arab 9.8 5.6 2.0 2.1 6.5 3.2 1.5 1.9
Latin American 11.9 6.8 2.4 2.8 7.6 3.8 1.4 2.4
Southeast Asian 8.1 5.5 1.1 1.5 4.5 2.1 0.8 1.5
Other 14.1 8.3 1.7 4.1 8.6 4.8 1.5 2.3
White 9.4 5.6 1.7 2.1 6.1 3.2 1.3 1.6
Industry in year t
Mining, oil and gas 9.7 6.3 1.8 1.7 5.7 3.7 1.1 0.8
Construction 8.8 5.2 1.7 1.9 4.8 2.5 0.7 1.7
Manufacturing 8.5 4.6 1.6 2.2 4.4 2.0 0.7 1.7
Low-skilled services 8.4 4.4 1.5 2.5 4.6 2.2 0.8 1.5
Highly skilled services 9.2 5.6 1.4 2.2 4.4 2.3 0.6 1.5
Public services 11.8 7.3 2.2 2.2 8.1 4.4 1.9 1.8
Other 10.0 6.0 1.8 2.1 5.1 2.6 0.8 1.7
Unknown 8.9 5.9 1.5 1.5 4.9 2.8 0.8 1.3
Firm size in year t
Fewer than 20 employees 9.5 5.8 1.6 2.1 4.6 2.4 0.7 1.5
20 to 99 employees 9.6 5.3 1.9 2.4 5.6 2.9 0.9 1.7
100 to 499 employees 8.8 5.1 1.4 2.4 6.6 3.3 1.2 2.0
500 employees or more 9.6 5.5 1.8 2.3 6.5 3.4 1.4 1.6
Overall 9.5 5.5 1.7 2.3 6.1 3.2 1.2 1.7
Unionized in year t
Yes 10.5 6.6 2.1 1.7 7.5 4.0 1.9 1.6
No 9.3 5.3 1.6 2.4 5.3 2.7 0.9 1.7
Tenure in year t
Six years or more 8.1 4.5 1.4 2.3 5.5 2.8 1.2 1.5
Less than six years 9.9 5.8 1.8 2.3 7.1 3.9 1.3 2.0
Province of employment
Newfoundland and Labrador 12.1 10.8 1.1 0.2 9.8 7.8 1.8 0.2
Prince Edward Island 17.9 15.1 2.4 0.5 8.3 6.0 2.2 0.1
Nova Scotia 7.3 5.7 1.1 0.6 6.6 4.8 1.6 0.2
New Brunswick 9.1 7.1 1.8 0.2 5.2 3.9 1.2 0.1
Quebec 8.5 5.4 2.4 0.7 4.8 2.7 1.6 0.5
Ontario 7.9 2.9 1.1 3.9 5.2 1.6 0.8 2.8
Manitoba 11.1 6.2 3.3 1.6 6.5 3.5 2.1 0.9
Saskatchewan 11.4 6.5 2.0 2.9 7.7 3.3 1.4 3.1
Alberta 7.5 5.0 2.1 0.4 4.0 2.7 1.2 0.2
British Columbia 16.4 11.4 2.1 2.9 12.1 8.6 1.5 2.0
Cohort
2009 10.4 6.1 2.0 2.3 6.6 3.5 1.4 1.7
2010 10.0 5.7 2.0 2.2 6.4 3.3 1.3 1.7
2011 9.0 5.1 1.7 2.3 6.1 3.2 1.2 1.7
2012 8.8 5.2 1.3 2.3 5.8 3.0 1.2 1.7
2013 9.0 5.2 1.5 2.3 5.6 2.9 1.1 1.6
This is an empty cell number
Sample size 57,939 57,939 57,939 57,939 2,354,637 2,354,637 2,354,637 2,354,637

Regression analyses

Table 3 shows the extent, if any, to which prime-aged displaced workers are more likely than other workers to enroll in PSE or to change fields of study. Probit models of the likelihood of making the following transitions are estimated: 1) entering PSE, 2) changing fields of study at some point during the three years following reference year t. The goal is to quantify the degree to which, among observationally equivalent individuals, displaced workers are more likely than workers in the comparison group to make these transitions. To compare individuals with similar observable characteristics, the following explanatory variables are included in sex-specific probit models: age in year t, education in 2006, broad field of study in 2006, immigration status, population group, province of employment in year t and cohort. The categories used to define these explanatory variables are shown in Table 2. Whenever probit models pool data for men and women, a female indicator is added to the list of indicators.


Table 3
Likelihood of entering postsecondary education or a new field of study, workers displaced from 2009 to 2013 and other workers
Table summary
This table displays the results of Likelihood of entering postsecondary education or a new field of study. The information is grouped by Likelihood of entering (appearing as row headers), Postsecondary education, Broad new field of study and Detailed new field of study, calculated using average marginal effects units of measure (appearing as column headers).
Likelihood of entering Postsecondary education Broad new field of study Detailed new field of study
average marginal effects
Fields of study
Both sexes
Displaced workers 0.031Note *** 0.019Note *** 0.022Note ***
Other workers Note ...: not applicable Note ...: not applicable Note ...: not applicable
Baseline rate (%) 6.2 3.2 3.5
Sample size (number) 2,412,576 2,412,576 2,412,576
Men
Displaced workers 0.031Note *** 0.018Note *** 0.022Note ***
Other workers Note ...: not applicable Note ...: not applicable Note ...: not applicable
Baseline rate (%) 5.3 2.9 3.2
Sample size (number) 1,082,796 1,082,796 1,082,796
Women
Displaced workers 0.030Note *** 0.020Note *** 0.022Note ***
Other workers Note ...: not applicable Note ...: not applicable Note ...: not applicable
Baseline rate (%) 6.9 3.5 3.7
Sample size (number) 1,329,780 1,329,780 1,329,780

All else being equal, displaced men and women are about 3 percentage points more likely than other workers to enroll in PSE. Depending on whether changes in fields of study are defined at the one-digit level or the two-digit level, the likelihood of displaced men changing fields of study is between 1.8 and 2.2 percentage points higher than that of other male workers. For women, the corresponding differences vary between 2.0 and 2.2 percentage points. In all cases, the differences are precisely estimated. Overall, these numbers confirm the findings of previous Canadian studies, i.e., confirm that job loss leads to modest increases in PSE enrollment. They also indicate that jobs loss induces, in absolute terms, modest increases in the likelihood of re-skilling.

Table 4 focuses on displaced workers (n=57,939) and examines which ones have a relatively high likelihood of enrolling in PSE or changing fields of study after job loss. Because of the large number of binary indicators that can potentially be used and the relatively small samples that are available when estimating sex-specific models for displaced workers, the set of explanatory variables used in Table 4 is narrower than that used in Table 3 and excludes province and cohort indicators. Nevertheless, the findings reported below generally hold when province and cohort indicators are added to the models.


Table 4
Likelihood of displaced workers entering postsecondary education or a new field of study after job loss, by personal characteristics
Table summary
This table displays the results of Likelihood of displaced workers entering postsecondary education or a new field of study after job loss. The information is grouped by Likelihood of entering: (appearing as row headers), Postsecondary education, A new broad field of study, A new detailed field of study, Both sexes, Men and Women, calculated using average marginal effects units of measure (appearing as column headers).
Likelihood of entering: Postsecondary education A new broad field of study A new detailed field of study
Both sexes Men Women Both sexes Men Women Both sexes Men Women
average marginal effects
Gender
Men Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Women 0.0116Note *** Note ...: not applicable Note ...: not applicable 0.0074Note ** Note ...: not applicable Note ...: not applicable 0.0068Note ** Note ...: not applicable Note ...: not applicable
Age in year t
30 to 44 Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
45 to 54 -0.0375Note *** -0.0319Note *** -0.0439Note *** -0.0267Note *** -0.0235Note *** -0.0302Note *** -0.0295Note *** -0.0275Note *** -0.0321Note ***
Education in 2006
College Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Certificate below a bachelor's degree 0.0109Note * 0.0102 0.0102 0.0101Note * 0.0056 0.0129Note * 0.0115Note ** 0.0063 0.0154Note *
Bachelor's degree 0.0128Note *** 0.0104Note * 0.0124Note * 0.0058Note * 0.0038 0.0046 0.0061Note * 0.0037 0.0060
Above a bachelor's degree 0.0159Note ** 0.0166Note * 0.0118Table 4 Note  0.0098Note * 0.0105Table 4 Note  0.0049 0.0109Note ** 0.0135Note * 0.0050
Field of study in 2006
Education 0.0146Note * 0.0197 0.0146Table 4 Note  0.0098Note * 0.0200Table 4 Note  0.0088 0.0080 0.0190Table 4 Note  0.0060
Visual and performing arts, and communications technologies 0.0144Note * -0.0013 0.0301Note ** 0.0205Note *** 0.0074 0.0324Note *** 0.0198Note *** 0.0074 0.0314Note ***
Humanities 0.0311Note *** 0.0284Note ** 0.0351Note *** 0.0274Note *** 0.0183Note * 0.0348Note *** 0.0413Note *** 0.0349Note *** 0.0472Note ***
Social and behavioural sciences and law 0.0132Note ** 0.0243Note ** 0.0089 0.0179Note *** 0.0253Note *** 0.0160Note *** 0.0230Note *** 0.0290Note *** 0.0214Note ***
Business, management and public administration Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Physical and life sciences and technologies 0.0316Note *** 0.0375Note ** 0.0274Note * 0.0476Note *** 0.0488Note *** 0.0471Note *** 0.0463Note *** 0.0475Note *** 0.0462Note ***
Mathematics, computer and information sciences 0.0045 0.0021 0.0118 0.0145Note ** 0.0065 0.0273Note *** 0.0140Note ** 0.0061 0.0286Note ***
Architecture, engineering, and related technologies 0.0053 0.0065 0.0020 0.0010 -0.0032 0.0162Note * 0.0133Note *** 0.0111Note * 0.0188Note *
Agriculture, natural resources and conservation 0.0397Note *** 0.0256Note * 0.0651Note *** 0.0444Note *** 0.0290Note ** 0.0669Note *** 0.0459Note *** 0.0337Note ** 0.0654Note ***
Health and related fields 0.0069 0.0234Note * 0.0027 0.0081Note * 0.0237Note ** 0.0049 0.0082Note * 0.0253Note ** 0.0043
Personal, protective and transportation services 0.0139Table 4 Note  0.0233Note * 0.0018 0.0175Note ** 0.0147Table 4 Note  0.0187Table 4 Note  0.0174Note * 0.0171Note * 0.0165
Other 0.1348 Note ...: not applicable 0.2192 -0.0177 Note ...: not applicable -0.0082 -0.0197 Note ...: not applicable -0.0113
Immigration status
Canadian-born Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Landed 10 years ago or less 0.0044 0.0082 0.0002 -0.0002 0.0054 -0.0072 -0.0008 0.0047 -0.0071
Landed more than 10 years ago -0.0161Note *** -0.0167Note ** -0.0143Note * -0.0131Note *** -0.0126Note ** -0.0135Note ** -0.0145Note *** -0.0150Note ** -0.0136Note *
Population group
South Asian -0.0226Note *** -0.0129 -0.0319Note *** -0.0235Note *** -0.0186Note ** -0.0275Note *** -0.0257Note *** -0.0199Note ** -0.0308Note ***
Chinese -0.0082 -0.0049 -0.0110 -0.0020 -0.0042 -0.0004 -0.0020 -0.0024 -0.0018
Black 0.0603Note *** 0.0694Note *** 0.0497Note ** 0.0303Note *** 0.0296Note * 0.0293Note * 0.0368Note *** 0.0378Note ** 0.0339Note *
Filipino -0.0252Note ** -0.0057 -0.0415Note *** -0.0250Note *** -0.0166Table 4 Note  -0.0309Note *** -0.0208Note ** -0.0087 -0.0304Note ***
Arab 0.0074 0.0070 0.0073 0.0047 -0.0067 0.0207 0.0053 -0.0024 0.0156
Latin American 0.0313Note * 0.0231 0.0392Table 4 Note  0.0193Table 4 Note  0.0116 0.0263 0.0199Table 4 Note  0.0143 0.0250
Southeast Asian -0.0027 0.0506Table 4 Note  -0.0733Note *** 0.0084 0.0398 -0.0323Note ** 0.0049 0.0378 -0.0367Note **
Other 0.0510Note *** 0.0408Note *** 0.0588Note *** 0.0323Note *** 0.0222Note * 0.0407Note *** 0.0338Note *** 0.0252Note * 0.0409Note ***
White Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Baseline rate (%) 9.5 8.8 10.1 5.5 5.0 6.0 6.1 5.7 6.4
Sample size (number) 57,939 27,415 30,524 57,939 27,415 30,524 57,939 27,415 30,524

Table 4 shows that, all else being equal, displaced women are more likely than displaced men to enroll in PSE after job loss or to enter a new field of study. On average, the likelihood of women entering PSE exceeds that of men by about 1.2 percentage points. The likelihood of women entering a new field of study exceeds that of men by 0.7 percentage point, regardless of the way changes in fields of study are defined.

In line with Morissette and Qiu (2021), Table 4 indicates that older male and female displaced workers are less likely than their younger counterparts to enter PSE after job loss. The difference between workers aged 45 to 54 and those aged 30 to 44 in the likelihood of entering postsecondary education averages 3.2 percentage points for men and 4.4 percentage points for women. Since 8.8% of displaced men and 10.1% of displaced women entered PSE after job loss, these differences are important in relative terms. Displaced men and women aged 45 to 54 are also less likely than those aged 30 to 44 to enter new fields of study after job loss. Across age groups, these differences in likelihood amount to about 2.5 percentage points for men and 3.0 percentage points for women.

Table 4 also shows that the amount of re-skilling varies across initial fields of study. In most initial fields of study, displaced women are more likely to undergo re-skilling than displaced women who previously studied in business, management and public administration. For example, displaced women who previously studied in physical and life sciences and technologies are, on average, almost 5 percentage points more likely to enter new fields of study than their counterparts who studied in business, management and public administration. Given that, on average, 6% of displaced women enter new fields of study after job loss, this 5 percentage point difference is worth noting. By contrast, the likelihood of changing fields of study is not statistically different for displaced women who were previously trained in education or health and related fields, compared with those who studied in business, management and public administration. Relative to business, management and public administration, the likelihood of displaced men changing fields of study is also higher in humanities; social and behavioural sciences and law; physical and life sciences and technologies; agriculture, natural resources and conservation; and health and related fields. Displaced men and women who previously studied in business, management and public administration are never more likely to change fields of study than their counterparts who previously studied in other fields. This suggests a certain degree of stability in the type of learning that individuals coming from business, management and public administration experience.

Differences in the amount of re-skilling are also observed across population groups. Compared with their White counterparts, South Asian men and women are between 1.9 and 3.1 percentage points less likely to enter new fields of study after job loss. A similar pattern is observed for Filipino women or women in “Other” population groups. By contrast, Black men and women are between 3 and 4 percentage points more likely than White men and women to change fields of study after job loss.

Taken together, the numbers in Table 4 confirm previous findings—for example, the relatively low likelihood of older displaced workers entering PSE after job loss—while highlighting new patterns not yet documented in Canada. One key finding is that the likelihood of re-skilling varies not only across sex and age group, but also across initial fields of study and population groups. Moreover, the differences uncovered by multivariate analyses are often important in relative terms.

Which fields of study do displaced workers enter after job loss?

The results shown in the previous section quantify the degree to which displaced workers enter new fields of study after job loss, but provide no details on the specific fields that workers with a given background choose after displacement. Table 5 fills this gap. It provides a pre-job displacement versus post-job displacement field of study matrix comparing the field of study chosen after displacement with the field of study reported previously in the 2006 Census of Population for displaced workers who entered PSE after job loss. The focus is on displaced workers for which the categories “New fields of study” or “Same field of study” shown in Table 2 can be identified. Displaced workers who fall under the “Unknown” category shown in columns 4 and 8 of Table 2 are excluded from Table 5. Separate matrices are produced for men (n=1,835) and women (n=2,394).


Table 5
Transition matrices, initial field of study compared with field of study after job loss, male and female displaced workers entering postsecondary education after job loss
Table summary
This table displays the results of Transition matrices MEN (n=1,835)
Field of study selected after job loss, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, Not for credit, Multiple and Overall, calculated using percent units of measure (appearing as column headers).
MEN (n=1,835)
Field of study selected after job loss
1 2 3 4 5 6 7 8 9 10 11 12 Not for credit Multiple Overall
percent
Initial field
1 26.7 0.0 18.8 7.5 5.4 0.0 5.0 16.2 0.0 4.1 3.7 4.1 7.5 1.1 3.7
2 10.8 11.4 14.1 6.9 10.5 0.0 5.7 20.7 0.0 1.7 7.2 2.7 6.8 1.5 5.3
3 15.1 2.9 29.6 5.0 15.6 0.0 1.2 10.2 0.6 4.1 3.4 2.4 8.9 1.1 6.4
4 9.3 0.9 25.4 15.4 18.3 0.0 2.4 11.2 3.7 3.9 3.7 1.4 4.5 0.0 9.5
5 5.1 0.9 22.1 2.0 24.2 0.5 3.4 19.9 1.3 2.9 5.0 2.4 8.9 1.4 13.9
6 10.6 1.1 21.9 5.3 14.9 4.1 4.6 22.8 2.8 2.4 3.3 2.5 1.3 2.3 6.0
7 3.7 1.1 16.3 3.3 13.7 0.8 20.0 21.0 0.0 3.3 5.6 2.1 6.0 3.0 8.0
8 2.7 0.9 25.9 2.7 7.8 0.4 3.4 38.2 0.6 1.6 3.4 4.3 7.1 0.8 33.8
9 0.0 0.2 21.0 2.1 4.0 0.0 0.0 26.5 12.6 6.7 9.7 2.3 11.5 3.4 4.2
10 4.3 0.0 18.6 6.8 16.3 6.1 1.8 15.3 1.8 17.5 3.6 0.0 5.3 2.6 4.9
11 4.0 0.0 23.2 1.9 10.6 0.8 1.0 26.9 0.0 1.7 15.5 5.8 2.9 5.8 4.2
12 § § § § § § § § § § § § § § 0.0
Overall 6.4 1.5 23.0 4.7 12.9 0.8 4.4 25.2 1.6 3.4 4.8 3.0 6.7 1.5 100.0

Table 5
Transition matrices, initial field of study compared with field of study after job loss, male and female displaced workers entering postsecondary education after job loss (continued)
Table summary
This table displays the results of Transition matrices WOMEN (n=2,394)
Field of study selected after job loss, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, Not for credit, Multiple and Overall, calculated using percent units of measure (appearing as column headers).
WOMEN (n=2,394)
Field of study selected after job loss
1 2 3 4 5 6 7 8 9 10 11 12 Not for credit Multiple Overall
percent
Initial field
1 37.4 0.2 17.2 12.0 11.6 0.8 1.6 0.0 0.5 6.4 0.0 1.3 9.0 2.1 10.1
2 15.7 10.9 21.3 12.5 13.9 0.0 4.2 3.3 0.6 6.8 0.0 3.4 5.3 1.8 7.0
3 21.6 1.3 22.9 8.7 20.6 0.0 2.8 1.1 0.3 10.2 1.4 2.0 5.0 2.1 10.7
4 12.3 2.4 21.6 14.6 19.8 0.6 0.5 1.3 1.3 11.5 0.7 4.7 7.0 1.7 15.1
5 5.8 0.4 24.4 8.1 32.7 0.5 1.1 2.6 0.3 8.7 3.5 2.7 7.8 1.4 26.2
6 6.6 0.0 17.5 9.7 14.6 5.6 1.7 1.7 6.6 15.6 5.3 5.9 8.9 0.3 4.4
7 9.7 0.9 22.7 11.2 20.0 2.9 4.5 6.6 2.9 8.5 1.7 4.2 4.2 0.0 4.1
8 6.9 0.0 25.1 9.0 27.1 0.0 1.6 8.9 2.3 7.4 0.7 3.1 5.1 2.8 4.3
9 9.6 0.0 22.2 7.8 10.3 1.1 0.0 7.3 11.7 9.9 4.0 0.0 6.5 9.6 3.3
10 7.4 1.3 18.8 9.7 13.8 0.5 1.3 1.6 0.6 29.4 2.5 2.1 9.6 1.4 12.2
11 3.7 0.0 22.3 4.7 22.6 0.0 1.0 3.4 0.0 18.4 5.0 2.6 5.2 11.0 2.4
12 § § § § § § § § § § § § § § 0.1
Overall 12.9 1.6 21.7 10.2 21.1 0.8 1.6 2.5 1.4 11.9 2.0 3.0 7.2 2.1 100.0

The results confirm that a minority of the displaced men and women considered in Table 5 stay in the same field of study after job loss. This can be seen by looking at the cells on the diagonal of each matrix. For example, depending on the initial field of study, between 4.1% and 38.2% of displaced men entered the same field of study as that observed in 2006 after losing their jobs. For displaced women, the corresponding estimates vary between 4.5% and 37.4%.

The fields of study selected after job loss differ between men and women. The two most popular fields for men are architecture, engineering, and related technologies (25.2%) and humanities (23.0%), and are selected by close to half of displaced men. By contrast, the two most popular fields for women are humanities (21.7%) and business, management and public administration (21.1%), selected by roughly 4 in 10 displaced women.

Admittedly, these dissimilarities partly reflect gender differences in the field of study observed in 2006, prior to job loss. A more meaningful comparison focuses on gender differences within a given initial field of study. When this comparison is done, interesting patterns emerge. For example, regardless of the initial field of study considered, the proportion of displaced workers choosing architecture, engineering, and related technologies (Field 8) is between 9.1 and 29.4 percentage points higher for men than for women.Note  Conversely, the proportion of displaced workers choosing health and related fields (Field 10) after job loss is, within most fields of study, between 5.1 and 16.6 percentage points lower for men than for women. This suggests that displaced men tend to stay away from health-related occupations after job loss (Miller 2017).

To test whether gender differences in the field selected after job loss remain after conditioning on workers’ initial field of study, a series of probit models are estimated. Each model estimates the likelihood of displaced workers selecting a given field of study (e.g., humanities, education) after job loss. Eleven models are estimated, one for each of the fields of study shown in Table 2 (with the exception of “other,” where small sample sizes preclude estimation). In each model, data for displaced men and women are pooled together and the following set of explanatory variables is used: an indicator for women, an indicator for workers aged 45 to 54, indicators of educational attainment and indicators for the fields of study observed in 2006, prior to job loss.Note  The following question is asked: Among displaced men and women of similar age, education and initial fields of study, are there gender differences in the field of study selected after job loss?

The results in Table 6 indicate that, all else being equal, women are more likely than men to choose the following fields after job loss: education; social and behavioural sciences and law; business, management and public administration; and health and related fields. For example, the likelihood of entering health and related fields is, on average, 7.5 percentage points higher for women than for men. This is a substantial difference given that 8.2% of displaced men and women, collectively, chose that field after job loss. Likewise, the likelihood of displaced workers entering social and behavioural science and law or business, management and public administration is about 6 percentage points higher, on average, for women than for men.


Table 6
Gender differences in the likelihood of entering a given field of study, conditional on the field of study observed in 2006, displaced workers who enrolled in postsecondary education after job loss
Table summary
This table displays the results of Gender differences in the likelihood of entering a given field of study. The information is grouped by Likelihood of entering field (appearing as row headers), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11, calculated using average marginal effects units of measure (appearing as column headers).
Likelihood of entering field 1 2 3 4 5 6 7 8 9 10 11
average marginal effects
Men Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable Note ...: not applicable
Women 0.0409Note *** -0.0019 -0.0015 0.049Note *** 0.0629Note *** -0.0001 -0.0244Note *** -0.1777Note *** -0.0017 0.0751Note *** -0.0299Note ***
Baseline rate (%) 10.0 1.5 22.3 7.8 17.5 0.8 2.9 12.5 1.5 8.2 3.3
Sample size (number) 4,229 4,229 4,229 4,229 4,229 4,229 4,229 4,229 4,229 4,229 4,229

By contrast, women are much less likely than men to choose architecture, engineering, and related technologies after job loss: the gender difference averages roughly 18 percentage points. Women are also slightly less likely than men to enter mathematics, computer and information sciences (-2.4 percentage points) or personal, protective and transportation services (-3.0 percentage points).

In sum, Table 6 highlights significant gender differences in the fields of study that displaced workers choose after job loss. These differences are observed even after controlling for their initial fields of study.

Table 7 provides additional information on the types of educational transitions that displaced workers experience.

The first panel of Table 7 shows that regardless of displaced workers’ initial educational attainment, the most frequent option selected after job loss is one-year programs. This finding is consistent with the fact that the sample considered in the study consists of adults, many of whom might face substantial time constraints. For example, between 53.7% and 60.0% of displaced men with identifiable “New fields of study” or “Same field of study” categories selected a one-year program after job loss. For displaced women, the corresponding percentages vary between 47.9% and 58.3%.

The second panel of Table 7 shows that career, technical and professional training programs (level 2 programs) are generally the preferred option of displaced workers after job loss. For example, almost half of displaced women with a college diploma chose this option after losing their jobs, compared with more than 60% of their male counterparts.

Taken together, the numbers in Table 7 suggest that many displaced workers choose short-term applied or career-oriented programs after job loss.


Table 7
Duration and level of postsecondary education program selected by displaced workers entering postsecondary education after job loss, by education level in 2006
Table summary
This table displays the results of Duration and level of postsecondary education program selected by displaced workers entering postsecondary education after job loss 1 year, 2 years, 3 years and Overall, calculated using percent units of measure (appearing as column headers).
1 year 2 years 3 years Overall
percent
I. Duration of program selected after job loss
Men (n=1,835)
Education in 2006
College 60.0 26.7 13.3 47.2
Certificate below bachelor 55.1 35.1 9.8 8.5
Bachelor's degree 56.2 29.5 14.4 29.2
Above bachelor's degree 53.7 23.2 23.0 15.1
Overall 57.5 27.7 14.8 100.0
Women (n=2,394)
Education in 2006
College 58.3 27.0 14.7 45.0
Certificate below a bachelor's degree 46.8 38.0 15.3 9.9
Bachelor's degree 51.7 29.5 18.8 33.1
Above a bachelor's degree 47.9 31.5 20.5 12.0
Overall 53.7 29.5 16.8 100.0

Table 7
Duration and level of PSE program selected by displaced workers entering postsecondary education after job loss, by education level in 2006 (continued)
Table summary
This table displays the results of Duration and level of PSE program selected by displaced workers entering postsecondary education after job loss Level, Multiple, 1, 2, 3, 4 and 5, calculated using percent units of measure (appearing as column headers).
Level Multiple
1 2 3 4 5
percent
II. Level of postsecondary education programs selected after job loss
Men (n=1,835)
Education in 2006 (census, HCDD)
College 2.2 61.3 9.5 1.7 22.8 2.5
Certificate below a bachelor's degree 0.0 52.7 17.2 6.0 20.1 4.0
Bachelor's degree 1.2 43.8 13.0 17.4 21.4 3.2
Above a bachelor's degree 2.2 36.3 11.8 28.3 15.8 5.6
Overall 1.7 51.7 11.5 10.7 21.1 3.3
Women (n=2,394)
Education in 2006 (census, HCDD)
College 4.0 48.9 18.9 2.3 21.4 4.6
Certificate below a bachelor's degree 3.9 35.9 23.2 7.1 25.2 4.6
Bachelor's degree 2.6 33.6 16.2 20.8 22.9 4.0
Above a bachelor's degree 2.4 22.7 16.0 28.1 25.6 5.2
Overall 3.3 39.4 18.1 12.0 22.8 4.4

Earnings growth following job loss

So far, displaced workers and workers in the comparison group have been tracked from year t-1 to year t+4 to measure their transitions (or lack thereof) into PSE. This section somewhat narrows the sample initially selected to track workers from year t-1 to year t+5 and compare the earnings growth they experienced during that period. As mentioned earlier, all workers considered had positive earnings in year t+5 and did not attend PSE that year. The goal of this section is to answer the following question: How does the earnings growth experienced by various groups of displaced workers (those who do not enroll in PSE, those who change fields of study and those who stay in the same field of study) compare in the medium term?

Table 8 provides descriptive evidence on this issue. On average, displaced workers who did not enter PSE after job loss saw their real annual earnings increase by 0.12 log points (or roughly 12%) over the 6 six-year period ranging from year t-1 to year t+5. Displaced workers who enrolled in PSE after job loss fared better—their real annual earnings increased by 0.19 log points, on average. Stronger earnings growth for this group is observed for both men and women, but appears to be limited to workers aged 30 to 44 and those who had at most a bachelor’s degree in 2006.


Table 8
Average change in log real earnings of displaced workers from the year prior to job loss to the fifth year after job loss
Table summary
This table displays the results of Average change in log real earnings of displaced workers from the year prior to job loss to the fifth year after job loss . The information is grouped by Displaced workers (appearing as row headers), Average change in log real earnings, Entered PSE after job loss, Entered PSE, No, Yes, New
field, Same
field and Unknown, calculated using logarithmic values and number units of measure (appearing as column headers).
Displaced workers Average change in log real earnings
Entered PSE after job loss Entered PSE
No Yes New
field
Same
field
Unknown
logarithmic values
All 0.12 0.19 0.23 0.17 0.11
Gender
Men 0.07 0.13 0.18 0.04 0.09
Women 0.18 0.24 0.27 0.29 0.12
Age in year t
30 to 44 0.21 0.27 0.31 0.20 0.22
45 to 54 -0.01 0.01 0.03 0.09 -0.07
Education in 2006
College 0.11 0.21 0.24 0.18 0.16
Certificate below a bachelor's degree 0.10 0.23 0.32 0.02 0.17
Bachelor's degree 0.16 0.20 0.27 0.13 0.08
Above a bachelor's degree 0.12 0.07 0.05 0.30 -0.05
PSE program duration after job loss
One year Note ...: not applicable 0.18 0.20 0.23 0.11
Two years Note ...: not applicable 0.19 0.27 0.11 -0.01
Three years Note ...: not applicable 0.27 0.31 0.12 0.41
Level of PSE program after job loss
1 Note ...: not applicable 0.21 0.21 1.36 0.02
2 Note ...: not applicable 0.19 0.23 0.07 0.17
3 Note ...: not applicable 0.29 0.25 0.34 0.45
4 Note ...: not applicable 0.22 0.25 0.18 Note ...: not applicable
5 Note ...: not applicable 0.16 0.22 0.50 0.10
Multiple Note ...: not applicable 0.15 0.27 -0.69 0.13
   number
Sample size 42,466 3,957 2,283 714 960

Among displaced workers who enrolled in PSE, those who changed fields of study saw their earnings increase the most, on average. Earnings growth for this group averaged 0.23 log points, compared with 0.17 log points for their counterparts who stayed in the same field of study, and 0.11 log points for others who enrolled in PSE.

The relatively strong earnings growth of displaced workers who changed fields of study is limited to men, however. Displaced women who changed fields of study did not experience stronger earnings growth (0.27 log points) than displaced women who stayed in their initial fields (0.29 log points).

Part of these differences in earnings growth might reflect compositional effects. Results not shown indicate that displaced workers who changed fields of study after job loss were about two years younger (39.9 years) than those who did not enter PSE after displacement (42.0 years). The former group was also slightly more likely to have had a bachelor’s degree or higher education than the latter prior to job loss.Note 

Table 9 controls for these compositional effects. The first panel shows earnings growth differences between displaced workers who entered PSE after job loss and those who did not. Results are shown for both sexes as well as for men and women separately. Changes in log real earnings from year t-1 to year t+5 are regressed on a) a binary indicator identifying displaced workers who entered PSE after job loss and b) controls for a quadratic term in age, education indicators and, when data for men and women are pooled, a binary indicator for women. The first line shows the parameter estimates obtained for the binary indicator identifying displaced workers who entered PSE after job loss when no controls are used; these parameter estimates replicate the log earnings differences observed in Table 9. The second line shows the corresponding parameter estimates obtained after controlling for age, education and sex. The numbers indicate that controlling for age, education and sex reduces the earnings growth differences between displaced workers who entered PSE after job loss and those who did not, from 0.065 to 0.067 log points to roughly 0.030 log points. However, the remaining difference is no longer statistically significant at conventional levels. These estimates suggest that among postsecondary-educated displaced workers of similar ages and education, those who entered PSE after job loss did not experience faster earnings growth, on average, from year t-1 to year t+5 than those who did not enter PSE.


Table 9
Earnings growth differences relative to displaced workers who did not enroll in postsecondary education after job loss
Table summary
This table displays the results of Earnings growth differences relative to displaced workers who did not enroll in postsecondary education after job loss Both genders, Women and Men, calculated using logarithmic values units of measure (appearing as column headers).
Both genders Women Men
logarithmic values
I. Displaced workers who enrolled in postsecondary education after job loss
No controls 0.0669Note ** 0.062Table 9 Note  0.0654
With controls 0.0326 0.0285 0.0368
II. Various groups of displaced workers who enrolled in postsecondary education after job loss
Broad field of study after job loss
No controls
Changed fields of study 0.1093Note *** 0.0965Table 9 Note  0.1156Note ***
Chose initial field of study 0.0446 0.1150Table 9 Note  -0.0283
Other -0.0122 -0.0507 0.0252
With controls
Changed fields of study 0.0686Note * 0.0583 0.0808Note *
Chose initial field of study 0.0073 0.0752 -0.0611
Other -0.0298 -0.0692 0.0139
Sample size (number) 46,423 23,907 22,516
Detailed field of study after job loss
No controls
Changed fields of study 0.0954Note ** 0.0914Table 9 Note  0.0959Note **
Chose initial field of study 0.0867 0.1424Table 9 Note  -0.0215
Other -0.0133 -0.0509 0.0231
With controls
Changed fields of study 0.0569Table 9 Note  0.0533 0.0608Table 9 Note 
Chose initial field of study 0.0401 0.1017 -0.0505
Other -0.0308 -0.0694 0.0118
Sample size (number) 46,423 23,907 22,516

The second panel of Table 9 investigates earnings growth differences between various groups of displaced workers: a) those who did not enter PSE after job loss, b) those who went through re-skilling, i.e., changed fields of study, c) those who stayed in their initial fields of study and d) others who enrolled in PSE. The upper half of the second panel shows log earnings differences—with no controls—between the last three groups and group a), i.e., displaced workers who did not enter PSE after job loss. The lower half shows the corresponding log earnings differences obtained after controlling for age, education and sex. These differences indicate that when re-skilling is defined using broad fields of study, displaced men who went through re-skilling experienced faster earnings growth (about 0.08 log points higher) than men who did not enroll in PSE after job loss. No statistically significant difference is detected between groups c) and d), and group a) for displaced men. When re-skilling is defined using 41 two-digit fields of study, the faster earnings growth of displaced men who went through re-skilling amounts to 0.06 log points, but is imprecisely estimated.

Results for displaced women tell a different story: after controlling for age and education, no statistically significant difference is detected between various groups of displaced workers, regardless of how re-skilling is defined.

Table 10 assesses the robustness of these results using an alternative sample that tracks displaced workers from year t-3 to year t+5. The numbers indicate that the Table 9 findings are not robust: a) displaced men who changed fields of study no longer experience faster earnings growth than men who did not enter PSE and b) displaced women who stayed in the same field of study now experience faster earnings growth than those who did not enter PSE.


Table 10
Earnings growth differences among displaced workers, year t-3 to year t+5
Table summary
This table displays the results of Earnings growth differences among displaced workers Both genders, Women and Men, calculated using logarithmic values units of measure (appearing as column headers).
Both genders Women Men
logarithmic values
I. Displaced workers who enrolled in postsecondary education after job loss
No controls 0.0678Note * 0.0873Table 10 Note  0.0413
With controls 0.0338 0.0545 0.0056
II. Various groups of displaced workers who enrolled in postsecondary education after job loss
Broad field of study after job loss
No controls
Changed fields of study 0.0922Note * 0.0886 0.0925Note *
Chose initial field of study 0.0742 0.2484Note ** -0.1041
Other 0.0136 -0.0141 0.0405
With controls
Changed fields of study 0.0528 0.0529 0.0501
Chose initial field of study 0.0320 0.2029Note * -0.1413
Other -0.0038 -0.0330 0.0207
Sample size (number) 30,004 15,543 14,461
Detailed field of study after job loss
No controls
Changed fields of study 0.0799Note * 0.0884 0.0712Table 10 Note 
Chose initial field of study 0.1263 0.2813Note ** -0.1494
Other 0.0135 -0.0146 0.0411
With controls
Changed fields of study 0.0418 0.0516 0.0288
Chose initial field of study 0.0764 0.2385Note * -0.1814
Other -0.0038 -0.0335 0.0210
Sample size (number) 30,004 15,543 14,461

These results of Tables 9 and 10 must be interpreted with caution because no attempt has been made to deal with the potential selectivity of displaced workers who changed fields of study (or stayed in the same field). For this reason, the results shown in these two tables cannot be given a causal interpretation.

Concluding remarks

While a significant literature has documented the substantial and persistent earnings losses often experienced by displaced workers, relatively little is known about the educational strategies that prime-aged displaced workers use to cope with job loss. Specifically, the extent to which laid off Canadian workers enter new fields of study after losing their job or simply upgrade their skills and remain within their initial fields of study, is currently unknown.

Using a combination of datasets, this study fills this information gap. The study shows that

  1. prime-aged displaced workers are slightly more likely than other workers to enroll in PSE or to change fields of study. The likelihood of entering PSE or changing fields of study over a three-year period is between 2 and 3 percentage points higher among displaced workers than among other workers.
  2. among prime-aged displaced workers, the likelihood of entering PSE or new fields of study after job loss varies across not only sex and age groups, but also initial fields of study and population groups. In many cases, the differences uncovered by multivariate analyses, although modest in absolute terms, are important in relative terms.
  3. the fields of study selected after job loss differ between men and women. The two most popular fields for men are architecture, engineering, and related technologies (25.2%) and humanities (23.0%), which are selected by close to half of displaced men. By contrast, the two most popular fields for women are humanities (21.7%) and business, management and public administration (21.1%), which are selected by roughly 4 in 10 displaced women.
  4. all else being equal, women are more likely than men to choose the following fields after job loss: education; social and behavioural sciences and law; business, management and public administration; and health and related fields. By contrast, women are much less likely than men to choose architecture, engineering, and related technologies and slightly less likely than men to enter mathematics, computer and information sciences or personal, protective and transportation services.
  5. the evidence as to whether displaced workers who change fields of study experience faster earnings growth than those who do not enroll in PSE after job loss is inconclusive.

These findings have a number of interesting implications. First, they provide a fresh perspective on the amount of a specific type of lifelong learning—going through re-skilling while entering PSE—that workers undertake over a given period of time. Specifically, they show that over a three-year period, between 3% and 6% of Canadian prime-aged workers with postsecondary credentials—displaced or not—enter PSE and change fields of study. Second, they uncover important differences in the fields of study that men and women choose after job loss. In particular, they show that all else being equal, displaced men who go back to school are less likely than displaced women to enter health-related fields of study after job loss. In a context where population aging is likely to lead to substantial employment growth in health-related occupations, the relatively low propensity of men to enter health-related fields of study might limit their post-displacement employment opportunities. More generally, the numbers show that gender differences in workers’ fields of study exist, not only prior to entering the labour market, but also later in their careers. Finally, the earnings-growth differences observed between displaced men who changed fields of study and those who stayed out of school provide some evidence, although not strong, that transitions into PSE might boost the earnings of some displaced workers after job loss. A thorough assessment of this question requires exogenous variation in the likelihood of workers making such transitions and is left for further research.

References

Ci, W., Frenette, M., & Morissette, R. (2016). Do Layoffs Increase Transitions to Postsecondary Education Among Adults? (Analytical Studies Branch Research Paper Series. No. 380). Statistics Canada.

Couch, K. A., & Placzek, D. W. (2010). Earnings losses of displaced workers revisited. American Economic Review,100, 572–589.

Foote, A. & Grosz, M. (2020). The effect of local labor market downturns on postsecondary enrollment and program choice. Education Finance and Policy, 15(4): 593–622.

Frenette, M., Upward, R., & Wright, P. W. (2011). The Long-Term Earnings Impact of Post-secondary Education Following Job Loss (Analytical Studies Branch Research Paper Series, No. 334), Statistics Canada.

Hijzen, A., Upward, R., & Wright, P. W. (2010). The income losses of displaced workers. Journal of Human Resources,45, 243–269.

Jacobson, L. S., Lalonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. American Economic Review, 83, 685–709.

Kletzer, L. G., & Fairlie, R. W. (2003). The long-term costs of job displacement for young adult workers. Industrial and Labor Relations Review, 56, 682–98.

Miller, C.C. (2017, January 4). Why Men Don’t Want the Jobs Done Mostly by Women. The New York Times.

Morissette, R., Qiu, H., & Chan, P. C. W. (2013). The risk and cost of job loss in Canada, 1978-2008. Canadian Journal of Economics, 46(4), 1480–1509.

Morissette, R., & Qiu, T. H. (2020). Turbulence or steady course? Permanent layoffs in Canada, 1978-2016 (IRPP Study 76). Institute for Research on Public Policy.

Morissette, R., & Qiu, T. H. (2021). Adjusting to job loss when times are tough. (IRPP Study 82). Institute for Research on Public Policy.

Stevens, A. H. (1997). Persistent effects of job displacement: the importance of multiple job losses. Journal of Labor Economics,15, 165–188.

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