LMIC Senior Economist Behnoush Amery presented on post-secondary education and gender earning differentials at the Canadian Research Data Centre Network’s National Conference in Halifax, Nova Scotia.

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Gender Earnings Differentials
Across Earnings Quantiles
Evidence from the linked PSIS-T1FF through the ELMLP
(Preliminary Findings)
CRDCN Conference 2019
Presented by: Behnoush Amery
Young Jung, Elba Gomez, Tony Bonen

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Introduction
• From Census 1981 to 2016, the share of working-age population (15-64) with PSE increased
o 20 percentage points increase for women (from 36% to 56%)
o 12 percentage points increase for men (from 42% to 54%)
• Based on LFS 2018: 66% of labour force have PSE credentials
• This study investigates gender earnings differences across the earnings distribution using the newly released platform
ELMLP which provides opportunity to link administrative datasets
56%
36%
54%
42%
Women
Men
Labour force characteristics Women Men
Labour force with PSE credential 33.0% 32.9%
Share of employment for PSE holders 95.5% 95.2%
Portion employed full-time among PSE holders 78.9% 91.4%

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Data
• Education and Labour Market Longitudinal Platform (ELMLP) includes three core administrative datasets: Post-
secondary Student Information System (PSIS); Registered Apprenticeship Information System (RAIS); and T1 Family
Files (T1FF)
• Caveat: employment information is limited (no information on occupations or hours of work)
o However, controlling for field of study would indirectly account for a significant part of the gender differences in
occupations as discussed in the literature
• Using the linked PSIS-T1FF data:
o Focus on graduate cohort 2010 – track them for 5 years since graduation, from 2011 to 2015
o Only Canadians (excluded international students)
o Included only those with paid-employment income (reported T4) in all 5 years (balanced panel)
o Excluded those with self-employment income in all 5 years and those who return to school for full-time studies

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Summary Statistics Table – 2010 PSE graduates Total Sample
690,720
Female
409,760
Male
340,040
Median age at graduation 25 25 25
Median T4 earnings (Year 1-Year 5) $40,600-$55,000 $39,000-$49,100 $43,300-$64,100
Credential
College-level certificate or diploma 39% 57% 43%
Bachelor’s degree 45% 62% 38%
Graduate degree (Master’s and PhD) 14% 58% 42%
Professional degree 2% 62% 38%
Field of study
Education 12% 76% 24%
Visual and performing arts, and communications technologies 3% 65% 35%
Humanities 4% 61% 39%
Social and behavioural sciences, and law 14% 70% 30%
Business, management and public administration 22% 60% 40%
Physical and life sciences, and technologies 3% 55% 45%
Mathematics, computer and information sciences 3% 32% 68%
Architecture, engineering and related technologies 15% 15% 85%
Agriculture, natural resources and conservation 2% 49% 51%
Health and related fields 19% 84% 16%
Personal, protective and transportation services 4% 40% 60%

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Average earnings between women and men across PSE
credentials over five years since graduation

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Increase in earnings differences between women and men
across five earnings quantiles for graduate degree holders
Year1
Year5

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Increase in earnings differences between women and men
across five earnings quantiles for all four credentials

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Pooled-OLS Quantile Regression – Preliminary Findings
All Credentials (690,720)
Coefficients Q10 Q25 Q50 Q75 Q90
Gender -1123.4*** -1674.2*** -2219.4*** -3449.6*** -6532.3***
(-9.23) (-11.31) (-16.45) (-20.97) (-22.77)
Gender x t=2 -3497.4*** -2946.5*** -2753.9*** -3523.0*** -5027.5***
(-17.86) (-14.50) (-14.71) (-15.03) (-12.47)
Gender x t=3 -6122.7*** -5297.3*** -5077.7*** -7068.6*** -9744.5***
(-26.64) (-24.38) (-25.55) (-28.45) (-22.79)
Gender x t=4 -9145.7*** -8024.1*** -7599.2*** -10222.1*** -14292.7***
(-38.05) (-36.81) (-37.01) (-39.75) (-31.57)
Gender x t=5 -10427.3*** -10510.5*** -9501.6*** -12032.0*** -16054.3***
(-37.37) (-42.84) (-45.59) (-44.65) (-33.02)
• Dependent Variable: Annual Earnings (T4E >0)
• Explanatory variables: gender, PSE credentials, fields of study, age, age squared, number of T4s, number of kids
***p<0.001

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Gender Earnings Differences (%) by Quantile – Graduate
degree
0%
5%
10%
15%
20%
25%
30%
35%
40%
1 2 3 4 5
Years since graduation
Q10
Q25
Q50
Q75
Q90

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Gender Earnings Differences (%) by Quantile – Bachelor’s
degree
0%
5%
10%
15%
20%
25%
30%
35%
40%
1 2 3 4 5
Years since graduation
Q10
Q25
Q50
Q75
Q90

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Gender Earnings Differences (%) by Quantile – College-level
degree
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5
Q10
Q25
Q50
Q75
Q90

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Gender Earnings Differences (%) by Quantile – Professional
degree
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
1 2 3 4 5
Q10
Q25
Q50
Q75
Q90

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Next Steps
• Removing (most) part-time employments by dropping the lower earnings quantile
• Apply more age restriction: 20-40 years-old (90% of the sample)
• Controlling for EI beneficiary users
• Matching sample to make more apple to apple comparison
• Oaxaca-Blinder Decomposition
• Use occupational information after Census has linked to ELMLP datasets

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Thank you
Research Team at LMIC:
• Behnoush Amery, Senior Economist behnoush.amery@lmic-cimt.ca
• Young Jung, Economist young.jung@lmic-cimt.ca
• Elba Gomez, Economist elba.gomez@lmic-cimt.ca
• Tony Bonen, Director, Research, Data and Analytics tony.bonen@lmic-cimt.ca