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LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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%
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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%
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
Average earnings between women and men across PSE
credentials over five years since graduation
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
Increase in earnings differences between women and men
across five earnings quantiles for graduate degree holders
Year1
Year5
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
Increase in earnings differences between women and men
across five earnings quantiles for all four credentials
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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
LABOUR MARKET INFORMATION COUNCIL
CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL
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

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Gender Earnings Differentials Across Earnings Quantiles: Evidence from the linked PSIS-T1FF through the ELMLP

  • 1. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 2. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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%
  • 3. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 4. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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%
  • 5. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL Average earnings between women and men across PSE credentials over five years since graduation
  • 6. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL Increase in earnings differences between women and men across five earnings quantiles for graduate degree holders Year1 Year5
  • 7. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL Increase in earnings differences between women and men across five earnings quantiles for all four credentials
  • 8. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 9. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 10. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 11. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 12. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 13. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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
  • 14. LABOUR MARKET INFORMATION COUNCIL CONSEIL DE L’INFORMATION SUR LE MARCHÉ DU TRAVAIL 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