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Earnings Inequality and theGender Pay Gap
Nicole Fortin Vancouver School of Economics andCanadian Institute for Advanced Research
1
State of the Art Lecture, CEA Meetings, June 4th 2016Click on Adobe Comment Tab to see Speaker’s Notes
Earnings Inequality and theGender Pay Gap
with the collaboration of Marie Drolet and Aneta Bonikowska
2
State of the Art Lecture, CEA Meetings, June 4th 2016
Gender Gap in Top Jobs
3
Gender Gap in Top Jobs
4
Earnings Inequality in Top Incomes
5
Earnings Inequality in Top Incomes
6Source: Veall (2012)
Increasing Earnings Inequality in Top Incomes and the Gender Pay Gap
• Apply the approach used in the analysis of earnings inequality in top incomes (developed by Thomas Piketty, Emmanuel Saez, and co-authors), as well as reweighing techniques à la DiNardo, Fortin and Lemieux (1996) [DFL] to the analysis of the gender pay gap
• Use all earnings data from the Canadian Longitudinal Worker Files (LWF, 1983-2010) supplemented by hourly wage data from the Labour Force Survey (LFS, 1997-2015)– Because couples file their income taxes separately, all earnings for T4
returns are available separately by gender in Canada
7
Increasing Earnings Inequality in Top Incomes and the Gender Pay Gap
• Questions of interest:1) What are the consequences of the under-representation of women in top
jobs for the overall gender pay gap?2) How is it contributing to the slowdown in the convergence of
female/male pay?3) What public policies and firm practices are effective to improve this
under-representation?
8
Canadian DataLongitudinal Worker File (LWF)• LWF is a 10% random sample of all
Canadian workers• Years: 1983-2010• integrates data from the T1 and T4
files of Canada (CRA) and the LEAP (Statistics Canada)
• Annual earnings from all jobs, include bonuses, honorariums, etc.
• Selected if > half of minimum wage earnings equivalent
• Select workers age 25 to 64
Labour Force Survey (LFS) Public Use
• Monthly survey on approximately 100,000 individuals rotating 6-months panel sample design
• Years: 1997-2015• Hourly wage of employees from
main job• Selected if > half the minimum
wage• Select workers age 25 to 64
9
Canadian DataLongitudinal Worker File (LWF)
• No self-employment income• No labour supply information• Top coded at P99.99 ≈ $2,000,000
in 1983 to ≈$10,000,000 in 2000• Available covariates: union
coverage, age, industry• CPI adjusted to 2010$CAN
• No self-employment income• Number of weeks worked
unavailable• Top-coding (P99.9) from ≈$95/hour
in 1997 ≈ $125/hour in 2015 • At 2080 (=52wk*40hrs) hrs/year,
from $200,000 to $260,000• Available covariates: age, union,
education, occupation, industry, firm size, etc.
10
Labour Force Survey (LFS) Public Use
Trends
1) Evolution of female/male labour force participationa) Extensive margin (LFP)b) Intensive margin (hours of work)
2) Evolution of female/male average wage and earnings ratios3) Evolution of female shares across top percentiles of the overall
distribution of wage and earnings4) Counterfactuals with alternative simulations
11
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015
Part
icip
atio
n Ra
teCanadian Labour Force Participation Rate - Ages 25 to 64
Men Women
Steep Growth in Women’s Labour Force Participation* Followed by a Leveling-Off
Source: Fortin, Drolet and Bonikowska (2016), LFS Public use files, 1976-2015*Labour force participants include employed (at work or on-leave) and unemployed individuals 12
8.7
Decline in LFP after the Great Recession in the US
13Source: Blau and Kahn (2016)
The Women’s Liberation Movement of the 1960s and The “Pill”
• Goldin and Katz (2002) and Bailey (2006) point out to important changes in women’s LFP occurring in the 1960’s
• Women born after the mid-1950s had access to reliable contraception• More likely to pursue higher education and enter life-long careers• Accompanied by a decline in traditional gender roles attitudes which
stabilized in the mid-1990s in the U.S. (Fortin, 2015)• Before married women were more likely `secondary workers’ who entered
the labour market when kids were in school • Mulligan and Rubinstein (2013) argue that the closing of the gender pay
gap is largely due to changing selection of women into the labour market
14
Generational Effects in the Growth of Women’s LFP
Source: Fortin, Drolet and Bonikowska (2016), LFS public use files, ages 25 to 64 year 15
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015
Part
icip
atio
n Ra
teWomen's Labour Force Participation by Synthetic Birth Cohort
<1920 1921-39 1940-45 1946-53 1954-58
1959-65 1966-75 1976-85 1985-90 All
Generational Effects in the Growth of Women’s LFP
Source: Fortin, Drolet and Bonikowska (2016), LFS public use files, ages 25 to 64 year 16
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015
Part
icip
atio
n Ra
teWomen's Labour Force Participation by Synthetic Birth Cohort
1940-45 1959-65 All
4432
54
3434
47
Continued Gender Convergence?
• According to the Mincer-Polachek hypothesis (1974), gender differences in experience and labour force attachment are the key determinants of the gender wage gap.
• Blau and Kahn (2016) found that declining gender differences in experience in the United States accounted for 18-31 % of wage convergence between men and women over the 1980-2000 period.
• Going forward, Goldin (2014) suggested that the impact of work force interruptions for family responsibilities should be understood in the context of temporal flexibility (or the lack thereof) in impacting the gender wage gap.
17
Less Convergence in Gender Gap in Hours
18Source: Fortin, Drolet and Bonikowska (2016), LFS data, ages 25 to 64 year, employed with positive hours of work, usual hours from all jobs
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015
Gender Ratio in Average Total Weekly Hours by Synthetic Birth Cohort
<1920 1921-39 1940-45 1946-53 1954-58
1959-65 1966-75 1976-85 1985-90 All
Gender Gap in Hours andIncreasing Earnings Inequality in Top Incomes
• Kuhn and Lozano (2008) had shown increases in long hours of work (>48 hours a week) among highly educated highly-paid older men was greatest in detailed occupations and industries with larger increases in residual wage inequality.
• Pointing to some high penalty for flexibility in some high wage occupations, Goldin (2014) further conjectures that rewards to working long hours are an obstacle for the gender gap in pay to vanish
• Cortes and Pan (2015) find that highly competitive jobs (O*NET characteristics) also have long hours
• Cortes and Pan (2016) find that across countries long hours lowers the share of married women in corresponding occupations
19
Trends
1) Evolution of female/male labour force participationa) Extensive margin (LFP) : substantial convergenceb) Intensive margin (hours of work): less convergence
2) Evolution of female/male “raw” wage and earnings ratios: Continuing progress?
3) Evolution of female shares across top percentiles of the overall distribution of wage and earnings
4) Counterfactuals with alternative simulations
20
What is the ratio of women’s to men’s earnings on average in Canada?
Source: Drolet (2011) 25 to 54 year olds, various data sources.21
What is the ratio of women’s to men’s earnings on average in Canada?
• “Hourly Wage” ratio ≈ 85% is the preferred measure to consider whether employers treat women fairly and should be used in statements such as
“women earn 85 cents out of every $1 men earn”• “Annual Earnings” ratio ≈ 65% mixes the number of hours worked with
how much is earned by hour provides a better measure of the welfare of women
• More women work part-time, many women working full-time full-year work less hours a week than men (clerical vs. industrial workers)
• But the “All Annual Earnings” measure is the only one available for the very top income groups
22
What is the ratio of women’s to men’s earnings on average in Canada?
• The focus of all annual earnings can also be justified by the less favorable gender earnings ratios found by Frenette (2014) over the life-cycle
• In terms of the present value of total cumulative earnings (1991-2000) from the LWF (combined with the 1991 Census), they are even lower
PV Cumulative Earnings ratio ≈ 57% for university graduates and college graduates, ≈ 53% for high school graduates
23
Generational Effects in the Gender Pay Gap
Source: Fortin, Drolet and Bonikowska (2016), LFS data, ages 25 to 64 year, hourly wage on the main job 24
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Gender Gap in Hourly Wages by Synthetic Birth Cohort
1935-39 1940-45 1946-53 1954-58 1959-651966-75 1976-85 1986-88 All
3131
41
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Gender Gap in Annual Earnings by Synthetic Birth Cohort
1935-39 1940-45 1946-53 1954-581959-65 1966-75 1976-85 All
Generational Effects in the Gender Pay Gap
25Source: Fortin, Drolet and Bonikowska (2016), LWF data, ages 25 to 64 year, 3-year moving average annual earnings from all jobs
3131
41
Slowdown of the Progress in Gender Pay Ratio
Longitudinal Worker File (LWF) Labour Force Survey (LFS)
26
Annual Earnings
Start End %Δ %Δ/ year
1983-1996 0.58 0.64 9.8 0.81997-2010 0.63 0.67 6.2 0.5
Hourly Wage
Start End %Δ %Δ/ year
1997-2010 0.81 0.85 5.0 0.42011-2015 0.86 0.85 -0.3 -0.1
Standard Decomposition of the Gender Pay Gap
• The Oaxaca-Blinder decomposition starts with gender-specific OLS regressions of individual characteristics on (log) wages:
𝑌𝑌𝑔𝑔 = 𝑋𝑋𝑔𝑔′𝛽𝛽𝑔𝑔 + 𝜀𝜀𝑔𝑔, g = m, f• Constructs a counterfactual wage such as “what would be the average
wage of women if they had the same characteristics as men” 𝑌𝑌𝑓𝑓𝑚𝑚 = 𝑋𝑋𝑚𝑚′ 𝛽𝛽𝑓𝑓 = 𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑚𝑚 × 𝑝𝑝𝑝𝑝𝑞𝑞𝑝𝑝𝑞𝑞𝑓𝑓
• Divides the average gender pay gap into “explained” and “unexplained” part �𝑌𝑌𝑚𝑚 − �𝑌𝑌𝑓𝑓 = (𝑌𝑌𝑓𝑓𝑚𝑚 − �𝑌𝑌𝑓𝑓) + (�𝑌𝑌𝑚𝑚−𝑌𝑌𝑓𝑓𝑚𝑚) = ( 𝑋𝑋𝑚𝑚′ − 𝑋𝑋𝑓𝑓′)𝛽𝛽𝑓𝑓+ 𝑋𝑋𝑚𝑚′ (𝛽𝛽𝑚𝑚 − 𝛽𝛽𝑓𝑓)
explained unexplained
27
Gender Pay Gap Largely “Unexplained” by Human Capital Variables
• For the United States, Blau and Kahn (2016) using human capital variables, including actual experience from the PSID, find a notable decline in the unexplained gap—from 0.341 log points in 1980 to 0.197 log points in 2010.
• But as a share of the gender gap in both years, the unexplained portion is actually a larger share of gap in 2010 (85%) than in 1980 (71%).
• For Canada, Baker and Drolet (2010) also report some progress in the unexplained gap from 0.163 log points in 1981 to 0.141 log points in 2008.
• But this represents an increase, from 1981 (61%) to 2008 (85%), in the share of gap that is unexplained by education, occupation and industry.
28
29Source: Blau and Kahn (2016)
Gender Pay Gap Largely “Unexplained” by Human Capital Variables
• Baker and Drolet (2010) explain that in many dimensions, such as education, women increasingly have an advantage over men.
• But because women’s wages have not seen commensurate increases, these are countervailing factors to explain the gap.
• They argue that most significant exception to this is the industrialdistribution of employment in which men maintain a significant advantage.
30
Industry Composition Largest Single Explanatory Factor* in the Private Sector
• Yet, Schirle(2015) finds that in most provinces more than half of the gap is unexplained
31Source: Schirle (2015), LFS 2014, hourly wages of private sector full-time employees, ages 25-59* with the exception of Manitoba
Trends
1) Evolution of female/male labour force participationa) Extensive margin (LFP)b) Intensive margin (hours of work)
2) Evolution of female/male average wage and earnings ratios: Slower progress in recent years, share of the gap unexplained has increased, industry may remain a potent explanatory variable
3) Evolution of female shares across top percentiles of the overall distribution of wage and earnings
4) Counterfactuals with alternative simulations
32
Increasing Earnings Inequality and the Gender Pay Gap
• When residual inequality experienced stupendous increases in the 1980s, Blau and Kahn (1997) coined the term “swimming upstream” to characterize women’s pursuit of pay equality in the face of countervailing currents.
• Have recent increases in top incomes lead to similar effects, therefore accounting for the slower progress in the gender pay and growing unexplained (by traditional factors) share?
• To the extent that some of the increases in top incomes are associated with excesses in rent seeking, curtailing those excesses would slow the countervailing currents
33
Soaring Top Incomes in the United States
•
25%
30%
35%
40%
45%
50%
1917
1922
1927
1932
1937
1942
1947
1952
1957
1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012
Top
10%
Inco
me
Shar
eTop 10% Pre-tax Income Share in the US, 1917-2013
Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash market income including realized capital gains and excluding government transfers. 34
Mostly among the top 1%
0%
5%
10%
15%
20%
25%
1913
1918
1923
1928
1933
1938
1943
1948
1953
1958
1963
1968
1973
1978
1983
1988
1993
1998
2003
2008
2013Sh
are
of to
tal i
ncom
e fo
r eac
h gr
oup
Decomposing Top 10% into 3 Groups, 1913-2013
Top 1% (incomes above $392,000 in 2013)Top 5-1% (incomes between $165,500 and $392,000)Top 10-5% (incomes between $116,500 and $165,500)
Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash market income including realized capital gains and excluding government transfers.
35
Gender Gap in Top Incomes
• Follow Guvenen, Kaplan, and Song (2014) in using the thresholds of the wage and earnings distribution for men and women combined
• Depart from the traditional literature on the glass ceiling which compares the pay gap at percentiles of the gender-specific distributions
• Depart from most of the literature which uses the logarithm of wages or earnings in order to emphasize the top end
• Allow for the construction of counterfactuals to study the under-representation of women in top income groups
• Study the role of industrial segregation within income groups
36
Thresholds of Top Incomes - 2010Longitudinal Worker File (LWF)
Annual Earnings for all jobs1) Top 0.1% > $662,8602) Top 1% > $206,7853) Top 10% > $92,000
Labour Force Survey (LFS)
Hourly wages on the main job1) Top 0.1% > $66 ( $128,705) 2) Top 1% >$53 ($116,922)3) Top 10% >$35 ($80,352)
at 2080 hours
37
Larger Increases for Top Incomes!
300,000
500,000
700,000
900,000
1,100,000
1,300,000
1,500,000
1,700,000
1,900,000
2,100,000
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Canadian All Earnings Trends
Bottom 90% Next 9% Next 0.9% Top 0.1% (right axis)
38Source: Fortin, Drolet and Bonikowska (2016), LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
Larger Increases for Top Earners!
300,000
500,000
700,000
900,000
1,100,000
1,300,000
1,500,000
1,700,000
1,900,000
2,100,000
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Canadian All Earnings Trends
Bottom 90% Next 9% Next 0.9% Top 0.1% (right axis)
162%
34%
81%
15%
39Source: Fortin, Drolet and Bonikowska (2016), LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
Source: Lemieux and Riddell (2015), LAD data 40
Gender Differences in Hourly Wage Distributions
41Source: Fortin, Drolet and Bonikowska (2016), LFS 1997-2015, 25-64 years old, Hourly wage from the main job
Top 10% Top 1% Top 0.1%
0.0
1.0
2.0
3.0
4.0
5D
ensi
ty
25 50 75 100 125Hourly Wage ($2010)
Women Men
B. 2011-2015Top 10% Top 1% Top 0.1%
0.0
1.0
2.0
3.0
4.0
5D
ensi
ty
25 50 75 100 125Hourly Wage ($2010)
Women Men
A. 1997-2002
Source: Fortin, Drolet and Bonikowska (2016) Computation, LFS 1997-2015, 25-64 years old, Hourly wage from the main job
Gender Differences in Hourly Wage Distributions
42
Top 10% Top 1% Top 0.1%
0.0
1.0
2.0
3.0
4.0
5D
ensi
ty
25 50 75 100 125Hourly Wage ($2010)
Women Men
B. 2011-2015Top 10% Top 1% Top 0.1%
0.0
1.0
2.0
3.0
4.0
5D
ensi
ty
25 50 75 100 125Hourly Wage ($2010)
Women Men
A. 1997-2002
3:2
4:18:1
Top 0.1%Top 10% Top 1%
Slower Convergence in Share of Women among Top Earners
43
0.00
0.10
0.20
0.30
0.40
0.50
0.60
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Share of Women in Selected Percentiles of Hourly Wages
All Bottom 90% Next 9% Next 0.9% Top 0.1%
Source: Fortin, Drolet and Bonikowska (2016), LFS 1997-2015, 25-64 years old, Hourly wages from the main job
Slower Convergence in Share of Womenamong Top Earners
0.00
0.10
0.20
0.30
0.40
0.50
0.60
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Share of Women in Selected Percentiles of Annual Earnings
All Bottom 90% Next 9% Next 0.9% Top 0.1%
Source: Fortin, Drolet and Bonikowska (2016), LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
44
Under-representation of women in top jobs makes for a less favorable overall gender pay ratio
Source: Fortin, Drolet and Bonikowska (2016), LFS 1997-2015, 25-64 years old, Hourly wages from the main job45
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Female-Male Average Hourly Wages Ratios by Selected Percentiles
All Bottom 90% Next 9% Next 0.9% Top 0.1% Median
Under-representation of women in top jobs slows progress in the overall gender pay ratio
Source: Fortin, Drolet and Bonikowska (2016), LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Female-Male Earnings Ratios by Earnings Percentile
All Bottom 90% Next 9% Next 0.9% Top 0.1%
0.10
46
0.15
Trends
1) Evolution of female/male labour force participationa) Extensive margin (LFP)b) Intensive margin (hours of work)
2) Evolution of female/male average wage and earnings ratios:3) Evolution of female shares across top percentiles of the overall
distribution of wage and earnings4) Counterfactuals with alternative simulations
a) Using male shares in selected percentile earningsb) Using male industrial distribution
47
Counterfactual Gender Pay Gaps and Reweighting
• Kline (2011) shows that the counterfactual (letting 𝐷𝐷𝑖𝑖 = 1 denote male),𝜇𝜇01 = 𝐸𝐸[𝑋𝑋𝑖𝑖�𝐷𝐷𝑖𝑖 = 1]′𝛽𝛽0
can be computed from an OB regression𝜇𝜇01 = 𝐸𝐸[𝑋𝑋𝑖𝑖|𝐷𝐷𝑖𝑖 = 1]′ × 𝐸𝐸[𝑋𝑋𝑖𝑖𝑋𝑋𝑖𝑖′|𝐷𝐷𝑖𝑖 = 0] −1𝐸𝐸[𝑋𝑋𝑖𝑖𝑋𝑋𝑖𝑖′|𝐷𝐷𝑖𝑖 = 0]
• Or using reweighting à la DFL
𝜇𝜇01 = 𝐸𝐸[𝑤𝑤(𝑋𝑋𝑖𝑖)𝑌𝑌𝑖𝑖|𝐷𝐷𝑖𝑖 = 0] where 𝑤𝑤(𝑋𝑋𝑖𝑖) ≡P( |𝑋𝑋𝑖𝑖 𝐷𝐷𝑖𝑖=1)P( |𝑋𝑋𝑖𝑖 𝐷𝐷𝑖𝑖=0) = 1−𝜋𝜋
𝜋𝜋𝑒𝑒(𝑋𝑋𝑖𝑖)
(1−𝑒𝑒(𝑋𝑋𝑖𝑖))with 𝜋𝜋 ≡ P(𝐷𝐷𝑖𝑖 = 1) and 𝑞𝑞(𝑋𝑋𝑖𝑖)= P( |𝐷𝐷𝑖𝑖 = 1 𝑋𝑋𝑖𝑖), under the assumptions of common support 𝑞𝑞(𝑋𝑋𝑖𝑖)< 1 and conditional independence (𝑌𝑌𝑖𝑖1,𝑌𝑌𝑖𝑖0) ⊥ |𝐷𝐷𝑖𝑖 𝑋𝑋𝑖𝑖
48
Counterfactual Gender Pay Gaps and Reweighting
• The sample analogues are: 𝜋𝜋 = 𝑁𝑁1𝑁𝑁
and 1−𝜋𝜋𝜋𝜋
= 𝑁𝑁0𝑁𝑁1
• If 𝑋𝑋𝑖𝑖 is a j-category variable, 𝑞𝑞(𝑋𝑋𝑖𝑖𝑗𝑗) = 𝑁𝑁1𝑗𝑗𝑁𝑁𝑗𝑗
and 𝑒𝑒(𝑋𝑋𝑖𝑖)(1−𝑒𝑒(𝑋𝑋𝑖𝑖))
= 𝑁𝑁1𝑗𝑗𝑁𝑁0𝑗𝑗
,
• So that reweighing observations requires only the ratio of shares in each j-category: 𝑤𝑤(𝑋𝑋𝑖𝑖𝑗𝑗)=𝑁𝑁0
𝑁𝑁1∗ 𝑁𝑁1𝑗𝑗𝑁𝑁0𝑗𝑗
= 𝑆𝑆1𝑗𝑗𝑆𝑆0𝑗𝑗
where 𝑆𝑆1𝑗𝑗 is the share of group 1 in category j • With conditional means, the overall mean is �𝑌𝑌0=∑𝑖𝑖 𝑆𝑆0𝑗𝑗𝑌𝑌0𝑗𝑗, so that
𝑌𝑌𝑜𝑜1 = ∑𝑗𝑗 𝑆𝑆1𝑗𝑗 ∑𝑖𝑖𝑆𝑆0𝑗𝑗𝑆𝑆0𝑗𝑗𝑌𝑌0𝑗𝑗 = ∑𝑗𝑗 𝑆𝑆1𝑗𝑗 ∑𝑖𝑖 𝑌𝑌0𝑗𝑗
49
If the proportion of women across professorial ranks was identical to men, the overall counterfactual average female salary would be:
51.8/100×146048 + 30.7/100×114595 + 17.6/100×99709 =128259.3, and the overall ratio would be 128382/134955(*100)=95% The salary gap explained by rank is 128259.3-120623.1 =7636.2 More that 53% of the gap is accounted for by the gender differences in the proportion of
faculty members across rank.
Female/ GenderMale Ratio Gap
Men All 968 100 134955 0.89 14332Women All 419 100 30.2 120623 Men Full 501 51.8 152494 0.96 6446Women Full 130 31 20.6 146048Men Associate 297 30.7 121483 0.94 6888Women Associate 184 43.9 38.3 114595Men Assistant 170 17.6 106806 0.93 7097Women Assistant 105 25.1 38.2 99709
Gender Rank Numbers % of All % of women
Average Salary
Table 1. Average Professorial Salaries at UBC in 2010
51
Variables: Model 1 % of gap Model 2 % of gap
Raw Gender Salary Differentials 14332.24 *** 14332.24 ***Accounted for by differences in characteristicsProfessorial Rank 7636.226 *** 53.28% 6647.376 *** 46.38%CRC, DUP 546.2663 * 3.81%Years in Rank 1180.126 ** 8.23%Departmental Dummies 3093.223 ** 21.58%Total Explained 7636.226 *** 53.28% 11466.99 *** 80.01%Total Unexplained 6696.018 *** 46.72% 2865.253 *** 19.99%
Table 2. Oaxaca-Blinder Decomposition of Average Professorial Salaries at UBC in 2010
Note: Using female coefficients. *** p<0.01, ** p<0.05, * p<0.1 See UBC (2011) for alternative specifications.
The more complete specification accounts for 80% of the gap, 46% of which from vertical segregation and 22% from horizontal segregation.
This leaves an unexplained gender gap of 2.2% of average professorial salary
If the shares of women in percentiles grouping* were the same as men’s, the gap in annual earnings would be 20 point lower
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Female-Male Earnings Ratios by Earnings Percentile (All Earners)
Simulated Ratio Actual Ratio
0.42
0.19 = 45%
*percentiles grouping: bottom 90%, next 9%, next 0.9%, top 0.1% 52
0.33
0.19 = 58%
Source: Fortin, Drolet and Bonikowska (2016), LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
If the shares of women in percentiles grouping* were the same as men’s, the gap would be 6-9 points lower
Source: Fortin, Drolet and Bonikowska (2016), LFS 1997-2015, 25-64 years old, Hourly wages on the main job*percentiles grouping: bottom 90%, next 9%, next 0.9%, top 0.1% 53
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Counterfactual Hourly Wage Ratio Substituting Male Shares in the Selected Wage Percentiles
Actual Ratio Simulated Ratio
0.07 = 45%
0.19
0.08 = 41%
0.15
Explanatory Variables Model 1 % of gap
Model 1 % of gap
Model 2 % of gap
Model 2 % of gap
1997 2015 1997 2015Raw Gender Wage Gap 4.66 *** 3.93 *** 4.66 *** 3.93 ***Accounted for by differences in characteristicsSelected Centiles 0.83 *** 17.9% 1.67 *** 42.4% 0.77 *** 19.1% 1.46 *** 37.1%Demographics (age, marital status, kids) 0.04 *** 0.8% 0.00 *** 0.0% 0.01 *** 0.2% 0.00 *** 0.0%Education -0.17 *** -3.6% -0.54 ** -13.8% -0.05 *** -1.3% -0.10 *** -2.6%Part-time, Union, Tenure 0.40 *** 10.0% -0.01 *** -0.2%Industry 0.22 *** 5.6% 0.32 *** 8.2%Occupation 0.19 *** 4.8% 0.07 *** 1.8%Province 0.01 ** 0.2% 0.03 *** 0.01 0.1% 0.03 *** 0.7%Total Explained 0.71 *** 15.2% 1.16 *** 29.5% 1.55 *** 38.6% 1.77 *** 45.1%Total Unexplained 3.95 *** 84.8% 2.77 *** 70.5% 3.11 *** 77.1% 2.16 *** 54.9%Note: Entries are male/female differences in the explanatory variables multiplied by the corresponding female coefficients. All variables, except tenure are categorical. There are 4 marital status and 7 education classes, 11 industry, 47 occupation
O-B Decomposition in LFS 1997 and 2015
Source: Fortin, Drolet and Bonikowska (2016), LFS 1997-2015, 25-64 years old, Hourly wages on the main job
Impact of Under-Representation in Top Jobs
• Over time, the under-representation of women in top jobs accounts for a growing share of the gender gap from 19% in 1997 to 37% in 2015, after accounting for the usual list of
factors (education, occupation, industry, etc.)• Even against industry and occupation, it is the most significantly
explanatory factor • It substantially reduces the unexplained portion of the gender gap which
had been growing over time in a puzzling way.
55
Industry Composition by Gender (LFS)
56
0 5 10 15 20
Ext. Resources/Const.
Transp/WholeS/WhareH.
Manufacturing
Agri/Fish/Forest
Public Admin
Prof/Scien/Manag Serv.
Other Services
Retail Trade
Education
F.I.R.E.
Health Care/Soc. Ass.
A. 1997-2002
Men Women
0 5 10 15 20
Ext. Resources/Const.
Manufacturing
Transp/WholeS/WhareH.
Agri/Fish/Forest
Prof/Scien/Manag Serv.
Public Admin
Other Services
Retail Trade
F.I.R.E.
Education
Health Care/Soc. Ass.
B. 2011-2015
Men Women
Source: Fortin, Drolet and Bonikowska (2016) computation, LFS 1997-2015, 25-64 years old
Industry Composition by Gender and Selected Centiles
57
A: 1997-2002Bottom
90%Next 9%
Next 0.9%
Top 0.1%
Bottom 90%
Next 9%
Next 0.9%
Top 0.1%
Agri/Fish/Forest 2.1 0.8 0.7 0.1 0.9 0.2 0.1 0.0Ext. Resources/Const. 11.4 10.7 8.7 9.3 2.1 2.4 2.7 0.0Manufacturing 26.4 18.7 19.9 23.0 11.1 5.4 7.7 5.3Transp/WholeS/WhareH 13.6 8.9 8.0 8.7 5.5 2.6 5.0 0.3Retail Trade 9.1 3.5 3.3 3.8 11.9 2.5 1.7 1.1 F.I.R.E. 3.7 8.0 11.5 12.1 8.9 8.0 14.2 14.0Prof/Scien/Manag Serv. 7.1 10.8 15.7 17.3 7.9 8.4 17.0 8.7Education 4.7 14.0 11.1 8.8 9.9 33.8 25.8 40.6Health Care/Soc. Ass. 3.8 2.8 3.1 3.2 20.4 19.1 7.6 12.2Other Services 11.4 8.2 8.8 7.7 14.5 7.4 10.9 12.9Public Admin 6.8 13.6 9.2 6.0 6.7 10.3 7.6 4.8
WomenMen
Source: Fortin, Drolet and Bonikowska (2016) computation, LFS 1997-2015, 25-64 years old
Industry Composition by Gender and Selected Centiles
58
B: 2011-2015Bottom
90%Next 9%
Next 0.9%
Top 0.1%
Bottom 90%
Next 9%
Next 0.9%
Top 0.1%
Agri/Fish/Forest 1.7 0.3 0.3 0.0 0.7 0.1 0.0 0.0Ext. Resources/Const. 14.5 14.5 12.9 17.3 2.7 3.5 5.8 2.8Manufacturing 20.3 13.7 15.7 12.1 8.1 4.3 5.2 8.5Transp/WholeS/WhareH 13.8 9.2 10.3 9.0 5.8 2.8 3.5 6.8Retail Trade 9.3 3.8 2.5 3.4 12.1 3.5 4.3 2.5 F.I.R.E. 4.4 8.5 11.1 13.6 8.4 9.0 12.4 11.8Prof/Scien/Manag Serv. 9.1 14.1 19.9 18.1 8.8 7.4 14.3 13.5Education 5.0 11.0 8.7 9.0 10.5 26.1 21.1 25.0Health Care/Soc. Ass. 4.1 3.6 2.5 3.0 22.1 24.7 11.6 12.3Other Services 11.7 7.2 7.9 6.3 13.9 5.5 6.2 6.0Public Admin 6.3 14.0 8.2 8.1 6.8 13.2 15.6 10.7
Men Women
Source: Fortin, Drolet and Bonikowska (2016) computation, LFS 1997-2015, 25-64 years old
What if women worked in the same industrial sectors as men?
59
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Gender Ratio in Average Hourly Wages by Selected Percentiles
All Bottom 90% Next 9% Next 0.9% Top 0.1%
Rw_All Rw_B90 Rw_N9 Rw_N0.0 Rw_T0.1
Source: Fortin, Drolet and Bonikowska (2016) Computation, LFS 1997-2015, 25-64 years old, Hourly wages on the main job
What if women worked in the same industrial sectors as men?
60
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Gender Ratio in Average Hourly Wages by Selected Percentiles
Bottom 90% Next 9% Rw_B90 Rw_N9
Source: Fortin, Drolet and Bonikowska (2016) Computation, LFS 1997-2015, 25-64 years old, Hourly wages on the main job
Similar Impact of Industrial Composition on the Annual Earnings Ratio
61
0.5
0.6
0.7
0.8
0.9
1
1.1
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Gender Ratio in Annual Earnings by Selected Percentiles
All Bottom 90% Next 9% Next 0.9% Top 0.1%Rw_all Rw_B90 Rw_N9 Rw_N0.9% Rw_T0.1
Source: Fortin, Drolet and Bonikowska (2016) Computation, LWF 1991-2010, 25-64 years old, Annual earnings from all jobs
Similar Impact of Industrial Composition on the Annual Earnings Ratio
62
0.7
0.75
0.8
0.85
0.9
0.95
1
1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
Gender Ratio in Annual Earnings by Selected Percentiles
Bottom 90% Next 9% Rw_B90 Rw_N9
Source: Fortin, Drolet and Bonikowska (2016) Computation, LWF 1991-2010, 25-64 years old, Annual earnings from all jobs
Impact of Industrial Composition
• Although issues of common support limit the analysis for the top 1% and 0.1%, overall women’s own choice of industrial sectors seem appropriate
• Among the top 9%-1%, women would almost reach parity if they works in the same industrial sectors as men, but in the bottom 90% would do worse
• Largely due to the health care sector, which is a well-paying sector in the bottom 90%, but less so in the next 9% (among the salaried workers we observe)
• It could be arguably different if we included self-employment income
63
Bottom-Line
• Looking back at the transformation of women’s work in Canada over the 20th century, Fortin and Huberman (2002) had argued that the decline in vertical segregation had contributed more to the improvement of women’s labour market outcomes than changes in horizontal segregation.
• With increasing earnings inequality in top incomes, further improvements in vertical segregation, “more women in top jobs” will be likely be even more important for further decline in the gender pay gap in the 21st
century• But unlike in the 20th century, further educational attainment alone will
not yield those changes!
64
Public Policy and Gender Pay Differentials
• Gender pay differentials “within” occupation “Equal Pay for Equal Work” • Gender pay differentials across “comparable” occupations, resulting from
horizontal segregation, are the focus of “Pay Equity” policies, implemented in the private sector of Canada’s two
most populous provinces: Ontario (1996) and Quebec (2001)• Gender pay differentials arising from the potential obstacles that women
face climbing (or not) the job ladder (vertical segregation) “Employment Equity”, enacted in the Federal jurisdiction in principle could
address disparities across the job ladder.
65
More Women in Tops Jobs! What to Do?
• In recent years, many countries have pushed for more general gender equality in decision-making with bolder moves. Both in the political sphere and on corporate boards.
• Many European countries implemented female quotas on the board of directors of firms on public stock exchanges.
• Some emerging countries are doing the same.
66
Quotas for corporate boards advance?
67
Source: Dizik, 2015
More Women in Tops Jobs! What to Do?
• Short of calling for gender quotas, the Canadian Securities Administrators of seven provinces and territories (CSA, 2015) implemented “comply-or-explain” female representation rules on January 1, 2015 (Shecter, 2014; McFarland, 2015).
• These rules require companies listed on their stock exchanges to disclose how many women they have on their boards and in their executive ranks.
• But many companies have shown bare `technical compliance’ with the reporting rules introduced last year and it is "simply not good enough," says Howard Wetston, the Ontario Securities Commission chair.
68
CAUS CAUS CAUS
AU
NZAUNZAUNZ
HK
ID
JP
MY
PH
SG
THHK
JP
MY
PHTH
ID
JP
MY PH SGTH AT
BEFRDE
GR
IE
IT
NLES
CH
GB
ATBECZFR
DEGR
HU IE
IT
NL
PL
PT
RUESCH GB
ATBECZ
FR
DE
GR HUIE
IT
NLPL
PTRU
ES CHGBAT
BE
FR
DE
GRHU
IEIT
NLPL
PTRU
ESCHGB
DKFI
NO
SE
DK
FI
NO
SE
DK
FI
NO
SE
DK
FI
NO
SE0
1020
3040
Wom
en o
n Bo
ards
(%)
40 45 50Female Employment Share (%)
Can/US AU/NZ AsiaEurope Nordic Countries
Years: 2006, 2009, 2011, 2013, 2014
Women on Boards and Employment Share
Sources: GMI, European PWN, Deloitte (2015), World Bank Indicators
AU
AUAU
ATAT
ATAT
BE BEBE BE
CZCZ
DK
DKDKDK
FIFI
FIFI
FRFR
FRFR
DEDE
DEDE
GR
GR GR GR HKHKHU
HUHUID
IDIE IEIEIE
ITIT IT
IT
JPJPJP
MYMYMY NL
NLNL
NL
NZNZ
NZ
NO
NONONO
PH
PH
PH PL
PLPL
PT PTPT
RURURUSG SGES
ESES ES
SESESESE
CHCH CHCHTHTHTH
GBGB
GBGB
AUATATBE BE
DK
FRFR
ITIT
MY NL
NLNL
ES
AUAUATATBE BE
DKDKDKFRFR
GR
GR GR GRIT
ITMYMY
NL
NO
NONONO
ESES ES
FI
HKHK
JPJP
NZNZ
PL
PLGB
GBGB
FIFIFI
JP
NZPL GB
010
2030
40W
omen
on
Boar
ds (%
)
40 45 50Female Employment Share (%)
No Regulation or Quota Pre-Quota Post-QuotaPre-Regulation Post-Regulation Fitted values
Years: 2006, 2009, 2011, 2013, 2014
Women on Boards and Employment Share
Sources: GMI, European PWN, Deloitte (2015), World Bank Indicators
More Women in Tops Jobs! Does it Help?
• Female CEOs/Directors have mixed results on firm performance (returns on asset, on equity, profits, Tobin Q, etc.) in firm-fixed effects models– Positive or no effect in Denmark [Smith et al. (2006), Parotta and
Smith (2013)] and in Italy [Amore et al. (2013)]– Negative or no effect in the US [Wolfers (2006), Adams and Ferreira
(2009)], in Norway [Ahern and Dittmar (2012)]• Bertrand, Black, Jensen, and Lleras-Muney (2014) show mixed results on
relative female wages: the Norwegian quotas increased representation of women among top 5 highest earners, but had no effect at other points in the distribution or on the gender pay gap.
71
ATAT
ATATAU
AUAUAUBE
BE
BE BECHCHCZCZ
CZCZ DEDEDEDE
DKDK
DKDK
ESES
ESES
FI FI
FIFI
FRFRFR
FRGB
GB
GBGB
GR
GR
GR
GR
HUHUHUHU
IDID
IE IE
IEIE
IT IT IT IT
MY
MYMY
NLNL
NL
NL
NONO
NONO
PHPH
PLPLPLPL
PT
PT
PTPT
SESESESE
TH
TH
ATAT
AUAUBE
BE
DK
ES
FRFR
IT IT
MYNL
NLNL
ATAT
AUAUBE BE
DK
DKDK
ES
ESES
FR
FR
GR
GR
GR
GR
IT ITMYMYNL
NONO
NONOFIGB
GB
GB
PLPLPLFI
FIFI
GB
PL15
2535
45W
omen
in S
enio
r Man
agem
ent (
%)
35 40 45 50Female Employment Share (%)
No Regulation or Quota Pre-Quota Post-QuotaPre-Regulation Post-Regulation Fitted values
Years: 2006, 2009, 2011, 2013, 2014
Women in Senior Management and Employment Share
Sources: ILO, World Bank Indicators
Women Fail to Move from Bottom 90% to Next 9%in Early Career (age 30)
73
0.000
0.005
0.010
0.015
0.020
0.025
0.030
0.035
0.04083
_84
84_8
5
85_8
6
86_8
7
87_8
8
88_8
9
89_9
0
90_9
1
91_9
2
92_9
3
93_9
4
94_9
5
95_9
6
96_9
7
97_9
8
98_9
9
99_0
0
00_0
1
01_0
2
02_0
3
03_0
4
04_0
5
05_0
6
06_0
7
07_0
8
08_0
9
09_1
0
Share of Men and Women Moving from Bottom 90% to Next 9%- Recent Synthetic Cohorts
Men 1960 Men 1970 Men 1980 Men All
Women 1960 Women 1970 Women 1980 Women All
30
3030
30
30
30
Source: Fortin, Drolet and Bonikowska (2016) Computation, LWF 1983-2010, 25-64 years old, Annual earnings from all jobs
Public Policy and Gender Pay Differentials
• Likely the better policies are those that “level the playing field”, but without lowering women’s attachment to the labour marketo Maternity-leave benefits, parental leave provisions But gender neutrality is an issue! (Antecol, Bedard, and Stearns, 2016)o Affordable high-quality child care
• Firm practices are likely also important: o On-site child care and o Flexible hours of work o Paying attention to gender biases at work
74
Why so Few Women in Top Jobs? Paths for Future Research
Hypotheses from Labour/Behavioral Economics
— Women shy away from competition (Gneezy, Niederle and Rustichini, 2003; Niederle and Vesterlund, 2007)
— Women cannot say “no” to non-promotable tasks (Vesterlund, 2015)— Negotiating divide (Babcock and Laschever, 2003, 2009) — The importance of money vs. people (Fortin, 2008), vs. work flexibility
(Blau and Kahn, 2016; Goldin, 2014)— Differential treatment by customers (i.e. discrimination) (80 cents eBay,
Kricheli-Katz and Regev, 2016)
75
Why so Few Women in Top Jobs? Paths for Future Research
Hypotheses from Identity Theory/Social Psychology— Glass cliff phenomenon (Ryan and Haslam, 2007), — Failure of romance of leadership to take hold (Kulich, Ryan,and Haslam,
2007) — Recognition deficit emanating from role incongruity (Eagly and Karau,
2002) — Weak work networks within the firm or the industry (Lalanne and
Seabright, 2011) — Preferences for deontological over utilitarian judgments, weaker team
spirit (Kennedy and Kray, 2013; Friesdorf, Conway, and Gawronski, 2015)
76
Why so Few Women in Top Jobs? Recognition Deficit
Source: Maller and Kossof, McKinsey & Company, 2013 *C-level: Chief Executive Officer, Chief Financial Officer, etc.77
Why so Few Women in Top Jobs? Preferences ….
Source: Barsch and Yee, McKinsey & Company, 2012 *C-suite: Chief Executive Officer, Chief Financial Officer, etc.78
Stay Tuned!
Thank you!
79
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