New Century,
Old Disparities
Gender Wage Gaps in Latin
America
Hugo Ñopo (based on
work with Juan Pablo Atal,
Alejandro Hoyos, and
Natalia Winder)
The One Slide Presentation
What is this paper about?
»
»
Harmonized and comparable
measures of gender and ethnic wage
gaps for 18 countries in the region
A refined answer to the old question:
»
»
To what extend gender and ethnic
differences in individuals’
characteristics can explain the
differences in earnings?
Findings? New Insights?
»
Gender wage gaps between 8% and
28%, dropping around 4 points in 15
years (1992-2007).
»
Higher gaps among older people,
those with lower income, with
secondary education, self-employed,
informal and those in small firms
»
Surprising role for occupational and
sector segregation
»
An across-the-board reduction over
the last decade, but especially
among those with kids at home, parttime workers and those previously
found with higher gaps
Methodological improvements:
»
Matching and a decomposition that
recognizes not only differences on
average characteristics but also on
their distribution; and most
importantly, on their supports
Outline
»
Setup of the Problem. Methodological considerations
» Blinder-Oaxaca decompositions
» Matching
» Combining the two tools
»
Empirical Results. LAC (circa 2005; 1992-2007)
» The Data
» For Ethnic and Gender Gaps
»
»
»
»
For Gender Gaps
»
»
Averages
Distributions
The role of occupational and sector segregation
Evolution
Conclusions
1. Setup of the Problem. Methodological
considerations
»Blinder-Oaxaca
decompositions
»Matching
»Combining
the two tools
Gender Differences in:
»
Wages
»
Individual Characteristics
» Age
» Education
»
Individual Characteristics
»
»
»
»
Urban and Rural Area
Presence of children in the HH
Presence of other income earner in the HH
Job Characteristics
» Occupation
» Sector
» Type of employment
» Part – time
» Formality
» Firm size
Blinder-Oaxaca Decompositions
»
The wage gap is separated into two additive
components
»
»
One attributable to the existence of differences in
the average characteristics of females and males
The other attributable to the existence of
differences in the rewards that females and males
get for the same characteristics
»
»
Discrimination
Unobservable characteristics
Blinder-Oaxaca Decomposition.
Linear Setup
Critiques
»
Recent data violates key implications of the Mincerian model
» Hansen (1999)
» Heckman, Lochner and Todd (2001)
»
B-O is informative only about the average decomposition, no clues
about the distribution of the components
» Jenkins (1994)
» DiNardo, Fortin and Lemieux (1996)
» Donald, Green and Paarsch (2000)
» Bourguignon, Ferreira and Leite (2008)
»
The comparison should be restricted only to comparable individuals.
The failure to recognize this fact may bias the estimates in the gap
decomposition
» Barsky, Bound, Charles and Lupton (2001)
»
The relationship governing characteristics and wages is not necessarily
linear
Matching. Impact Evaluation
»
»
»
Treatment effects
Identification of counterfactual situations
Extensively used in the Program Evaluation
literature
»
»
»
»
»
Rubin (1977)
Heckman, Ichimura and Todd (1998)
Heckman, LaLonde and Smith (1998)
Angrist (1998)
Dehejia and Wahba (1998)
The Main Counterfactual Question
What would the distribution of earnings for
males be, in the case that their individual
characteristics follow the distribution of the
characteristics for females?
The Matching Algorithm
For each possible value of the vector of characteristics x:
»
Select all females with these characteristics nF(x)
»
Select all males with these characteristics nM(x)
»
If nF(x)=0 and nM(x)>0  unmatched males
»
If nF(x)>0 and nM(x)=0  unmatched females
»
If nF(x)>0 and nM(x)>0  reweight:
»
Each female with 1
»
Each male with nF(x)/nM(x)
Maids
CEOs
The Matching Algorithm

Result:
A sample of matched females and males with the
same distribution of observable individual
characteristics (but not necessarily the same
distribution of earnings).
A sample of unmatched females and another of
unmatched males
This Matching Approach is…
A non-parametric alternative to B-O decompositions that
has advantages in terms of:
»
»
»
»
Simplicity
Avoiding the estimation of earnings equations
Flexibility
It “contains” all possible propensity scores
Identification/Correct specification
Recognizing that the supports of empirical distributions of
characteristics do not completely overlap (the failure to
recognize this leads to an overestimation of the unexplained
component of the wage gap)
Information
Allowing us to compute directly the distribution of the
unexplained effects, not just the average
The New Decomposition and Matching
2. Empirical Results. LAC (circa 2005)
The Data
Gender Gaps
Averages
Distributions
The role of occupational and sector segregation
The Data
2006
Number of
Observations*
41,287
31 urban regions
Encuesta Continua de Hogares (ECH)
2006
4,959
National
Brasil
Pesquisa Nacional por Amostra de Domicilio (PNAD)
2007
133,764
National
Chile
Encuesta de Caracterizacion Socioeconomica Nacional (CASEN)
2006
85,968
National
Colombia
Encuesta Continua de Hogares (ENH)
2005
52,388
National
Costa Rica
Encuesta de Hogares de Propositos Multiples (EHPM)
2006
13,810
National
Dominican Republic
Encuesta Nacional de Fuerza de Trabajo (ENFT)
2003
9,718
National
Ecuador
Encuesta de Empleo, Desempleo y Subempleo (ENEMDU)
2007
15,611
National
Guatemala
Encuesta Nacional de Condiciones de Vida (ENCOVI)
2006
18,865
National
Honduras
Encuesta Permanente de Hogares de Propositos Multiples (EPHPM)
2007
23,278
National
Mexico
Encuesta Nacional Empleo (ENE), Segundo Trimestre
2004
131,348
National
Nicaragua
Encuesta Nacional de Hogares sobre medicion de Niveles de Vida (EMNV)
2005
9,838
National
Panama
Encuesta de Hogares (EH)
2003
17,368
National
Paraguay
Encuesta Permanente de Hogares (EPH)
2006
5,592
National
Peru
Encuesta Nacional de Hogares (ENAHO)
2006
27,665
National
El Salvador
Encuesta de Hogares de Propositos Multiples (EHPM)
2005
16,856
National
Uruguay
Encuesta Continua de Hogares (ECH)
2005
20,351
Urban
Venezuela
Encuesta de Hogares Por Muestreo (EHM), Segundo Semestre
2004
47,880
National
Country
Name Of The Survey
Year
Argentina
Encuesta Permanente de Hogares (EPH), Segundo Semestre
Bolivia
* Workers between 18 and 65, after eliminating observations with incomplete data or outliers in wage
Coverage
The pooled data set
»
»
»
»
Covering all Latin American countries (except
rural Argentina and Uruguay)
Use of expansion factors, so the size of the
economies are properly represented (all but
Mexico)
Income measures are normalized to 2002 PPP
USD, deflated by nominal GDP
After that, average females (minorities) income
is normalized to one
Relative Wages by Characteristics
All
Age
18 to 24
25 to 34
35 to 44
45 to 54
55 to 65
Education
None or Primary Incomplete
Primary Complete or Secondary Incomplete
Secondary Complete or Tertiary Incomplete
Tertiary Complete
Presence of children (<12) in the household
No
Yes
Presence of other household member with labor income
No
Yes
(Base: Average female wage = 100)
Male
Female
110.00
100.00
(Base: Average minority wage = 100)
Non Minority
Minority
137.78
100.00
79.62
106.57
122.45
127.15
113.02
74.94
100.90
108.72
111.30
97.84
98.44
133.62
149.45
159.80
151.24
77.86
98.18
109.45
113.49
100.08
73.06
95.27
141.67
201.99
71.08
75.98
118.10
178.94
108.72
113.36
155.67
223.68
74.67
90.79
127.12
160.16
117.03
102.20
105.04
95.92
144.65
130.73
104.40
96.32
108.78
110.81
101.95
99.40
140.48
136.67
96.32
101.90
Source: Authors’ calculations using Household Surveys circa 2005.
Relative Wages by Characteristics
All
Urban
No
Yes
Type of Employment
Employer
Self - Employed
Employee
Part time
No
Yes
Formality
No
Yes
Small fim
No
Yes
(Base: Average female wage = 100)
Male
Female
110.00
100.00
(Base: Average minority wage = 100)
Non Minority
Minority
137.78
100.00
91.34
116.76
92.45
101.60
92.47
145.73
67.96
108.13
195.34
95.94
109.59
180.11
88.81
101.53
264.33
134.96
130.84
215.35
95.12
97.81
105.04
158.32
92.22
123.55
133.00
169.18
94.31
132.72
95.81
128.38
86.82
116.70
113.45
160.03
83.90
120.98
115.90
85.28
113.72
78.13
152.10
122.92
113.79
87.60
Source: Authors’ calculations using Household Surveys circa 2005.
Relative Wages by Characteristics
All
Occupation
Professionals and technicians
Directors and upper management
Administrative personnel and intermediary level
Merchants and sellers
Service workers
Agricultural workers and similar
Non-agricultural blue-collars, drivers and similar
Armed forces
Occupations not classified above
Economic Sector
Agriculture, Hunting, Forestry and Fishing
Mining and Quarrying
Manufacturing
Electricity, Gas and Water supply
Construction
Wholesale and Ratail Trade and Hotels and Restorants
Transport, Storage
Financing Insurance, Real Estate and Business Services
Community, Social and Personal Services
(Base: Average female wage = 100)
Male
Female
110.00
100.00
(Base: Average minority wage = 100)
Non Minority
Minority
137.78
100.00
208.68
212.50
134.02
106.60
93.43
63.41
95.59
105.58
110.52
182.18
176.66
107.66
93.28
70.87
80.37
70.41
116.23
89.93
236.98
271.72
136.48
117.47
94.99
85.29
126.07
409.12
170.26
180.33
210.97
114.02
102.22
79.85
57.69
102.14
260.14
161.39
59.13
144.27
115.51
153.89
97.33
106.62
115.73
150.50
153.91
54.03
175.90
85.42
165.60
109.31
88.84
125.02
149.12
110.13
87.64
195.63
136.94
178.43
124.16
132.34
158.21
196.78
153.21
58.31
144.83
103.91
151.34
94.51
102.71
129.27
143.38
112.32
Source: Authors’ calculations using Household Surveys circa 2005.
Descriptive statistics
Age
Education (%)
None or Primary Incomplete
Primary Complete or Secondary Incomplete
Secondary Complete or Tertiary Incomplete
Tertiary Complete
Presence of children in the household (%)
Presence of other member with labor income (%)
Urban (%)
Type of Employment (%)
Employer
Self - Employed
Employee
Part time (%)
Formality (%)
Small fim (%)
Source: Authors’ calculations using Household Surveys circa 2005.
Non
Minority
Minority
37.04
36.37
Men
Women
37.05
36.59
20.90
44.51
29.08
5.51
47.41
60.18
73.40
15.89
37.60
37.96
8.56
55.26
76.41
82.53
14.93
38.65
38.39
8.04
49.34
70.81
85.08
24.82
42.99
27.61
4.59
54.49
65.96
79.76
4.93
27.96
67.11
9.30
43.56
52.39
2.30
26.22
71.49
24.84
44.11
54.22
4.46
24.06
71.48
13.21
52.23
49.23
2.51
28.19
69.30
14.81
43.42
70.08
Descriptive statistics
Age
Occupation (%)
Professionals and technicians
Directors and upper management
Administrative personnel and intermediary level
Merchants and sellers
Service workers
Agricultural workers and similar
Non-agricultural blue-collars, drivers and similar
Armed forces
Occupations not classified above
Economic Sector (%)
Agriculture, Hunting, Forestry and Fishing
Mining and Quarrying
Manufacturing
Electricity, Gas and Water supply
Construction
Wholesale and Ratail Trade and Hotels and Restorants
Transport, Storage
Financing Insurance, Real Estate and Business Services
Community, Social and Personal Services
Non
Minority
Minority
37.04
36.37
Men
Women
37.05
36.59
9.62
3.32
5.02
9.16
11.84
15.55
32.04
0.77
12.67
15.10
2.76
10.52
17.19
32.52
7.05
9.41
0.08
5.39
13.62
4.75
9.61
12.39
18.96
11.96
27.59
0.01
1.11
8.48
2.27
6.48
11.40
24.30
16.69
29.00
0.00
1.38
18.07
0.95
16.70
0.85
12.08
20.96
8.97
3.10
18.32
3.78
0.14
15.31
0.22
0.79
27.86
1.94
3.10
46.86
12.21
0.78
16.83
0.64
7.30
23.98
6.56
3.72
27.99
16.92
0.70
14.49
0.50
9.63
21.89
5.35
1.70
28.83
Source: Authors’ calculations using Household Surveys circa 2005.
Gender Wage Gap Decompositions
∆
∆0
∆M
∆F
∆X
% Men in CS
% Women in CS
Age
+ Education
+ Presence of children
in the HH
10.00%
8.88%
0.00%
0.00%
1.11%
100.00%
100.00%
10.00%
17.16%
0.07%
-0.03%
-7.20%
99.80%
99.94%
10.00%
17.40%
0.20%
-0.11%
-7.49%
99.28%
99.78%
Source: Authors’ calculations using Household Surveys circa 2005.
+ Presence of other
income earner in the
HH
+ Urban
10.00%
17.93%
0.24%
-0.38%
-7.80%
97.66%
99.12%
10.00%
18.80%
-0.28%
-0.58%
-7.94%
94.67%
97.90%
Gender Wage Gap Decompositions by Job
Related Characteristics
∆
∆0
∆M
∆F
∆X
% Men in CS
% Women in CS
Demographic
Set
& type of empl.
& Part-time
& Formality
& Sector
& Occupation
& Small firm
Full Set
10.00%
18.80%
-0.28%
-0.58%
-7.94%
94.67%
97.90%
10.00%
17.23%
1.10%
-1.19%
-7.14%
87.25%
95.12%
10.00%
27.30%
-0.29%
-2.03%
-14.98%
91.26%
93.46%
10.00%
17.99%
-0.14%
-1.03%
-6.82%
90.82%
96.36%
10.00%
23.59%
-5.02%
-0.33%
-8.25%
64.26%
87.96%
10.00%
16.84%
-0.82%
-1.12%
-4.89%
72.96%
86.79%
10.00%
18.83%
-0.19%
-0.88%
-7.75%
90.75%
96.28%
10.00%
19.47%
-2.02%
-2.92%
-4.53%
27.26%
44.71%
Source: Authors’ calculations using Household Surveys circa 2005.
Unexplained Gender Wage Gaps by country
[%]
Argentina
Bolivia
Brasil
Chile
Colombia
Costa Rica
Dominican Republic
Ecuador
Guatemala
Honduras
Mexico
Nicaragua
Panama
Peru
Paraguay
El Salvador
Uruguay
Venezuela
Latin America
0.5
-5.5
20.5
10.9
-0.9
-5.8
-3.1
-3.2
-3.3
5.6
2.6
1.5
-8.6
18.3
6.2
3.3
5.7
0.4
10.0
+ Presence of
+ Part-time, Formality,
children in the HH, Occupation, Economic
Age and
Presence of other
Sector, Type of
education [%]
income earner in the Employment and Small
HH and Urban [%]
Firm [%]
14.2
-1.8
29.7
19.3
7.1
13.7
16.6
16.4
0.3
16.3
7.8
20.3
13.6
19.4
16.0
11.9
26.3
13.9
17.2
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
12.6
3.0
31.4
18.6
6.3
13.6
17.3
13.6
-0.7
16.3
10.5
19.3
16.2
25.9
13.8
16.0
27.5
13.8
18.8
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
10.8
17.8
26.4
13.1
7.3
17.9
23.9
5.6
17.7
24.2
15.3
28.4
10.4
23.5
6.9
11.3
23.4
12.3
19.5
Source: Authors’ calculations using Household Surveys circa 2005.
*Statistically different than zero at the 99% level
†Statistically different than zero at the 95% level
*
*
*
*
*
*
*
*
*
*
†
*
*
*
*
Gender Wage gap Decompositions by Country
NIC (∆=1.5%)
BRA (∆=20.5%)
HON (∆=5.6%)
DOM (∆=-3.1%)
PER (∆=18.3%)
URU (∆=5.7%)
CRI (∆=-5.8%)
BOL (∆=-5.5%)
GUA (∆=-3.3%)
MEX (∆=2.6%)
CHL (∆=10.9%)
VEN (∆=0.4%)
SLV (∆=3.3%)
ARG (∆=0.5%)
PAN (∆=-8.6%)
COL (∆=-0.9%)
PRY (∆=6.2%)
ECU (∆=-3.2%)
-60%
∆0
∆M
∆F
-40%
-20%
0%
20%
40%
60%
∆X
Venezuela
Uruguay
El Salvador
Paraguay
Peru
Panama
Nicaragua
Mexico
Honduras
Guatemala
Ecuador
Dom. Republic
Costa Rica
Colombia
Chile
Brasil
Bolivia
Argentina
Percentage of Femlae Wage
Confidence Intervals for the Unexplained
Gender Gap
35%
30%
25%
20%
15%
10%
5%
0%
-5%
-10%
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
Females have more
schooling, but they
do not earn more
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
+ Part Time
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
+ Part Time
+ Formality
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
+ Part Time
+ Formality
+ Occupation
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
+ Part Time
+ Formality
+ Occupation
+ Sector
Unexplained Gender Wage Gaps by
Percentiles of the Wage Distribution
70%
60%
50%
40%
30%
20%
10%
0%
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100
Wage Percentile
Age
+ Education
+ Presence of Children in HH
+ Presence of other wage earner in HH
+ Urban
+ Type of Employment
+ Part Time
+ Formality
+ Occupation
+ Sector
+ Small Firm
Unexplained Gender Gaps
Unexplained Gender Gaps
Unexplained Gender Gaps
Unexplained Gender Gaps
Unexplained Gender Gaps
Unexplained Gender Gaps
The Role of Job Tenure
The Role of Job Tenure II
A look at the evolution of Gender Wage Gaps
∆
∆0
∆M
∆F
∆X
% CS Males
% CS Females
∆
∆0
∆M
∆F
∆X
% CS Males
% CS Females
Age
+ Education
16.32%
13.44%
0.00%
0.00%
2.88%
100.00%
100.00%
16.32%
25.17%
0.39%
-0.01%
-9.23%
99.46%
99.88%
Age
+ Education
8.80%
9.73%
0.00%
0.00%
-0.92%
100.00%
100.00%
8.80%
22.21%
0.03%
0.01%
-13.44%
99.86%
99.97%
Period 1 (CIRCA 1992)
+ Presence of
+ Presence of
Other Wage
Children in the
Earner in the
Household
Household
16.32%
16.32%
25.42%
23.96%
0.50%
0.80%
0.05%
-0.02%
-9.65%
-8.41%
98.20%
93.47%
99.52%
98.88%
Period 2 (CIRCA 2007)
+ Presence of
+ Presence of
Other Wage
Children in the
Earner in the
Household
Household
8.80%
8.80%
22.21%
21.88%
0.04%
-0.25%
0.02%
0.07%
-13.47%
-12.90%
99.26%
97.42%
99.78%
99.41%
+ Urban
+ Type of
Employment
+ Time Worked
16.32%
25.00%
0.02%
0.13%
-8.83%
89.34%
97.40%
16.32%
23.99%
2.23%
0.26%
-10.16%
79.62%
92.79%
16.32%
33.68%
1.29%
-1.43%
-17.22%
65.55%
80.66%
+ Urban
+ Type of
Employment
+ Time Worked
8.80%
22.56%
-0.89%
0.16%
-13.03%
95.28%
98.74%
8.80%
20.75%
-0.33%
0.37%
-11.98%
89.61%
96.36%
8.80%
29.56%
-2.07%
0.43%
-19.12%
79.42%
89.04%
Drop in Gender Earnings Gaps (15-year span)
The Gap has Dropped in Most Countries
And it Has Dropped Especially at Both
Extremes of the Earnings Distribution
It Has Dropped more among Low-Educated
People
It has dropped more in rural areas
It Has Dropped more among the Self-Employed
It Has Dropped more among those with
Children at Home
It Has Dropped more among Part-Time
Workers
It has dropped for older cohorts
(matching after matching)
Cohort
Decomposition
Counterfactual Jump if no Change in X's
15 to 29 in 1992 Part of the Jump due to changes in X's
Total Jump
Counterfactual Jump if no Change in X's
30 to 44 in 1992 Part of the Jump due to changes in X's
Total Jump
Counterfactual Jump if no Change in X's
45 to 59 in 1992 Part of the Jump due to changes in X's
Total Jump
Education
Presence of
Children in the
Household
5.21
2.47
7.67
-4.82
1.04
-3.79
-16.55
4.13
-12.42
6.98
0.70
7.67
-5.13
1.35
-3.79
-12.54
0.12
-12.42
Presence of
other wage
earner in the
Household
7.30
0.38
7.67
-3.91
0.13
-3.79
-12.40
-0.02
-12.42
Urban
Type of
Employment
Time Worked
Full Set
6.52
1.16
7.67
-5.67
1.89
-3.79
-14.29
1.86
-12.42
7.82
-0.14
7.67
-3.05
-0.72
-3.79
-11.56
-0.86
-12.42
6.54
1.14
7.67
-4.20
0.42
-3.79
-12.91
0.49
-12.42
6.98
0.70
7.67
-6.72
2.94
-3.79
-28.75
16.33
-12.42
3. Conclusions
Methodological Advantages/Disadvantages
Messages
Advantages/Disadvantages
 It is not necessary to estimate earnings equations (no functional
form assumption)
 Better assessment. The traditional approach seems to deliver
biased results when the differences in supports are not taken into
account
 Once the matching has been done, it is straightforward to:
» Explore the distribution of the unexplained wage gap
» Explore not only wage gaps but also gaps for other labor market
outcomes (participation, unemployment, unemployment spells,
segregation)
 Curse of Dimensionality. The method does not allow us to use too
many explanatory variables.
 It does not take into account selection into the labor markets
Summary
Gender wage gaps
» Between 8% and 28%.
» older people, those with lower income, with secondary
education, self-employed, informal and those in small
firms
» Some “CEO effects” (in some countries)
» Somewhat surprising segregation effects
» An overall reduction over the last 15 years, especially for
those segments of the labor markets with the highest
disparities.
»
And the reductions are not explained by changes in the
labor markets’ composition