Gender Earnings Gaps in the Worldgesd.free.fr/iza5736.pdf · gender wage-gap in the private sector in favor of men (Tansel, 2004) East Asia and the Pacific: The impact of the economic

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Forschungsinstitut zur Zukunft der ArbeitInstitute for the Study of Labor

Gender Earnings Gaps in the World

IZA DP No. 5736

May 2011

Hugo ÑopoNancy DazaJohanna Ramos

Gender Earnings Gaps in the World

Hugo Ñopo Inter-American Development Bank

and IZA

Nancy Daza National Planning Department, Colombia

Johanna Ramos

National Planning Department, Colombia

Discussion Paper No. 5736 May 2011

IZA

P.O. Box 7240 53072 Bonn

Germany

Phone: +49-228-3894-0 Fax: +49-228-3894-180

E-mail: [email protected]

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IZA Discussion Paper No. 5736 May 2011

ABSTRACT

Gender Earnings Gaps in the World* This paper documents gender disparities in labor earnings for sixty-four countries around the world. Disparities are partially attributed to gender differences in observable socio-demographic and job characteristics. These characteristics are used to match males and females such that gender earnings disparities are computed only among individuals with the same characteristics, as in Ñopo (2008). After comparing males and females with the same characteristics we found that the earnings gap falls within a range between 8% and 48% of average females’ earnings, being more pronounced in South Asia and Sub-Saharan Africa. The unexplained earnings gaps are more pronounced among part-time workers and those with low education. JEL Classification: C14, D31, J16, O57 Keywords: gender, wage gaps, matching Corresponding author: Hugo Ñopo Inter-American Development Bank Colombia Country Office Carrera 7ma 71‐21, Torre B, Piso 19 Bogotá Colombia E-mail: [email protected]

* Any mistake within the paper is our own and the findings herein do not necessarily represent the views of the Inter-American Development Bank or its Board of Directors. The comments of Felipe Balcazar are especially acknowledged.

1. Introduction and Literature Review

The literature on gender disparities has been abundant. Several pieces have

examined not only the magnitude but also the reasons behind earnings gaps

between men and women, its persistence, evolution, and its impact on economic

welfare and development. Among the aspects that have been usually attributed to

explain the differences are the personal and job characteristics of women (age,

education, experience, occupation, working time, job status, type of contract), the

labor market structure (occupational segregation by gender, level of formality),

and institutional, cultural and social norms and traditions. The literature varies

not only in terms of methodologies and results, but also in the policy

recommendations aimed to improve the opportunities and economic participation

of women. This paper pretends to contribute to this literature providing a

comprehensive view of earnings disparities in the world, comparing different

regions with the same methodological approach and attempting to identify

commonalities across the globe.

We made a descriptive analysis of the data set by regions regarding gender

socio-demographic and labor characteristics, then, we use Ñopo (2008) matching

procedure to compute and analyze the wage gap, attributing disparities to these

characteristics. But first, in this section, we briefly summarize the literature by

world’s regions highlighting only some relevant pieces for our endeavor. We also

provide an annex that contains a more comprehensive table (but by no means

exhaustive) of the literature reviewed with summaries for each reviewed paper.

Globally, one of the first patterns that arises is that economic development or

market liberalization does not mean narrower gender differences. Different studies

have shown that there is no relationship between economic growth and the

narrowing of earnings gaps against women (Hertz et al., 2008; Blau and Kahn,

2001; Tzannatos, 1999). This result has been robust to different methodologies and

data sets. Weichselbaumer et al. (2007) report that the unexplained component of

the gender gap, estimated with Oaxaca-Blinder decompositions, has been

negatively related with further liberalization of markets.

Other reasons that have been found to be linked to gender earnings disparities

are: sectorial segregation to lower wage sectors against women (Tzannatos, 1999),

lower female net supply and wage structure (Blau and Kahn, 2001), labor market

liberalization and institutional frame in each country (Weichselbaumer et al., 2007;

Blau and Kahn, 2001; Cornish, 2007 and Tzanatos 1999), among others.

The magnitude and heterogeneity of the gender earnings gap notoriously varies

across studies. Blau et al. (2001) report that the gap is as low as 14.4% for Slovenia

and as high as 85% for Japan. Along with Japan, Switzerland, United States, Great

Britain and Russia also show high gender earnings disparities in this study. On the

lowest extreme of gender gaps, along with Slovenia, many other Eastern European

countries can be found. Fetherolf (2001) reports gender earnings disparities shows

on a range that goes from 53.5% (Rep. of Korea) to 106% (Swaziland), with all other

countries varying in a range between 65 and 92%. The countries in the OECD did

not have a significant narrower wage gap than other countries with similar

development levels. Hausmann et al. (2010) report Oceania as the region with the

lowest gender earnings gap and North America, the United Kingdom and Asia on

the other extreme with the highest gaps. Some brief accounts of the literature by

region are summarized as follows.

Sub-Saharan Africa: Different endowments, different opportunities. Labor force

education, work allocation with gender selection, and different unemployment

rates by gender seem to be the key drivers of gender earnings disparities in this

region. For instance, in Ethiopia, education accounts for around one-fifth of pay

differences and it works as a passport to enter into the public sector, a sector that

offers better wages and labor conditions (Kolev and Suarez, 2010; Suarez, 2005).

For a more comprehensive set of countries, it has been found an important role for

education on reducing wage differences (Kolev and Sirven, 2010). It has been also

reported that women tend to work more hours than men but they tend to be found

more often among unpaid family workers and domestic workers (Suarez, 2005;

Wodon and Ying, 2010). Unemployment is more prevalent among women but the

relationship between education and unemployment has not been conclusive

(Nordman et al., 2010). All in all, almost one-half of observed gender earnings

disparities fail to be explained by observable characteristics.

Europe and Central Asia: transition economies with segregation. The economic

and political transition of last decades has received special attention in the ECA

region. Most studies agree on the relative improvement of females’ wages in most

countries of the region (Brainerd, 2000). Increased wage inequality in Eastern

Europe have worked towards depressing female relative wages, but these losses

have been more than offset by gains in rewards to observed skills and by a decline

in the unobservable component of the earnings gap. Still, female segregation into

low-wage occupations emerges as the main contributor to the gender pay gap

(Simon). Along similar lines, the public-private divide seems to play an important

role as well. When controlling for observed characteristics and sample selection,

public administration wages are higher than private sector wages in the case of

men, except at the university level where the wages are equal. State-owned

enterprises’ wages are higher than those in the private sector. Further, while wages

of men and women are at parity in the public administration sector, there is a large

gender wage-gap in the private sector in favor of men (Tansel, 2004)

East Asia and the Pacific: The impact of the economic and political reforms. It

has been documented that the economic liberalization policies of 1986 did not have

an important effect on reducing the gender wage gap. For the last decades there is

no clear agreement on the tendency that the gender earnings gaps have followed.

The overall difference shave narrowed but the unexplained component of the gap,

overall, has not (Liu, 2001, 2004; Son, 2007). The results seem to show that it has

reduced in some percentiles of the earnings distribution (Pham and Reilly, 2006).

The reduction of the gap, when observed, has been mainly due to a reduction on

observed gender differences in characteristics. However, the unexplained

component of the earnings gap seems to be explaining most of the observed gaps.

Education also plays an important role in explaining wage differentials in this

region. In Indonesia it has been documented that earnings disparities by gender

shows an inverted U profile with respect to education (Pirmana, 2006). The

evidence for Mongolia shows that early career wages are not different between

genders. Despite this, on later stages of their careers women earn less than males,

but higher educated women partially overcome such gap (Pastore, 2010).

Western Europe: Occupational and industry segregation. Part of the literature

shows that wage differentials are mainly explained by the female segregation into

low-wage jobs (Daly et al., 2006), but it has also been documented the existence of

significant inter-industry wage differentials in all countries for both sexes (Gannon

et al., 2006). Other studies support the idea that gender pay gaps are typically

bigger at the top of the wage distribution and that the gender pay gap differs

significantly across the public and private sector wage distribution of each country

(Arulampalam et al., 2004).

This paper presents a comprehensive view of gender earnings gaps in the

world, simultaneously exploring many of the issues highlighted in this brief

literature review. This will be done, however, with the advantage of exploring

them from a comparative perspective, applying the same methods for all data

across the world. In order to perform such world comparison, the data from

different sources (national household surveys) is harmonized first, making them

comparable. This is shown in the next section. After that, the main section of this

paper is devoted to the comparison of unexplained gender earnings gaps for

different world regions. Next, an attempt of linking unexplained gender earnings

gaps to some political and social world indicators is made and finally the paper

closes with some brief concluding remarks.

2. The Data

This exercise of gender earnings gaps decompositions has been performed for 64

countries. The data sources have been any sort of nationally representative

household survey available with information on labor earnings and observable

characteristics of the individuals and their jobs1. The countries have been grouped

into regions: East Asian and Pacific (EAP), Europe and Central Asia (ECA), Middle

East and North Africa (MENA), South Asia (SA), Western Europe and Sub-Saharan

Africa (SSA). Note that this paper does not include the Latin America and the

Caribbean (LAC) regions2. The data from all countries was pooled restricting the

analysis to working individuals between 18 and 65 years old, reporting positive

earnings at their main activity and with no missing information on their

demographic characteristics.

The demographic characteristics considered for the analysis are: age, region

(urban/rural), education (measured in levels), marital status, and presence of

children (younger than 12 years old) at the household, presence of elderly (older)

than 65 years old at the household and presence of other household members who

generate labor income. On top of these demographics, information on job

characteristics has also been used: hours of work per week, employment status,

occupation, economic sector and formality (social security coverage). Labor hourly

earnings have been expressed in constant 2008 dollars using PPP-corrected

exchange rates and GDP deflators. All labor characteristics considered in the

analysis, including earnings, have been considered only for the main occupation.

The expansion factors from each survey have been used such that when pooling all

data the number of expanded observations per country is proportional to their

corresponding population sizes.

Not all the surveys have the same individuals’ information. Hence, the

estimations have been carried out for two groups of countries based on data

availability. The first group, the full set of countries, uses formality as control

variable. This comprises 21 countries from SSA, MENA, ECA and EAP regions.

The second group allows controlling for economic sector; this group comprises 14

1 For more details about the harmonization of the data sets, see Montenegro and Hirn (2009).

2 The gender earnings gaps decomposition for these countries can be found in two companion papers: Atal, Ñopo and

Winder (2009) and Hoyos and Ñopo (2010).

countries from SA and Western Europe regions3. The whole countries in the

analyses allow the inclusion of the hours of work per week and type of

employment and occupation, variables.

Table 1 displays the list of available countries on each group classified by

region, including the number of available observations (that is, those that remain

after dropping observations with missing values, zero labor income, or those out of

the range 18 to 65 years old) after sequentially adding hours of work per week,

type of employment, occupations, economics sector and formality into the analysis.

3 These regions are controlled for economic sector because for the first region all the individuals are informal (that is,

they are not covered by social security) and in the second region all the individuals are formal (covered by social security), in this way social security is not a proper control for informality.

Table 1. Available Countries by Set and Region

Region Country Year

Set

Observations* Weighted

Observations + Hours of work

+Type of employment

+Occupation +

Economic Sector

Full Set

SS

A

COTE D'IVOIRE 2002 X X 8,835 1,848,307

CAMEROON 2007 X X 9,942 3,542,248

COMOROS 2004 X X X X X 1,939 63,388

CONGO 2005 X X X X X 7,442 6,180,549

ETHIOPIA 2005 X X 20,663 2,014,380

GABON 2005 X X 7,918 300,853

GHANA 2005 X X X X X 8,653 4,518,128

KENYA 2005 X X X X 7,284 3,966,704

MADAGASCAR 2001 X X X X X 2,731 1,227,875

MOZAMBIQUE 1996 X X X X 1,877 526,543

MAURITANIA 2000 X X 3,602 178,802

MAURITIUS 2003 X X X X 9,069 9,069

MALAWI 2005 X X 3,056 718,149

NIGER 2002 X X 1,515 60,348

NIGERIA 2003 X X X X X 1,745 3,217,024

RWANDA 2005 X X X X 3,569 887,725

CHAD 2002 X X 4,943 918,357

TANZANIA 2006 X X X X X 11,707 5,524,172

UGANDA 2005 X X X X 3,271 2,301,786

NO. OF COUNTRIES

19 19 11 11 6

TOTAL

119,761 38,004,407

ME

NA

EGYPT 1998 X X X X 2,873 6,622,328

MOROCCO 1991 X X X 1,900 2,607,931

TUNISIA 2001 X X X X X 25,520 1,249,731

YEMEN 2005 X X X X X 7,158 1,241,521

NO. OF COUNTRIES

4 4 4 3 2

TOTAL

37,451 11,721,511

EC

A

ALBANIA 2002 X X X X X 2,155 416,072

BULGARIA 2008 X X X X X 3,689 2,539,627

BOSNIA AND HERZEGOVINA 2001 X X X X X 3,482 669,402

CZECH REPUBLIC 2008 X X X X X 7,990 3,074,162

ESTONIA 2008 X X X X X 4,978 552,748

CROATIA 2004 X X X X X 4,831 1,083,146

HUNGARY 2008 X X X X X 7,142 3,241,095

KYRGYZSTAN 1997 X X 2,238 915,574

LITHUANIA 2008 X X X X 4,826 1,425,343

LATVIA 2008 X X X X X 4,478 844,832

MOLDOVA 2002 X X X X X 3,541 843,473

MONTENEGRO 2006 X X 555 112,875

POLAND 2008 X X X X X 7,754 8,747,305

ROMANIA 2008 X X X X 6,242 7,408,127

RUSIA 2003 X X X X 28,219 36,900,000

SLOVAKIA 2008 X X X X X 6,480 2,120,510

TAJIKISTAN 2003 X X X X X 4,664 1,202,027

TURKEY 2005 X X 70,785 70,785

NO. OF COUNTRIES

18 18 15 15 12

TOTAL

174,049 72,167,103

SA

MALDIVES 2004 X 1,427 25,808

NEPAL 2003 X X X X

442 537,722

NO. OF COUNTRIES

2 1 1 1

TOTAL

1,869 563,530

EA

P

MICRONESIA 2000 X X 12,330 12,330

INDONESIA 2002 X X 104,811 28,200,000

CAMBODIA 2004 X X X X 7,466 1,238,972

MONGOLIA 2002 X X X X X 2,631 403,883

VIETNAM 2002 X X X X 24,502 14,800,000

NO. OF COUNTRIES

5 5 3 3 1

TOTAL

151,740 44,655,185

WE

ST

ER

N E

UR

OP

E

AUSTRIA 2008 X X X X

5,243 3,289,700

BELGIUM 2008 X X X X

5,732 4,031,928

CYPRUS 2008 X X X X

4,091 350,609

GERMANY 2008 X X X X

11,324 33,800,000

DENMARK 2008 X X X

11,324 33,800,000

SPAIN 2008 X X X X

13,025 18,000,000

FINLAND 2008 X X X X

11,913 2,240,843

GREECE 2008 X X X X

5,820 4,113,921

IRELAND 2008 X X X X

4,124 1,671,177

ICELAND 2008 X X X X

4,079 143,664

ITALY 2008 X X X X

18,605 21,700,000

LUXEMBOURG 2008 X X X X

4,310 198,882

NORWAY 2008 X X X

6,350 2,077,142

PORTUGAL 2008 X X X X

3,966 4,012,968

SWEDEN 2008 X X X

8,443 4,074,758

UNITED KINGDOM 2008 X X X X

7,585 23,100,000

NO. OF COUNTRIES

16 16 16 13

TOTAL

125,934 156,605,592

Source: Authors’ calculations using Household Surveys (World Bank)

Tables 2a and 2b show descriptive statistics by region. Table 2a presents the

descriptive statistics regarding the demographic set of variables; Table 2b presents

the job-related variables. In most cases the descriptive statistics are shown for the

full set of variables. The descriptive statistics obtained for the more restricted sets

of variables (that is, those including more comprehensive sets of countries) depict

similar results.4

Regarding the gender composition of the labor force it is possible to distinguish

three groups of regions. First, MENA and SA show more than seventy percent of

males on their active labor force; second, SSA have around sixty percent of males;

third, ECA and Western Europe have only slightly more males than females; and

fourth EAP show slightly less males than females on their labor force. Regarding

the urban/rural split and gender composition MENA highlights. While almost half

of working males in this region are located in urban areas, it is nine out of ten

females who do so. In all other regions of the world the urban/rural split does not

differ much between males and females.

Educational differences are also interesting to highlight. SSA, MENA and SA

show a high fraction of females with no education or primary incomplete,

although in MENA the corresponding percentage of males is even higher. On the

other extreme of the educational distribution, in all regions but SA the percentage

of females achieving post secondary education surpasses that of males.

The gender differences in marital status and household composition are also

salient. In all regions the proportion of married males surpasses that of females. In

SSA and SA the proportion of widowed females is around 10%. In SSA, ECA and

Western Europe it is interesting to highlight that also around 10% of females are

divorced. The proportion of never married among working women in MENA is

interestingly higher than the corresponding proportion for males. ECA highlights

as the region of the world with the lowest presence of children in the workers’

households (such indicator cannot be computed for Western Europe). SSA in turn

highlights as the region of the world with the highest presence on elderly in the

workers’ households, slightly higher for males than for females. In all regions of

the world the proportion of females living with another labor-income-generator at

home is higher than that of males.

The job-related differences by gender, depicted in Table 2b for all regions under

analysis, are also salient. Part-time work (defined in this paper as working 20

hours or less per week) is more prevalent among females than males across the

globe, but this is especially the case in Western Europe. Also, SSA and SA highlight

as having a high proportion of males doing part-time work.

Self-employment is prevalent in SSA both for males and females, but especially

for the latter (although it is important to note that this is not possible to identify in

4 Using Kolmogorov-Smirnov tests we conclude at the 90% confidence that the distributions of characteristics do not

differ across the sets of variables, for both males and females. That is, restricting the dataset due to data availability does not bias the sample.

MENA and SA). Regarding occupations and economic sectors, all regions show

some degree of segregation by gender but it is ECA the region that shows it the

highest occupational segregation. In this region ―Professionals and technicians‖

and ―Service workers‖ are clearly segments with higher female prevalence;

contrasting ―Administrative personnel and intermediary level‖ and ―Machine

operators‖ which are male-dominated occupations. EAP and Western Europe

show the lowest fraction of the labor force working on elementary occupations.

Regarding formality, Sub-Saharan Africa show a higher fraction of formal working

males than formal working females but in MENA, ECA and EAP the situation is

reversed (in SA and Western Europe it is not possible to measure formality).

Tab

le 2

a.

Desc

rip

tiv

e S

tati

stic

s b

y R

eg

ion

–D

em

og

rap

hic

Ch

ara

cteri

stic

s +

S

SA

M

EN

A

EC

A

SA

++

EA

P

WE

ST

ER

N E

UR

OP

E +

+

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

All

6

2

38

83

17

54

46

77

23

47

53

56

45

Ag

e

15

-24

11.0

1

6.2

1

9.8

2

5.2

1

0.9

9

.1

18.6

1

4.0

1

2.5

1

0.3

8

.3

8.4

25

-34

29.3

3

0.7

3

2.4

4

0.0

3

0.5

2

7.1

3

1.0

3

0.4

2

6.9

2

9.7

2

3.1

2

4.0

35

-44

28.0

2

6.5

2

5.9

2

3.7

2

5.0

2

8.3

2

3.4

2

6.9

3

3.7

3

5.7

3

0.5

3

0.8

45

-54

21.1

1

8.6

1

7.0

9

.6

23.2

2

8.3

1

6.8

1

9.0

1

8.7

2

1.1

2

5.5

2

5.6

55

-65

10.6

8

.1

4.8

1

.4

10.4

7

.2

10.2

9

.7

8.3

3

.2

12.7

1

1.3

Urb

an

No

58.4

5

5.3

4

4.4

1

1.1

4

5.8

4

1.4

8

4.6

9

2.1

2

6.8

2

4.4

1

8.7

1

5.8

Yes

4

1.6

4

4.7

5

5.6

8

8.9

5

4.2

5

8.6

1

5.4

7

.9

73.2

7

5.6

8

1.3

8

4.2

Ed

uca

tio

n

Non

e or

Pri

mar

y I

nco

mp

lete

2

1.8

3

3.4

1

8.2

9

.2

2.7

2

.0

71.1

9

2.8

5

.5

2.5

7

.1

5.0

Pri

mar

y C

om

ple

te o

r S

econ

dar

y I

nco

mp

lete

5

1.6

4

3.1

4

9.4

3

4.3

6

8.8

5

8.8

2

7.9

7

.2

18.3

1

1.6

6

2.9

5

9.8

Sec

on

dar

y C

om

ple

te

11.3

5

.7

18.6

3

6.8

7

.6

9.4

0

.9

0.0

2

6.9

2

4.0

5

.0

7.0

Post

Sec

on

dar

y

15.4

1

7.8

1

3.8

1

9.8

2

0.8

2

9.9

0

.1

0.0

4

9.2

6

1.9

2

5.0

2

8.1

Ma

rita

l S

tatu

s

Mar

ried

or

Liv

e to

get

her

7

8.9

6

5.8

6

8.9

4

4.4

6

6.4

6

3.9

8

8.8

8

2.7

7

7.5

6

8.5

6

0.1

5

6.9

Div

orc

ed/S

epar

ated

3

.4

10.7

0

.6

2.9

4

.3

10.1

1

.5

0.7

2

.3

7.5

6

.1

10.6

Wid

ow

/er

1.4

9

.2

0.4

3

.0

0.9

4

.4

1.7

1

1.1

2

.3

7.7

0

.7

2.3

Sin

gle

1

6.3

1

4.2

3

0.1

4

9.7

2

8.4

2

1.5

8

.0

5.5

1

8.0

1

6.3

3

3.1

3

0.1

Prese

nce

of

ch

ild

ren

in

th

e h

ou

seh

old

No

21.2

1

9.8

2

8.0

5

0.0

8

5.1

8

8.7

1

3.2

1

5.7

3

6.0

3

5.5

-

-

Yes

7

8.8

8

0.2

7

2.0

5

0.0

1

4.9

1

1.3

8

6.8

8

4.3

6

4.0

6

4.5

-

-

Prese

nce

of

eld

erl

y i

n t

he

ho

use

hold

No

79.5

8

5.5

9

8.9

9

9.5

9

1.8

9

3.3

9

8.3

9

9.6

9

9.4

9

9.2

9

9.6

9

9.5

Yes

2

0.5

1

4.5

1

.1

0.5

8

.2

6.7

1

.7

0.4

0

.6

0.8

0

.4

0.5

Prese

nce

of

oth

er

mem

ber w

ith

la

bo

r i

nco

me

No

46.6

3

3.0

5

7.3

3

3.5

2

8.1

2

3.4

6

3.1

4

1.5

2

5.8

2

4.4

3

9.3

3

1.5

Yes

5

3.4

6

7.0

4

2.7

6

6.5

7

1.9

7

6.6

3

6.9

5

8.5

7

4.2

7

5.6

6

0.7

6

8.5

Ob

serva

tio

ns

(Weig

hte

d)

12,7

99

,673

7,9

31

,46

2

2,0

57

,65

0

433

,60

2

13,5

78

,354

11,7

56

,044

412

,11

2

125

,61

0

191

,53

9

212

,34

4

64,7

50

,428

51,9

48

,315

Ob

serva

tio

ns

(Un

wei

gh

ted

)

Nu

mb

er o

f C

ou

ntr

ies

20,3

04

13,9

13

6

24,4

46

8,2

32

2

32,4

90

28,6

94

12

344

98

1

1,2

49

1,3

82

1

21,1

01

13,4

98

13

So

urc

e: A

uth

ors

’ ca

lcu

lati

on

s u

sin

g H

ou

seh

old

Su

rvey

s (W

orl

d B

an

k).

+ F

or

SA

an

d W

este

rn E

uro

pe

resu

lts

are

rep

ort

ed u

sin

g t

he

Eco

nom

ic s

ecto

r S

et, g

iven

th

e fa

ct t

ha

t so

cia

l se

curi

ty i

s n

ot

a p

rop

er c

on

tro

l fo

r in

form

alit

y.

Tab

le 2

b.

Desc

rip

tiv

e S

tati

stic

s b

y R

eg

ion

– J

ob

Rela

ted

Ch

ara

cteri

stic

s

S

SA

M

EN

A

EC

A

SA

E

AP

W

ES

TE

RN

EU

RO

PE

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

All

6

2 3

8 8

3 1

7 5

4 4

6 7

7 2

3 4

7 5

3 5

6 4

5 H

ou

rs o

f w

ork

pe

r w

eek

0

-20

Ho

urs

1

2.6

15.

9 4

.3

7.0

2

.1

4.3

1

4.6

18.

7 1

.3

1.3

2

.5

17.

9 2

1-4

0 H

ou

rs

34.

1 3

8.9

26.

5 2

7.8

65.

9 7

7.1

32.

9 4

5.4

58.

9 6

7.6

53.

2 6

2.9

Mo

re t

ha

n 4

0 H

ou

rs

53.

3 4

5.2

69.

2 6

5.2

32.

1 1

8.6

52.

4 3

6.0

39.

8 3

1.1

44.

4 1

9.3

Ty

pe

of

Em

plo

ym

ent

Em

plo

yee

4

4.1

27.

0 1

00.0

1

00.0

9

7.6

98.

1 1

00.0

1

00.0

9

8.4

98.

5 8

4.4

91.

7 E

mp

loy

er

3.6

2

.8

- -

0.3

0

.2

- -

0.4

0

.9

5.0

2

.0

Sel

f-E

mp

loy

ed

52.

2 7

0.3

0.0

0

.0

2.0

1

.7

- -

1.2

0

.6

10.

6 6

.3

Occ

up

ati

on

P

rofe

ssio

nal

s a

nd

tec

hn

icia

ns

12.

2 8

.3

14.

6 2

6.7

22.

5 3

8.5

0.6

2

.4

30.

4 4

6.3

28.

7 3

6.0

Dir

ecto

rs a

nd

up

per

m

ana

gem

ent

6.5

4

.8

10.

6 2

.4

6.3

4

.0

- -

9.5

4

.5

10.

0 5

.5

Ad

min

istr

ativ

e p

erso

na

l a

nd

in

term

edia

ry l

evel

1

4.8

12.

0 1

9.4

26.

8 3

0.8

19.

8 3

3.0

5.5

2

0.7

20.

9 3

1.2

24.

2 S

erv

ice

wo

rker

s 1

2.9

23.

8 1

6.4

7.0

8

.8

16.

8 1

.5

0.0

1

1.9

15.

8 7

.7

20.

5 S

kil

led

ag

ricu

ltu

re

21.

3 2

1.9

4.8

0

.5

1.5

1

.1

0.6

0

.5

1.7

1

.3

2.7

1

.1

Ma

chin

e o

per

ato

rs

4.6

0

.6

8.2

1

7.4

18.

1 6

.1

2.7

0

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17.

7 3

.1

11.

3 2

.3

Arm

ed f

orc

es

0.5

0

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0

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0.0

0

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0

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Ele

men

tary

occ

up

ati

on

s 2

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28.

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2.0

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6 6

1.6

91.

6 8

.2

8.0

8

.4

10.

4 E

con

om

ic S

ect

or

Ag

ricu

ltu

re, H

un

tin

g,

Fo

rest

ry a

nd

Fis

hin

g

45.

0 4

4.4

14.

1 2

.0

7.6

5

.5

57.

0 2

7.5

3.5

2

.3

3.6

1

.7

Min

ing

an

d Q

ua

rry

ng

9

.7

4.5

2

.5

0.4

-

- 1

2.0

12.

2 6

.9

3.6

-

- M

an

ufa

ctu

rin

g

14.

6 1

3.1

9.4

4

1.4

33.

8 2

1.2

5.6

1

5.5

5.5

9

.1

25.

1 1

1.4

Ele

ctri

city

, Ga

s a

nd

Wa

ter

sup

ply

0

.9

0.2

1

7.0

0.8

-

- 5

.6

9.5

5

.5

2.2

-

-

Co

nst

ruct

ion

2

.7

0.2

1

0.4

2.3

1

1.9

1.5

2

.9

9.7

7

.1

2.7

1

2.8

1.9

W

ho

lesa

le a

nd

Ret

ail,

Tra

de

an

d H

ote

ls a

nd

Res

tau

ran

ts

11.

9 2

7.2

7.8

7

.6

12.

2 1

9.6

5.8

9

.9

5.2

9

.9

16.

3 2

0.7

Tra

nsp

ort

, Sto

rag

e 4

.7

0.2

1

9.4

4.9

9

.8

4.4

2

.0

5.7

1

1.6

4.0

8

.1

3.6

F

inan

ce a

nd

Bu

sin

ess

Ser

vic

es

2.0

1

.2

2.1

4

.6

1.7

3

.8

2.7

6

.0

3.7

4

.2

3.8

4

.7

Co

mm

un

al S

erv

ices

6

.4

5.3

1

7.2

35.

8 2

0.5

40.

8 3

.6

3.0

4

7.0

58.

6 2

5.3

47.

0 O

ther

s n

ot

wel

l sp

ecif

ied

2

.1

3.6

0

.1

0.1

2

.5

3.2

2

.9

1.2

4

.0

3.5

5

.0

9.1

F

orm

ali

ty

No

7

8.0

86.

8 5

2.5

10.

6 8

.1

6.9

-

- 1

2.0

10.

4 -

- Y

es

22.

0 1

3.2

47.

5 8

9.4

91.

9 9

3.1

- -

88.

0 8

9.6

- -

Ob

serv

ati

on

s (W

eig

hte

d)

12

,79

9,67

3

7

,93

1,46

2

2

,05

7,65

0

43

3,60

2

13

,57

8,35

4

11

,75

6,04

4

41

2,11

2

12

5,61

0

19

1,53

9

21

2,34

4

64

,75

0,42

8

51

,94

8,31

5

Ob

serva

tio

ns

(Un

wei

gh

ted

)

Nu

mb

er o

f C

ou

ntr

ies

20,3

04

13,9

13

6

24,4

46

8,2

32

2

32,4

90

28,6

94

12

344

98

1

1,2

49

1,3

82

1

21,1

01

13,4

98

13

So

urc

e: A

uth

ors

’ ca

lcu

lati

on

s u

sin

g H

ou

seh

old

Su

rvey

s (W

orl

d B

an

k)

Tables 3a and 3b show additional descriptive statistics. In this case these

statistics are earnings averages for different segments of the labor markets. As

before, the first table uses the demographic set of variables and the second the job-

related characteristics. Both tables correspond to measures of hourly labor

earnings, normalized such that the average of females’ earnings in each region is

set equal to 100.

SA highlights as the region with the highest earnings disparities as males earn

on average 48% more than females. On the other extreme are EAP and MENA with

gender earnings gaps of 10% and 8% of average females’ earnings respectively.

Note that these are simple comparison of average earnings for all working males

and females. These gaps are not taking into account the gender differences in

observable characteristics yet. That will be analyzed in the next section.

The earnings pattern over the life cycle shows no surprise. Younger workers (15-

24) tend to earn less than prime-agers. When getting close to retirement age (55-

65), females’ earnings decrease more than those of males. The earnings patterns

with respect to education show no surprises as well. Higher educated workers

earn more than those with lower education. The gender differences across those

patterns, however, differ (and this will be analyzed latter after the earnings gaps

decompositions).

Individuals’ earnings in urban areas tend to be higher than those in rural areas.

Married males tend to earn higher than the rest of the population. Those with no

elderly at home tend to earn higher than their counterparts with at least one

elderly at home. The only exception to that happens among females in SSA (recall

that SSA is also the region of the world where workers tend to live more with their

elderly relatives).

Regarding occupations, is no surprise that ―Directors and upper management‖

and ―Professionals and technicians‖ tend to have higher earnings than those at

other occupations. Interestingly, ―Armed forces‖ are also a high-paying occupation

for women in Sub-Saharan Africa. This may reflect that females who join the army,

generally, do not do so in lower-hierarchy positions. Regarding economic sectors,

it is also no surprising to verify that finance and business services are at the top

earnings.

Tab

le 3

a.

Earn

ing

s D

istr

ibu

tio

n b

y R

eg

ion

– D

em

og

rap

hic

Ch

ara

cteri

stic

s

S

SA

M

EN

A

EC

A

SA

E

AP

W

ES

TE

RN

EU

RO

PE

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

M

ale

F

em

ale

All

1

35

100

1

08

100

1

18

100

1

48

100

1

10

100

1

23

100

Ag

e

15

-24

99.

0 7

0.5

88

48

80.

4 6

7.5

164

.1

245

.3

101

.6

90.

6 6

2.9

57.

9

25

-34

136

.1

104

.9

102

1

06

114

.5

96.

1 1

43.6

8

9.2

105

.6

103

.8

105

.4

90.

8

35

-44

135

.2

109

.9

116

1

30

132

.1

107

.1

141

.1

72.

7 1

13.9

1

00.0

1

30.3

1

04.9

45

-54

139

.9

103

.9

128

1

34

120

.7

105

.6

148

.0

67.

8 1

17.9

9

9.6

140

.2

112

.2

55

-65

154

.3

98.

9 1

26

117

1

25.8

1

05.4

1

42.9

6

3.4

98.

5 9

7.7

140

.6

110

.1

Urb

an

No

1

20.5

8

9.6

101

6

7 1

01.2

8

4.5

137

.3

98.

5 8

4.7

80.

6 1

03.4

8

9.9

Yes

1

54.1

1

12.9

1

14

104

1

31.8

1

11.0

2

03.3

1

17.6

1

18.8

1

06.2

1

27.3

1

01.9

Ed

uca

tio

n

No

ne

or

Pri

mar

y I

nco

mp

lete

1

06.8

7

9.6

99

48

73.

3 5

5.5

139

.8

83.

1 9

2.4

96.

8 8

6.4

67.

5

Pri

mar

y C

om

ple

te o

r S

eco

nd

ary

In

com

ple

te

119

.4

102

.2

91

55

101

.6

80.

8 1

59.6

3

17.5

9

1.9

72.

1 1

05.8

8

5.9

Sec

on

da

ry C

om

ple

te

192

.5

129

.0

100

8

7 1

07.2

8

8.9

311

.1

0.0

8

8.5

78.

9 1

21.6

1

03.6

Po

st S

eco

nd

ary

1

81.8

1

23.7

1

95

226

1

81.2

1

44.3

6

23.3

0

.0

129

.7

113

.5

176

.1

134

.8

Ma

rita

l S

tatu

s

Ma

rrie

d o

r L

ive

tog

eth

er

144

.3

108

.8

119

1

38

127

.3

100

.5

147

.5

99.

2 1

12.4

9

9.6

135

.8

103

.9

Div

orc

ed/

Sep

ara

ted

8

9.6

90.

6 8

8 1

01

113

.9

114

.3

102

.5

59.

9 8

8.2

105

.5

133

.0

102

.4

Wid

ow

/er

1

10.9

8

8.4

86

78

101

.4

94.

2 1

26.8

7

1.6

75.

8 9

6.7

119

.4

99.

5

Sin

gle

9

8.3

73.

8 8

4 6

7 9

6.7

92.

9 1

60.6

1

74.5

1

04.7

1

00.9

9

7.3

91.

8

Pre

sen

ce o

f ch

ild

ren

in

th

e h

ou

seh

old

No

1

27.8

1

22.2

1

01

90

118

.6

101

.5

142

.9

122

.5

117

.5

109

.1

- -

Yes

1

36.3

9

4.5

111

1

10

113

.3

88.

5 1

48.2

9

5.8

105

.2

95.

0 -

-

Pre

sen

ce o

f e

lder

ly i

n t

he

ho

use

ho

ld

No

1

41.7

9

7.6

108

1

00

118

.7

100

.3

148

.0

100

.0

109

.7

100

.2

122

.8

100

.0

Yes

1

06.6

1

14.1

1

53

78

107

.2

96.

4 1

19.0

1

06.5

8

9.1

79.

2 1

23.2

9

2.1

Pre

sen

ce o

f o

the

r m

emb

er w

ith

la

bo

r in

com

e

No

1

28.6

1

07.1

1

10

92

121

.9

106

.7

154

.5

141

.1

97.

7 9

4.8

126

.7

105

.1

Yes

1

06.6

1

14.1

1

53

78

107

.2

96.

4 1

19.0

1

06.5

8

9.1

79.

2 1

23.2

9

2.1

So

urc

e: A

uth

ors

’ ca

lcu

lati

on

s u

sin

g H

ou

seh

old

Su

rvey

s (W

orl

d B

an

k)

Tab

le 3

b. E

arn

ing

s D

istr

ibu

tio

n b

y R

eg

ion

- J

ob

Rela

ted

Ch

ara

cteri

stic

s

S

SA

M

EN

A

EC

A

SA

E

AP

W

ES

TE

RN

EU

RO

PE

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

Ma

le

Fe

ma

le

All

1

35

100

1

08

100

1

18

100

1

48

100

1

10

100

1

23

100

Ho

urs

of

wo

rk p

er

wee

k

0-2

0 H

ou

rs

339

.4

233

.4

372

3

69

275

.2

188

.9

329

.3

266

.8

280

.8

242

.1

228

.1

103

.2

21

-40

Ho

urs

1

23.3

8

5.0

123

1

37

118

.6

100

.1

144

.0

75.

7 1

17.0

1

06.9

1

18.4

1

00.2

M

ore

th

an

40

Ho

urs

9

3.3

66.

1 8

6 5

5 1

05.9

7

9.2

98.

9 4

4.0

93.

1 7

9.3

122

.2

96.

5

Ty

pe

of

Em

plo

ym

ent

Em

plo

yee

1

08.2

9

8.4

108

1

00

117

.6

100

.2

147

.5

100

.0

109

.6

99.

5 1

21.9

9

9.9

Em

plo

yer

4

75.7

1

71.7

-

- 1

25.9

9

7.2

- -

156

.9

142

.8

170

.0

130

.8

Sel

f-E

mp

loy

ed

133

.0

97.

8 0

0 1

27.6

8

8.7

- -

93.

4 1

22.3

1

07.7

9

0.9

Occ

up

ati

on

P

rofe

ssio

nal

s a

nd

tec

hn

icia

ns

210

.9

128

.7

183

2

12

158

.5

128

.0

234

.9

719

.4

130

.2

111

.6

161

.9

128

.7

Dir

ecto

rs a

nd

up

per

ma

na

gem

ent

158

.8

149

.5

117

1

62

199

.5

170

.2

- -

144

.0

129

.6

168

.5

120

.4

Ad

min

istr

ativ

e p

erso

nn

al a

nd

in

term

edia

ry

lev

el

139

.7

117

.8

102

6

8 1

02.5

8

9.2

171

.6

208

.9

101

.1

96.

5 1

03.5

9

4.9

Ser

vic

e w

ork

ers

150

.6

114

.7

103

5

3 9

7.4

73.

5 2

91.2

0

.0

88.

3 8

3.1

96.

7 7

2.1

Sk

ille

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3. Gender Earnings Gap Decompositions

Tables 4a and 4b show the decompositions for the gender gaps in hourly

earnings (at the main occupation), measured as a percentage of the average

females’ earnings. SSA, MENA and ECA are shown in Table 4a, while SA, EAP

and Western Europe are in Table 5b. The structure of the tables is the same across

regions. The components of the gender earnings gaps are shown in columns

(labeled as Delta 0, Delta M, Delta F and Delta X) , together with the percentages of

males and females in the common support (labeled as CSF and CSM).5

The matching variables that are sequentially added are shown as consecutive

lines. First, the “Demographic set” of variables is added: age, urban status,

education, marital status, presence of child in the household, presence of an elder

in the household and presence of other income-generator in the household (the

first and obvious matching variable within the pooled data set is ―country‖). As

mentioned, these are sequentially added as matching variables to measure the

extent to which the observed gender earnings gaps can be attributed to gender

differences in observed characteristics. On top of the “Demographic set” some job-

related variables are added, but with replacement. That is, first the number of

hours per week is added as a matching variable to the “Demographic set”; then the

type of employment variable replaces the number of hour per week as a matching

variable; then the occupation variable takes the place type of employment and so

on with economic sector and formality. Last, the ―All variables‖ line includes all

demographic and job-related variables in the matching.

It can be noted that the most comprehensive set of matching variables is the one

that shows the lowest measures of common support for both males and females.

These are particularly low for SA. This is a common feature of all non-parametric

methods (the curse of dimensionality). The inclusion of a comprehensive set of

variables may constraint the comparison of males and females to a small (and

perhaps non-representative) set of individuals. For that reason, the discussion of

the results below will be done considering the demographic set of variables first

and all variables afterwards.

In ECA, EAP and Western Europe, the gender earnings gap that remains after

matching on demographic characteristics (that is, after comparing males and

females with the same observable characteristics regarding the demographic set)

are higher than their corresponding original earnings gap (the one that does not

account for gender differences in characteristics). This is also the case for the LAC

5 See Ñopo (2008) for a detailed description of the components and the common supports.

region (Atal et al., 2009). Women show observable characteristics that would make

them more attractable to the labor markets (and hence, better paid), but this is

actually not the case. In the other three regions under analysis, SSA, MENA and

SA, the unexplained gender earnings gap that remain after matching on

demographic characteristics are below their corresponding original gap.

When analyzing the role of each particular variable on the explanation of the

earnings gaps, it is interesting to note that age moves down the unexplained wage

gap in MENA, reflecting that males tend to inhabit the prime-age segments of the

distribution in greater proportion than women. The inclusion of education as a

matching variable moves up the counterfactual earnings gaps in MENA, ECA and

EAP, reflecting that higher school achievements for females are not necessarily

compensated in the labor markets. In contrast, the inclusion of education moves

down the counterfactual earnings gap in SA. Marital status is a variable that

substantially contributes to the explanation of the earnings gap. The inclusion of

such variable in the matching reduces the counterfactual earnings gap in SSA, SA

and EAP.

The further inclusion of job-related characteristics move the unexplained

component of the earnings gaps up and down, with variability depending on the

variable to include on the matching and the region of the world. The inclusion of

hours of work per week moves up the counterfactual earnings gaps in all regions

under analysis. Including type of employment leaves the gap unaltered with

respect to the demographic set in all regions but SSA where it drops. Adding

occupation as a matching variable increases the counterfactual gap in SSA in SA

leaving it almost unaltered in the rest of the regions. In no region, however, the

unexplained gender earnings gaps show a reduction after introducing occupation

as a matching variable. This apparently paradoxical result, which has also been

found for Latin America, suggest that the reduction of gender occupational

segregation is a wrong target when trying to reduce gender earnings disparities

(Calonico and Ñopo, 2009). A slightly similar story can be depicted for economic

sectors. The inclusion of formality, in those regions where the data allows it, leaves

the counterfactual earnings gap almost unaltered. All in all, the inclusion of all job-

related characteristics moves the unexplained component of the earnings gaps

down in two regions (SA and Western Europe) and up in the other four (SSA,

MENA, ECA and EAP).

A related feature is that the Delta F and Delta M components of the earnings gap

also have the potential to increase. Delta M, the component of the earnings gap

that can be attributed to the existence of certain combination of observable

characteristics to which males reach but females do not, is positive in two regions

(SSA and SA), negative in MENA and statistically zero the other regions. Females

in SSA and SA suffer from a sort of glass-ceiling or barriers to the access to certain

well paid segments of the labor markets which males can access. Interestingly, the

same two regions show display a negative measure of Delta F, suggesting that

there are also some other well paid segments of the labor markets to which females

access and males not. The access barriers in these two regions work for both, males

and females, but females suffer from barriers that imply higher earnings

limitations for them. Regarding Delta F as well it is interesting to note that EAP

and to a lesser extent Western Europe display a positive component. This may be

an indication of the existence of certain females’ confinements within the labor

markets with earnings that are below the average of the rest of the markets.

Table 4a. Gender Earnings Gaps Decompositions by Region

SSA

Delta=34.50%

Delta 0 Delta M Delta F Delta X CSM CSF

Dem

ogra

phi

c se

t

Country 37.18% 0.00% 0.00% -2.68% 100.00% 100.00%

+ Age 33.96% 0.00% 0.00% 0.54% 100.00% 100.00%

+ Urban 36.99% 0.00% 0.00% -2.49% 100.00% 100.00%

+ Education 34.83% 0.30% -0.15% -0.48% 99.15% 99.95%

+ Marital Status 25.80% -0.35% -0.20% 9.25% 96.90% 98.22%

+ Presence of child in the household 30.09% -0.54% -0.56% 5.52% 95.94% 95.61%

+ Presence of older in the household 28.72% -0.92% 0.08% 6.62% 95.37% 93.93%

+ Presence of other member with income in the household 28.85% -1.03% 0.79% 5.90% 92.63% 90.67%

Job

Rel

ated

V

aria

bles

& Hours of work per week 39.49% 0.44% -0.10% -5.32% 85.08% 82.52%

& Type of Employment 17.81% 8.10% -0.16% 8.76% 88.48% 87.68%

& Occupation 37.96% 0.04% 2.06% -5.56% 74.62% 79.69%

& Economic Sector 45.57% 0.88% 1.25% -13.19% 73.13% 79.64%

& Formality 27.51% -1.14% 1.06% 7.08% 89.05% 89.22%

All Variables 31.02% 16.62% -9.36% -3.78% 46.50% 55.38%

MENA

Delta=8.25%


Dem

ogra

phi

c se

t

Country 6.08% 0.00% 0.00% 2.17% 100.00% 100.00%

+ Age -3.74% 0.00% 0.00% 11.98% 100.00% 100.00%

+ Urban -0.46% -0.04% 0.00% 8.75% 98.47% 100.00%

+ Education 4.92% 0.33% 0.00% 3.00% 94.56% 100.00%

+ Marital Status 3.87% 1.31% 0.11% 2.95% 87.81% 99.34%

+ Presence of child in the household 3.88% 0.92% 0.11% 3.35% 83.58% 97.76%

+ Presence of older in the household 3.80% 1.35% 0.10% 3.00% 82.84% 97.44%

+ Presence of other member with income in the household 6.09% 0.60% 0.06% 1.49% 73.92% 96.39%

Job

Rel

ated

V

aria

bles


& Type of Employment 6.09% 0.60% 0.06% 1.49% 73.92% 96.39%

& Occupation 7.21% 2.74% 1.80% -3.50% 52.63% 91.94%

& Economic Sector 7.90% 3.18% 2.88% -5.72% 46.24% 90.29%

& Formality 6.92% 1.99% 0.27% -0.94% 68.28% 95.44%

All Variables 12.24% -5.40% 3.55% -2.15% 25.50% 77.68%

ECA

Delta=17.80%


Dem

ogra

phi

c se

t

Country 17.88% 0.00% 0.00% -0.08% 100.00% 100.00%

+ Age 19.00% 0.00% 0.00% -1.19% 100.00% 100.00%

+ Urban 19.99% 0.02% 0.00% -2.21% 99.92% 100.00%

+ Education 26.67% -0.09% 0.00% -8.78% 99.61% 99.97%

+ Marital Status 25.55% -0.26% 0.12% -7.61% 99.01% 97.88%

+ Presence of child in the household 25.49% -0.23% 0.13% -7.59% 98.89% 97.59%

+ Presence of older in the household 25.38% -0.21% 0.16% -7.53% 98.62% 97.30%

+ Presence of other member with income in the household 25.74% -0.22% -0.98% -6.74% 97.71% 96.04%

Job

Rel

ated

V

aria

bles


& Type of Employment 25.68% -0.18% -0.91% -6.78% 96.90% 95.67%

& Occupation 25.05% -0.57% -0.42% -6.26% 88.07% 87.89%

& Economic Sector 27.07% -0.55% -1.20% -7.51% 80.40% 78.67%

& Formality 25.55% -0.36% -0.91% -6.48% 96.86% 95.69%

All Variables 27.49% -0.38% -0.12% -9.18% 47.28% 52.87%


Table 4b. Gender Earnings Gaps Decompositions by Region SA

Delta=47.51%


Dem

ogra

phi

c se

t

Country 47.51% 0.00% 0.00% 0.00% 100.00% 100.00%

+ Age 46.58% 0.00% 0.00% 0.93% 100.00% 100.00%

+ Urban 42.33% 1.82% 0.00% 3.36% 95.63% 100.00%

+ Education 33.04% 6.55% 0.00% 7.92% 78.27% 100.00%

+ Marital Status 28.05% 8.53% 0.31% 10.62% 72.82% 98.73%

+ Presence of child in the household 25.82% 10.34% 0.65% 10.69% 70.20% 97.62%

+ Presence of older in the household 28.51% 9.94% 1.21% 7.85% 68.64% 96.90%

+ Presence of other member with income in the household 21.38% 13.15% 4.03% 8.95% 60.59% 88.00%

Job

Rel

ated

V

aria

bles

& Hours of work per week 28.64% 11.38% -5.54% 13.03% 43.58% 78.25%

& Type of Employment 21.38% 13.15% 4.03% 8.95% 60.59% 88.00%

& Occupation 42.27% 18.99% -14.26% 0.52% 47.07% 82.04%

& Economic Sector 47.61% 26.41% -12.32% -14.20% 33.67% 44.34%

All Variables 18.84% 57.38% -30.27% 1.57% 10.62% 21.11%

EAP

Delta=9.62%


Dem

ogra

phi

c se

t

Country 9.62% 0.00% 0.00% 0.00% 100.00% 100.00%

+ Age 10.53% 0.00% 0.00% -0.91% 100.00% 100.00%

+ Urban 11.34% 0.00% 0.00% -1.73% 100.00% 100.00%

+ Education 15.04% 0.05% 0.00% -5.48% 99.50% 100.00%

+ Marital Status 11.63% -0.89% 0.94% -2.06% 96.44% 93.81%



+ Presence of other member with income in the household 13.40% -2.95% 0.90% -1.73% 89.15% 86.34%

Job

Rel

ated

V

aria

bles

& Hours of work per week 16.43% -4.04% -0.37% -2.41% 81.70% 80.82%

& Type of Employment 13.97% -2.61% 0.41% -2.16% 87.85% 85.26%

& Occupation 11.92% -7.33% 3.96% 1.07% 68.73% 70.52%

& Economic Sector 11.95% -4.44% 1.22% 0.88% 65.47% 70.03%

& Formality 13.84% -3.60% 1.81% -2.43% 84.80% 82.46%

All Variables 14.49% -16.35% 7.97% 3.51% 30.97% 39.78%

WESTER EUROPE

Delta=22.80%


Dem

ogra

phi

c se

t

Country 24.04% 0.00% 0.00% -1.23% 100.00% 100.00%

Age 23.73% 0.00% 0.00% -0.93% 100.00% 100.00%

+ Urban 24.17% 0.00% 0.00% -1.37% 100.00% 100.00%

+ Education 25.78% -0.02% 0.00% -2.96% 99.94% 99.99%

+ Marital Status 26.07% -0.09% 0.04% -3.21% 99.74% 99.42%



+ Presence of other member with income in the household 25.69% -0.21% 0.08% -2.76% 98.96% 98.52%

Job

Rel

ated

V

aria

bles

& Hours of work per week 38.97% -0.26% -0.30% -15.60% 96.72% 93.83%

& Type of Employment 24.58% 0.15% -0.04% -1.88% 96.30% 97.48%

& Occupation 24.91% -2.13% 0.76% -0.74% 90.38% 93.17%

& Economic Sector 26.99% -1.60% 0.68% -3.26% 85.62% 86.88%

All Variables 21.76% -2.86% 5.91% -2.00% 44.09% 47.08%


Table 5. Original and Unexplained Components of the Gender Wage Gap, by

Country

Country Delta

Delta 0

Demographic Variables

All Variables

CONGO 52.50% 33%* 20.47%

COMOROS 50.20% 40.46%* 85.26%*

GHANA 44.20% 27.22%* 57.34%*

MADAGASCAR 42.63% 23.73%* -1.91%

TANZANIA 38.02% 45.11% 41.41%*

NIGERIA -14.17% -8.20% 3.76%

SSA 34.50% 28.85%* 31.02%*

YEMEN 23.36% 10.13% 11.81%

TUNISIA 4.04% 5.65%* 12.16%*

MENA 8.25% 6.09%* 12.24%*

ESTONIA 39.01% 45.14%* 48.7%*

CZECH REPUBLIC 33.18% 32.78%* 35.19%*

ALBANIA 30.07% 35.15%* 48.75%*

SLOVAKIA 26.74% 32.4%* 30.06%*

TAJIKISTAN 25.17% 37%* 19.63%

LATVIA 25.01% 38.89%* 42.47%*

BULGARIA 21.97% 30.11%* 31.33%*

CROATIA 13.97% 22.11%* 19.78%*

HUNGARY 13.76% 24.8%* 26.05%*

POLAND 10.25% 20.66%* 26.79%*

MOLDOVA 8.88% 2.73% -4.84%

BOSNIA AND HERZEGOVINA 5.73% 9.81% 3.15%

ECA 17.80% 25.74%* 27.49%*

NEPAL 47.51% 21.38% 18.84%

SA 47.51% 21.38% 18.84%

MONGOLIA 9.62% 13.4%* 14.49%*

EAP 9.62% 13.4%* 14.49%*

UNITED KINGDOM 38.02% 37.55%* 24.62%*

CYPRUS 33.47% 31.61%* 23.43%*

LUXEMBOURG 31.33% 28.01%* 20.16%*

GERMANY 29.55% 26.64%* 20.9%*

ICELAND 25.00% 30.05%* 42.27%*

FINLAND 22.10% 26.49%* 26.35%*

PORTUGAL 20.71% 36.34%* 40.75%*

SPAIN 14.21% 19.02%* 20.83%*

ITALY 13.04% 19.87%* 24.16%*

BELGIUM 12.84% 13.14%* 11.8%*

GREECE 12.43% 16.76%* 17.61%*

IRELAND 11.02% 8.11%* 7.42%

AUSTRIA 10.40% 11.54%* 13.47%* WESTERN EUROPE 22.80% 25.69%* 21.76%*


* Statistically different than zero at the 99% level

4. Beyond averages. Exploring the distribution of unexplained gender earnings differences

One of the advantages of the matching approach is that it allows an exploration of

unexplained gender differences in pay within different segments of the labor

markets. In this way we can report that (these results are shown on Figures 1 to

12):

For SSA the highest unexplained gender differences in pay are found among

those who live with another labor income generator within their households,

those working in ―communal services‖ and those holding informal jobs.

For MENA the highest unexplained gaps are found among younger workers

(15-24) with none or primary education, with no presence of elderly at their

households, working either part-time or over-time (but not full-time) and

among those with lower earnings.

In ECA the situation slightly differs as the highest unexplained gaps are found

among married part-time workers, living with elderly and with no other labor

income generator at home. Is SA, as in MENA, the highest earnings gaps are

among those with none or primary incomplete education; and as in ECA, for

those with no other income generator at home. Additionally, the unexplained

earnings gaps in SA are high among those working in elementary occupations.

EAP is the only region of the world for which there is no clear segment of the

market in which the earnings gaps are more pronounced. The unexplained

earnings gaps are almost equally pronounced across all segments.

The situation in Western Europe shows some similarities, but also some

differences with respect to what happens in other regions of the world. On one

hand, two elements in Western Europe that contrast with the rest of the world

are that the unexplained gender earnings gaps are more pronounced among

older worker (35 years old and older) and in urban areas. On the other hand, as

in MENA and SA, those with no education or primary incomplete are those who

suffer from the highest unexplained earnings disparities. Along the same line of

similarities with respect to other regions of the world, part-time workers suffer

from higher unexplained gaps, as in MENA and ECA. And similarly to ECA as

well, married workers suffer from high unexplained gaps; but the gaps are also

high among divorced people in Western Europe.

Figure 1. Confidence Intervals for the Unexplained Gender Earnings Gap

(after Controlling for Demographic and Job-Related Characteristics) by Different Characteristics - SSA Region

-20%

0%

20%

40%

60%

80%

100%

120%

15-24 25-34 35-44 45-54 55-65

Age

20%

25%

30%

35%

40%

45%

50%

55%

60%

Urban Rural

Urban/Rural

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

None or Primary incomplete

Primary Complete / Secondary Incomplete

Secondary Complete Post Secondary

Education

-10%

0%

10%

20%

30%

40%

50%

Married or Live Together

Divorced/ Separated

Widow/er Single

Marital Status

-10%

0%

10%

20%

30%

40%

50%

60%

70%

No Yes

Presence of Children in the Household

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

No Yes

Presence of Elderly in the Household

-10%

0%

10%

20%

30%

40%

50%

60%

70%

No Yes

Presence of other Household Member with Labor

Income

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

0-20 21-40 More than 40 hours

Hours of work per week

-50%

0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

Employee Employer Self-Employed

Type of Employment

-400%

-300%

-200%

-100%

0%

100%

200%

300%

400%

Professionals and technicians

Directors and upper

management

Administrative personnel and intermediary

level

Service Workers

Skilled agriculture

Machine opeators

Armed Forces Elementary Occupations

Occupation

-300%

-200%

-100%

0%

100%

200%

300%

400%

500%

600%

Agriculture, Hunting,

Forestry and Fishing

Mining and Quarryng

Manufacturing Electricity, Gas and Water

supply

Construction Wholesale and Retail, Trade

and Hotels and

Restaurants

Transport, Storage

Finance and Business Services

Communal Services

Other not well specified

Economic sector

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Informal Formal

Formality


Figure 2. Confidence Intervals for the Unexplained Gender Earnings Gap (after Controlling for Demographic and Job-Related Characteristics)

by Different Characteristics - MENA Region

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

15-24 25-34 35-44 45-54 55-65

Age

-15%

-10%

-5%

0%

5%

10%

15%

20%

Urban Rural

Urban/Rural

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%




Education

-40%

-30%

-20%

-10%

0%

10%

20%

30%


Divorced/ Separated

Widow/er Single

Marital Status

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

No Yes


-20%

-10%

0%

10%

20%

30%

40%

No Yes


-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

No Yes


Income

-5%

0%

5%

10%

15%

20%

25%

30%



0%

2%

4%

6%

8%

10%

12%

Employee

Type of Employment

-15%

-5%

5%

15%

25%

35%

45%

55%

65%


Directors and upper

management

Administrative personnel and

intermediary level

Service Workers Skilled agricultureMachine opeators Elementary Occupations

Occupation

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%



Mining and Quarryng


supply


and Hotels and Restaurants

Transport, Storage

Finance and Business

Services

Communal Services

Economic sector

-30%

-20%

-10%

0%

10%

20%

30%

40%

Informal Formal

Formality



by Different Characteristics - ECA Region

10%

15%

20%

25%

30%

35%

15-24 25-34 35-44 45-54 55-65

Age

14%

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

Urban Rural

Urban/Rural

15%

20%

25%

30%

35%

40%

45%

50%




Education

-15%

-5%

5%

15%

25%

35%

45%


Divorced/ Separated

Widow/er Single

Marital Status

17%

19%

21%

23%

25%

27%

29%

31%

33%

No Yes


13%

15%

17%

19%

21%

23%

25%

27%

29%

31%

No Yes


15%

17%

19%

21%

23%

25%

27%

29%

31%

33%

No Yes


Income

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%



-60%

-40%

-20%

0%

20%

40%

60%


Type of Employment

15%

20%

25%

30%

35%

40%

45%


Directors and upper

management


intermediary level

Service Workers

Skilled agriculture

Machine opeators

Elementary Occupations

Occupation

0%

10%

20%

30%

40%

50%

60%

Agriculture, Hunting, Forestry

and Fishing

Mining and Quarryng,

Manufacturing,

Electricity, Gas and Water supply

Construction Wholesale and Retail, Trade and

Hotels and

Restaurants

Transport, Storage Finance and Business Services

Communal Services


Economic sector

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Informal Formal

Formality



by Different Characteristics - SA Region

-100%

-50%

0%

50%

100%

150%

200%

250%

15-24 25-34 35-44 45-54 55-65

Age

0%

20%

40%

60%

80%

100%

120%

Urban Rural

Urban/Rural

-140%

-90%

-40%

10%

60%

110%

None or Primary incomplete Primary Complete / Secondary Incomplete

Education

-120%

-70%

-20%

30%

80%

130%


Divorced/ Separated

Widow/er Single

Marital Status

-40%

-20%

0%

20%

40%

60%

80%

100%

No Yes


0%

10%

20%

30%

40%

50%

60%

No Yes


-45%

-25%

-5%

15%

35%

55%

75%

95%

115%

No Yes


Income

-70%

-50%

-30%

-10%

10%

30%

50%

70%

90%

110%

130%



-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Employee

Type of Employment

-120%

-70%

-20%

30%

80%

130%

Administrative personnel and intermediary level


Occupation

-300%

-200%

-100%

0%

100%

200%

300%

400%

500%

600%

Agriculture, Hunting, Forestry and Fishing


Manufacturing, Electricity, Gas and

Water supply


Hotels and Restaurants


Communal Services

Economic Sector


Figure 5. Confidence Intervals for the Unexplained Gender Earnings Gap

(after Controlling for Demographic and Job-Related Characteristics) by Different Characteristics - EAP Region

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

15-24 25-34 35-44 45-54 55-65

Age

-2%

3%

8%

13%

18%

23%

Urban Rural

Urban/Rural

-25%

-15%

-5%

5%

15%

25%

35%

45%

None or Primary

incomplete

Primary Complete /

Secondary Incomplete

Secondary

Complete

Post Secondary

Education

-35%

-25%

-15%

-5%

5%

15%

25%


Divorced/ Separated

Widow/er Single

Marital Status

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

No Yes


6%

8%

10%

12%

14%

16%

18%

Yes


3%

5%

7%

9%

11%

13%

15%

17%

19%

21%

23%

No Yes


Income

-35%

-15%

5%

25%

45%

65%

85%

105%

125%

145%

165%



-110%

-90%

-70%

-50%

-30%

-10%

10%

30%

Employee Self-Employed

Type of Employment

-150%

-100%

-50%

0%

50%

100%

150%

Professionals and

technicians

Directors and upper

management


intermediary level

Service Workers

Skilled agriculture

Machine opeators


Occupation

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

120%



Mining and Quarryng


supply


and Hotels and Restaurants

Transport, Storage

Finance and Business

Services

Communal Services


Economic sector

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

Informal Formal

Formality



by Different Characteristics – Western Europe Region

3%

8%

13%

18%

23%

28%

33%

15-24 25-34 35-44 45-54 55-65

Age

12%

14%

16%

18%

20%

22%

24%

26%

28%

Urban Rural

Urban/Rural

15%

17%

19%

21%

23%

25%

27%

29%

31%

33%

35%

None or Primary incomplete Primary Complete / Secondary Incomplete


Education

5%

10%

15%

20%

25%

30%

35%

40%


Divorced/ Separated

Widow/er Single

Marital Status

0%

10%

20%

30%

40%

50%

60%

No Yes


22%

23%

24%

25%

26%

27%

28%

29%

No Yes


Income

20%

40%

60%

80%

100%

120%

140%



7%

12%

17%

22%

27%

32%

37%


Type of Employment

5%

10%

15%

20%

25%

30%

35%

40%

45%


Directors and upper

management


intermediary level

Service Workers Skilled agricultureMachine opeators Elementary Occupations

Occupation

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Agriculture, Hunting, Forestry

and Fishing


Manufacturing,

Electricity, Gas and Water supply


Hotels and

Restaurants


Communal Services


Economic sector


Figure 7. Unexplained Gender Earnings Gap by Percentiles of the Earnings Distribution

of Males and Females-SSA

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Earnings Percentil

Demographic Set Full Set


Figure 8. Unexplained Gender Earnings Gap by Percentiles of the Earnings Distribution

of Males and Females-MENA

-20%

-10%

0%

10%

20%

30%

40%

50%

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Earnings Percentil



Figure 9. Unexplained Gender Earnings Gap by Percentiles of the Earnings Distribution of Males and Females-ECA

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Earnings Percentil



Figure 10. Unexplained Gender Earnings Gap by Deciles of the Earnings Distribution of Males and Females-SA

-20%

0%

20%

40%

60%

80%

100%

120%

140%

160%

1 2 3 4 5 6 7 8 9 10

Earnings Percentil



Figure 11. Unexplained Gender Earnings Gap by Percentiles of the Earnings

Distribution of Males and Females-EAP

-10%

0%

10%

20%

30%

40%

50%

60%

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Earnings Percentil



Figure 12. Unexplained Gender Earnings Gap by Percentiles of the Earnings Distribution of Males and Females-Western Europe

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

Earnings Percentil



Table 6 (below) summarizes all the information from Figures 1 to 12 describing the

segments of the labor markets for which the unexplained gender earnings gaps are more

pronounced. The most salient regularities that can be traced in most of the regions under

analysis are two: part-time workers and those with lower educational achievement suffer

from the highest unexplained gender earnings gaps. It is interesting to note that this also

shows some similarities with respect to Latin America.6

Table 6. Labor Market Segments with Highest Unexplained Gender

Earnings Gap by Region

SSA MENA ECA SA EAP WESTERN EURO PE

Age Young (15-54) Older (35 +)

Urban/Rural Urban

EducationNone/ Primary

Incomplete

None/ Primary

Incomplete

None/ Primary

Incomplete

Marital StatusMarried or Live

together

Married or Live

together and Divorced

Presence of children in the household

Presence of elderly in the household No Yes

Presence of other member with labor income Yes No No

Hours of WorkPart time and

Over time Part time Part time

Type of Employment

OccupationElementary

Occupations

Economic SectorCommunal

Services

Job Formality Informal

Earnings Percentiles Poorer

Dem

og

rap

hic

set

Jo

b R

ela

ted

Va

ria

ble

s


6 See Atal, Nopo and Winder (2009).

5. Gender Earnings Gap and the Economic, Cultural and Political Characteristics.

Having shown the heterogeneity on unexplained gender earnings gaps across the world, this section will explore the cross-country linkages of these disparities and other socio-economic and political variables. Figures 13 through 15 illustrate the correlation between the unexplained gender earnings gaps (the one that remains after controlling for the full set of matching variables described above) and GDP per-Capita, Institutionalized democracy and Predominant religion respectively.

Figure 13 plots GDP per capita, measured in 2005 PPP terms, against the unexplained component of the wage gap. The negative relationship between the two variables that the figure depicts is weak, as judged by the R-squared coefficient (0.0066). Without considering Luxemburg within the analysis the R-squared would increase (0.0377). Bigger economies tend to show smaller gender disparities, but the relationship is not too strong.

Figure 14 plots Institutionalized Democracy against the unexplained component of the wage gap, showing a positive relationship between both. Countries with more institutionalized democracies tend to show bigger unexplained gender disparities, although, as above, the relationship is not too strong.

Figure 15 show bar diagrams of the unexplained component of the gender earnings gaps groups by the predominant religion in the countries. The results show no clear pattern. If any, the unexplained gender earnings gaps are slightly higher in Muslim countries than in the rest of the world.

The results from comparing the unconditional gender earnings gaps with the same socio-economic and political indicators (available upon request) deliver similar results.

Figure 13. Unexplained Component of the Gender Earnings Gap against GDP per capita

Source: Authors’ calculations using World Bank Indicators.

Figure 14. Unexplained Component of the Gender Earnings Gap against Democracy Level

Source: Authors’ calculations using Policy IV Indicators. The Institutionalized Democracy indicator is an eleven-point scale (0-10) derived from indicators on the competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the chief executive (See Annex 2 for details)

Figure 15. Unexplained Component of the Gender Earnings Gap and Religion, by Country

Source: United Nations, CIA World Factbooks. The figure reports the religion that shows the largest group of adherents in each country.

Concluding Remarks

This paper has presented gender earnings disparities for an as comprehensive as possible list of countries. A prominent result is the vast heterogeneity of gender differentials. An important component of those earnings differentials cannot be explained on the basis of gender differences in observable characteristics that the labor markets rewards. At a cross-country level, the gaps cannot be completely linked neither to socio-economic nor to political indicators. Much of the earnings gaps are yet to be explained.

Among the regularities that can be observed across the globe highlights the role of part-time work, a predominantly female way of participating in the labor markets which particularly suffers from higher unexplained gender disparities in pay. Another regularity, seen in most of the regions, is the fact that unexplained gender earnings disparities tend to be more pronounced among low-educated workers, and part-time workers. These regularities on the descriptive statistics of gender earnings gaps may serve as indications of areas for which more analytical work, with a stronger emphasis on causality, is needed for advancing the understanding of gender disparities.

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An

nex 1

.

Reg

ion

Lit

era

ture

Rev

iew

Co

un

try

A

uth

ors

a

nd

Ye

ar

Da

ta

Ma

in F

ind

ing

s M

eth

od

olo

gy

INT

ER

NA

TIO

NA

L C

OM

PA

RIS

ON

12

4 co

un

trie

s in

E

ast

& S

ou

ther

n

Afr

ica

W

est

Afr

ica

Eas

t A

sia

Pac

ific

, S

ou

th A

sia,

E

ast

& C

entr

al

Eu

rop

e,

Res

t o

f E

uro

pe,

M

idd

le E

ast,

N

ort

h A

fric

a,

Am

eric

as

Tz

an

na

tos

(19

99)

ILO

Dat

a B

ase

Th

e p

ap

er

exa

min

es

the

lev

el

an

d

chan

ges

in

fe

ma

le

an

d

ma

le

pa

rtic

ipa

tio

n

rate

s,

emp

loy

men

t se

gre

gat

ion

an

d f

emal

e re

lati

ve

to m

ale

wag

es a

cro

ss t

he

wo

rld

eco

no

my

. It

is

pre

sen

ted

a

d

eco

mp

osi

tio

n

of

the

eco

no

my

-wid

e fe

ma

le

rela

tiv

e w

age

in

emp

loy

men

t ef

fect

(ch

ang

es i

n s

ecto

ral

emp

loy

men

t),

fem

ale

wag

e ef

fect

(ch

ang

es i

n g

end

er p

ay

gap

w

ith

in s

ecto

rs)

an

d s

tru

ctu

ral

wag

e ef

fect

(ch

ang

es i

n m

ale

ea

rnin

gs)

. It

fi

nd

s su

ffic

ien

t ev

iden

ce

sup

po

rtin

g

tha

t la

bo

r m

ark

ets

in

dev

elo

pin

g

cou

ntr

ies

are

tr

an

sfo

rmed

in

th

e se

nse

th

at

gen

der

dif

fere

nti

als

in

em

plo

ym

ent

an

d p

ay

are

na

rro

win

g

mu

ch f

ast

er t

ha

n i

n i

nd

ust

ria

liz

ed.

Gro

wth

ben

efit

s w

om

en a

t la

rge,

in

equ

alit

ies

can

ha

ve

sig

nif

ica

ntl

y a

dv

erse

eff

ects

on

wel

fare

, a

nd

ma

rket

-ba

sed

dev

elo

pm

ent

alo

ne

can

be

a

wea

k i

nst

rum

ent

for

red

uci

ng

in

equ

alit

y.

Dec

om

po

siti

on

o

f th

e ec

on

om

y-w

ide

fem

ale

rela

tiv

e w

age.

Au

stra

lia,

Au

stri

a,

Bri

tain

, B

ulg

aria

, C

anad

a, C

zech

R

epu

bli

c, E

ast

Ger

man

y,

Wes

t

Ger

man

y,

Hu

ng

ary

, Ir

elan

d,

Isra

el, I

taly

, Ja

pan

, N

eth

erla

nd

s, N

ew

Zea

lan

d,

No

rwa

y,

Po

lan

d,

Ru

ssia

, S

lov

enia

, S

wed

en,

Sw

itze

rlan

d,

US

A.

Bla

u a

nd

K

ah

n

(20

01)

Inte

rnat

ion

al

Surv

ey

Pro

gra

mm

e

Usi

ng

mic

ro-d

ata

fo

r 2

2 c

ou

ntr

ies

ov

er 1

98

5-9

4 p

erio

d,

it w

as f

ou

nd

th

at

mo

re c

om

pre

ssed

m

ale

wag

e st

ruct

ure

s a

nd

lo

wer

fem

ale

net

su

pp

ly a

re b

oth

ass

oci

ate

d w

ith

lo

wer

gen

der

p

ay

g

ap.

Th

e ex

ten

t o

f co

llec

tiv

e b

arg

ain

ing

co

ver

ag

e in

ea

ch

cou

ntr

y

is

sig

nif

ica

ntl

y

ass

oci

ate

d w

ith

th

e g

end

er p

ay

gap

. M

ore

ov

er,

a l

arg

e p

art

of

the

dif

fere

nce

in

th

e g

end

er

dif

fere

nti

al

bet

wee

n h

igh

ga

p a

nd

lo

w g

ap c

ou

ntr

ies

is e

xp

lain

ed b

y t

he

dif

fere

nce

s a

cro

ss

thes

e co

un

trie

s in

ov

era

ll w

ag

e st

ruct

ure

, an

d i

n t

he

dif

fere

nce

s in

fem

ale

net

su

pp

ly.

T

he

Juh

n,

Mu

rph

y a

nd

Pie

rce

dec

om

po

siti

on

su

gg

este

d a

str

on

g r

ole

fo

r w

age

ineq

ua

lity

a

nd

wag

e se

ttin

g i

nst

itu

tio

ns

in a

ffec

tin

g g

end

er p

ay

gap

.

Juh

n, M

urp

hy

a

nd

Pie

rce

dec

om

po

siti

on

C

orn

ish,

(20

07

)

It i

s es

tim

ate

d t

ha

t w

om

en e

arn

ab

ou

t 7

8%

of

wh

at

men

ma

ke.

Th

e p

rin

cip

al

rea

son

s fo

r th

e ex

iste

nce

of

gen

der

pa

y d

iscr

imin

ati

on

are

th

e o

ccu

pa

tio

na

l se

gre

gat

ion

an

d t

he

glo

ba

l tr

end

to

war

ds

gre

ate

r in

form

alit

y a

risi

ng

fro

m m

ark

et l

iber

ali

za

tio

n.

Fo

r th

e m

ost

pa

rt o

f th

e w

orl

d,

exis

tin

g

lab

or

ma

rket

m

ech

an

ism

s h

av

e n

ot

ma

de

sig

nif

ica

nt

pro

gre

ss

in

rem

edy

ing

th

is g

lob

al

gen

der

pa

y g

ap.

Mea

sure

s th

at

can

del

iver

in

crea

ses

in w

om

en’s

pa

y

to r

edu

ce t

his

dis

crim

ina

tio

n a

re c

riti

cal

to t

hei

r su

rviv

al

an

d p

rosp

erit

y.

Art

icle

Met

a-a

nal

ysi

s: 6

2 co

un

trie

s;

Mic

ro-d

ata:

58

cou

ntr

ies.

Wei

chse

lba

um

er,

Win

ter-

Eb

mer

and

Met

a-a

na

lysi

s co

nd

uct

ed b

y

Wei

chse

lba

um

er a

nd

Win

ter-

It i

s u

sed

tw

o v

ery

dif

fere

nt

ap

pro

ach

es t

o e

xp

lore

th

e re

lati

on

bet

wee

n m

ark

et o

rien

tati

on

a

nd

gen

der

wag

e d

iffe

ren

tial

s in

in

tern

ati

on

al

da

ta.

Th

e fi

rst

ap

pro

ach

em

plo

ys

met

a-

an

aly

sis

da

ta a

nd

tak

es a

dv

an

tag

e o

f th

e fa

ct t

ha

t m

an

y s

tud

ies

alr

ead

y e

xis

t w

hic

h u

se

na

tio

na

l d

ata

so

urc

es t

o t

he

bes

t p

oss

ible

ex

ten

t. T

he

seco

nd

ap

pro

ach

use

s co

mp

ara

ble

Oax

aca

- B

lin

der

d

eco

mp

osi

tio

n

Zw

eim

üll

er.

(20

07

)

Eb

mer

(2

005

);

Inte

rnat

ional

So

cial

Surv

ey

Pro

gra

mm

e

(IS

SP

)

18

85

-200

0

mic

ro d

ata

. In

ea

ch c

ase

s, i

t is

ca

lcu

late

d t

he

gen

der

ea

rnin

g g

ap u

sin

g O

axa

ca-

Bli

nd

er

dec

om

po

siti

on

. U

sin

g b

oth

da

ta b

ase

s, i

t is

ob

tain

ed t

he

con

clu

sio

n a

bo

ut

the

exis

ten

ce o

f a

stro

ng

n

ega

tiv

e co

rrel

ati

on

b

etw

een

co

mp

etit

ive

ma

rket

s a

nd

g

end

er

wag

e g

aps,

in

p

art

icu

lar

wh

en

com

pet

itiv

e m

ark

ets

are

m

easu

red

b

y

the

com

po

nen

ts

―fr

ee

tra

de‖

, ―

ab

sen

ce o

f re

gu

lati

on

‖ a

nd

―le

gal

str

uct

ure

‖.

Mo

re m

ark

et o

rien

tati

on

mig

ht

be

rela

ted

to

g

end

er w

age

gap

s v

ia i

ts e

ffec

ts o

n c

om

pet

itio

n i

n p

rod

uct

an

d l

abo

r m

ark

ets

an

d t

he

gen

era

l a

bse

nce

of

reg

ula

tio

n i

n t

he

eco

no

my

.

Su

b‐S

ahar

an:

Gh

ana,

Mal

awi,

N

iger

ia;

So

uth

& E

ast

Asi

a:

Ban

gla

des

h,

Ind

on

esia

, N

epal

, V

ietn

am;

E

aste

rn E

uro

pe

&

Cen

tral

Asi

a:

A

lban

ia,

Bu

lga

ria,

T

ajik

ista

n:

Lat

in A

mer

ican

an

d t

he

Car

ibb

ean

: E

cua

do

r G

uat

emal

a

Nic

arag

ua

P

anam

a

Her

tz,

Win

ters

, d

e

la O

,

Quiñ

ones

,

Dav

is Z

ezza

(20

08

).

RIG

A-L

da

tase

t It

is

use

d t

he

Oax

aca

-Bli

nd

er d

eco

mp

osi

tio

n t

o u

nd

erst

an

d t

he

det

erm

ina

nts

of

wag

e-g

aps

bet

wee

n m

en a

nd

wo

men

, b

etw

een

urb

an

an

d r

ura

l w

ork

ers,

an

d b

etw

een

th

ose

em

plo

yed

in

th

e ru

ral

ag

ricu

ltu

ral

ver

sus

the

rura

l n

on

-ag

ricu

ltu

ral

sect

ors

, fo

r th

e 1

4 d

evel

op

ing

an

d

tra

nsi

tio

n e

con

om

ies.

Th

e a

ver

ag

e g

end

er g

ap

in

da

ily

wag

es a

cro

ss t

he

14

co

un

trie

s w

as

on

th

e o

rder

of

25

per

cen

t in

fa

vo

r o

f m

en. T

her

e w

as n

o c

lea

r re

gio

na

l p

att

ern

to

th

e si

ze

of

the

raw

wag

e d

iffe

ren

ce,

yet

th

ere

is a

cle

ar

reg

ion

al

dif

fere

nce

in

th

e b

rea

kd

ow

n b

etw

een

it

s ex

pla

ined

an

d u

nex

pla

ined

co

mp

on

ents

. T

he

av

era

ge

un

exp

lain

ed s

ha

re o

f th

e w

age

gap

was

ver

y h

igh

, a

t ro

ug

hly

90

per

cen

t. W

hil

e th

e g

eog

rap

hic

an

d s

ecto

ria

l w

age

gap

s sh

ou

ld r

esp

on

d t

o c

han

ges

in

th

e le

vel

of

hu

ma

n c

ap

ital

, a

nd

in

th

e lo

cati

on

of

no

nfa

rm

emp

loy

men

t o

pp

ort

un

itie

s, i

n o

ther

wo

rds,

to

eco

no

mic

dev

elo

pm

ent,

th

ere

seem

s to

be

no

ev

iden

ce t

ha

t th

e g

end

er w

ag

e p

rem

ium

res

po

nd

s to

eco

no

mic

gro

wth

per

se.

Oax

aca

-Bli

nd

er

dec

om

po

siti

on

SS

A

Co

te

D'iv

oir

e,

Eth

iop

ia,

Ken

ya,

C

am

ero

on

, G

ha

na

, M

ad

ag

asca

r,

Ma

uri

tan

ia,

Ma

law

i,

Nig

eria

, U

gan

da

Ko

lev

an

d

Sir

ven

W

orl

d B

an

k

Su

rvey

-ba

sed

H

arm

on

ized

In

dic

ato

rs

Pro

gra

m 2

00

0

Par

tici

pa

tio

n i

n p

rod

uct

ive

emp

loy

men

t in

urb

an

are

as

was

ap

pre

ciab

ly l

ow

er f

or

wo

men

, y

et

cou

ntr

ies

wit

h

mo

re

fav

ora

ble

em

plo

ym

ent

ou

tco

mes

fo

r m

en

als

o

ha

d

hig

her

em

plo

ym

ent

rati

os

am

on

g w

om

en a

nd

les

s g

end

er d

isp

ari

ties

in

em

plo

ym

ent.

In

mo

st

cou

ntr

ies.

U

nem

plo

ym

ent

was

la

rgel

y

an

u

rba

n

ph

eno

men

on

, a

ffec

tin

g

wo

men

d

isp

rop

ort

ion

ate

ly.

Wo

men

wer

e o

ver

rep

rese

nte

d a

mo

ng

th

e u

nd

erem

plo

yed

. L

ow

-pa

id

wo

rk w

as a

n i

mp

ort

an

t is

sue

in s

even

co

un

trie

s fo

r w

hic

h d

ata

wer

e a

va

ila

ble

, a

ffec

tin

g

bo

th m

en a

nd

wo

men

. In

mo

st c

ou

ntr

ies,

wo

men

ex

per

ien

ced

a d

isa

dv

an

tag

e in

ea

rnin

gs.

W

om

en

ten

ded

to

b

e u

nd

erre

pre

sen

ted

in

th

e in

du

stry

a

nd

se

rvic

e se

cto

rs

an

d

ov

erre

pre

sen

ted

in

ag

ricu

ltu

re.

Fo

r b

oth

men

an

d w

om

en,

edu

cati

on

did

no

t se

em t

o b

e a

sso

cia

ted

wit

h l

ow

er u

nem

plo

ym

ent

an

d h

igh

er e

mp

loy

men

t. T

he

retu

rns

fro

m e

du

cati

on

o

n e

arn

ing

s w

ere

imp

ort

an

t, a

nd

ed

uca

tio

n a

lso

ha

d a

po

siti

ve

effe

ct o

n g

end

er w

age

equ

ity

.

Ra

tio

s a

nd

In

dic

ato

rs

Eth

iop

ia

Ko

lev

an

d

Su

arez

L

ab

or

Fo

rce

Su

rvey

20

05

On

av

era

ge

wo

men

’s m

on

thly

wag

es r

epre

sen

ted

in

20

05

on

ly a

bo

ut

55

per

cen

t o

f m

en’s

w

ages

.

No

m

ore

th

an

5

0

per

cen

t o

f th

e o

bse

rved

w

age

gap

co

uld

b

e a

ttri

bu

ted

to

ex

pla

ined

dif

fere

nce

s in

ch

ara

cter

isti

cs,

lea

vin

g a

lar

ge

fra

ctio

n o

f th

e g

ap u

nex

pla

ined

. A

n

on

-neg

lig

ible

pro

po

rtio

n o

f th

e g

end

er w

age

gap

—a

t le

ast

11

per

cen

t b

ut

no

mo

re t

ha

n 2

3

per

cen

t o

n a

ver

ag

e—w

as e

xp

lain

ed b

y t

he

dif

fere

nce

s in

ed

uca

tio

n e

nd

ow

men

ts b

etw

een

m

en a

nd

wo

men

. Jo

b c

ha

ract

eris

tics

wer

e fo

un

d t

o b

e sy

stem

atic

ally

les

s fa

vo

rab

le f

or

wo

men

.

Min

cer

equ

ati

on

s,

Co

tto

n-

Neu

ma

rk

dec

om

po

siti

on

p

roce

du

re

Ma

da

gas

car

No

rdm

an

, R

ak

oto

ma

na

na

, an

d

Ro

bil

lia

rd

En

qu

ête

pér

iod

iqu

e a

up

rès

des

m

énag

es

(EP

M)

200

1

an

d 2

00

5

Reg

ard

ing

la

bo

r a

llo

cati

on

, p

art

icip

ati

on

of

wo

men

in

th

e M

ala

gas

y l

abo

r m

ark

et a

pp

ears

to

be

hig

h,

an

d i

t in

crea

sed

bet

wee

n 2

00

1 a

nd

20

05.

O

ver

all

, th

e st

ruct

ure

of

emp

loy

men

t ch

ang

ed b

etw

een

20

01

an

d 2

005

. T

he

evo

luti

on

in

em

plo

ym

ent

stat

us

can

be

exp

lain

ed i

n

pa

rt b

y s

om

e o

f th

e sh

ock

s ex

per

ien

ced

by

th

e M

ala

ga

sy l

abo

r m

ark

et b

etw

een

20

01

an

d

20

05

. T

he

stu

dy

fo

un

d a

str

on

g p

osi

tiv

e im

pa

ct o

f ed

uca

tio

n o

n t

he

pro

ba

bil

ity

of

get

tin

g a

p

aid

jo

b,

for

bo

th m

en a

nd

wo

men

. R

egar

din

g g

end

er i

neq

ua

lity

in

ea

rnin

gs,

th

e re

sult

s sh

ow

th

at

the

av

era

ge

gen

der

wag

e g

ap i

s re

lati

vel

y s

ma

ll a

nd

sta

ble

ov

er t

ime.

Acr

oss

w

age

emp

loy

men

t se

cto

rs,

the

gen

der

gap

ap

pea

rs t

o b

e lo

wes

t in

th

e p

ub

lic

sect

or

an

d

hig

hes

t in

th

e in

form

al s

ecto

r.

Oax

aca

an

d

Neu

ma

rk’s

D

eco

mp

osi

tio

n

Ben

in,

Ken

ya,

M

ad

ag

asca

r,

Ma

uri

tiu

s,

Mo

rocc

o,

Sen

ega

l, a

nd

U

gan

da

No

rdm

an

a

nd

Wo

lff

Inv

estm

ent

Cli

ma

te

Ass

essm

ent

(IC

A)

surv

eys

Th

is s

tud

y m

ak

es u

se

of

ma

tch

ed e

mp

loy

er-e

mp

loy

ee d

ata

co

llec

ted

in

sev

en A

fric

an

co

un

trie

s to

sh

ed l

igh

t o

n t

he

ma

gn

itu

de

of

the

gen

der

wag

e g

ap i

n t

he

ma

nu

fact

uri

ng

se

cto

r. R

aw

gen

der

gap

s ca

lcu

late

d a

t th

e m

ean

of

the

sam

ple

s te

nd

to

hid

e si

gn

ific

an

t d

iffe

ren

ces

in t

he

ma

gn

itu

de

of

the

gap

s a

lon

g t

he

wag

e d

istr

ibu

tio

n. T

hey

in

ves

tig

ated

th

e b

elie

f th

at

dif

fere

nce

s a

mo

ng

th

e se

ven

Afr

ica

n c

ou

ntr

ies

mig

ht

be

a r

esu

lt o

f th

e p

rese

nce

o

f se

lect

ivit

y e

ffec

ts, t

hro

ug

h g

end

er d

iffe

ren

ces

in a

cces

s to

job

s.

Qu

anti

le

reg

ress

ion

, F

ield

s d

eco

mp

osi

tio

n,

Mea

n a

nd

q

uan

tile

d

eco

mp

osi

tio

n

Ta

nz

an

ia

Par

ra a

nd

W

od

on

S

AM

20

01

co

nst

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ed

by

Th

url

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003

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ex

og

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incr

ease

in

th

e d

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d f

or

an

y o

f th

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ou

ld h

elp

(a

t th

e m

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in)

to c

lose

th

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etw

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to

tal

pa

y f

or

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wo

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s, a

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wee

n t

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l p

ay

fo

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uca

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on

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uca

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row

th i

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lab

or

inco

me.

Str

uct

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l P

ath

A

na

lysi

s (S

PA

) o

n S

oci

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acc

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m

atr

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(S

AM

s)

Eth

iop

ia

Su

árez

L

ab

or

Fo

rce

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rvey

(L

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) 2

00

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Th

ere

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str

on

g g

end

er-b

ase

d d

ivis

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of

lab

or

in E

thio

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, w

hic

h i

s m

uch

mo

re a

cute

in

ru

ral

are

as.

Wo

men

wo

rk m

ore

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d f

or

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ger

ho

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th

an

men

in

th

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ou

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, w

hil

e th

e re

ver

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s tr

ue

in t

he

lab

or

ma

rket

. W

om

en s

pen

d m

ore

tim

e a

t w

ork

th

an

men

, th

is

ph

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men

on

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ein

g

ob

serv

ed

to

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gre

ate

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ten

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ru

ral

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as.

W

om

en

are

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earl

y

dis

ad

va

nta

ged

in

ter

ms

of

job

all

oca

tio

n.

Un

pa

id f

am

ily

wo

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s a

cco

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t fo

r th

e h

igh

est

sha

re o

f fe

mal

e w

ork

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wh

ile

the

ma

jori

ty o

f m

ale

wo

rker

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re s

elf-

emp

loy

ed.

As

they

b

eco

me

edu

cate

d

an

d

rea

ch h

igh

er

lev

els

of

edu

cati

on

, m

en

an

d,

to

a

gre

ate

r ex

ten

t,

wo

men

, st

ron

gly

in

crea

se t

hei

r ch

ance

s o

f w

ork

ing

in

th

e p

ub

lic

sect

or,

wh

ich

is

the

mo

st

rew

ard

ing

wag

e-em

plo

ym

ent

sect

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bec

au

se i

t o

ffer

s th

e h

igh

est

earn

ing

s a

nd

pro

tect

ion

.

Des

crip

tiv

e S

tati

stic

s,

Mu

ltin

om

ial

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it

reg

ress

ion

s,

tob

it m

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Sie

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Leo

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Wo

do

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an

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In

teg

rate

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Ho

use

ho

ld

Su

rvey

Wo

men

are

fo

un

d t

o w

ork

mu

ch m

ore

th

an

men

on

do

mes

tic

task

s, e

spec

iall

y i

n r

ura

l a

rea

s.

Fo

r m

an

y c

hil

dre

n,

the

bu

rden

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do

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tic

wo

rk i

s h

igh

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wel

l, r

each

ing

mo

re t

ha

n

20

ho

urs

per

wee

k o

n a

ver

ag

e in

so

me

case

s. A

cces

s to

ba

sic

infr

ast

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ure

ser

vic

es (

wat

er

an

d e

lect

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ma

kes

a l

arg

e d

iffe

ren

ce i

n t

he

am

ou

nt

of

tim

e sp

ent

on

do

mes

tic

wo

rk.

Des

crip

itiv

e S

tati

stic

s, O

LS

Rep

ub

lic

of

Co

ng

o

Ba

ckin

y-

Yet

na

an

d

Wo

do

n

Ho

use

ho

lds

Ex

pen

dit

ure

(E

CO

M)

surv

ey

La

bo

r in

com

e te

nd

s to

be

con

tro

lled

by

men

. T

he

resu

lts

pre

sen

ted

her

e sh

ow

th

at,

wh

en

wo

men

co

ntr

ol

a h

igh

er s

ha

re o

f to

tal

lab

or

inco

me

wit

hin

th

e h

ou

seh

old

, th

e h

ou

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te

nd

s to

all

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te l

arg

er s

ha

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of

its

reso

urc

es t

o i

nv

estm

ents

th

at

ben

efit

th

eir

chil

dre

n. T

he

evid

ence

her

e su

gg

ests

th

at

in t

he

Rep

ub

lic

of

Co

ng

o,

as

in o

ther

co

un

trie

s, t

he

un

itar

y

ho

use

ho

ld h

yp

oth

esis

do

es n

ot

ho

ld w

ell.

Des

crip

tiv

e S

tati

stic

s,

Sta

nd

ard

R

egre

ssio

n

An

aly

sis

Nig

eria

U

rdin

ola

a

nd

uen

tin

W

od

on

Co

re W

elfa

re

Qu

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na

ire

Ind

ica

tor

(CW

IQ)

surv

eys

20

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Mo

st o

f h

ou

seh

old

dec

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ma

de

by

men

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om

en p

art

icip

ate

mo

re o

ften

in

dec

isio

ns

on

ex

pen

dit

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r fo

od

, h

eath

, a

nd

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uca

tio

n,

bu

t ev

en i

n t

hes

e a

rea

s, m

en m

ore

oft

en

tha

n

no

t re

ma

in

the

ma

in

dec

isio

n

ma

ker

s.

Th

e d

ecis

ion

-ma

kin

g

po

wer

o

f w

om

en

is

esp

ecia

lly

lo

w a

mo

ng

po

or

ho

use

ho

lds,

in

pa

rt,

bec

au

se i

n s

uch

ho

use

ho

lds,

th

e li

kel

iho

od

th

at

wo

men

wil

l b

e th

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ain

co

ntr

ibu

tor

of

ho

use

ho

ld i

nco

me

is m

uch

lo

wer

as

wel

l. T

his

st

ud

y f

ou

nd

th

at

incr

easi

ng

th

e co

ntr

ibu

tio

n a

bil

ity

of

wo

men

to

ho

use

ho

ld i

nco

me

lea

ds

to h

igh

er d

ecis

ion

-ma

kin

g p

ow

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or

them

wit

hin

th

e h

ou

seh

old

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Biv

aria

te p

rob

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tech

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ME

NA

Eg

yp

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19

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Eg

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do

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div

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bet

wee

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pu

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Th

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f th

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con

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Str

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dju

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Pro

gra

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in 1

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b q

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Eg

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th

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ativ

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plo

ym

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sha

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n t

he

pu

bli

c se

cto

r. R

eal

mo

nth

ly w

ages

are

co

nsi

sten

tly

hig

her

fo

r m

en t

ha

n w

om

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Oax

aca

d

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mp

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19

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20

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Th

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all

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m A

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C

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top

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at

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Th

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Eu

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pro

ach

.

bet

wee

n

men

a

nd

w

om

en

for

bo

th

yea

rs

incr

ease

in

si

ze

a

s w

e m

ov

e u

p

the

wag

e d

istr

ibu

tio

n.

Per

form

ing

th

e Ju

hn

et

al.

dec

om

po

siti

on

at

dif

fere

nt

qu

anti

les

for

bo

th y

ears

re

vea

ls

tha

t th

e m

ag

nit

ud

e o

f th

e g

end

er

spec

ific

a

nd

w

age

stru

ctu

re

effe

cts

are

n

ot

ho

mo

gen

eou

s a

cro

ss t

he

dis

trib

uti

on

.

Bu

lga

ria

, C

zec

h

Rep

ub

lic,

H

un

gar

y,

Kh

az

ak

stan

, L

atv

ia,

Po

lan

d,

Ru

ssia

, S

lov

ak

ia,

Uk

rain

a,

Uz

bek

ista

n,

Yu

go

sla

via

New

ell

an

d R

eill

y

(20

01)

Bu

lgar

ian

Ho

use

ho

ld

Bu

dg

et S

urv

ey,

So

cial

S

trat

ific

atio

n S

urv

eys,

P

oli

sh L

abo

ur

Fo

rce

Su

rvey

s, F

RY

Lab

ou

r F

orc

e S

urv

eys,

Lat

via

n

Ho

use

ho

ld B

ud

get

S

urv

ey,

Ru

ssia

n

Lo

ng

itu

din

al

Mo

nit

ori

ng

Su

rvey

s,

Uk

rain

e L

ivin

g

Sta

nd

ard

s M

easu

rem

ent

Su

rvey

, K

azak

hst

an L

abo

ur

Fo

rce

Su

rvey

, Th

e E

uro

pea

n U

niv

ersi

ty

Inst

itu

te a

nd

Ess

ex

Un

iver

sity

Su

rvey

in

U

zbek

ista

n

Th

e g

end

er

pa

y

gap

h

as

no

t ex

hib

ited

, in

g

ener

al,

a

n

up

war

d

ten

den

cy

ov

er

the

tra

nsi

tio

na

l p

erio

d t

o w

hic

h a

va

ila

ble

da

ta r

elat

e. M

ost

of

the

gen

der

pa

y g

ap i

s a

scri

bed

to

th

e 'u

nex

pla

ined

' co

mp

on

ent

usi

ng

co

nv

enti

on

al

dec

om

po

siti

on

s a

nd

th

is m

ay

pa

rtly

be

att

rib

uta

ble

to

th

e p

rox

y m

easu

re f

or

lab

ou

r fo

rce

exp

erie

nce

use

d i

n t

his

stu

dy

. Q

ua

nti

le

reg

ress

ion

an

aly

sis

ind

ica

tes

tha

t, i

n a

ll b

ut

on

e co

un

try

, th

e ce

teri

s p

arib

us

gen

der

pa

y g

ap

rise

s a

s w

e m

ov

e u

p t

he

wag

e d

istr

ibu

tio

n.

Oax

aca

-Bli

nd

er

dec

om

po

siti

on

Tu

rkey

T

an

sel

(20

04)

Ho

use

ho

ld

Ex

pen

dit

ure

S

urv

ey (

19

94)

Wh

en

con

tro

lled

fo

r o

bse

rved

ch

ara

cter

isti

cs

an

d

sam

ple

se

lect

ion

, fo

r m

en,

pu

bli

c a

dm

inis

tra

tio

n w

ages

are

hig

her

th

an

pri

vat

e se

cto

r w

ages

ex

cep

t a

t th

e u

niv

ersi

ty l

evel

w

her

e th

e w

ages

are

at

pa

r. S

tate

ow

ned

en

terp

rise

wa

ges

fo

r m

en a

re h

igh

er t

ha

n p

riv

ate

sect

or

wag

es.

Sim

ila

r re

sult

s a

re o

bta

ined

fo

r w

om

en.

Fu

rth

er,

wh

ile

wa

ges

of

men

an

d

wo

men

are

at

pa

rity

in

th

e p

ub

lic

ad

min

istr

ati

on

, th

ere

is a

lar

ge

gen

der

wa

ge-

gap

in

th

e p

riv

ate

sect

or

in f

av

or

of

men

. P

riv

ate

ret

urn

s to

sch

oo

lin

g a

re f

ou

nd

to

be

low

er i

n t

he

no

nco

mp

etit

ive

pu

bli

c ra

ther

th

an

in

th

e co

mp

etit

ive

pri

va

te s

ecto

r.

Oax

aca

-Bli

nd

er

dec

om

po

siti

on

.

Bu

lga

ria

D

imo

va

a

nd

Ga

ng

(2

004

)

Inte

gra

ted

H

ou

seh

old

S

urv

eyes

(1

995

, 19

97

a

nd

20

01)

Wh

ile

skil

led

lab

or’

s p

att

ern

of

rea

llo

cati

on

in

to t

he

pu

bli

c se

cto

r re

ma

ins

rou

gh

ly t

he

sam

e o

ver

ti

me,

th

e in

flo

w

of

hig

hly

ed

uca

ted

la

bo

rers

in

to

the

pri

vat

e se

cto

r a

nd

se

lf-

emp

loy

men

t in

crea

ses.

Th

ese

chan

ges

co

inci

de

wit

h t

he

ero

sio

n o

f th

e re

turn

s to

ob

serv

ed

skil

ls

in

the

pri

vat

e se

cto

r a

nd

se

lf-e

mp

loy

men

t,

wh

ile

the

pu

bli

c se

cto

r co

nti

nu

es

to

rew

ard

all

ty

pes

of

edu

cati

on

at

hig

her

th

an

th

e el

emen

tary

lev

el.

Ea

rnin

gs

equ

ati

on

s a

fter

co

rrec

tin

g f

or

sele

ctio

n b

ias.

Ru

ssia

L

ehm

an

n

an

d

Wa

dsw

ort

h (

20

01

)

Ru

ssia

n

Lo

ng

itu

din

al

Mo

nit

or

Su

rvey

(1

99

4,

19

95

, 199

6

an

d 1

99

8)

Th

e m

edia

n g

end

er w

age

gap

wo

uld

be

aro

un

d t

wen

ty-f

ive

po

ints

hig

her

th

an

th

e a

ctu

al

ob

serv

ed g

ap.

Sim

ila

rly

, th

e co

un

terf

act

ua

l ra

tio

of

mea

n g

rad

ua

te p

ay

to

mea

n p

ay

of

tho

se

wit

h

pri

ma

ry

edu

cati

on

is

a

rou

nd

tw

enty

p

oin

ts

low

er

tha

n

ob

serv

ed.

Th

e p

ara

met

ers

of

the

cou

nte

rfa

ctu

al

wag

e d

istr

ibu

tio

ns

are

ver

y s

imil

ar

to t

he

pa

ram

eter

s o

f th

e o

bse

rved

wag

e d

istr

ibu

tio

ns

of

tho

se n

ot

in a

rrea

rs.

Fo

r th

ose

wis

hin

g t

o s

tud

y a

spec

ts

of

wag

e d

iffe

ren

tial

s a

nd

in

equ

ali

ty i

n R

uss

ia,

it m

ay

be

fea

sib

le t

o u

se t

he

sub

set

of

tho

se

no

t in

arr

ears

an

d s

till

get

clo

se t

o t

he

tru

e p

op

ula

tio

n p

ara

met

ers.

Co

un

terf

act

ua

l d

istr

ibu

tio

ns

Cze

ch

Rep

ub

lic,

B

ulg

ari

a,

Hu

ng

ary

, P

ola

nd

, R

uss

ia,

Slo

va

kia

an

d

Uk

rain

e

Eli

za

bet

h

Bra

iner

d

(20

00)

Ho

use

ho

ld

surv

eys

tak

en

bef

ore

an

d

aft

er t

he

imp

lem

enta

tio

n o

f m

ark

et

refo

rms

Th

e re

sult

s in

dic

ate

a c

on

sist

ent

incr

ease

in

fem

ale

rela

tiv

e w

ages

acr

oss

Ea

ster

n E

uro

pe,

a

nd

a s

ub

stan

tial

dec

lin

e in

fem

ale

rela

tiv

e w

ages

in

Ru

ssia

an

d U

kra

ine.

Wo

men

in

th

e la

tter

co

un

trie

s h

av

e b

een

pen

ali

zed

by

th

e tr

emen

do

us

wid

enin

g o

f th

e w

age

dis

trib

uti

on

in

th

ose

co

un

trie

s. I

ncr

ease

d w

age

ineq

ual

ity

in

Ea

ster

n E

uro

pe

ha

s a

lso

dep

ress

ed f

emal

e re

lati

ve

wag

es, b

ut

thes

e lo

sses

ha

ve

bee

n m

ore

th

an

off

set

by

gai

ns

in r

ewar

ds

to o

bse

rved

sk

ills

an

d b

y a

n a

pp

are

nt

dec

lin

e in

dis

crim

ina

tio

n a

ga

inst

wo

men

.

Juh

n-M

urp

hy

-P

ierc

e d

eco

mp

osi

tio

n

SA

Ho

ng

Ko

ng

, K

ore

a,

Sin

gap

ore

, T

aiw

an

, In

do

nes

ia,

Ma

lay

sia,

P

hil

ipp

ines

, T

ha

ila

nd

, Ja

pa

n,

Ind

ia,

Ch

ina

Men

g

(19

98)

F

emal

e la

bo

ur

pa

rtic

ipa

tio

n i

n m

ost

Asi

an

co

un

trie

s is

clo

sely

lin

ked

to

na

tio

na

l ec

on

om

ic

dev

elo

pm

ent.

Als

o,

it h

as

bee

n f

ou

nd

th

at

thes

e ch

an

ges

in

tec

hn

olo

gy

an

d w

orl

d-t

rad

e p

att

ern

s h

av

e ca

use

d A

sia

n w

om

en t

o p

art

icip

ate

mo

re i

n t

he

no

n-a

gri

cult

ura

l se

cto

r.

Gen

der

w

age

dif

fere

nti

als

a

re

hea

vil

y

infl

uen

ced

b

y

cult

ure

a

nd

la

bo

ur-

ma

rket

in

stit

uti

on

al

sett

ing

s b

ut

ha

ve

litt

le t

o d

o w

ith

eco

no

mic

dev

elo

pm

ent

Lit

. Rev

iew

So

uth

an

d

Ea

st

Asi

a;

L

ati

n

Am

eric

a

Ca

mp

s ,

Ca

mo

u,

Ma

ub

rig

ad

es a

nd

M

ora

-Sit

ja

(20

06)

Un

ited

N

ati

on

s d

ata

sets

.

In t

he

Ea

st A

sia

n,

the

ero

sio

n o

f th

e g

end

er g

ap s

eem

s to

be

ma

inly

ex

pla

ined

by

th

e

Sto

ple

r-S

am

uel

son

an

d B

eck

er s

imp

le m

od

el.

Wit

h t

he

exce

pti

on

of

Ch

ina

, th

e ex

po

sure

to

in

tern

ati

on

al

tra

de

op

enn

ess

act

s a

s a

n e

ng

ine

of

ero

sio

n o

f th

e g

end

er w

age

dif

fere

nce

s.

Th

e im

pro

vem

ent

of

wo

men

’s c

on

dit

ion

in

mo

st o

f th

e ca

ses

ha

s fu

rth

er c

on

seq

uen

ces

for

the

an

aly

sis

of

wag

e in

equ

ali

ty.

Sin

ce t

rad

itio

na

lly

wo

men

ha

ve

bee

n a

t th

e b

ott

om

of

the

wag

e h

iera

rch

y,

thei

r ec

on

om

ic i

mp

rov

emen

t a

lso

na

rro

ws

wag

e d

isp

ersi

on

an

d i

nco

me

ineq

ual

ity

.

Pan

el D

ata

Mo

del

s,

Gin

i In

dex

w

ith

in m

en a

nd

w

ith

in w

om

en

EA

P

Ind

on

esia

P

irm

ana

(2

006

) T

he

Na

tio

na

l L

ab

ou

r F

orc

e S

urv

ey

(SA

KE

RN

AS

)

Th

e re

sult

of

esti

ma

tin

g M

ince

ria

n e

arn

ing

s eq

uat

ion

sh

ow

s th

at

fact

ors

as

hu

ma

n c

ap

ital

, so

cio

-dem

og

rap

hy

-eco

no

mic

ch

ara

cter

isti

c a

nd

lo

cati

on

fa

cto

rs

aff

ects

si

gn

ific

an

tly

in

div

idu

al

earn

ing

s.

Th

e p

rofi

le o

f ea

rnin

gs

ineq

ual

ity

by

gen

der

see

ms

to b

e a

n ―

inv

erte

d

U‖

fa

shio

n,

wit

h t

he

ma

le-f

emal

e ea

rnin

gs

gap

na

rro

win

g a

s ed

uca

tio

na

l a

ttai

nm

ent

wen

t u

p.

Th

e re

sult

s a

lso

su

gg

est

tha

t th

e in

du

stri

al

aff

ilia

tio

n o

f fe

mal

e w

ork

ers

mat

ter.

Min

cer

equ

ati

on

s;

Oax

aca

-Bli

nd

er

dec

om

po

siti

on

Mo

ng

oli

a

Pas

tore

(2

010

) S

cho

ol

to

Wo

rk

Su

rvey

(S

WT

S)

Fro

m t

he

esti

ma

tio

n o

f d

eter

min

an

ts o

f g

end

er d

iffe

ren

ces

in e

arl

y c

are

er,

it w

as f

ou

nd

th

at,

on

av

era

ge,

fem

ale

wa

ges

are

no

t lo

wer

th

an

th

ose

of

ma

les.

Ho

wev

er,

the

con

dit

ion

al

gen

der

gap

bec

om

es s

ign

ific

an

t a

nd

siz

eab

le f

or

the

ov

er-2

0s.

Th

e d

eco

mp

osi

tio

n s

ho

ws

tha

t m

ost

o

f th

e g

ap

is

du

e to

d

iffe

ren

ces

in

the

way

th

e m

ark

et

va

lues

th

e sa

me

char

act

eris

tics

of

men

an

d w

om

en.

If w

ages

wer

e p

aid

eq

ua

lly

, w

om

en s

ho

uld

ha

ve

11

.7

per

cen

t m

ore

fo

r th

eir

hig

her

ed

uca

tio

n a

ttai

nm

ent

an

d o

ver

all

22

per

cen

t m

ore

.

Juh

n-M

urp

hy

-P

ierc

e d

eco

mp

osi

tio

n

Vie

tnam

L

iu (

20

01)

V

ietn

am

Liv

ing

Sta

nd

ard

Surv

eys

(VL

SS

).

As

con

seq

uen

ce o

f th

e D

oi

Mo

i re

form

s (e

con

om

ic r

efo

rms

init

iate

d i

n 1

98

6 w

ith

th

e g

oa

l o

f cr

eati

ng

a s

oci

ali

st-o

rien

ted

ma

rket

eco

no

my

), a

bso

lute

gen

der

ea

rnin

gs

gap

ha

s ri

sen

o

ver

tim

e in

th

e p

riv

ate

sect

or;

dis

crim

ina

tio

n h

as

incr

easi

ng

ly a

cco

un

ted

fo

r m

ore

of

the

gen

der

ea

rnin

gs

dif

fere

nce

s, a

nd

it

acc

ou

nts

fo

r m

ore

of

the

gap

in

pri

vat

e se

cto

r th

an

in

p

ub

lic

sect

or

in 1

99

7-9

8 t

han

in

19

92

-93

.

Ap

ple

ton

- H

od

din

ott

- K

rish

na

n.

Dec

om

po

siti

on

Vie

tnam

L

iu

(20

04b

)

VL

SS

. U

sin

g J

uh

n e

t a

l. (

19

91

) d

eco

mp

osi

tio

n a

nd

da

ta o

ver

th

e p

erio

d 1

99

2–9

3 a

nd

19

97–9

8,

it i

s sh

ow

ed t

ha

t ch

ang

es i

n o

bse

rved

va

ria

ble

s h

av

e te

nd

ed t

o n

arr

ow

it,

bu

t th

e g

ap e

ffec

t h

as

ten

ded

to

w

iden

it

, w

ith

th

e n

et

effe

ct

bei

ng

o

ne

of

litt

le

cha

ng

e.

Th

e ex

per

ien

ce

of

Vie

tnam

, il

lust

rate

s th

e im

po

rtan

ce o

f d

iscr

imin

ati

on

as

an

ob

stac

le t

o g

end

er w

age

gap

co

nv

erg

ence

.

Juh

n-M

urp

hy

-P

ierc

e d

eco

mp

osi

tio

n

Vie

tnam

P

ha

m a

nd

R

eill

y

(20

06)

Vie

tnam

-Ho

use

ho

ld-

Liv

ing

Sta

nd

ard

Surv

eys

(VH

LS

S).

It i

s ex

am

ined

th

e ev

olu

tio

n o

f th

e g

end

er p

ay

gap

fo

r th

e w

age

emp

loy

ed o

ver

th

e p

erio

d

19

93

to

20

02,

an

d i

t is

fo

un

d t

ha

t th

e tr

an

siti

on

in

to m

ark

et-o

rien

ted

eco

no

my

ha

ve

ha

d a

si

gn

ific

an

t im

pa

ct o

n t

he

lab

ou

r m

ark

et i

n V

ietn

am

an

d h

av

e a

cted

to

red

uce

gen

der

wag

e d

isp

ari

ties

in

th

e w

age

emp

loy

men

t se

cto

r. T

he

dec

om

po

siti

on

an

aly

sis

sug

ges

ts t

ha

t th

e tr

eatm

ent

effe

ct i

s re

lati

vel

y s

tab

le a

cro

ss t

he

con

dit

ion

al

wag

e d

istr

ibu

tio

n.

Qu

an

tile

R

egre

ssio

n

An

aly

sis

Th

aila

nd

an

d

Vie

tna

m

So

n (

20

07

) V

ietn

am

:

VL

SS

Thai

land

:

Lab

or

Fo

rce

Surv

eys

Dev

elo

pm

ent

of

a d

eco

mp

osi

tio

n m

eth

od

olo

gy

to

ex

pla

in t

he

wel

fare

dis

pa

rity

bet

wee

n

ma

le

an

d

fem

ale

w

ork

ers

in

term

s o

f th

ree

com

po

nen

ts:

seg

reg

atio

n,

dis

crim

ina

tio

n

(ea

rnin

g d

iffe

ren

tial

bet

wee

n m

ale

s a

nd

fem

ales

wit

hin

occ

up

ati

on

s),

an

d i

neq

ual

ity

. It

w

as f

ou

nd

th

e g

end

er d

isp

ari

ty i

n w

elfa

re i

s la

rgel

y c

on

trib

ute

d b

y t

he

lab

or

ma

rket

d

iscr

imin

ati

on

ag

ain

st f

emal

e w

ork

ers,

an

d t

he

oth

er t

wo

co

mp

on

ents

pla

y a

sm

all

er r

ole

in

ex

pla

inin

g t

he

gen

der

wel

fare

gap

.

Ind

ex o

f w

elfa

re

dis

pa

rity

WE

ST

ER

N E

UR

OP

E

Au

stra

lia

, F

ran

ce, J

ap

an

a

nd

Bri

tain

An

ne

Dal

y,

Ak

ira

K

aw

agu

chi

an

d X

in

Men

g

(20

06)

Au

stra

lian

W

ork

pla

ce

Ind

ust

rial

Rel

atio

ns

Su

rvey

(A

WIR

95

),

Fre

nch

dat

a ar

e

from

199

2 F

ren

ch

Lab

ou

r C

ost

and

Wag

e S

tru

ctu

re

Su

rvey

, Ja

pan

dat

a ar

e fr

om

th

e B

asic

Su

rvey

of

Wag

e

Str

uct

ure

in

19

90

an

d B

rita

in d

ata

are

dra

wn

fro

m t

he

Bri

tish

Work

pla

ce

Em

plo

yee

Rel

atio

ns

Su

rvey

1998

(W

ER

S98

)

Fem

ale

seg

reg

atio

n

into

lo

w-w

age

stru

ctu

res

emer

ges

a

s th

e m

ain

co

ntr

ibu

tor

to

the

gen

der

pa

y g

ap,

wit

h f

ema

le s

egre

gat

ion

in

to l

ow

-wag

e w

ork

pla

ces

as

an

ou

tsta

nd

ing

o

rig

in

of

bo

th

the

gen

der

p

ay

g

ap

in

all

E

uro

pea

n

eco

no

mie

s a

nd

o

f in

tern

ati

on

al

dif

fere

nce

s in

its

siz

e. O

n t

he

oth

er h

an

d,

inte

rna

tio

na

l d

isp

ari

ties

in

glo

ba

l ch

ara

cter

isti

cs

of

the

wag

e st

ruct

ure

, a

nd

in

pa

rtic

ula

r in

th

e ex

ten

t o

f w

age

ineq

ual

ity

, a

re n

ot

ma

jor

det

erm

ina

nts

of

inte

r-co

un

try

dif

fere

nce

s in

th

e si

ze

of

the

gen

der

wag

e g

ap i

n E

uro

pe.

A

fin

al

po

int

of

con

cern

is

tha

t cr

oss

-co

un

try

dif

fere

nce

s in

th

e o

rig

in a

nd

th

e m

ag

nit

ud

e o

f th

e g

end

er

gap

in

p

ay

a

re

pa

rtic

ula

rly

si

gn

ific

an

t b

etw

een

th

e n

ew

mem

ber

s o

f th

e E

uro

pea

n U

nio

n,

wh

ich

su

gg

ests

th

e ex

iste

nce

of

a r

ema

rka

ble

div

ersi

ty i

nto

th

is g

rou

p o

f co

un

trie

s.

Up

da

tes

19

80s

Bo

b G

reg

ory

's

wo

rk

wit

h

Bec

ker

(1

975

) a

nd

Min

cer

(19

74)

dec

om

po

siti

on

.

Bel

giu

m,

Den

ma

rk,

Ita

ly, I

rela

nd

, S

pa

in a

nd

U

nit

ed

Kin

gd

om

Ro

ber

to

Pla

sma

n

an

d

Sa

lim

ata

S

isso

ko

(2

004

)

19

95

Euro

pea

n

Str

ust

ure

of

Ear

nin

gs

Surv

ey (

ES

ES

),

gat

her

ed b

y

Euro

stat

.

Th

e ev

iden

ce

sho

w

tha

t th

e si

gn

ific

an

ce

of

dif

fere

nce

s in

h

um

an

ca

pit

al

in

mo

del

ing

g

end

er p

ay

dif

fere

nti

als

va

ries

acr

oss

co

un

trie

s. N

ever

thel

ess,

a c

om

mo

n f

act

am

on

g a

ll

cou

ntr

ies

un

der

stu

dy

is

tha

t th

ese

char

act

eris

tics

ex

pla

in l

ess

tha

n 5

0%

of

the

pa

y g

ap.

Inte

rna

tio

na

l co

mp

ari

son

s o

f w

age

dif

fere

nti

als

con

firm

th

at

bo

th g

end

er-s

pec

ific

fa

cto

rs

an

d w

age

stru

ctu

re p

lay

an

im

po

rta

nt

role

as

gen

der

wag

e g

ap i

s co

nce

rned

. T

he

stri

kin

g

resu

lts

of

the

ad

ap

tati

on

o

f th

e O

axa

ca-B

lin

der

d

eco

mp

osi

tio

n

for

inte

rnat

ion

al

com

pa

riso

ns

are

th

at

cou

ntr

ies,

wh

ich

rec

ord

th

e lo

wes

t g

end

er w

age

gap

an

d g

end

er

dif

fere

nce

s in

ob

serv

ed p

rod

uct

ivit

y c

ha

ract

eris

tics

as

wel

l a

s h

igh

lev

els

of

pro

du

ctiv

e ch

ara

cter

isti

cs.

Oax

aca

an

d

Bin

der

d

eco

mp

osi

tio

n,

Bla

u a

nd

Kh

an

d

eco

mp

osi

tio

n,

an

d B

row

n,

Mo

on

an

d

Zo

loth

d

eco

mp

osi

tio

n

Au

stri

a,

Bel

giu

m,

Bri

tain

, D

enm

ark

, D

inla

nd

, F

ran

ce,

Ger

ma

ny

, Ir

elan

d, I

taly

, N

eth

erla

nd

s a

nd

Sp

ain

.

Wij

i A

rula

mp

al

am

, Ali

son

L

. Bo

oth

a

nd

Ma

rk

L. B

rya

n

(20

04)

Euro

pea

n

Co

mm

unit

y

Ho

use

ho

ld

Pan

el (

EC

HP

)

Th

e g

end

er p

ay

gap

s a

re t

yp

ical

ly b

igg

er a

t th

e to

p o

f th

e w

age

dis

trib

uti

on

, a

fin

din

g t

ha

t is

co

nsi

sten

t w

ith

th

e ex

iste

nce

of

gra

ss c

eili

ng

s. F

or

som

e co

un

trie

s g

end

er p

ay

gap

s a

re

als

o b

igg

er a

t th

e b

ott

om

of

the

wag

e d

istr

ibu

tio

n,

a f

ind

ing

th

at

is c

on

sist

ent

wit

h s

tick

y

flo

ors

. T

he

gen

der

pa

y g

ap i

s ty

pic

all

y h

igh

er a

t th

e to

p t

ha

n t

he

bo

tto

m e

nd

of

the

wag

e d

istr

ibu

tio

n,

sug

ges

tin

g t

ha

t g

lass

es c

eili

ng

s a

re m

ore

pre

va

len

t th

an

sti

cky

flo

ors

an

d t

ha

t th

ese

pre

va

il i

n t

he

ma

jori

ty o

f o

ur

cou

ntr

ies.

Th

e g

end

er p

ay

gap

dif

fers

sig

nif

ica

ntl

y

acr

oss

th

e p

ub

lic

an

d p

riv

ate

sect

or

wag

e d

istr

ibu

tio

n o

f ea

ch c

ou

ntr

y.

Qu

an

tile

re

gre

ssio

n

An

aly

sis

Sp

ain

C

ata

lin

a

Am

ued

o-

Do

ran

tes

an

d S

ara

d

e la

Ric

a

(20

05)

19

95

and

20

02

Sp

anis

h W

age

Str

uct

ure

Surv

eys

(EE

S-

95

and

EE

S-

02

)

Th

e ra

w g

end

er w

age

ga

p d

ecre

ase

d f

rom

0.2

6 t

o 0

.22

ov

er t

he

cou

rse

of

sev

en y

ears

. H

ow

ever

, ev

en a

fter

acc

ou

nti

ng

fo

r w

ork

ers'

hu

ma

n c

ap

ital

, jo

b c

har

act

eris

tics

, fe

mal

e se

gre

gat

ion

in

to

low

er-p

ay

ing

in

du

stri

es,

occ

up

ati

on

s,

esta

bli

shm

ents

, a

nd

o

ccu

pa

tio

ns

wit

hin

est

ab

lish

men

ts,

wo

men

sti

ll e

arn

ed a

pp

rox

ima

tely

13

per

cen

t a

nd

16

per

cen

t le

ss

tha

n s

imil

ar

ma

le c

ou

nte

rpa

rts

as

for

19

95

an

d 2

002

, re

spec

tiv

ely

. M

ost

of

the

gen

der

wag

e g

ap i

s a

ttri

bu

tab

le t

o w

ork

ers’

sex

. Y

et,

fem

ale

seg

reg

ati

on

in

to l

ow

er-p

ay

ing

occ

up

ati

on

s w

ith

in e

sta

bli

shm

ents

, es

tab

lish

men

ts a

nd

in

du

stri

es a

cco

un

ted

fo

r a

siz

ab

le a

nd

gro

win

g

fra

ctio

n o

f th

e fe

mal

e-m

ale

wag

e d

iffe

ren

tia

l.

Ba

yar

d,

Hel

lers

tein

, N

eum

ark

an

d

Tro

ske

esti

ma

tio

n,

po

ole

d O

LS

, fi

xed

-eff

ects

, a

ug

men

ted

p

oo

led

OL

S.

Un

ited

S

tate

s,

Un

ited

K

ing

do

m,

Fin

lan

d,

Den

ma

rk,

Ger

ma

ny

, N

eth

erla

nd

s,

Bel

giu

m,

Au

stri

a,

Irel

and

, F

ran

ce, I

taly

, S

pa

in,

Po

rtu

gal

an

d

Gre

ece.

Cla

ud

ia

Oli

vet

ti

an

d

Ba

rba

ra

Pet

ron

go

lo

(20

06)

Pan

el S

tud

y o

f

Inco

me

Dynam

ics

(PS

ID)

for

the

US

and

the

Euro

pea

n

Co

mm

unit

y

Ho

use

ho

ld

Pan

el S

urv

ey

(EC

HP

S)

for

Euro

pe.

Per

iod

19

94

-200

1.

Rec

ov

er i

nfo

rma

tio

n o

n w

ag

es f

or

tho

se n

ot

in w

ork

s in

a g

iven

yea

r u

sin

g a

lter

nat

ive

imp

uta

tio

n t

ech

niq

ues

. Im

pu

tati

on

is

ba

sed

on

(i)

wa

ge

ob

serv

ati

on

s fr

om

oth

er w

aves

in

th

e sa

mp

le,

(ii)

o

bse

rva

ble

ch

ara

cter

isti

cs

of

the

no

n-e

mp

loy

ed

an

d

(iii

) a

st

ati

stic

al

rep

eate

d-s

amp

lin

g

mo

del

. T

he

au

tho

rs

esti

ma

te

med

ian

w

age

gap

s o

n

the

resu

ltin

g

imp

ute

d w

age

dis

trib

uti

on

s a

nd

ob

tain

hig

her

med

ian

wag

e g

aps

on

im

pu

ted

ra

ther

th

an

a

ctu

al

wag

e d

istr

ibu

tio

ns

for

mo

st c

ou

ntr

ies

in t

he

sam

ple

. C

orr

ecti

on

fo

r em

plo

ym

ent

sele

ctio

n

exp

lain

s m

ore

th

an

a

h

alf

o

f th

e o

bse

rved

co

rrel

ati

on

b

etw

een

w

age

an

d

emp

loy

men

ts g

aps.

Hec

km

an

's

two

-sta

ge

pa

ram

etri

c a

pp

roa

ch

Sp

ain

S

ara

de

la

Ric

a, J

uan

J.

Do

lad

o

an

d

Va

nes

a

Llo

ren

s (2

005

)

Euro

pea

n

Co

mm

unit

y

Ho

use

ho

ld

Pan

el (

EC

HP

-

99

)

In c

on

tra

st w

ith

th

e st

eep

pa

tter

n f

ou

nd

fo

r o

ther

co

un

trie

s, t

he

flat

ter

evo

luti

on

of

the

gap

in

Sp

ain

hid

es a

co

mp

osi

tio

n e

ffec

t w

hen

th

e sa

mp

le i

s sp

lit

by

ed

uca

tio

n.

Fo

r th

e g

rou

p

wit

h c

oll

ege/

tert

iary

ed

uca

tio

n,

we

fin

d a

hig

her

un

exp

lain

ed g

ap a

t th

e to

p t

ha

n a

t th

e b

ott

om

of

the

dis

trib

uti

on

, in

acc

ord

an

ce w

ith

th

e co

nv

enti

on

al

gla

ss c

eili

ng

hy

po

thes

is,

wh

ile

for

the

gro

up

wit

h l

ow

er e

du

cati

on

, th

e g

ap i

s m

uch

hig

her

at

the

bo

tto

m t

ha

n a

t th

e to

p o

f th

e d

istr

ibu

tio

n,

Qu

an

tile

re

gre

ssio

n

An

aly

sis

an

d

Oax

aca

-Bli

nd

er

dec

om

po

siti

on

.

Annex 2.

Weights of Democracy Indicator

Authority Coding Scale Weight

Competitiveness of executive Recrudiment (XRCOMP):

(1) Selection +2

Opennes of Executive Recruitmen (XROPEN):

only if XRCOMP is coded Selection (1)

(1) Closed +1

(2) Dual/designation +1

Contraints on Chief Executive (XCONST):

(1) Unlimited authority +3

(2) Intermediate category +2

(3) Slight to moderate limitations +1

Regulation of participation (PARREG)

(4) Restricted +2

(3) Sectarian +1

Competitiveness of Participation (PARCOMP):

(1) Repressed +2

(2) Suppressed +1

Source: Policy IV Indicators

Documents

Gender Earnings Gaps in the Worldgesd.free.fr/iza5736.pdf · gender wage-gap in the private sector in favor of men (Tansel, 2004) East Asia and the Pacific: The impact of the economic