Occupational Segregation by Gender in the Uruguayan Labor
Market (1986-1997) 1
Máximo Rossi (*)
Abstract
The objective of this paper has been to analyze the evolution of
occupational segregation by gender in Uruguay and in particular if
the evolution was associated to the wage gap trends. There are high
level of segregation in Uruguay and they were not important changes
during the 1990s
JEL: J71)
(*) Department of Economics, University of Uruguay (mito@decon.edu.uy).
1
I thank the research assistant from Zuleika Ferré and Tatiana Rossi
1
I.- Introduction
The objective of this paper is to study the evolution of the
occupational segregation by gender in the Uruguay during 1986-97.
One of the main characteristics of the Uruguayan labor market
during the last 25 years is the women participation rate increase in
the labor market, accompanied with a high level of wage
discrimination and occupational segregation. An important question
now is: the high level of participation in the labor market with
investment in human capital has implied a decrease in the degree of
wage discrimination and occupational segregation? In this paper I
will consider only the question referred to the occupational
segregation.
The occupational segregation by gender (females are gathered
disproportionally in some occupations) is one of the causes of a lower
structure of payments for women respect to men .
Several arguments have been developed that try to explain the
origin of the segregation. One explanation is related with
discrimination in the labor market.
The crowding hypothesis (Bergman 1974)
states that
employer´s discriminate against females by excluding them from
occupations considered being male jobs. These jobs are occupied by
males and few women have the opportunity to get one. Then, males
and females are segregated into different occupations. Occupations
considered female occupation pay less than occupations considered
mainly male occupations despite the fact that all workers are
qualified for both occupations.
2
The quality sorting hypothesis (Macpheerson and Hirsch,
1995) states that women concentrate into low wages jobs due to
discrimination.
Then the structure of the occupation by gender
become an index of labor quality. Males who are, in relative terms,
less productive accept low wages in those occupations that are
primarily
female
occupations.
occupations of low wages,
Consequently
with
time
the
crowded by females, attract low
productive men and force to leave all women of high productivity.
Consequently, in these occupations we find workers with low
productivity and low wages.
From the hypothesis not related with the discrimination in the
labor market, Polachek (1985) explain that individuals who expect
to leave and to enter several times into labor market will choose
occupations in which the penalty associated to this intermittence is
lowest. These occupations will have a flat wage profiles.
Becker (1985) arguments that women who expect to spend a lot
of time at home choose a job that imply less effort. Other authors
outline that the women have preferences on the outside for work
conditions of the market while the men think more of economic
terms.
In this paper I analyze changes in the occupational segregation
during 1986-97 and I do not intend to confirm those hypothesis. This
period is interesting because it is accompanied by an important
decrease in the gap between males and females wages and females
invest more in human capital relative to males.
3
II.- Participation rates and wages in the labor market (males and
females)
a) Participation rate
The growth of women
participation
rate is one of the
outstanding facts from 70s: the increment was
more than 50%
(participation rate: 1969 27,4% to 1997 46.1%)
During the period 1991-97 the women participation rate (25-55
years old) grew 5.1 points passing from 64.6% to 69.7%.
97%
Males: participation rate
actividad de los hombres
96.8%
96.7%
96.6%
97%
96.5%
96.4%
96.0%
96%
96%
95.5%
95%
1991
1992
1993
1994
1995
1996
1997
Source: Fernanda Rivas and Máximo Rossi (2001)
4
Females: participation rate
70%
69.6%
69%
69.7%
68.7%
68%
67.3%
67.5%
67%
66.7%
66%
65%
64.6%
64%
1991
1992
1993
1994
1995
1996
1997
Source: Fernanda Rivas and Máximo Rossi (2001)
High males an females participation rate are verified in the
high educational levels
b) Average wage for male over average wage for female
The following graph shows the evolution of the wage-ratio for
full time workers.
Wage ratio
0.40
0.361
0.35
0.323
W
m/ 0.30
W
f - 0.25
1
0.307
0.303
0.247
0.20
0.234
0.210
0.15
1991
1992
1993
1994
1995
1996
1997
Source: Fernanda Rivas and Máximo Rossi (2001)
During the period 1991-97 the average wage for males is
higher than the average wage for females, but the ratio decreases .
5
Nevertheless persists the phenomenon of wage discrimination.
In this work I break the wage gap into three factors: wage
advantage for male, wage disadvantage for female and differences in
human capital and occupations. During 1991-97 these three
components move improving the market situation of females. But
differences in human capital and occupations are the mainly factars
that explain changes in the wage gap.
Evolution and decomposition of wage gap.
Variation: 1991 - 97
Total
Advantage
Ventaja
male
-0.0004
Differences
Human capital and
Total
female
-0.0280
occupations
-0.0764
Gap
-0.1047
Disavantage
Montevideo
0.0151
-0.0441
-0.0944
-0.1234
RUC
-0.0006
-0.0032
-0.0615
-0.0653
Contribution to the total vatiation
Differences
Advantage
Total
Montevideo
RUC
Disavantage
Human capital and
Total
male
0.35%
female
ujer
26.72%
occupations
72.93%
Gap
100.00%
-12.25%
0.92%
35.76%
4.92%
76.49%
94.16%
100.00%
100.00%
Note: RUC: Rest of Urban Country
Source: Fernanda Rivas and Máximo Rossi, (2001).
6
III.- Methodology
To analyze the trends in occupational segregation by gender in
the labor market I use Duncan Index. The differences in the
distribution of females and males along the structure of the
occupations can summarize through a segregation index, this index
can be interpreted as the proportion of women who would have to
change occupations for the occupational distribution of men and
women to be the same.
D
t

1
 
2 t mit
f
it
Where mit and fit are the proportion of males and the proportion of
females working in the occupation i at time t.
The Duncan index indicates the proportion of women that
would be necessary to change occupation to achieve perfect
integration (the proportion of men and women are the same in each
occupation).
This index takes values between 0 and 1. The minimum is
reached with the complete integration and the maximum is reached
when all the occupations are completely masculine or completely
feminine.
The index of Duncan is very sensitive at the level of aggregation
of the occupations: larger aggregation of the occupations minor is the
segregation that is captured by the index.
7
For the Household Survey I used bootstrapping (with 1000
replications) in order to obtain the bootstrap-standard errors of
Duncan index (Efron and Tibshirani, 1992 and Stine, 1990).
There are decompositions of the Duncan index (See Blau, 1998)
and Dolado, Felgueroso and Jimeno, 2002), but I did not estimate
because I did not find important changes about the level of
segregation.
IV.- Data
For this paper I used de Household Surveys and the Population
Census from the Insituto Nacional de Estadística de Uruguay.
These surveys give information on urban population in two
major regions: Montevideo, the capital city, where more than half the
total population lives, and the Rest of the Urban Country.
This survey has been conducted monthly, with the same layout,
since 1981 and bears individual data on monthly wage income, nonwage income, age, sex, educational levels, occupation, working hours
and other relevant variables.
The variable of interest in this study is the occupation (two
digits)
that the individuals hold (women and men). Since the
occupational structure is relatively stable and with the objective of
working with an important number of individuals two year periods
were added: 1997-1996, 1994-1993, 1990-1989 and 1987-1986 for
Montevideo and the RUC.
8
I worked with the last two Population Census with occupations
at three digits.
V.- Results
1.- Population Census (1985-1996)
In the Annex II the distribution of the occupations is presented
between men and women for Montevideo and RUC (three digits of
Population Census).
According to Duncan Index the segregation levels are high
among the Uruguayan labor force, being quite higher in RUC than
in Montevideo: 51.2% in Montevideo and 57.4% in the RUC. This
implies that (both regions) more than half of the women would have
to change occupation to achieve a perfect integration.
Duncan Index- Population Census: 1985 y 1996
Census
Montevideo
RUC
1985
55.6
68.9
1996
51.2
57.4
In the period that separates the two Census segregation
diminished (7.9% in Montevideo and 11.5% in the RUC).
Nevertheless the segregation continues being very high.
9
2.- Household survey (1986/87 – 1996-97)
The Household Surveys allow us to analyze the evolution of
total segregation and for Montevideo and RUC by age and
education.
The Survey also show us that the changes are not important in
the evolution of the segregation at Country, Montevideo and RUC
level. In Montevideo is around 50% and 60% in RUC.
Taking 1996-97 respect to 1986-87 was found that main change
are given in the group with university education: the segregation
falls 17%.
Duncan Index: Household survey, Country
Years
Duncan Index
Standard
error
95% Conf. Interval
1996-97
0.5578
0.0034
0.5509 - 0.5646
1993-94
0.5675
0.0034
0.5607 - 0.5742
1989-90
0.5691
0.0035
0.5621 - 0.5762
1986-87
0.5759
0.0032
0.5695 - 0.5823
Duncan Index: Household survey, Montevideo
Years
Duncan Index
Standard
error
95% Conf. Interval
1996-97
0.5010
0.0049
0.4913 - 0.5107
1993-94
0.5180
0.0047
0.5086 - 0.5275
1989-90
0.5224
0.0048
0.5129 - 0.5320
1986-87
0.5161
0.0048
0.5065 - 0.5257
10
Duncan Index: Household Index, Rest of the Urban Country
Years
Duncan Index
Standard
error
95% Conf. Interval
1996-97
0.6290
0.0044
0.6203 - 0.6378
1993-94
0.6291
0.0044
0.6203 - 0.6379
1989-90
0.6261
0.0045
0.6174 - 0.6347
1986-87
0.6307
0.0041
0.6226 - 0.6388
Duncan index: Household survey, country (by age).
Years
Duncan Index
Standard
error
95% Conf. Interval
Age1
1996-97
0.5822
0.0069
0.5686 - 0.5958
1986-87
0.6057
0.0071
0.5917 – 0.6197
1996-97
0.5615
0.0049
0.5518 – 0.5711
1986-87
0.5865
0.0047
0.5772 – 0.5958
1996-97
0.5539
0.0054
0.5431 – 0.5646
1986-87
0.5808
0.0058
0.5693 – 0.5923
Age 2
Age 3
11
Duncan index: Household survey, Country (by education).
Years
Duncan
Index
Standard error
95% Conf. Interval
Education <=
12 years
1996-97
0.5893
0.0035
0.5624 - 0.5962
1986-87
0.5851
0.0035
0.5782 – 0.5921
1996-97
0.3823
0.0054
0.3635 – 0.4012
1986-87
0.4690
0.0108
0.4477 – 0.4903
Education >
12 years
VI.- Conclusions
The objective of this paper has been to analyze the evolution of
occupational segregation by gender in Uruguay and in particular if
the evolution was associated to the wage gap trends.
The facts were:
- The male-female wage gap falls a lot during the last decade.
- Differences in human capital investment explain this reduction.
- Discrimination component explains the remainder wage
differential.
- There are high level of segregation in Uruguay. Around 50%
of females would have to change occupation to achieve a
perfect integration.
12
- They were not important changes on segregation . Only
women with university experienced a decrease on segregation
(falls 17%).
13
References:
Blau, F., Simpson, P. y Anderson, D. 1998, Continuing progress?
Trends in occupational segregation in the United States over the
1970´s and 1980´s. National Bureau of Economics Research. Working
Papers 6716.
Blau, F., 1998, Trends in the well-being of american women, 19701995, Journal of Economic Literature Vol. XXXVI (march).
Becker (1985), Human Capital, effort and the sexual división of
labor. Journal of Labor Economics, 3(1).
Bergman, B. (1974), Occupational segregation, wages and profits
when employers discriminate by Race and sex. Eastern Economics
Journal, 1.
Diez de Medina, R y Rossi, M., 1989, La mujer en el mercado de
trabajo uruguayo: participación, dedicación, segregación y
discriminación. Cuartas Jornadas Anuales de Economía. Banco
Central del Uruguay.
Duncan, G. And Duncan, B., 1955, A Methological analysis of
segregation indexes. American Sociological Review 20.
Dolado, J., Felgueroso, F. y Jimeno, J., (2002), Recent trends in
occupational segregation by gender: a look across the atlantic. IZA
Discussant Paper Nº 524, July.
Fortin, Nicole and Huberman. Michael ( 2001), Occupational Gender
Segregation: Public Policies and Economic Forces. Working Papers
Cirano, Centre Interuniversitaire de Recherche an Analyse des
Organizations. 2001RP-05.
Green, W., 1993, Econometric analysis, Prentice Hall, Englewood
Cliffs.
Macpheerson, D.
y Hirsch, B. (1995), Wages and gender
composition. Why do women`s jobs pay less?. Journal of Labor
Economics, 13.
Neuman, S. and Silber J., 1994, The econometrics of labor market
segregation and discrimination. Journal of Econometrics. March.
14
Polachek, W. (1985), Occupational segregation: A defense of human
capital predictions. Journal of Human Resources, 20(3).
Rivas, F. y Rossi, M., Wage Discrimination in Uruguay(1991-1997).
Deaprtamento de Economía, FCS, UDELAR, Doceumento de
Trabajo/2001.
15
Table Nº1: Labor Force Structure. Montevideo
1986
1989
1994
1997
14 - 24 years old
0.201
0.204
0.212
0.232
25 - 34 years old
0.236
0.232
0.223
0.201
35 - 44 years old
0.190
0.192
0.212
0.211
45 - 64 years old
0.332
0.330
0.312
0.318
> 65 years old
0.041
0.042
0.040
0.038
Education < 6 years
0.131
0.113
0.089
0.069
Education 6 - 12 years
0.743
0.744
0.733
0.709
Education = 12 years
0.011
0.013
0.023
0.048
Education 13 - 15 years
0.044
0.055
0.071
0.082
Education > 16 years
0.070
0.075
0.085
0.093
Age
Education
16
Table Nº2: Labor Force Structure: Women. Montevideo
1986
1989
1994
1997
Age 14 - 24 years old
0.206
0.204
0.212
0.216
Age 25 - 34 years old
0.264
0.260
0.223
0.222
Age 35 - 44 years old
0.212
0.222
0.212
0.219
Age 45 - 64 years old
0.289
0.283
0.312
0.298
Age > 65 years old
0.029
0.031
0.040
0.044
Education < 6 years
0.115
0.100
0.068
0.054
Education 6 - 12 years
0.717
0.719
0.681
0.626
Education = 12 years
0.005
0.002
0.018
0.066
Education 13 - 15 years
0.058
0.072
0.102
0.112
Education > 16 years
0.105
0.107
0.132
0.142
Age
Education
17
Table Nº 3: Labor Force Structure: Men. Rest of the Urban Country.
1986
1989
1994
1997
Age 14 - 24 years old
0.217
0.215
0.238
0.232
Age 25 - 34 years old
0.224
0.214
0.202
0.201
Age 35 - 44 years old
0.216
0.216
0.213
0.211
Age 45 - 64 years old
0.310
0.319
0.311
0.318
Age > 65 years old
0.033
0.036
0.036
0.038
Education < 6 years
0.261
0.231
0.192
0.162
Education 6 - 12 years
0.697
0.726
0.735
0.752
Education = 12 years
0.003
0.004
0.027
0.038
Education 13 - 15 years
0.012
0.015
0.018
0.022
Education > 16 years
0.027
0.024
0.028
0.026
Age
Education
18
Table Nº4: Labor Force Structure: Women. Rest of the Urban Country.
1986
1989
1994
1997
Age 14 - 24 yerars old
0.223
0.211
0.219
0.225
Age 25 - 34 years old
0.268
0.254
0.225
0.211
Age 35 - 44 years old
0.246
0.244
0.252
0.241
Age 45 - 64 years old
0.244
0.271
0.279
0.297
Age > 65 years old
0.019
0.020
0.025
0.027
Education < 6 years
0.196
0.169
0.139
0.114
Education 6 - 12 years
0.689
0.724
0.692
0.696
Education = 12 years
0.002
0.002
0.044
0.071
Education 13 - 15 years
0.024
0.022
0.042
0.052
Education > 16 years
0.089
0.084
0.083
0.067
Age
Education
19