Gender Discrimination in Uruguay’s Labor Market



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).

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.

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. Consequently with time the occupations of low wages, 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.

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%.

Source: Fernanda Rivas and Máximo Rossi (2001)

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.

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 .

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.

Note: RUC: Rest of Urban Country

Source: Fernanda Rivas and Máximo Rossi, (2001).

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.

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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.

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, non-wage 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.

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.

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 |

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 |

| Age 2 | | | |

|1996-97 | 0.5615 | 0.0049 | 0.5518 – 0.5711 |

|1986-87 | 0.5865 | 0.0047 | 0.5772 – 0.5958 |

| Age 3 | | | |

|1996-97 | 0.5539 | 0.0054 | 0.5431 – 0.5646 |

|1986-87 | 0.5808 | 0.0058 | 0.5693 – 0.5923 |

Duncan index: Household survey, Country (by education).

|Years |Duncan Index |Standard error |95% Conf. Interval |

|Education 12 years | | | |

|1996-97 | 0.3823 | 0.0054 | 0.3635 – 0.4012 |

|1986-87 | 0.4690 | 0.0108 | 0.4477 – 0.4903 |

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.

- They were not important changes on segregation . Only women with university experienced a decrease on segregation (falls 17%).

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, 1970-1995, 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.

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.

Table Nº1: Labor Force Structure. Montevideo

| |1986 |1989 |1994 |1997 |

|Age | | | | |

|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 | | | | |

|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 |

Table Nº2: Labor Force Structure: Women. Montevideo

| |1986 |1989 |1994 |1997 |

|Age | | | | |

|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 | | | | |

|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 |

Table Nº 3: Labor Force Structure: Men. Rest of the Urban Country.

| |1986 |1989 |1994 |1997 |

|Age | | | | |

|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 | | | | |

|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 |

Table Nº4: Labor Force Structure: Women. Rest of the Urban Country.

| |1986 |1989 |1994 |1997 |

|Age | | | | |

|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 | | | | |

|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 |

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[1] I thank the research assistant from Zuleika Ferré and Tatiana Rossi

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