UPPSALA UNIVERSITET Nationalekonomiska institutionen Examensarbete/D ht 06 Åsa Malmberg

Evaluating the gender wage gap

in Sweden

Författare: Författare: Författare: Författare: Åsa MalmbergÅsa MalmbergÅsa MalmbergÅsa Malmberg

Handledare: Handledare: Handledare: Handledare: Eva MörkEva MörkEva MörkEva Mörk

1

ABSTRACTABSTRACTABSTRACTABSTRACT Using mainly quantile regressions, this paper evaluates the gender wage gap throughout the

conditional wage distribution in Sweden. The gender wage is found to increase at the upper tail of

the wage distribution, indicating an enforcement of the glass ceiling effect recorded in earlier studies.

The results also indicate that the earlier noted trend of diminishing wage differences at the bottom of

the wage distribution now is turning. The increase of overall wage inequalities coincides with a

general increase in wage dispersion among high-income and low-income individuals. It is also noted

that there are substantial differences in returns to productivity characteristics between the public

and the private sectors, and that both the highest and the lowest unexplained gender wage gap is

found in the public sector.

Keywords: Glass ceiling, gender wage gap, conditional wage distribution, quantile regression

2

IIIINDEXNDEXNDEXNDEX

ABSTRACTABSTRACTABSTRACTABSTRACT 1

1. INTRODUCTION1. INTRODUCTION1. INTRODUCTION1. INTRODUCTION 3

2. THEORETICAL BACKG2. THEORETICAL BACKG2. THEORETICAL BACKG2. THEORETICAL BACKGROUNDROUNDROUNDROUND 5

2.1 PREVIOUS EVIDENCE 6

3. EMPIRICAL METHOD3. EMPIRICAL METHOD3. EMPIRICAL METHOD3. EMPIRICAL METHOD 8

3.1 BASIC LOG WAGE ANALYSIS 8 3.2 QUANTILE REGRESSIONS 9

4. DATA4. DATA4. DATA4. DATA 11

4.1 DATA SOURCE 12 4.2 DATA DESCRIPTION & SUMMARY STATISTICS 12

5. EMPIRICAL RESULTS5. EMPIRICAL RESULTS5. EMPIRICAL RESULTS5. EMPIRICAL RESULTS 17

5.1 OLS ESTIMATION OF LOG WAGES 17 5.2 QUANTILE REGRESSIONS USING BASIC VARIABLES 21 5.3 EXPLANATIONS 24 5.3.1 Quantile Regressions by Gender 24 5.3.1 Quantile Regressions using an Extended Model 27

6. DISCUSSION6. DISCUSSION6. DISCUSSION6. DISCUSSION 30

7. CONCLUSION7. CONCLUSION7. CONCLUSION7. CONCLUSION 32

8. REFERENCES8. REFERENCES8. REFERENCES8. REFERENCES 34

APPENDIX A APPENDIX A APPENDIX A APPENDIX A –––– DATA DESCRIPTION DATA DESCRIPTION DATA DESCRIPTION DATA DESCRIPTION 36

APPENDIX B APPENDIX B APPENDIX B APPENDIX B –––– DATA PRESENTATION DATA PRESENTATION DATA PRESENTATION DATA PRESENTATION 38

3

1. 1. 1. 1. IntroductionIntroductionIntroductionIntroduction

In spite of the growing numbers of anti-discrimination laws, the wage gap between men and women is

still wide throughout the western world, even after controlling for quantifiable productivity

characteristics, such as age and education.1 This development has been of great interest to economic

research, since a strong trend of diminishing gender wage inequality and wage convergence has been

observed from the 1960s. The trend changed in the 1980s, and wage differences between men and

women started to increase, and seem to be growing in a large number of countries. The term glass ceiling

was introduced to explain the lacking representation of women on the top management positions in

society. Women seem to advance on the career path until a certain point, but advancement to the top of

the occupational hierarchy appears to be impeded by an informal barrier – the glass ceiling. This barrier

disregards factors that normally influence career development, such as education and earlier career

achievements.

Sweden often sets a good example when it comes to gender equality in such areas as female

representation in politics, the comprehensive childcare systems, and the high female labor force

participation. Sweden has however also been shown to have a very segregated labor market, with women

dominating the public sector, and men dominating the private. The labor market is also segregated on

the horizontal level - the representation of women on the top of the professional hierarchy is very low,

especially compared to many other countries.2

A study of the evolution of the gender wage gap was performed by Albrecht et al. (2003), hereafter

Albrecht et al., using Swedish data from the 60s through the 90s. Their study covered the years 1968 to

1998, and showed that there was considerable wage convergence between men and women until the

1980s, after which the trend turned, and the wage differences at the top of the wage distribution

increased. With the assumption that the presence of a glass ceiling would imply a larger gender wage gap

toward the top of the income distribution, Albrecht et al. used quantile regressions to study the entire

1 De la Rica et al. (2005), Blau & Kahn (1992)

2 Bihagen & Ohls (2006)

4

income distribution. It was found that the gap between women’s and men’s wages was wide at the top of

the conditional income distribution, and rather small at the bottom of the distribution.

There are no strong theoretical reasons for why Sweden should have a large gender wage gap at the top

of the wage distribution. On the opposite - there are reasons to believe that women at the bottom of the

wage distribution have less bargaining power, and would therefore be more subjected to discrimination

and to the power of the company. Women who have invested time and money in enriching their human

capital should also be more attractive to employers. Nevertheless, the presence of a glass ceiling has been

recorded in Sweden, as well as in a large number of countries, and comparing with older data, it seems to

be a recent phenomenon that requires more attention. In a country with a strong opinion of

unconditional equality between men and women, increasing gender wage differences should be

alarming. It is now time to see if the trend of increasing wage differences between men and women at

the top of the wage distribution is still growing, which, if so, should be of interest to policy makers

trying to encourage equal terms for men and women on the labor market. The aim of this study is

therefore to evaluate the gender wage gap in Sweden in 2004, and, by comparing with data from

Albrecht et al. from 1998, assess if the gender wage gap has increased or decreased since 1998. This study

will take the form of a replication study that evaluates the recent development, applying a method very

similar to the one used by Albrecht et al. I will, however, extend the analysis to separate tests for the

public and the private sector, since sector affiliation has been shown to play great importance in wage

development. After assessing the size of the gender wage gap, there will also be an attempt to explain

parts of the gap.

The following section accounts for the theoretical background and previous studies on the subject. In

section 3, I present the empirical approach, after which a presentation of the data and its sources follows

in section 4. The empirical results are presented in section 5, which is followed by a discussion in section

6. Finally, the conclusions are presented in the last section.

5

2222. Theoretical B. Theoretical B. Theoretical B. Theoretical Backgroundackgroundackgroundackground

Several theories have attempted to explain the gap in income levels between men and women. The

human capital theory, originally outlined by Adam Smith, and later developed by economists like Jacob

Mincer and Gary Becker, points out that it is variations in the individual’s knowledge and experience, i.e.

productivity, that explain the variation in wages. Women taking time out of work will experience a

slower wage growth due to foregone experiences and loss of work-specific knowledge associated with

time off from work. The human capital theory presumes that men and women have the same returns to

productivity characteristics, and that these are the same disregarding the type of job interruption. Even

though differences in human capital characteristics seem to explain the wage gap between men and

women, discrimination and other gender-specific factors cannot be ruled out as explanations of the

gender gap.

The screening, or signal, theory is another approach to the understanding of the gender wage gap. The

theory says that it is not education per se that raises an individual’s productivity, but having an education

gives the employer a signal of a high-productive person. The same mechanism works to explain gender

differences in employment. Since there is imperfect information on the labor market, the employer will

evaluate the individual according to readily observable signals. An individual taking time off from work

signals a lesser commitment to the job, thus not meriting the same wage development as individuals that

are more loyal to the workplace. Women taking time off to care for children thus signal a lower

productivity, which is put forward as an explanation of the generally lower wage level among women.

The theory of statistical discrimination suggests that men and women are treated differently on the labor

market because there is a lack of information about each applicant, and the employer will therefore

ascribe the applicants’ characteristics that are common to the group that the employer assumes the

applicant belongs to. Thus, it is assumed that a female applicant in fertile age will have children and will

have to take a lot of time off from work. Women are also more prone to take shorter periods of time off

from work when the children are sick etc.. Since men are not as predisposed as women to use the

parental leave system, hiring a man implies higher odds of a long-term employment without

interruptions. This could be an explanation of why men are more prone to advance to the higher

6

positions on the labor market, as well as an explanation of the parts of the gender wage gap that exist

between men and women with the same qualifications.3

2222.1.1.1.1 Previous E Previous E Previous E Previous Evidencevidencevidencevidence

The field of gender wage gaps has been thoroughly explored in a vast number of studies. Usually, a

method comparing logged wages is employed, and the gender wage gap is inferred at the mean of the

distribution of wages.4 In an intra-country comparison, especially separating the U.S. from other

countries, Blau & Kahn use the wage structure as an explanation of the international differences in the

gender pay gap.5 The same authors found that the narrowing of the U.S. gender wage gap slowed down

in the 1990’s, which was attributed to gender-specific factors, as well as wage structure.6

The research mentioned gives a good idea of the level of differences between men’s and women’s wages

in general. But there is also growing literature evaluating the gender gap at different points of the wage

distribution, thus pointing to differences in the gender wage gap between low-income takers and high-

income takers.7 Using data from the U.K., Fitzenberger & Wunderlich find that the gender wage gap was

decreasing for all full-time workers in the period 1975 to 1995. This decline was, however, much smaller

in the upper part of the wage distribution for high-skilled workers, than for the lower parts of this wage

distribution. The result indicates that the gender wage gap is decreasing in the wage distribution for low-

skilled individuals, but for the high-skilled, women face a disadvantageous wage development.8 Datta

Gupta et al. find that the gender wage gap is increasing at the highest decile in Denmark, but decreasing

at the highest decile in the US, indicating a strong presence of a glass ceiling effect in Denmark and but

not in the U.S.9 In Switzerland, opposing observations were made, where the Swiss wage distribution is

found to have large unexplained gender wage differences at the lower part of the wage distribution, and

3 Björklund et al., (1996)

4 De la Rica et al.,(2005)

5 Blau & Kahn, (1992)

6 Blau & Kahn, (2004)

7 De la Rica et al., (2005)

8 Fitzenberger & Wunderlich, (2002)

9 Datta Gupta et al., (2003)

7

a constant decline in gender wage gap when moving to the higher parts of the wage distribution. In fact,

at the higher wage levels, most of the gender wage differences could be attributed to labor market

characteristics, implying an absence of a glass ceiling in Switzerland.10 Using quantile regressions and an

Oaxaca-Blinder decomposition when studying the wage structure in Spain, De la Rica et al. find evidence

that for groups with lower education, the gender gap is wider at the bottom than at the top of the wage

distribution, a phenomenon referred to as sticky floors. The gender gap was also found to be expanding

for the group with tertiary education.11 Albrecht et al. employed a similar approach when examining the

possible effect of a glass ceiling in Sweden during the 1990’s. The authors find that the wage gap in

Sweden increases across the conditional wage distribution, accelerating towards the top of the wage

distribution, which gives evidence of a glass ceiling that separates men and women. The evidence

presented indicates that this is a new phenomenon that could not be observed in the 1968 data, and that

this trend was only minor in 1981. Hence, they identify a trend of increasing differences between the

wages of men and women at the top of the income distribution in Sweden, and that gender differences in

returns to the labor market characteristics could be the most important factor affecting gender wage

inequality. It is also pointed out that their data demonstrates little evidence of a glass ceiling in the

United States, even though the U.S. has an average gender wage gap that is much larger than the one in

Sweden.12 A more recent study by Bihagen & Ohls of the Swedish gender wage gap shows instead that

women face greater difficulties at the lower hierarchical levels than at the higher ones, thus

contradicting the results of Albrecht et al. It is often claimed that the private sector has higher gender

wage differences than the public sector, since the public sector often is characterized by low-skilled jobs

with a more rigid wage development, where wages are set in accordance with union agreements.

However, the authors find evidence that challenges this notion.13

10 Bonjour & Gerfin, (2001)

11 De la Rica et al. ,(2005)

12 Albrecht et al. ,(2003)

13 Bihagen & Ohls, (2006)

8

3333. . . . EmpiricalEmpiricalEmpiricalEmpirical Method Method Method Method

There are different approaches when studying wage structures and wage differences between groups. In

this paper, I will apply two separate techniques to enable a thorough examination of the possible wage

differences between men and women in Sweden, and to compare the outcomes of different procedures. I

will start by a log wage analysis through a simple OLS method. This analysis will be followed by a

dissection of the wage distribution through quantile regressions on several sets of data. Following Kee,

there will also be a separate analysis of the private and the public sectors.14 Finally, there will be an

attempt to explain the wage differences by controlling for a number of variables of a more endogenous

character.

3333.1 .1 .1 .1 Basic Basic Basic Basic Log Wage ALog Wage ALog Wage ALog Wage Analysisnalysisnalysisnalysis

Gender differences have often been studied within the human capital model, and wage inequalities have

to a large extent been explained by differences in human capital attributes. Women are likely to accrue

less labor market skills, and have lower incentives to work when anticipating longer periods of parental

leave.15 The most common tool when measuring wage dispersion has been to compare average log wages

of different groups, through ordinary OLS estimation. In accordance with the human capital model,

commonly included variables for the determination wage are for example age, level of education, work

experience etc.16 Using this approach, it is assumed that men and women have the same expected

productivity, and the variable of interest then is the gender variable that is used to establish possible

gender differences. This technique will yield an estimate of the wage discrimination faced by the average

woman, thus giving an indication as to the overall extent of the gender wage gap.

14 Kee, (2006)

15 Blau & Kahn, (2000)

16 It should be noted that a wide range of variables have been used in such estimations. The choice of variables could vary depending on for example availability of data

and aim of the author. Examples of commonly used variables are: training, age, education, tenure, marital status, health, unemployment, experience, part time, fixed term,

casual contracts, region, sector, urban/rural area, year and union membership.

9

I will initiate my empirical research by running simple OLS regressions on variables often considered as

determinants of wage. These will be age, age squared, level of education (divided into six groups), and

immigration background, and will through the paper be referred to as the basic control variables. The log

wage analysis will be presented in a table including the equivalent figures for 1992 and 1998, as

presented by Albrecht et al.. This will enable a comparison of the development of raw gender wage gap

for a 12-year period. To catch another aspect of the nature of the gender wage gap, there will be a

comparison of different cohorts. Further, the simple regression analysis will include separate regressions

for individuals working in the private and the public sectors. Those regressions will give indications of

differences in returns to the basic productivity characteristics in the different sectors.

A limitation of regression estimates is that if an independent variable is incorrectly excluded from the

regression, the gender wage gap could be over- or underestimated. It is equally important to avoid the

inclusion of irrelevant variables in the model, which could lead to overspecification of the model. The

apparent aim is thus to include variables that measure productivity in an effective way.17 Because of the

limitations of the log wage analysis, i.e., only analyzing the average citizen, this approach will only be

used to give a brief overview of the data material. For an in-depth study of differences along the wage

distribution, the framework used by Albrecht et al., which is based on quantile regressions, will yield

more specific information on the gender wage gap. When using quantile regressions, I will face the same

problems in the specification of the model, as when using the OLS model. The gain from using quantile

regression is that it will give me a tool for analyzing a broader spectrum of the population, i.e., possible

differences between low-, high-, and middle-income takers.

3333.2.2.2.2 Quantile R Quantile R Quantile R Quantile Regressionegressionegressionegressionssss

First used by Koenker & Bassett, and here following a method developed by Buchinsky, quantile

regression (QR) is a semi-parametric technique used to estimate conditional quantile functions.18 The

method has been widely used on wage data when studying wage differences between different groups in

17 Blau, Kahn (2000)

18 Koenker & Bassett (1978), Buchinsky (1998)

10

society. Where ordinary linear regressions yield estimates of the relationship between one or several

variables and the conditional mean of Y, QR estimates a model using the conditional median, which

gives a more complete and comprehensive causal analysis, than simple linear regression. The method

estimates the θth quantile of a variable, in this case log wage, conditional on a number of variables, with

the assumption that qθ, the conditional quantile of y, is linear in x (i.e. qθ = xβ(θ). The model can be

written: iii uxwage θθβ +=ln and Quantθ (ln wagei | xi ) = xi βθ . Xi represents the vector of exogenous

variables and βθ represents the vector of parameters. Quantθ (lnwi | xi ) represents the conditional quantile

of ln wage given x. 19

The coefficient vector is found through the following equation:

The coefficient vector, b(θ), is understood as the estimated returns to individual characteristics at the θth

quantile of the distribution. The advantage of this technique is that it allows for the possibility that

characteristics have different returns at different parts of a distribution, which is not the case with

ordinary least square analyses.20

The QR analysis will be initiated with regressions on pooled data on men and women combined, to

determine to what extent the gender wage gap at different percentiles can be explained by differences in

labor market characteristics, that is, differences non-attributable to gender. The underlying assumption

will be that men and women are paid the same wage for identical labor market characteristics, i.e., that

they have the same returns to these characteristics. This analysis will include the basic control variables

used in the OLS analysis. These regressions will yield a gender coefficient that will be used to

approximate the extent of the gender differences that cannot be explained by labor market

characteristics, and will be carried out for seven percentiles - the 5th, 10th, 25th, 50th, 75th, 90th, and the 95th

percentile. The results will be compared with the raw gender wage gap to se how the gender wage gap

evolves when consideration is taken to basic control variables. There will be comparisons with Albrecht

19 Buchinsky, (1998)

20 Albrecht et al., (2003)

−−+− ∑∑<≥ )(:)(:)(

)()1()(minθβθβθβ

θβθθβθiiii xyi

iiii

xyi

xyxy

11

et al.’s data from 1998, to get an idea of the development of the wage gap. There will also be separate

regressions for the public and the private sector, to see how sector affiliation affects the wage level.

Comprehensive tables of the results will be presented in Appendix B.

After controlling for the basic control variables, the analysis will proceed to explanations of the observed

gender wage gap. The earlier regressions have been performed under the assumption that men and

women have the same returns to characteristics. This assumption will now be relaxed by running

separate quantile regressions for men and women to see if the two groups have differing returns to the

same labor market characteristics. After the regressions using the basic control variables, variables with a

more endogenous character will be added to the model, to see how these affect the gender coefficient.

The additional variables include sector, field of education, marital status, single parenthood, having

children aged 1-6, and full-time/part-time employment. These are considered more endogenous since,

for example, field of education could be correlated with sector of employment, marital status could be

correlated to having children, etc.

It is important to remember that using additional explanatory variables won’t necessarily improve the

explanatory power of the model, since there is a risk of collinearity between one or several of the

variables used in the tests. To get an idea of how well the models explain the variation within the model,

R2 values will be recorded. Quantile regressions instead yield a pseudo-R2, which could be interpreted in

a similar mode as the R2 value, and will also be recorded in my empirical presentation.

4444. . . . Data Data Data Data

In this part of the paper, the sources of data will be presented. There will also be a presentation of the

variables used, and summary statistics. I will start the data presentation will start by presenting the basic

control variables, then proceeding to the additional control variables that will be used later on in the

analysis. A more detailed data description and variable definitions are found in Appendix A.

12

4.1 Data S4.1 Data S4.1 Data S4.1 Data Sourceourceourceource

The analysis in this paper will based on data from the LINDA database, a register-based longitudinal data

set, that mainly use data from the Income Register and the Population Censuses. It has been developed

by Statistics Sweden and Uppsala University, and consists of a large panel of individuals and families,

with data from 1960, to currently 2004. The database is updated every year and additional variables are

constantly added.21 Further, the sample is random and representative for the whole population, and

covers 3.35% of the population in the ages 18 to 65.

The advantages of this database are that it has a wide selection of variables, and that it contains a large

number of observations on each variable. Therefore, the extent of the dataset offers great opportunities

for assessing the many aspects that might affect wage differences between men and women. A

shortcoming is that information on actual work experience is missing from the LINDA database.

Albrecht et al. solved this problem by including data from other databases that covered this aspect. Since

the use of several databases is outside the scope of this paper, I only intend to use the LINDA database,

which will cause some limitations when it comes to comparing my results with the ones from Albrecht.

The variable work experience will instead be approximated with the variable age, and the full-time/part-

time variable.

4444.2 .2 .2 .2 Data Description & Summary SData Description & Summary SData Description & Summary SData Description & Summary Statisticstatisticstatisticstatistics

The dependent variable in this study will be logged monthly wages. Log wages are commonly used

when studying wages. In a histogram, regular wages are often found more skewed than log wages.

The use of logged wages therefore offers the benefit of using a variable that is normally distributed,

which will reduce the problem of heteroscedacity and possible influence from outliers.22 The dataset

excludes the self-employed.

21 Edin & Fredriksson, (2000)

22 Study material, Statistik B/Tillämpad Ekonometri VT 2005, p.4

13

The variable of interest in the analysis will be gender. In the classical mincerian wage equation,

women are found to have lower wages than men, wherefore the gender dummy is expected to yield a

negative wage coefficient.

One of the basic control variables that will be used in the empirical section is age. Age, or years of

work experience, is fundamental to the mincerian wage equation. In addition, age squared will be

used to catch the fact that age and wage do not follow a linear correlation, and that wage only

increases with age up to a certain point, after which the wage curve levels out. Since work

experience is missing in the LINDA database, age will be used as an approximation of work

experience.

Table 1. Descriptive Statistics

Ln wage - women

Ln wage - men

Number of children

Extent of work - women

Extent of work - men

Mean 9.955 10.138 1.500 83.194 95.277

Maximum 12.583 13.633 12 100 100

Minimum 9.393 9.393 0 4.5 5

St.deviation 0.240 0.346 1.181 23.708 16.406

Men Women Men Women

Level of Level of Level of Level of educationeducationeducationeducation::::23232323

Field of Field of Field of Field of educationeducationeducationeducation:24

Level 1 0.039 0.027 Field 0 0.200 0.199

Level 2 0.092 0.067 Field 1 0.059 0.143

Level 3 0.467 0.475 Field 2 0.026 0.030

Level 4 0.083 0.038 Field 3 0.130 0.195

Level 5 0.293 0.383 Field 4 0.032 0.018

Level 6 0.026 0.009 Field 5 0.400 0.044

Field 6 0.020 0.007

Age 42.9 42.9 Field 7 0.064 0.298

Private sector 0.684 0.308 Field 8 0.069 0.065

Public sector 0.316 0.692

Part time job 0.010 0.468 Single parent 0.029 0.109

No. of No. of No. of No. of observationsobservationsobservationsobservations

78593 104008

23 Level 1 - Incomplete comprehensive school (less than 9 years), Level 2 - Comprehensive school (9 or 10 years), Level 3 - Upper secondary school (up to 3 years), Level 4

- Post-secondary schooling (up to 2 years), Level 5 - Post-secondary schooling (for at least 3 years), Level 6 - Completed doctoral degree

24 0) General education, 1) Pedagogical- & teacher training, 2) Humanities & arts, 3) Social science, jurisprudence, business & administration, 4) Natural science, mathematics & computer, 5) Technology & production, 6) Agriculture, forestry & veterinary services, 7) Healthcare, medical service & social care, 8) Services.

14

Another important variable is education, that in this material is divided into six categories; Level 1 -

incomplete comprehensive school (less than 9 years), level 2 - comprehensive school (9 or 10 years),

level 3 - upper secondary school (up to 3 years), level 4 - post-secondary schooling (up to 2 years),

level 5 - post-secondary schooling (for at least 3 years), level 6 - completed doctoral degree. When it

comes to level of education, there has been a steady trend of a growing level of education among the

whole population, and especially among women who are in one sense more highly educated than

men. In the LINDA database 2004, education is divided into six different levels. As a whole, 59.8% of

the men and 56.9% of the women have one of the three lowest levels of education. Consequently,

40.2% of the men and 43% of the women are found on the upper scale of the education ladder. The

corresponding figures from 1998, taken from Albrecht, were 70.9% of the men and 66.5% of the

women on the lower scale of education, and 28.5% of the men and 33.4% of the women on the

upper scale of education.25 However, men still dominate the highest level of education - the doctoral

level. It should be remembered that education is a variable with some endogeneity problems. It

cannot be ruled out that, for example, men are encouraged to explore certain areas, and that they

therefore end up in educations that lead to a better paid job. On the other hand, Table 1 shows that

the dispersion of men and women at the different levels of education are quite similar. The

exceptions are among those with a post-secondary schooling; 63% of those with a level 5 education

are women, and among those with a completed doctoral degree, two thirds are men.

Immigrants are often found to have lower wages and to face more difficulties on the labor market

than native Swedes. Therefore, immigrant background, in this study defined as being born outside of

Sweden, Norway, Finland or Denmark, will be included in the QR. The immigrant variable is

expected to yield a negative coefficient if the individual is born outside of “Sweden”. Albrecht et al.

found that the immigrants wage gap was steady at around 10%, i.e., immigrants tend to have 10 %

lower wages than native Swedes, irrespective of position on the wage distribution.26

The following variables will be used as additional control variables added later in the analysis to

evaluate explanation of the possible gender wage gap.

25 Figures taken from Albrecht et al.(2003)

26 Albrecht et. al., (2003)

15

One aspect of wage analysis is field of education, since wage varies greatly between different fields.

Again, this variable holds an endogeneity problem, since choice of field is correlated with, for

example, level of education and gender. It is worth mentioning that the division of the field of

education is very general, and one field could include both high status and low status jobs, such as

healthcare, medical services & social care, which for example includes both doctors and assistant

nurses. Even when men and women work within the same field, women are often claimed hold less

prestigious and low-pay jobs compared to men. The use of field of education will however give a hint

as to the importance of industry on the wage level. It should be noted that the categorization of the

fields of education in LINDA has changed somewhat since 1998, why comparisons with earlier

results should be made with some caution.

Further, Table 1 shows that women dominate the public sector, and men dominate the private

sector. This disparity is often put forward as an explanation for women having lower wages than

men.

In the wage analysis, I will include information on family status. Research has shown that being

married has a positive impact on men’s wages, but none, or a negative effect, on women’s. It is

however hard to know if married men have higher wages because they get a lot of “ground service”

from their wives, thus giving them more time to focus on their careers, or if making more money

makes it more likely that a man will get married (that is, if money makes the man more attractive),

or if a combination of the two is the explanation. Single-parenthood will also be included as a

dummy variable. Table 1 show that women are single parents more often than men. A single parent

could be expected to have less opportunity to focus on a career, which could reflect negatively upon

the individuals wage curve.

When analyzing family structure, number and age of children will also be included. In general, small

children need more attendance than older children, why having small children could be expected to

a have a negative impact on the wage, especially for women. Moreover, in Sweden, people with

children under the age of eight are allowed to cut down to part time work, which could have a

negative impact on the wage. I assume that children start school at an age of seven, in which case,

16

individuals are no longer counted as having small children. I expect to find that having small

children has a negative impact on the wages of women, but I do not expect to find the same relation

for men. It is also possible that people postpone parenthood until they make enough money to

support them, in which case having small children will yield a positive coefficient.

Yet one interesting aspect is the extent of part-time work. Women more often than men work in

fields where part time work is a common alternative, as for example in elderly care. Individuals with

children under the age of 8 are allowed to cut down the number of working hours. This benefit is

usually exploited by women, which could be reflected in a slower wage development for these

women. Table 1 shows that around 47% percent of women in Sweden work less than 95%. The

equivalent number for men is 1%! Among part-time working individuals with children aged 1-6,

89% are women. Among full-time working individuals with children aged 1-6, 66% are men. The

extent of work is therefore highly relevant when it comes to comparing the productivity

characteristics of men and women, even though the variable could be through to be correlated with,

for example, having small children.

To get a general idea of the overall wage distribution in Sweden, percentile ratios are presented in

Table 2. For example, the P90/P10 ratio is the quota between the log wages for the individual(s) at

the 90th percentile, divided by the log wage for the individual(s) at the 10th percentile. A low quota

implies a more equal income distribution - a P90/P10 ratio of 1.5 suggests that the wage is 50%

higher at the 90th percentile than at the 10th percentile.

Table 2. Percentile Ratio Quotas for the years 1992, 1998 and 2004

1992 1998 2004

Men Women Men Women Men Women

P90/P10P90/P10P90/P10P90/P10 1.96 1.56 2.13 1.63 1.08 1.05

P90/P50P90/P50P90/P50P90/P50 1.55 1.32 1.65 1.35 1.05 1.03

P50/P10P50/P10P50/P10P50/P10 1.26 1.18 1.29 1.21 1.03 1.02

Note: Men (n =78 593), women (n =104 593) in 2004. The data from 1992 and 1998 are taken from Albrecht et al.

Comparing the three percentile quotas in from 1998 with the ones from 2004, it appears that the

overall wage dispersion has been compressed greatly during the six year period covered by this study.

17

It also appears that the wage dispersion is smaller among women’s wages than among men, which is

compatible with Albrecht et al.’s results from 1992 and 1998. The general debate concerning wage

inequalities in Sweden has rather been about the growing wage inequalities, since there are signs of

increasing wage dispersion. It is therefore reasonable to believe that the incompatibility of my results

and Albrecht et al.’s could be ascribed to differing dimensions of the datasets when it comes to, for

example, the age of the individuals.

5555. Empirical . Empirical . Empirical . Empirical RRRResultsesultsesultsesults

In this section, the empirical evidence of the research will be presented. First, the results from log wage

analysis, compared with results from 1998 will be presented. Thereafter, I proceed to the results from the

quantile regressions. The empirical part will be completed with an analysis of the possible explanations

of an observed gender gap. A selection of the results will be presented in the text. For a more thorough

presentation of the results, complete tables are found in Appendix B.

5555.1 .1 .1 .1 OLS Estimation ofOLS Estimation ofOLS Estimation ofOLS Estimation of Log W Log W Log W Log Wagesagesagesages

In 2004, men had an average monthly wage of 27 332 sek, and women made on average 21 840 sek.

When using this simple average, there is no information on differences between men and women in

productivity characteristics that could explain these differences. It could for example be the case,

that men generally have a higher level of education, which would explain this income gap. It is

therefore a measure of limited analytical scope, and a poor measure of the income differences

between men and women. Simple linear regression using logged monthly wages, and different

parameters of productivity, gives a more comprehensive understanding of the topic.

In Table 3, the evolution of the logged wages of men and women is displayed. The raw gender gap is

the difference between men’s and women’s log wages. The difference in log wage is then equivalent

to the wage difference in percent. By that manner, Albrecht et al. show that the raw average gender

18

.0

.1

.2

.3

.4

.5

.6

10 20 30 40 50 60 70 80 90 100

COHORT_1COHORT_2

COHORT_3

Log wage gap

wage gap was 15% in 1992, 16% in 1998, and the current data shows a raw gender wage gap of 18%.

This raw gender wage gap, could serve as a first indication of gender differences in pay. At this stage

however, it is necessary to add other dimensions of the wage structure, to explore possible gender

inequalities.

Table 3. Log Wage Comparisons for the years 1992, 1998 and 2004

1992 1998 2004

Men Women Men Women Men Women

Log wageLog wageLog wageLog wage 9.64 9.49 9.87 9.71 10.14 9.96

Note: Men (n =78 593), women (n =104 593) in 2004. The data from 1992 and 1998 are taken from Albrecht et al.

A cohort analysis could further illustrate the nature of the wage gap. In the presence of a glass

ceiling, it would be expected that the gender wage gap is higher in the upper cohorts. In the

beginning of a career, men and women with a similar background are more or less on the same

starting point. The average age for people’s first child in Sweden is around 30 years, wherefore I

expect that the gender wage differences would start to grow among individuals in their 30’s-40’s,

since career interruptions are expected to stall the wage development and affect the career

opportunities for women.

Figure 1. Gender Log Wage Differences by Cohort 2004.

19

Figure 1 illustrates the raw gender wage gaps for three different cohorts. Cohort 1, representing

individuals between 18-33 years, is clearly separated from the two older cohorts, and at the lowest

percentiles, the gender wage gap in this cohort is close to zero. The two upper cohorts have a very

similar pattern when it comes to the distribution of the raw gender wage gap. There is a raw gender

wage gap of a few percent even at the bottom of the wage distribution. The raw gender wage gap

then increases throughout the distributions, ranging from around 30, to nearly 60 percent at the

highest income levels. Thus, it is reasonable to believe that wage differences between men and

women are much lower among younger individuals. The differences between the cohorts could have

several explanations. It is only in the past few years that women have caught up on the level of

education, and are today just about as educated as men. Another possible explanation is that many

women have children in their thirties, and that a longer period of parental leave results in a slower

wage development compared to men in the same age, who tend to use the right to parental leave to a

much lower extent. The results could also be a sign that the younger generation is more well-

prepared and well-informed about gender inequalities, and less likely to accept wage inequalities due

to gender. Another feature of Figure 1 is that in the two older cohorts, there is a clear leap around

the 75th percentile. It is difficult at this level of the analysis to find an explanation to this leap. The

other results are, however, in accordance with earlier studies that have shown the presence of a glass

ceiling. It is also reasonable to believe that there are smaller wage inequalities among the younger

generations.

Although pointing to interesting gender differences, these results are not enough to prove the

presence of a glass ceiling. It could still be the case that the wage differences between men and

women can be attributed to differences in education or other productivity characteristics. The results

from the simple regressions presented in Table 4 could further illustrate the nature of the gender

wage gap. The first two columns show the estimates on data for the years 1998 and 2004.27 The last

two columns show estimates on separate datasets for the groups of individuals working in the private

and the public sector.

27 The results for 1998 are taken from Albrecht et al.

20

The first columns in Table 4 show that the gender coefficient has increased since 1998, that is, the

gender wage differences are increasing. It is also noteworthy that the gender wage gap actually

increases with the use of the basic explanatory variables. When it comes to returns to education,

there seem to be inconclusive trends. For the individuals with finished comprehensive school (level

2), and upper secondary schooling (level 3), the returns to education are decreasing, especially for the

level 3 individuals. Individuals with a shorter post-secondary schooling (level 4), and those with a

completed doctoral degree, are facing higher returns to education, while those with a post-secondary

schooling of at least three years (level 5), have lower returns to education compared to 1998. The

negative coefficient for immigration background has decreased since 1998, indicating that the

Swedish labor market has become somewhat more equal with respect to ethnical background.

Table 4. OLS Regression Comparisons of Log Wages for the years 1998 and 2004, and Separate Regressions for the Public and Private Sector Regressions for 2004

OLS - 1998 OLS OLS OLS OLS ---- 2004 2004 2004 2004 OLS-2004 Private sector

OLS OLS OLS OLS –––– 2004 2004 2004 2004 PublicPublicPublicPublic sector sector sector sector

Raw gender wage gap

-0.162 -0.180 -0.150 -0.178

Gender dummy -0.174 (0.0015)

-0.192

(0.0012)

-0.153 (0.0019)

-0.126

(0.0014)

Age 0.031 (0.0005)

0.026

(0.0004)

0.035 (0.0007)

0.015

(0.0004)

Age^2/100 -0.029 (0.0006)

-0.025

(0.0004)

-0.034 (0.0007)

-0.011

(0.0005)

Level of education:Level of education:Level of education:Level of education:

Level 2 0.068 (0.0037)

0.061

(0.0039)

0.062 0.0055)

0.071

(0.0051)

Level 3 0.171 (0.0035)

0.102

(0.0034)

0.121 (0.0049)

0.115

(0.0044)

Level 4 0.234 (0.0035)

0.289

(0.0041)

0.330 (0.0058)

0.224

(0.0054)

Level 5 0.391 (0.0035)

0.331

(0.0035)

0.446 (0.0052)

0.344

(0.0044)

Level 6 0.600 (0.0084)

0.636

(0.0056)

0.734 (0.0110)

0.686

(0.0059)

Immigration background

-0.127 (0.0050)

-0.093

(0.0021)

-0.146 (0.0033)

-0.048

(0.0023)

Constant 8.841 (0.0152)

9.314

(0.0079)

9.127 (0.0119)

9.416

(0.0092)

R2 adjusted 0.345 0.345 0.445

Note: Men (n =78 593), women (n =104 008) for the 2004 data. Private sector (n =85 803), public sector (n =96 798) for the 2004 data. Standard errors in parentheses. P<0.01 for all values. Levels of education: Level 1 - Incomplete comprehensive school (less than 9 years)(control group), Level 2 - Comprehensive school (9 or 10 years), Level 3 - Upper secondary school (up to 3 years), Level 4- Post-secondary schooling (up to 2 years), Level 5 - Post-secondary schooling (for at least 3 years), Level 6 - Completed doctoral degree.

21

When comparing sector affiliation, Table 4 demonstrates considerable differences in returns to the

basic labor market characteristics. It is interesting to note that the raw gender wage gap is higher in

the public sector than in the private. After controlling for the basic control variables, the gender

dummy increases slightly in the private sector, whereas in the public sector, the gender dummy

decreases a few percent. Further, on all levels of education except at the 2nd level, the returns to

education are higher in the private sector than in the public sector. One should bear in mind that

because of the strong domination of women in the public sector, it is reasonable to believe that

women fall behind in the wage league, because the public sector has lower rewards for education.

Comparisons of the immigrant dummy reveal that the wage penalty for immigration background is

much larger in the private sector than in the public. This could be a reflection of the fact that the

public sector is more restricted by anti-discrimination laws, and has an ambition to set a good

example when it come to equal opportunities.

This log wage analysis gives an indication that there is indeed a considerable unexplained wage gap

between men and women. The differences in the simple regressions on the private and the public

sectors also imply that the differences in sector affiliation could be an important explanation to the

gender wage differences. Further, the analysis has so far only focused on the average citizen. To

discern gender differences at different parts of the income distribution, the analysis will now proceed

to the study of quantile regressions.

5555.2 .2 .2 .2 QuQuQuQuaaaannnntile Regressions usingtile Regressions usingtile Regressions usingtile Regressions using Basic Variables Basic Variables Basic Variables Basic Variables

I will start the QR analysis by testing data including both men and women, to elucidate the extent to

which the gender gap at various percentiles can be explained by individual differences in labor

market characteristics. These factors will all be controlled for in the following quantile regressions,

imposing the restriction that men and women are paid the same rewards for their characteristics. In

Table 5A, I present gender coefficients from quantile regressions on the 5th, 50th and 95th percentiles

of data from 2004. For comparison, I will also include Albrecht’s results from 1998. For comparison,

the raw gender wage gap, where no account is taken of labor market characteristics, is included in

22

the first two panels. A complete table of the regressions on the 2004 data is found in Table B 1, in

Appendix B.

The raw gender wage gap is increasing throughout the wage distribution, and is overall larger in

2004 than in 1998. This increase could be attributed to gender differences in labor market variables.

The two second panels illustrate the gender dummy coefficient after controlling for the labor market

characteristics age, age squared, six different levels of education and immigration background-

variables (the basic control variables). It is interesting to see that the wage gap increases when going

from the raw wage gender gap to the gender wage difference estimated with the basic control

variables, for all quantiles except at the 95th percentiles, where the gender wage difference is

decreasing. One explanation of this phenomenon, which is pointed out by Albrecht et al., is that

women generally have a higher level of education than men, except when it comes to doctoral

degrees. Another explanation is that immigrants generally have lower wages then native Swedes, and

since a working immigrant is more likely to be a man, than a woman, this could reflect upon the

gender wage gap.

Table 5A. Quantile Log Wage Regressions for 1998 and 2004

Note: Men (n =78593), women (n =104 008) for the 2004 data. Standard errors in parentheses. P<0.01 for all values. The values from 1998 are taken from Albrecht et al.

Table 5B. Quantile Log Wage Regression by Sector 2004 including Interaction Terms 5th 5th

interaction 50th 50th

interaction 95th 95th

interaction Public sector -0.034

(0.0021) -0.034 (0.0021)

-0.102 (0.0013)

-0.100 (0.0015)

-0.300 (0.0040)

-0.291 (0.0055)

Private sector -0.080 (0.0021)

-0.084

-0.143 (0.0019)

-0.145

-0.203 (0.0068)

-0.214

Note: Private sector (n =85 803), public sector (n =96 798). Standard errors in parentheses. P<0.01 for all values.

5th 50th 95th-

Raw gender wage gap 1998 -0.048 -0.133 -0.387

Raw gender wage gap 2004

-0.060 -0.156 -0.416

Gender dummy 1998 -0.059 (0.0021)

-0.158 (0.0013)

-0.293 (0.0046)

Gender dummy 2004 -0.071

(0.0014) -0.170

(0.0010) -0.326

(0.0042)

23

The most striking feature of the data is that the gender gap seems to be increasing at all quantile

levels, compared to the 1998 data. These results indicate that the trend of diminishing wage

differences between men and women at the lower levels of the income distribution, as distinguished

by Albrecht et al. and others, now is turning in Sweden. It is also apparent that the gender gap

increases with higher quantiles, i.e., at the higher quantiles, the wage gap between men and women

is higher than at the lower levels of the income distribution. At the 5th quantile the wage gap has

increased with around 1.2 percentage units from 1998 to 2004. At the 50th percentile, there increase

is also 1.2 percentage units, and at the 95th percentile, the gender wage gap increased by 3.3

percentage unit, thus the greatest increase.

The OLS regressions in section 5.1 clearly indicated that there are substantial differences in the

gender wage gap between the private and the public sector. To see if the unexplained gender wage

gap differs at varying points on the income distribution, separate sets of quantile regressions were

carried out for the public and for the private sector. Table 5B shows that that the gender wage gap

varies considerably along the wage distribution, and between the sectors. Since it cannot be excluded

that the wage level differs between the public and the private sectors, an interaction variable is

included in the analysis, enabling direct comparison of the coefficient from the public and the

private sectors.

As is apparent from Table 5B, the ordinary quantile regressions and the regressions including the

interaction variable, yield very similar coefficients, indicating that differing wage levels between the

sectors only play a minor, or non-existent, role in explaining the gender wage differences between

the sectors. At the 5th percentile of the public sector, the gender coefficient is very low, while the

private sector yields a slightly higher coefficient. At the 50th percentile of the public sector, the

gender coefficient is again lower than in the private sector at the same percentile. At the 95th

percentile, however, women in the private sector make around 20% percent less than men in the

private sector. For the public sector, the equivalent figure is around 30%.

I can at this stage clearly establish that there is an unexplained wage gap between men and women,

and that this gap increases towards the top of the conditional income distribution. The results also

24

indicate that the unexplained wage gap has grown since 1998, even at the lower parts of the income

distribution, and that the returns to the basic productivity characteristics could vary greatly between

the sectors, which could be an important source of gender wage inequalities. Further, it has been

showed that there are considerable differences in gender wage gap between the public and the

private sectors. The public sector seems to have both the highest and the lowest gender income gaps.

The next step is to see if this gender income gap can be explained by differing returns to labor market

characteristics between men and women, and if the income gap decreases if additional explanatory

variables are added to extend the model.

5.3 Explanations5.3 Explanations5.3 Explanations5.3 Explanations

To identify if there are gender differences in returns to productivity characteristics, separate quantile

regressions for men and for women were computed. The results are presented in Table 6 below,

which records the results for the 5th, 50th and the 95th percentile, including data from 1998. After

that, an extended model will be used to discern other possible explanations of the gender wage gap.

5.3.1 Quantile Regressions by Gender5.3.1 Quantile Regressions by Gender5.3.1 Quantile Regressions by Gender5.3.1 Quantile Regressions by Gender

The results from the quantile regressions results reveal considerable gender differences when it

comes to returns to productivity characteristics. Starting with returns to education, at the 5th

percentile, women have the highest returns to the two educational levels below post-secondary

schooling (level 2 and 3), and to the post-secondary education of at least three years (level 4). Men

have higher returns to the shorter post-secondary schooling (level 5), and to a doctoral degree (level

6). The biggest difference is at the doctoral level, where men have a 3.8 percentage unit higher

return than women, compared to education level 1. The comparisons with earlier data reveal small

changes since 1998. The return to the longer level of post-secondary school has increased a few

percentage units, and the returns to comprehensive school have decreased.

25

Table 6. Quantile Regressions by Gender for the years 1998 and 2004

Men

5th

1998 5555thththth

2004200420042004 50th

1998 50505050thththth

2004200420042004 95th

1998 95959595thththth

2004200420042004 Age 0.031

(0.0012) 0.031

(0.0007)

0.032 (0.0007)

0.033

(0.0007)

0.051 (0.0026)

0.049

(0.0023)

Age^2/100 -0.031 (0.0015)

-0.031

(0.0008)

-0.031 (0.0009)

-0.032

(0.0008)

-0.047 (0.0032)

-0.044

(0.0027)

Level of Level of Level of Level of educationeducationeducationeducation

Level 2 -0.003 (0.0081)

0.012

(0.0067)

0.057 (0.0052)

0.046

(0.0067)

0.219 (0.0184)

0.231

(0.0213)

Level 3 0.055 (0.0073

0.039

(0.0060)

0.154 (0.0048)

0.095

(0.0060)

0.406 (0.0172)

0.315

(0.0189)

Level 4 0.117 (0.0079)

0.100

(0.0069)

0.243 (0.0051)

0.287

(0.0069)

0.491 (0.0179)

0.593

(0.0217)

Level 5 0.199 (0.0077)

0.158

(0.0060)

0.419 (0.0050)

0.303

(0.0060)

0.761 (0.0176)

0.745

(0.0190)

Level 6

0.366 (0.0158)

0.413

(0.0086)

0.589 (0.0104)

0.634

(0.0087)

0.806 (0.0359)

0.915

(0.0275)

Immigrant background

-0.185 (0.0111)

-0.127

(0.0039)

-0.149 (0.0073)

-0.122

(0.0039)

-0.123 (0.0252)

-0.120

(0.0124)

Constant 8.763 (0.0243)

8.987

(0.0153)

8.929 (0.0146)

9.151

(0.0144)

8.727 (0.0509)

8.951

(0.0480)

Pseudo R2 0.157 0.158 0.193

Women 5th

1998 5555thththth

2004200420042004 50th

1998 50505050thththth

2004200420042004 95th

1998 95959595thththth

2004200420042004

Age 0.023 (0.0009)

0.019

(0.0005)

0.020 (0.0004)

0.014

(0.0004)

0.032 (0.0022)

0.028

(0.0018)

Age^2/100 -0.023 (0.0010)

-0.018

(0.0006)

-0.019 (0.0006)

-0.013

(0.0004)

-0.033 (0.0026)

-0.028

(0.0020)

Level of Level of Level of Level of educationeducationeducationeducation

Level 2 0.016 (0.0064)

0.014

(0.0054)

0.058 (0.0038)

0.064

(0.0042)

0.118 (0.0172)

0.136

(0.0187)

Level 3 0.064 (0.0063)

0.049

(0.0048)

0.124 (0.0037)

0.097

(0.0037)

0.221 (0.0172)

0.140

(0.0164)

Level 4 0.134 (0.0059)

0.088

(0.0060)

0.211 (0.0058)

0.236

(0.0046)

0.279 (0.0159)

0.511

(0.0208)

Level 5 0.218 (0.0059)

0.191

(0.0048)

0.332 (0.0058)

0.292

(0.0037)

0.559 (0.0162)

0.522

(0.0166)

Level 6

0.334 (0.0112)

0.375

(0.0090)

0.688 (0.0180)

0.626

(0.0070)

0.801 (0.0487)

0.892

(0.0310)

Immigrant background

-0.130 (0.0084)

-0.087

(0.0087)

-0.078 (0.0050)

-0.046

(0.0034)

-0.072 (0.0225)

-0.056

(0.0098)

Constant 8.884 (0.0181)

9.151

(0.0110)

9.059 (0.0098)

9.379

(0.0088)

9.143 (0.0448)

9.389

(0.0382)

Pseudo R2 0.181 0.190 0.174

Note: Men (n =78593), women (n =104 008). Standard errors in parentheses. P<0.01 for all values. Levels of education: Level 1 - Incomplete comprehensive school (less than 9 years) (reference group), Level 2 - Comprehensive school (9 or 10 years), Level 3 - Upper secondary schooling (up to 3 years), Level 4 - Post-secondary schooling (up to 2 years), Level 5 - Post-secondary schooling (for at least 3 years), Level 6 - Completed doctoral degree. The values from 1998 are taken from Albrecht et al..

26

At the 50th percentile, women have higher returns to the lower levels (level 2 and 3) of schooling,

and men have higher returns to all three levels of post-secondary schooling (level 5, 6 and 7). The

biggest difference is at the lower level of post-secondary schooling, where a man on average has a 5

percentage unit higher rate of return to education, compared to a woman. Comparing with 1998

data, the biggest change has been at the doctoral level (level 6). In 1998, women had a 10 percentage

unit higher return to a doctoral level than men, compared to level 1, whereas in 2004, men had a

slightly higher return to this level.

At the 95th percentile, men have higher returns to all five levels of education, and the differences are

considerable at all levels, except at the doctoral level (level 6), where the difference is moderate. For

example, among people with upper secondary schooling (level 3), men have a 17.5 percentage unit

higher return to education, compared to level 1. Among those who have completed at least three

years of university (level 5), the gender difference is around 22 percentage units, compared to level 1.

Comparing with the values from 1998, the gender differences for those with a shorter post-secondary

level of schooling (level 4) have decreased greatly, and at the doctoral level (level 6), the differences

have risen somewhat. These results strongly suggest that men have higher returns to education. The

particular high returns to education at the 95th percentile faced by males, is an important component

for understanding the concept of glass ceiling.

The importance of age/experience as a wage determinant is increasing, and age/experience is overall

more important as a determinant for wage than for men than for women. For example, for a 30-year-

old at the 5th percentile, a man will have a 37% higher annual wage increase than a woman. In 1998,

the equivalent figure was 26%. For a 30-year-old at the 95th percentile, a man will have a 45% higher

annual increase than a woman. For 1998, the figure was 40%. Thus, age seems to be more important

when explaining the gender wage differences at the upper part of the wage distribution, than at the

lower parts of the distribution.

For women, this decrease is somewhat larger than for men. Since age is used as an approximation of

experience, a decreasing importance of age also implies that age/experience is becoming less

important as a wage determinant. The most interesting feature of the age/experience variable is that

27

women have considerable lower returns to age/experience compared to men. For both men and

women, age/experience is of greatest importance at the 95th percentile.

When it comes to immigration background, there is a clear difference between immigrant men and

immigrant women. Trough all the percentiles, and in both the 1998 and 2004 data, immigrant men

face a greater wage penalty than immigrant women. For male immigrants, the wage penalty

decreases, however slightly, when moving up the wage distribution. For female immigrants, the

wage penalty is lowest at the 50th percentile and highest at the 5th percentile. The immigrant

coefficient has however decreased since 1998, which is a positive indication that the Swedish labor

market has become somewhat less unequal for immigrants.

The results presented above clearly show that a large part of the gender gap can be explained by

gender wage differences in returns to the basic productivity characteristics. The largest gender

differences were found in the 95th percentile, i.e., among high-income takers. The differences in

returns to education, at this part of the wage distribution, were generally large, except when it came

to the returns to a doctoral degree, where the differences were comparably low. At the 5th percentile

the gender differences were small, and at the 50th percentile, certain levels of education showed

larger gender differences. Over the three levels of the income distribution, the wage penalty for

immigration background varied between 4-7.6 percentage units, with the highest coefficient in the

50th percentile. The return to age/experience increased though the distribution, and was the highest

in the 95th percentile. Women were also found to have lower return to age/experience than men.

5.3.1 5.3.1 5.3.1 5.3.1 QuQuQuQuaaaannnntile Regressions usingtile Regressions usingtile Regressions usingtile Regressions using an an an an Extended Model Extended Model Extended Model Extended Model

The results presented earlier clearly suggests, that when controlling for basic productivity

characteristics, there is a substantial wage gap between men and women, and that the gap is

widening towards the top of the income distributions. Comparisons with data from earlier years also

prove a widening of the gap at all levels of the income distribution. The basic control variables do

not, however, cover all the factors that explain men’s and women’s productivity. In this section, I

28

will concentrate on finding additional explanations of the gender wage inequalities, and to some

extent compare the results with data from 1998. These tests will be conducted at the 5th, 50th and 95th

percentiles. Table 7 below compares the results from the earlier regressions using the basic control

variables with the results from the extended model using additional explanatory variables. A

complete table of the results is presented in Appendix B, Table B3.

Table 7. Quantile Regressions using an Extended Model for the years 1998 and 2004

Gender wage gap 5th - 1998

5555thththth ---- 2004200420042004

50th - 1998

50505050thththth ---- 2004200420042004

95th-- 1998

95959595th th th th ---- 2004200420042004

OLS - 1998

OLS OLS OLS OLS ---- 2004200420042004

Raw gender wage gap -0.048 -0.060 -0.133 -0.156 -0.387 -0.416 -0.162 -0.184

Gender dummy with basic control variables

-0.059 (0.0021)

-0.071

(0.0014)

-0.158 (0.0013)

-0.170

(0.0010)

-0.293 (0.0046)

-0.326

(0.0042)

-0.174 (0.0015)

-0.192

(0.0012)

Gender gap with basic control variables and field of education

-0.053 (0.0026)

-0.071

(0.0017)

-0.134 (0.0017)

-0.144

(0.0012)

-0.254 (0.0053)

-0.288

(0.0043)

-0.153 (0.0017)

-0.167

(0.0013)

(0.3807)

Gender gap with basic control variables, field of education and sector

-0.050 (0.0027)

-0.063

(0.0016)

-0.113 (0.0014)

-0.116

(0.0012)

-0.206 (0.0049)

-0.236

(0.0037)

-0.132 (0.0017)

-0.138

(0.0013)

(0.4171)

Gender gap with basic control variables, field of education, sector and family situation.

- -0.067

(0.0017)

(0.189)

- -0.117

(0.0012)

(0.249)

- -0.237

(0.0037)

(0.337)

- -0.139

(0.0013)

(0.421)

Gender gap with basic control variables, field of education, sector, family situation & full-time work.

- -0.051

(0.0015)

(0.200)

- -0.106

(0.0012)

(0.2520)

- -0.223

(0.0039)

(0.3386)

- -0.125

(0.0014)

(0.425)

Note: Men (n =78593), women (n =104 008). Standard errors in parentheses. P<0.01 for all values. Fields of education: 0) General education (control group) 1) Pedagogical- & teacher training, 2) Humanities & arts, 3) Social science, jurisprudence, business & administration, 4) Natural science, mathematics & computer, 5) Technology & production, 6) Agriculture, forestry & veterinary services, 7) Healthcare, medical service & social care, 8) Services. The values from 1998 are taken from Albrecht et al..

The first additional control variable added to the model is field of education, which is divided into 9

categories. When adding this control variable, the gender gap decreases at the 50th and 95th percentiles,

with the greatest decrease at the 95th percentile. On the 5th percentile, the gender wage gap is exactly the

same as when using the basic control variables. It should be reasonable to believe that the gender that

has the highest return to a certain skill also works within that field. Table 8 displays the relation between

the gender domination of a field, and which group has the highest returns to a certain field, at the 5th,

50th and 95th percentile. Both field 1 and 2 are dominated by women, and women also have the highest

29

returns to their skills at all three levels, compared to field 0. Field 3 is dominated by women, but men

have the highest returns to skills throughout the wage distribution. In field 4, 5 and 6, men dominate the

employees, but they only have the highest returns to skills at the 5th percentile of the wage distribution.

Women have higher returns to the other levels of the distribution. In field 6, an explanation could be

that men in that category tend to work within agriculture, with relatively low incomes, and women tend

to be veterinarians, with a higher income level. Field 7 is dominated by women, but men have the

highest returns to their skills. One probable explanation is that assistant nurses have low wages, and tend

to be women. The higher levels of the wage distribution are represented by doctors, of which a majority

is men. The last field, services is dominated by men, and men have the highest returns to skills at the 5th

and the 50th percentiles. Women have the highest returns at the 95th percentile. To summarize, there

seems to be a certain variation when it comes to gender and wage level, in the different fields studied in

this paper.

Table 8. Field of Education - Gender Domination and Returns to Skills in 2004

Note: Men (n =78593), women (n =104 008), Field 0, general education, is used as reference group.

The next variable added to the model is sector of employment. The importance of sector has already

been explored in part 5.1 and 5.2. In the quantile regressions of this section, adding sector to the model

gives a lower gender coefficient of one or a few percent, depending on the percentile.

Moving on to the family variables, adding marital status, single-parenthood and having children in the

ages 1-3 or 4-6, the gender wage gap actually increases at al three levels, if only very slightly. Separate

OLS and quantile regressions for men and women using all control variables were carried out to get an

idea of the coefficient for the individual family variables. These regressions indicate that married men

Field of education Gender domination

5th 50th 95th

1) Pedagogical- & teacher training Women W W W 2) Humanities and arts Women W W W 3) Social science, law, economics, administration Women M M M 4) Natural science, mathematics & computer Men M W W 5) Technology & production Men M W W 6) Agriculture, forestry & veterinary services Men M W W 7) Healthcare, medical services & social care Women W M M 8) Services Men M M W

30

have between 6-10% higher wages than unmarried men. For women, being married has almost no effect,

or possibly a negative one. The variable single-parenthood seems to yield inconclusive results. Among

men, single parents seem to make slightly more than non-single parents. Having small children in the

ages 1-3 or 4-6 years old yields a slightly positive coefficient for both women and men. The figures

probably reflect the presumption that people tend to wait with children until they have a stable

economy.

Finally, adding full-time work to the model yielded a still lower gender coefficient. Table B3 indicates

that working full-time has a considerable effect upon the wages of both men and women, why adding

the variable to the model was expected to yield a lower gender coefficient.

To summarize the results, adding more explanatory variables to the basic model indeed yield a lower

gender coefficient. Still, it should be remembered that the additional explanatory variables added to the

basic model struggle with endogeneity problems of varying degree, why the gender coefficients should

be interpreted with some caution. The results from the extended model still implies that the using the

basic control variables only, will not entirely cover all the aspects of the gender wage gap. Because of the

limited nature of this paper, it has not been possible to test other aspects of the determinants of the

gender wage gap. It can however be stated Sweden has an unexplained gender wage gap, that increases

throughout the wage distribution.

6666. Discussion. Discussion. Discussion. Discussion

The results in the previous section confirm earlier findings of the presence of a glass ceiling in

Swedish labor market. One striking outcome is however, that the gender wage gap is increasing at all

levels of the wage distribution, which was not recorded by the previous study by Albrecht et al..

Thus study thus sheds light on the more recent trend of increasing gender wage differences among

low-income takers. Edin & Rickardsson conclude in a study that the overall wage compression

during the 60s and 70s worked in favor for a decreased gender wage gap, since women, more often

than men, worked in the low-pay sectors. Smaller overall wage inequalities thus imply smaller

31

gender wage differences.28 Blau & Kahn draw similar conclusions when using wage structure as an

explanation of the gender wage gap. They state that decentralized wage setting works to increase

overall wage inequality, which in turn works for a reduction of gender wage differences.29 A recent

report by Nordström-Skans et al. states that the overall wage dispersion in Sweden has increased

since the 80’s, mostly due to the fact that companies are becoming more specialized on a certain level

of competence. The changed structure of competence could entail that a company employ mostly

high-skilled persons, placing the company among those that have a very high wage level.30 When the

overall wage inequality is increasing, women are the ones who loose the most. The conclusions of

the above mentioned studies could serve to understand some of my results. If there is a general trend

of increasing wage dispersion, this will also reflect upon the gender differences between men and

women, which could serve as an explanation the increasing gender wage differences recorded in this

paper.

Another precarious issue raised by the results of this paper, is of course the fact that, in spite of being

in the forefront of gender equity, Sweden has indeed a large gender wage gap. Generous parental

leave policies are generally considered to be an important component of a well-functioning modern

welfare state. The general trend among the richer European countries is that of longer periods of

leave, aimed at encouraging men to take more active part in the rearing of children. Such policies

facilitate for, and encourage, women who decide to continue a professional career after childbirth.

An article by Ruhm observes the presence of parental leave policies raises the labor force

participation by 3-4 %. However, Ruhm blames the extensive family policies for the wage differences

in countries like, for example, Sweden. The argument is that in a country where family policies are

lacking, women will find alternative ways of combining work and childrearing. For example, private

nannies and maids could be hired, to give women the possibility of returning to work a lot earlier

after childbirth, than Swedish women do. In minimizing the absence from work, women will not fall

behind in the wage league, and they will not loose as much job-specific knowledge, as women that

stay home for a longer time after childbirth. Instead of having a common family policy, each

28 Edin & Richardson, (2002)

29 Blau & Kahn (1996)

30 Nordström Skans et al., (2006)

32

employer could negotiate maternity leave conditions. Ruhm concludes that countries with policies

allowing a longer period of parental leave, will see a higher female employment, but these women

also face larger cutbacks in wage due to absence, than women in countries with less generous family

policies.31 This relation has been pointed out in several studies, where the more generous parental

policies of many European countries have been compared to the U.S., where such policies have only

existed since the 70s, and are very limited in scope. Although the overall wage gap between high-

income takers and low-income takers is relatively wide in the U.S., the wage gap between men and

women at similar positions is much smaller than in many European countries.32 Other studies,

following the arguments of Ruhm, state that the Nordic model does favor female labor force

participation, but women also loose career ground from the long periods of parental leave.33 Yet, it

cannot be concluded that generous family policies only act to weaken women’s positions on the labor

markets. In comparisons to other Scandinavian countries, it has been found that the generous

Swedish welfare system actually has worked to enforce women’s labor market positions.34 The

extension of the Swedish parental leave scheme, reserving three months of parental leave exclusively

for fathers, is one such step to increase gender equality when it comes to childcare.

7777. Conclusion. Conclusion. Conclusion. Conclusion

After analyzing a number of productivity characteristics, mainly through quantile regressions, this

paper can conclude that the glass ceiling effect, which has been observed by previous studies, appears

to persist and to increase. Moreover, and contrary to earlier findings, the gender wage gap is

increasing over time, throughout the conditional wage distribution. Thus, the gender wage

differences are increasing among both high-income takers and low-income takers. This development

seems to be linked to an overall wage dispersion, recorded in several recent studies.

31 Ruhm, (1998)

32 Datta Gupta et al.,(2003),

33 Datta Gupta et al.,(2006)

34 Rönsen & Sundström, (1996), Albrecht, et al.,(1999)

33

Another finding of this study is that the wage structures in the public and private sectors is one

important explanation for both the observed, and the unobserved, gender wage gap. There were

considerable differences between the sectors when it came to returns to education and other

productivity characteristics. Surprisingly, within the public sector, the unexplained gender

differences were found at the same time the lowest, among low-income takers, and the highest,

among high-income takers. Thus it can be concluded, that the glass-ceiling effect, as defined in this

paper, is higher in the public sector than in the private sector.

The trend of increasing wage differences in general, and gender wage differences in particular,

should be alarming to policy makers endeavoring a gender equal society. There is an immense

number of studies recording unexplained, and explained, gender wage differences. Perhaps the most

important difference between male and female labor market behavior in Sweden is that women tend

to take much longer periods of parental leave than men. Childrearing, for women, often also result in

a number of years of part-time work. Moreover, women are thought to choose to work within fields

that could more easily be combined with having children. There is a need for more economic

research on the structure of incentives concerning children and parental leave. Important issues are

what could be done to increase male participation in childrearing, and how the both horizontal and

vertical divisions of the labor market could be changed.

34

ReferencesReferencesReferencesReferences

Albrecht, James W., Edin Per Anders, Sundström Marianne, & Vroman Susanne B., (1999), “Career Interruptions and Subsequent Earnings: A Reexamination Using Swedish Data”, The Journal of Human Resources, Vol. 34, No. 2, pp. 294-311. Albrecht, James W., Björklund, Anders & Vroman, Susan B., (2003), "Is there a Glass Ceiling in Sweden?" Journal of Labor Economics, Vol. 21, No.1, pp. 145-177 Bihagen, Erik & Ohls, Marita, (2006), “The glass ceiling – where is it? Women's and men's career prospects in the private vs. the public sector in Sweden 1979–2000”, The Sociological Review, Vol. 54, No.1, pp. 20-47. Björklund, Anders, Edin, Per-Anders, Holmlund, Bertil & Wadensjö, E., (1996), Arbetsmarknaden, SNS Förlag Blau, Francine & Kahn, Lawrence, (1992), “The Gender Earnings Gap: Learning from International Comparisons”, American Economic Review, American Economic Association, vol. 82 No. 2, pp. 533-38 Blau, Francine & Kahn, Lawrence, (1996), “Wage Structure and Gender Earnings Differentials: an International Comparison”, Economica, Vol. 63, pp. 29-62 Blau, Francine & Kahn, Lawrence, (2000), “Gender Differences in Pay”, Journal of economic perspectives, Vol.14, pp. 75-99 Blau, Francine & Kahn, Lawrence, (2003), “Understanding International Differences in the Gender Pay Gap”, Journal of Labor Economics, Vol. 21, No.1, pp. 106-144 Bonjour, Dorothe & Gerfin, Michael, (2001), “The Unequal Distribution of Unequal Pay - An Empirical Analysis of the Gender Wage Gap in Switzerland”, Empirical Economics, 2001, 26, pp. 407-427 Buchinsky, Moshe, (1998), “Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research”, Journal of Human Resources , Vol 33, No.1, pp. 88-126 Datta Gupta, Nabanita, Oaxaca, Ronald L. & Smith, Nina, (2003), "Swimming Upstream, Floating Downstream: Comparing Women's Relative Wage Position in the U.S. and Denmark", IZA Discussion Paper No. 756. Datta Gupta, Nabanita, Smith, Nina, & Verner, Mette, (2006), ”Child Care and Parental Leave in the Nordic Countries: A Model to Aspire to?, IZA discussion paper No.2014 De la Rica, Sara, Dolado, J., Llorens, V., (2005), “Ceiling and Floors: Gender Wage Gaps by Education in Spain”, IZA discussion paper No.1483 Edin, Per-Anders & Fredriksson, Peter, (2000), LINDA – Longitudinal INdividual DAta for Sweden, Working Paper No.19, Nationalekonomiska Institutionen, Uppsala Universitet

35

Edin, Per-Anders & Richardson, Katarina, (2002) “Swimming with the Tide: Solidary Wage Policy and the Gender Earnings Gap”, Scandinavian Journal of Economics, Vol. 104, No 1, pp. 49-67. Fitzenberger, B. & Wunderlich, G., (2002), “Gender Wage Differences in West Germany: a Cohort Analysis”, German Economic Review, Vol.3, pp. 379-414 Kee, Hiau Joo, (2006), “Glass Ceiling or Sticky Floor? Exploring the Australian Gender Pay Gap”, The Economic Record, Vol. 82, No. 259, pp.408-427 Koenker, Roger & Bassett, Gilbert, (1978), “Regression Quantiles”, Econometrica, vol. 46, No.1 Nordström Skans, Oskar, Edin, Per-Ander, & Holmlund, Bertil, (2006), “Löneskillnader i svenskt näringsliv 1985-2000”, IFAU report 2006:8 Ruhm, Christopher J., (1998), “The Economic Consequences of Parental Leave Mandates: Lessons from Europe”, The Quarterly Journal of Economics, Vol. 113, No. 1, pp. 285-317 Rönsen, Marit & Sundström, Marianne, (1996), “Maternal employment in Scandinavia: A Comparison of the after-birth employment activity of Norwegian & Swedish women”, Journal of population economics, vol.9, No. 3, pp. 267-285 Studymaterial, Statistik B/Tillämpad Ekonometri, Uppsala Universitet, VT 2005

36

AppendixAppendixAppendixAppendix A A A A –––– Data D Data D Data D Data Description escription escription escription

Age Age Age Age

Age is measured at the 31st of December the income year.

Age of childrenAge of childrenAge of childrenAge of children

The age of children is divided into two categories, children aged 1-3, and children aged 4-6 years of age.

Children older than 6 years are supposed to start school, which is assumed to given the parents more time to

work.

Extent of workExtent of workExtent of workExtent of work

The dummy variable describes the extent of work in percent, ranging from no work at all, 0%, to full time

work, 100%. Individuals that work at a rate between 95% and 100% will be given a 1, and individuals that

work at less than a 95% rate are given a 0.

Field of highest achieved educationField of highest achieved educationField of highest achieved educationField of highest achieved education

The field of education is divided into nine categories with a number of subcategories. Here, only the primary

division of categories has been used

0 General educations 1 Pedagogical- & teacher education 2 Humanities & arts 3 Social science, jurisprudence, business, administration 4 Natural science, mathematics & computer 5 Technology & production 6 Agriculture, forestry & veterinary services 7 Healthcare, medical service & social care 8 Services

Level Level Level Level of educationof educationof educationof education

The LINDA database for 2004 contains information on six levels of education. For the years used by Albrecht et

al., LINDA had seven levels of education. The difference between the sets of data, is that for earlier years, a

distinction between two and three years of upper secondary education was made. For the more recent years,

this distinction has been removed since almost all upper secondary education lasts for three years.

37

Level 1 Incomplete comprehensive school (less than 9 years) Level 2 Comprehensive school (9 or 10 years) Level 3 Upper secondary school (up to 3 years) Level 4 Post-secondary schooling (up to 2 years) Level 5 Post-secondary schooling (for at least 3 years) Level 6 Completed doctoral degree

FamilyFamilyFamilyFamily status status status status

Family status is divided into a five category dummy variable.

1 Partner : Married couples, couples in partnership and cohabiting couples with common children 2 Single parents, including cohabitants without common children 3 Children 4 Singles, including cohabiting couples without common children, and children without a connection to anyone at

the address 5 Individuals with incomplete or contradictory information (code 000)

Gender Gender Gender Gender

Gender is caught by a dummy variable, taking the value 0 for men, and 1 for woman.

Monthly wageMonthly wageMonthly wageMonthly wage

The monthly wage, or the squared monthly wage, is used as measurement of wage. The wage variable uses data

on the wage of the individual, had he or she worked full time. This will facilitate comparisons between full-

time and part-time workers.

Natal countryNatal countryNatal countryNatal country

The individual’s country of origin was used to estimate possible presence of wage differences due to

discrimination against immigrants. Individuals born in Sweden, Norway, Finland or Denmark were all

classified as “Swedes”, and people from any of these countries are thus assumed to face the same labor market

conditions. People born in any other country are classified as “foreigner”.

SectorSectorSectorSector

The sector of employment is divided into five different categories; worker in the private sector (Ar), civil

servant in the private sector (Tj), county council sector (Lt), state sector (St), local municipality sector (Pk).

Workers in the private sectors, and civil servant in the private sector, are the categories counted to the private

sector. The others belong to the public sector. The sector affiliation is caught by a dummy taking the value 1 if

private sector.

38

Appendix BAppendix BAppendix BAppendix B –––– D D D Data Pata Pata Pata Presentationresentationresentationresentation

Table B 1. Estimated Gender Gap 2004, complete table 5555thththth 10th 25th 50505050thththth 75th 90th 95959595thththth OLS

Raw gender wage gap -0.060 -0.076 -0.109 -0.156 -0.229 -0.370

-0.416

Gender gap with basic control variables

-0.071

(0.0014)

-0.083 (0.0010)

-0.123 (0.0010)

-0.170

(0.0010)

-0.239 (0.0165)

-0.301 (0.0027)

-0.326

(0.0042)

-0.192 (0.0012)

Age 0.024

(0.0005)

0.023 (0.0003)

0.021 (0.0003)

0.021

(0.0003)

0.022 (0.0005)

0.025 (0.0009)

0.029

(0.0015)

0.026 (0.0004)

Age^2/100 -0.023

(0.0005)

-0. 023 (0.0004)

-0.021 (0.0004)

-0.020

(0.0004)

-0.021 (0.0006)

-0.023 (0.0010)

-0.026

(0.0016)

-0.025 (0.0004)

Level of education:Level of education:Level of education:Level of education:

Level 2 0.014

(0.0047)

0.020 (0.0033)

0.039 (0.0032)

0.055

(0.0035)

0.082 (0.0054)

0.123 (0.0089)

0.174

(0.0138)

0.071 (0.0039)

Level 3 0.046

(0.0043)

0.054 (0.0030)

0.076 (0.0029)

0.092

(0.0031)

0.122 (0.0048)

0.162 (0.0078)

0.208

(0.0122)

0.113 (0.0035)

Level 4 0.099

(0.0051)

0.123 (0.0036)

0.186 (0.0034)

0.231

(0.0037)

0.376 (0.0058)

0.481 (0.0094)

0.543

(0.0146)

0.299 (0.0042)

Level 5 0.180

(0.0043)

0.205 (0.0030)

0.246 (0.0029)

0.292

(0.0031)

0.379 (0.0049)

0.524 (0.0079)

0.617

(0.0124)

0.342 (0.0035)

Level 6 0.412

(0.0068)

0.463 (0.0048)

0.541 (0.0046)

0.630

(0.0050)

0.759 (0.0067)

0.852 (0.0127)

0.886

(0.0196)

0.647 (0.0056)

Immigration background

-0.104

(0.0027)

-0.099 (0.0019)

-0.083 (0.0018)

-0.073

(0.0019)

-0.067 (0.0030)

-0.071 (0.0049)

-0.073

(0.0076)

-0.088 (0.0021)

Constant 9.120

(0.0104)

9.172 (0.0072)

9.278 (0.0068)

9.402

(0.0071)

9.510 (0.0109)

9.587 (0.0183)

9.599

(0.0293)

9.301 (0.0080)

Note: Men (n =78593), women (n =104 008). Standard errors in parentheses. P<0.01 for all variables

39

Table B 2. Quantile Regressions by Gender 2004, complete table 5555thththth 10th 25th 50505050thththth 75th 90th 95959595hhhh OLS MenMenMenMen

Age 0.031

(0.0007)

0.030 (0.0007)

0.030 (0.0006)

0.033

(0.0007)

0.033 (0.0010)

0.039 (0.0014)

0.049

(0.0023)

0.036 (0.0007)

Age^2/100 -0.031

(0.0008)

-0.030 (0.0008)

-0.029 (0.0007)

-0.032

(0.0008)

-0.030 (0.0011)

-0.034 (0.0017)

-0.044

(0.0027)

-0.035 (0.0008)

Level of Level of Level of Level of educationeducationeducationeducation

Level 2 0.012

(0.0067)

0.020 (0.0064)

0.038 (0.0057)

0.046

(0.0067)

0.102 (0.0094)

0.181 (0.0138)

0.231

(0.0213)

0.078 (0.0065)

Level 3 0.039

(0.0060)

0.046 (0.0057)

0.073 (0.0050)

0.095

(0.0060)

0.164 (0.0084)

0.262 (0.0122)

0.315

(0.0189)

0.134 (0.0058)

Level 4 0.100

(0.0069)

0.141 (0.0066)

0.204 (0.0058)

0.287

(0.0069)

0.440 (0.0097)

0.562 (0.0141)

0.593

(0.0217)

0.330 (0.0067)

Level 5 0.158

(0.0060)

0.186 (0.0057)

0.238 (0.0051)

0.303

(0.0060)

0.510 (0.0085)

0.685 (0.0125)

0.745

(0.0190)

0.389 (0.0059)

Level 6 0.413

(0.0086)

0.464 (0.0082)

0.544 (0.0073)

0.634

(0.0087)

0.791 (0.0023)

0.860 (0.0180)

0.915

(0.0275)

0.663 (0.0085)

Immigrant background

-0.127

(0.0039)

-0.122 (0.0038)

-0.123 (0.0033)

-0.122

(0.0039)

-0.116 (0.0055)

-0.108 (0.0081)

-0.120

(0.0124)

0.135 (0.0039)

Constant 8.987

(0.0153)

9.033 (0.0147)

9.094 (0.0126)

9.151

(0.0144)

9.178 (0.0120)

9.082 (0.0301)

8.951

(0.0480)

9.038 (0.0140)

WomenWomenWomenWomen

Age 0.019

(0.0005)

0.017 (0.0004)

0.016 (0.0003)

0.014

(0.0004)

0.015 (0.0005)

0.021 (0.0010)

0.028

(0.0018)

0.018 (0.0004)

Age^2/100 -0.018

(0.0006)

-0.015 (0.0005)

-0.014 (0.0004)

-0.013

(0.0004)

-0.014 (0.0006)

-0.022 (0.0012)

-0.028

(0.0020)

-0.017 (0.0005)

Level of Level of Level of Level of educationeducationeducationeducation

Level 2 0.014*

(0.0054)

0.019 (0.0040)

0.039 (0.0038)

0.064

(0.0042)

0.081 (0.0062)

0.095 (0.0113)

0.136

(0.0187)

0.070 (0.0046)

Level 3 0.049

(0.0048)

0.060 (0.0036)

0.082 (0.0034)

0.097

(0.0037)

0.101 (0.0054)

0.099 (0.0100)

0.140

(0.0164)

0.098 (0.0040)

Level 4 0.088

(0.0060)

0.106 (0.0045)

0.159 (0.0043)

0.236

(0.0046)

0.319 (0.0069)

0.433 (0.0126)

0.511

(0.0208)

0.261 (0.0051)

Level 5 0.191

(0.0048)

0.216 (0.0036)

0.254 (0.0034)

0.292

(0.0037)

0.330 (0.0055)

0.411 (0.0100)

0.522

(0.0166)

0.311 (0.0041)

Level 6 0.375

(0.0090)

0.451 (0.0067)

0.539 (0.0064)

0.626

(0.0070)

0.770 (0.0103)

0.858 (0.0189)

0.892

(0.0310)

0.643 (0.0077)

Immigrant background

-0.087

(0.0087)

-0.079 (0.0021)

-0.059 (0.0020)

-0.046

(0.0034)

-0.047 (0.0032)

-0.050 (0.0059)

-0.056

(0.0098)

-0.055 (0.0024)

Constant 9.151

(0.0110)

9.188 (0.0082)

9.276 (0.0076)

9.379

(0.0088)

9.471 (0.0119)

9.473 (0.0223)

9.389

(0.0382)

9.322 (0.0089)

Note: Men (n =78593), women (n =104 008). Standard errors in parentheses. *= p<0.10, p<0.01 for all other variables.

40

Table B 3. OLS and QR Results for all Variables in 2004 Men Women

5th 50th 95th OLS 5th 50th 95th OLS

Age -0.021 (0.0009)

0.018 (0.0007)

0.024 (0.0022)

0.021 (0.0007)

0.018 (0.0006)

0.014 (0.0004)

0.010 0.0013

0.016 (0.0004)

Age^2 -0.021 (0.0009)

-0.014 (0.0008)

-0.015 (0.0025)

-0.017 (0.0008)

-0.016 (0.0006)

-0.012 (0.0005)

-0.006 (0.0015)

-0.014 (0.0005)

Level of Level of Level of Level of educationeducationeducationeducation

Level 2 0.013* (0.0065)

0.080 (0.0059)

0.232 (0.0190)

0.101 (0.0061)

0.018 (0.0053)

0.066 (0.0041)

0.151 (0.0122)

0.073 (0.0043)

Level 3 0.039 (0.0070)

0.159 (0.0063)

0.416 (0.0206)

0.203 (0.0064)

0.054 (0.0051)

0.119 (0.0039)

0.197 (0.0118)

0.127 (0.0041)

Level 4 0.124 (0.0081)

0.367 (0.0073)

0.7141 (0.0237)

0.416 (0.0075)

0.104 (0.0065)

0.253 (0.0050)

0.506 (0.015)

0.282 (0.0053)

Level 5 0.199 (0.0075)

0.487 (0.0069)

0.906 (0.023)

0.551 (0.0070)

0.184 (0.0055)

0.351 (0.0042)

0.639 (0.013)

0.385 (0.0044)

Level 6 0.458 (0.0098)

0.774 (0.0090)

1.063 (0.0297)

0.822 (0.0092)

0.394 (0.0092)

0.652 (0.0072)

0.992 (0.022)

0.680 (0.0076)

Immigration background

-0.134 (0.0038)

-0.118 (0.0035)

-0.102 (0.0111)

-0.136 (0.0036)

-0.085 (0.0028)

-0.051 (0.0022)

-0.022 (0.0065)

-0.063 (0.0023)

Field of Field of Field of Field of educationeducationeducationeducation

Field 1 -0.029 (0.0067

-0.182 (0.0060)

-0.478 (0.0197)

-0.229 (0.0062)

0.016 (0.0037)

-0.078 (0.0028)

-0.299 (0.0084)

-0.109 (0.0029)

Field 2 -0.074 (0.0076)

-0.158 (0.0071)

-0.232 (0.0232)

-0.188 (0.0073)

-0.041 (0.0049)

-0.057 (0.0038)

-0.107 (0.0118)

-0.082 (0.0040)

Field 3 -0.010 (0.0052)

0.014 (0.0047)

0.0842 (0.0153)

0.0145 (0.0048)

-0.011 (0.0029)

0.004** (0.0022)

0.025 (0.0066)

0.0061 (0.0023)

Field 4 -0.047 (0.0075)

-0.0478 (0.0069)

-0.185 (0.0225)

-0.088 (0.0070)

-0.040 (0.0060)

0.012 (0.0047)

-0.055 (0.0140)

-0.010* (0.0049)

Field 5 0.001*** (0.0045)

-0.019 (0.0041)

-0.152 (0.0134

-0.052 (0.0042)

-0.015 (0.0043)

0.017 (0.0033)

-0.006 (0.0098)

0.013 (0.0034)

Field 6 -0.040 (0.0083)

-0.110 (0.0076)

-0.271 (0.0244)

-0.153 (0.0078)

-0.066 (0.0086

-0.039 (0.0067)

-0.073*** (0.0199)

-0.0574 (0.0070)

Field 7 -0.014* (0.0062)

-0.000*** (0.0057)

0.088 (0.0172)

0.022 (0.0058)

0.027 (0.0029)

-0.007 (0.0022)

-0.018 (0.0065)

-0.012 (0.0023)

Field 8 -0.012*** (0.0060)

-0.059 (0.0053)

-0.159 (0.0171)

-0.087 (0.0055)

-0.024 (0.0036)

-0.043 (0.0028)

-0.074 (0.0084)

-0.053 (0.0029)

Sector 0.055 (0.0030)

0.149 (0.0024)

0.289 (0.0084)

0.174 (0.0025)

0.009 (0.0018)

0.088 (0.0013)

0.252 (0.0038)

0.108 (0.0014)

Marital status 0.066 (0.0032)

0.065 (0.0029)

0.094 (0.0085)

0.081 (0.0030)

0.012 (0.0025)

0.005 (0.0020)

-0.002*** (0.0058)

0.008 (0.0021)

Single parent 0.048 (0.0068)

0.026 (0.0062)

-0.005*** (0.0192)

0.032 (0.0063)

-0.005*** (0.0032)

-0.002*** (0.0025)

-0.001*** (0.0074)

-0.005 (0.0026)

Number of children 1-3

0.010 (0.0028)

0.004*** (0.0025)

-0.009*** (0.0081)

0.006 (0.0026)

0.014 (0.0022)

0.013 (0.0017)

0.013 (0.0050)

0.018 (0.0017)

Number of children 4-6

0.004*** (0.0027)

0.0076 (0.0025)

0.016* (0.0078)

0.012 (0.0025)

0.007 (0.0020)

0.008 (0.0015)

-0.000*** (0.0046)

0.010 (0.0016)

Full time 0.089 (0.0039)

0.067 (0.0033)

0.014*** (0.0099)

0.072 (0.0034)

0.044 (0.0016)

0.030 (0.0012)

0.057 (0.0032)

0.043 (0.0012)

Constant 9.027 (0.0160)

9.158 (0.0137)

9.146 (0.0470)

9.043 (0.0140)

9.144 (0.0115

9.320 (0.0087)

9.544 (0.0277)

9.261 (0.0091)

Pseudo R2 0.194 0.222 0.279 0.387 0.205 0.230 0.308 0.385

Note: Men (n =78593), women (n =104 008). Standard errors in parentheses. *= p<0.05 , ** = p<0.10 , ***= p>0.10, all other results p<0.01