public-private sector wage differentials in germany: evidence … · 2016. 2. 27. · 1...

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Public-private sector wagedifferentials in Germany:

evidence from quantile regression+

Blaise Melly

University of St. Gallen, Swiss Institute for International Economics andApplied Economic Research (SIAW), Dufourstrasse 48, 9000 St. Gallen,

Switzerland, email: [email protected],www.siaw.unisg.ch/lechner/melly.

April 2002

Abstract

This paper measures the differences in earnings distributions between publicsector and private sector employees in Germany in 2000. OLS resultssuggest that wages are lower in the public sector for men but higher forwomen. The use of quantile regressions indicates that the public sector wagepremium is highest at the lower end of the wage distribution and thendecreases monotonically as we move up the wage distribution. Separateanalyses by work experience and educational groups reveal that the mostexperienced employees and those with basic schooling do best in the publicsector.

Keywords: Quantile regression, public-private wage differential, Germany.

JEL classification: J3, J45.

+ I am grateful to Michael Lechner, Ruth Miquel and seminar participants at University of Konstanz for helpfulcomments. I thank Michael Lechner and the Centre for European Economic Research (ZEW), Mannheim, forletting me work with the full sample of the German Socio-Economic Panel (GSOEP).

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1. Introduction

Public sector pay has always attracted policy attention. Obviously the size of the public sectorwage bill has implications for both monetary and fiscal policy. The government remains byfar the largest employer in Germany. In 2000, 4.91 million people or 13.4 percent of the laborforce received his wage or salary from the public sector (Federal Statistical Office 2001).Furthermore, the wage settlements in the public sector could have a substantial impact overthose in the private sector. Because of this spillover effect, the existence of a public sectorwage premium may induce private sector employers to pay higher wages to their employees.The concern is that such general wage increases can jeopardize competitiveness in the globaleconomy and further fuel inflation.

There are a number of reasons that earnings differential between the private and the publicsector could exist. The public sector is subject to political constraints and not to profitconstraints. Therefore the political system may have different objectives from those of theprivate sector. Issues of pay equity and fairness can survive in the political market place morethan in the economic market place. Governments are also under pressure to be a modelemployer and not pay low wages to its less skilled workforce. Similarly, voters seem to refusethat high-level officials receive comparable remuneration to the high salaries of the privatesector. Nevertheless, the pursuit of theses equity goals could have a serious impact on theefficiency of the labor market. If the government pays too much, employees in the privatesector may decide to queue for relatively high-paying jobs in the public sector. Moreover, thispolicy leads to higher taxes or budget deficits. If the public sector pays wages that are too low,it will not find skilled and loyal employees. The consequences will be public services of poorquality.

Given these differences in the wage setting procedures and the possible consequences forthe labor market, many researchers have sought to ascertain whether an identical employeeworking in the same job in the public and in the private sector would earn the same or adifferent amount. Early research comparing the earnings of public sector employees has beenundertaken in the United States by Smith (1976 and 1977). She found that rates of pay werehigher for public sector than private sector employees and that the earnings premium waslarger for female than for male public sector employees. Subsequent research has taken up thesame question as Smith and confirmed her findings. Ehrenberg and Schwarz (1986) andGregory and Borland (1999) have surveyed this voluminous literature. Such wagecomparisons using German data are not as voluminous. Dustmann and Van Soest (1997) useddata from the German Socio-Economic Panel for the years 1984-93 to analyze developmentsand differences in public and private sector wage distributions. They found that conditionalwages are higher in the private sector for males but higher in the public sector for females.Dustmann and Van Soest (1998) estimated switching regression models for males and modelsthat endogenise education. Their later results are stronger than the results for men that theyreported in 1997.

Poterba and Rueben (1995) were the first to apply quantile regression to study public-private wage differentials. This is a natural arena for quantile regression, since there is asuspicion that the public sector compresses the distribution of earnings of employees who

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work in that sector relative to private sector employees. Therefore, the least squares estimateof the mean public sector wage premium gives an incomplete picture of the conditionaldistribution. Evidence to this effect is available for Canada (Mueller 1998), UK (Disney andGosling 1998) and Zambia (Nielsen and Rosholm 2001). Quantile regression has apparentlynot yet been applied to study the wage structure in the private and public sectors in Germany.This is the object of this paper.

Section 2 provides a brief introduction to quantile regression methods. Section 3describes the data set along with some descriptive statistics. The two following sectionsestimate conditional wage differentials. In section 4, the dummy variable approach is appliedand, in section 5, the decomposition approach is used. Section 6 gives some concludingremarks.

2. Introduction to quantile regression

The quantile regression model introduced by Koenker and Basset (1978) extends the notion ofordinary quantiles in the location model to a more general class of linear models in whichconditional quantiles have a linear form. The most common form is median regression (LeastAbsolute Deviations or LAD), where the object is to estimate the median of the dependentvariable, conditional on the value of the independent variables. The remaining conditionalquantiles are estimated by minimizing an asymmetrically weighted sum of absolute errors.Thus, these models can be used to characterize the entire conditional distribution of adependent variable given a set of regressors.

The point of departure is an elementary definition of the quantiles. Let 0 1.θ< < The thθquantile of the distribution of a random variable Y is a value θξ such that

( ) ( )Pr Y Fθ θθ ξ ξ= ≤ = (1)

where F is the cumulative distribution function of Y. Let { },...,i ny y be a random sample of

size n drawn from the distribution of Y. By analogy to (1), the sample quantile is estimated by

( ){ }nˆ inf y : F yθξ θ= ≥ (2)

where nF is the empirical distribution function of Y. To extend this idea to the estimation of

conditional quantile functions we need a new way to estimate the quantiles. By circumventingthe usual reliance on an ordered set of sample observations, we can define the quantiles as an

optimization problem. It is straightforward to show that θ̂ξ solves

( )1i i

i ii: y m i:y m

ˆ arg min y m y m .θξ θ θ≥ <

= − + − −

∑ ∑ (3)

Quantile regression extends these ideas to the linear model generating a new class of

statistics. Let { },...i nx x be vectors of explanatory variables that correspond to { },..., .i ny y It is

assumed that:

( )ii i i i iy x ’ u and Quant y x x ’ ,θ θ θ θβ β= + = (4)

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where ( )i iQuant y xθ denotes the θth quantile of iy conditional on .ix The θth regression

quantile is then defined in the same manner as the sample quantile by the minimizationproblem

( )1i i i i

i i i ii: y x ’ i: y x ’

ˆ arg min y x ' y x ' .θβ β

β θ β θ β≥ <

= − + − −

∑ ∑ (5)

The problem can be formulated as a linear program and simplex-based methods provide

efficient algorithms for most applications (Koenker and d’Orey 1987). Increasing θcontinuously from 0 to ,1 we can trace the entire distribution of iy conditional on .ix Let

( )1 p’ ’ ’ˆ ˆ ˆ,...,θ θβ β β= denote the estimated values obtained by solving (5) separately for p

alternative ’sθ and let ( )1 p’ ’ ’,...,θ θβ β β= denote the population’s true values. Under mild

regularity conditions (see for instance Buchinsky 1991), ( ) ( )ˆ , ,dn N 0θ θ θβ β Λ− → where

{ }, ,...,

,jk j k 1 pθΛ Λ=

=

{ }( ) ( )( ) ( ) ( )( )j k

1 1

jk j k j k u umin , f 0 x xx’ xx’ f 0 x xx’θ

Λ θ θ θ θ Ε Ε Ε− −

= − (6)

and ( )uf 0 xθ

denotes the density of the error term uθ evaluated at 0 conditional on .x The

bootstrap is known to estimate the distribution of ( )ˆn θ θβ β− consistently (Hahn 1995).

There are several alternative schemes for estimating the covariance matrix, for an overviewsee Buchinsky (1998). In the present application standard errors are obtained by design matrixbootstrap with 1000 replications.

In the case of homoscedasticity, ( ) ( )u uf 0 x f 0θ θ

= and any two quantile parameter vectors

1θβ and 2θβ should differ only in their intercepts but not in their slope coefficients. As

suggested in Koenker and Basset (1982), equality of all or selected subsets of slopeparameters across different quantiles may be tested using a Wald test. Different estimates atdistinct quantiles may be interpreted as differences in the response of the dependent variableto changes in the regressors at various points in the conditional distribution. Such effectscannot be captured by mean regressions.

Models for conditional quantile functions offer a number of advantages over more familiarmean regression (least squares) methods. They have inherited a natural interpretability and aninherent robustness from the behavior of the ordinary quantiles. When the error term is non-normal, quantile regression estimators may be more efficient than least squares estimators. Byreplacing a monolithic model of conditional central tendency with a family of models forconditional quantiles, we are able to achieve considerably greater flexibility and a much morecomplete view of the effect of the covariates on the dependent variable, allowing them toinfluence location, scale and shape of the response distribution.

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In application, a variety of heterogeneity phenomena are rendered observable. Primeexamples of economic applications in the literature include Buchinsky (1994, 1995),Chamberlain (1994) and Fitzenberger et al. (1995). Since then, there is a rapidly expandingempirical quantile regression literature. Buchinsky (1998) and Koenker and Hallock (2001)have recently surveyed it.

3. Data description

The analysis in this paper draws on data from the German Socio-Economic Panel (GSOEP)for the year 20001. After reunification, the panel was extended to include the eastern part ofGermany, but we focus here on West Germany only because undeniable economic differencessubsist between East and West Germany. Since many public sector jobs are not open toforeign nationals, the analysis is based on the subsample of Germans only. Furthermore, thesample is restricted to include those who were between 18 and 65 years old and were in full-time or part-time employment in 2000. Finally, all observations with a missing value for oneof the variables have been excluded. The final data set has 2972 observations.

As the sample includes only wage earners, the results must be interpreted conditional onthe selected sample. Issues of sample selection bias and the potential problem of endogeneityof sector choice and education are considered the scope of the present paper, whichconcentrates on distributional aspects. This is, of course, a more descriptive approach andsome caution must be exercised in interpreting the results.

Table A.1 in Appendix describes the variables we use for our descriptive analyses and inthe regressions. Table A.2 presents descriptive statistics for male and female public andprivate sector employees. Means of relevant variables show that average hourly earnings arehigher in the public sector than in the private sector. They also show that public sectoremployees are, on average, better educated than private sector employees. For instance, 19 percent of the employees in the public sector have achieved a university degree (Ed level 6),while they are only 9 per cent in the private sector. Public sector employees have acquiredmore labor market experience and tenure, too. These differences in work experience,education and tenure may explain the higher average wages of public sector employees.Another part of disparity between average compensation in the public and the private sectorsmay be due to the greater concentration of professionals and clerks in the public sector.

To get a picture of the unconditional wage dispersion in each sector, the 10th, 25th, 50th,75th and 90th percentiles of the log wage are displayed in Table 1. A measure of the wagedispersion, the 0.9-0.1 spread, is also shown in this table. For both genders, this spread isclearly higher in the private sector than in the public sector, indicating that the unadjustedearnings are more compressed in the public sector. For males, public sector employees at the1st decile of the public sector earnings distribution enjoy an earnings advantage over privatesector employees at the same point in the private sector distribution of wages; but the reverseholds for employees at the 9th decile of the public sector and private sector earnings

1 For an English language description of the GSOEP see SOEP Group (2001).

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distribution. With “higher floors” and “lower ceilings”, the public sector compresses theunconditional wage distribution.

Table 1: Percentiles of the log wage

10% 25% 50% 75% 90% 0.9-0.1 spreadPublic sector 2.907 3.154 3.376 3.585 3.780 0.873MalesPrivate sector 2.724 3.030 3.293 3.586 3.861 1.137Public sector 2.597 2.918 3.171 3.364 3.570 0.973FemalesPrivate sector 2.213 2.663 2.967 3.241 3.497 1.284

A first visual summary of the public and private sector wage distributions is provided inFigures 1 and 2 for men and women respectively. The density functions were estimated usingan Epanechnikov kernel estimator. Silverman’s rule of thumb was used to choose thebandwidths. It can be seen from these figures that the distributions are quite distinct betweensectors. For both genders, the public sector earnings distribution is characterized by a higherdensity function around the mode and a lower dispersion. Of course, differences in earningsdispersion between public and private sector employees using raw earnings data mayconfound the effects of a worker’s sector of employment with the effects of differences in thedistribution of worker characteristics between sectors. In order to control for these differencesand to study conditional wages and wage differentials, we will estimate wage regressions inthe next sections.

Figure 1: Kernel density estimates of the wage distributions for men

Note: Epanechnikov kernel density estimates; the bandwidths were chosen using Silverman’s rule of thumb.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5Log gross hourly wages

Private sector Public sector

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Figure 2: Kernel density estimates of the wage distributions for women

Note: Epanechnikov kernel density estimates; the bandwidths were chosen using Silverman’s rule of thumb.

4. Single equation method

This and the next section compare earnings of individual workers corrected for differences inproductivity-related characteristics and job attributes. In this section, the basic dummyvariable approach is applied and, in the next section, the decomposition approach will beused.

4.1 Public sector wage premium

Following Smith (1977), the first and basic methodological approach is identical to that usedin studies of sex, race, or union wage differentials. It involves estimating an earningsregression using pooled data for public sector and private sector employees and including adummy variable for a worker’s sector of employment. That is:

( )i i i iln wage x ’ p sec t uβ δ= + + , (7)

where ix includes experience (specified in quartic form), job tenure, marital status, part-time

status, education and occupation. The descriptive statistics of these variables are given inTable A.2. The reference educational category is Ed level 1 and the reference occupationalcategory is Professional.

Equation (7) has been estimated separately for males and females using standard OLS andquantile regressions. The estimates of the public sector dummy are reported in Table 2. Theestimates for the other variables reveal a familiar pattern and displayed in Table A.3. Thecoefficient on the public sector dummy estimated with OLS equals –7.9% for men, indicatingthat wages in the public sector are about 8 per cent lower than wages in the private sector. For

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5Log gross hourly wages

Private sector Public sector

8

females, on the other hand, the public sector appears to pay a significant premium of 8.8 percent. Both coefficients are strongly significant. If the distribution of the error terms issymmetric, mean and median regressions estimate the same quantity. The OLS and LAD

( 0.5θ = ) estimates are actually similar. At the median, the public sector pay premium is –9.1per cent for men and 7 per cent for women. Again, both coefficients are significant. Theseresults are similar to that of Dustmann and Van Soest (1997).

Table 2: Estimates of the public sector pay premium

Males Females

Coefficient Standard error Coefficient Standard error

OLS -0.079 0.020 0.088 0.026

θ = 0.10 0.034 0.037 0.166 0.044

θ = 0.25 -0.023 0.024 0.120 0.029

θ = 0.50 -0.091 0.021 0.070 0.026θ = 0.75 -0.161 0.020 0.005 0.026θ = 0.90 -0.150 0.037 0.009 0.032Note: Variables controlled for in the regressions are education, experience, occupation, marital status, tenure andpart-time status. Standard errors are obtained by bootstrapping (1000 repetitions) for quantile regression andusing the Huber-White estimator for OLS.

The OLS regression does not consider the possibility that the distribution of actual wagesaround their predicted values differ across sectors. In fact, both the unconditional and theconditional wage distribution in the public sector are more compressed than those in theprivate sector. Therefore, to complete the analysis, Table 3 presents also estimates from

quantile regressions with 0.10, 0.25, 0.75 and 0.90θ = . The estimated public sector wage

premium varies strongly with θ . For males this premium decreases from 3.4% at 0.10θ = to

–15% at 0.90θ = . For females, this premium varies from 16.6% at 0.10θ = to –0.9% at

0.90θ = . The quantile regressions reveal dispersion in the public sector that cannot becaptured using OLS. This sector compresses the pay dispersion and, therefore, reduces thewithin-group pay inequality.

The quantile regression model can be used to characterize the entire conditionaldistribution. Figure 3 and 4 present a concise visual summary of the quantile regression

results. 99 quantile regressions with 0.01,...,0.99θ = have been estimated separately for

males and females. For comparison, the OLS estimate is also shown. The monotone decreaseof the public sector wage premium as we go from the lowest to the highest quantiles appearsclearly. The pattern is the same for both sexes. The extreme quantiles are particularlyinteresting, although they must be interpreted with caution because the standard deviations arevery high at these points of the distribution. At the first percentile, the premium is 11% formen and 48% for women. On the other hand, at the last percentile, the wage penalty in thepublic sector is 16% for men and 24% for women!

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Figure 3: Estimated public sector wage premium for men

Premia are based on ordinary least sqares and quantile regressions. Variables controlled for in the regressions areeducation, experience, marital status, tenure and part-time status.

There is disagreement in the literature on discrimination about whether differences in theoccupational status of the individual should be controlled for. As can be seen in Table A.2, thepublic sector work force is predominantly white-collar with large proportions of workers inprofessional an clerical occupations and relatively few workers in sales and blue-collaroccupations. It is appropriate to control for such occupational differences if they are indicativeof what is necessary to do the job. However, if occupational titles are inflated in the publicsector to justify higher wages, then it is not appropriate to control for such occupationaldifferences since they in fact are a part of the public sector rents. As differences in occupationcould be the result of segregation, it is usual to run two regressions, with and without controlfor job characteristics. Therefore, the premiums estimated without control for jobcharacteristics (occupations and part-time status) are also plotted in Figures 3 and 4. Theyshow that the public sector wage premium is slightly lower without control for jobcharacteristics for the men but approximately 3.5% higher for women. So, as in Poterba andRuben (1994), the inclusion of occupational dummies reduces the absolute value of thepremium but the differences between the two specifications are much less important inGermany than in the USA.

How to interpret these results? It is likely to regard differences in the occupationaldistribution of males and females as indicative of discrimination and not of differential tastesby women for the jobs they want to hold. It has been found that the gender occupationalintegration proceeds more rapidly in the public sector than in the private sector (Gregory andBorland 1999). For example, 40% of the women in the public sector are employed in the

-0.3

-0.2

-0.1

0

0.1

0.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Quantiles

Quantileregressionwith controlforoccupation

OLS withcontrol foroccupation

Quantileregressionwithoutcontrol foroccupation

OLS withoutcontrol foroccupation

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professional occupational group when they are only 14% in the private sector. For women inprofessional occupations, the public sector is capital as employment opportunities: 54% arepublicly employed. For men, this proportion is only 31%. Thus, it seems that not only directlabor market discrimination but also occupational discrimination is less in the public sectorthan in the private sector. The results of section 5.2 will confirm these hypotheses.

Figure 4: Estimated public sector wage premium for women

Premia are based on ordinary least sqares and quantile regressions. Variables controlled for in the regressions areeducation, experience, marital status, tenure and part-time status.

4.2 Public sector wage premium stratified by educational attainment

Since the wage differential may vary across education levels, the public wage premium is nowestimated for four ranges of schooling. The observations are stratified in four educationalgroups: Low education (corresponds to Ed level 1 and 2), Medium education (Ed level 3),High education (Ed level 4 and 5) and University (Ed level 6). The public sector dummy isthen separated into 4 dummies, one for each level of education. The other regressors are thesame as in (7). Figures 5 and 6 combine the results by qualification and quantile to look at theimpact of public sector status on pay within and between educational groups.

The four panels of Figure 5 provide the information for men. Quantile regression estimatesshow the same pattern for the 4 educational categories: the public sector premium declines aswe move up the income distribution. Hence, for the 3 top education categories, the publicsector wage penalties become significant only at the highest quantiles: from the 3rd quartilefor the medium education group, from the median for those with high education and from the1st quartile for men with a university degree. Thus, the story of Figure 3 is confirmed: thepublic sector reduces the within-group inequality by compressing the wage distribution.

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Quantiles

Quantileregressionwith controlforoccupation

OLS withcontrol foroccupation

Quantileregressionwithoutcontrol foroccupation

OLS withoutcontrol foroccupation

11

The comparison of the four OLS or LAD estimates shows that the public sector wagepremium decreases monotonically as the educational qualification increases. At the mean, thepublic sector wage premium falls from 2% for the lowest education group at –2% for thesecond group, -10% for the third and –23% for the top education category. The picture is thesame at the median. Since the average wage increases with the number of years of schooling,there is an equalizing effect between educational groups attached to public sector status. Thepolitical pressure on the government not to pay low wages to its less skilled employees couldexplain why the return to education is higher in the private then in the public sector.

Figure 5: Public sector wage premium stratified by educational attainment for men

Note: Premia are based on ordinary least sqares and quantile regressions. Variables controlled for in the regres-sions are education, experience, occupation, marital status, tenure and part-time status. One (two, three) star(s)under the bar indicates that the estimated coefficient is statistically significant at the 10 (5, 1) per cent level.

Figure 6 illustrates the results for women. At least for the three lowest educationcategories, there is again evidence of a negative gradient in public sector premium across theincome quantiles, as shown in Figure 4. The premium is significant positive at the lowpercentiles for the low and medium education groups and significant negative at the highquantiles for the high education group. Thus, the within-group equalizing effect is clear forthese three categories. For women with a university degree, no estimate of the public sectordummy is significant different from zero, maybe due to the smaller number of observations.The evidence of within-group equalization of pay within the public sector is not apparent.

Low education

-0.4

-0.2

0

0.2

OLS 10th 25th**

50th 75th 90th

Quantile

Medium education

-0.4

-0.2

0

0.2

OLS 10th**

25th 50th 75th**

90th*

Quantile

High education

-0.4

-0.2

0

0.2

OLS**

10th 25th 50th***

75th***

90th***

Quantile

University

-0.4

-0.2

0

0.2

OLS***

10th 25th***

50th***

75th***

90th***

Quantile

12

As for men, there is evidence of an equalizing effect between groups attached to publicsector status. At the median or the mean, the public sector wage premium is strong significantpositive for the two lowest educational categories but negative for the two highest. While thelevel of the public sector wage premium is about 15% higher for women than for men, thedifferences between the educational levels are very similar for both sexes. Thus, it seems thatthe government tends to be a model employer and refuses to pay low wages to its lesseducated employees. An implicit minimum wage in the public sector arises from this policy.Similarly, the government has the tendency to underpay its most skilled workforce. Very highsalaries in the public sector seem to be not accepted by voters.

Figure 6: Public sector wage premium stratified by educational attainment for females


4.3. Public sector wage premium stratified by levels of experience

The public sector wage premium may also vary across different levels of experience. Insteadof the quartic equation in experience, the observations are now stratified in four experiencegroups: those with less than 11 years of experience, 11-20 years, 21-30 years and more than30 years. The public sector dummy is then interacted with the set of 4 indicator variables, onefor each level of potential experience. Figure 7 for men and Figure 8 for women combine theresults by experience and quantile in the same way as Figures 5 and 6 did.

Low education

-0.2

-0.1

0

0.1

0.2

0.3

OLS***

10th***

25th***

50th***

75th*

90th

Quantile

Medium education

-0.2

-0.1

0

0.1

0.2

0.3

OLS***

10th***

25th***

50th*

75th 90th

Quantile

High education

-0.2

-0.1

0

0.1

0.2

0.3

OLS*

10th 25th 50th 75th***

90th*Quantile

University

-0.2

-0.1

0

0.1

0.2

0.3

OLS 10th 25th 50th 75th 90th

Quantile

13

With a few exceptions, we observe again the equalizing effect of the public sector relativeto the private sector, with a negative gradient of premiums as we move up the incomedistribution. The public sector wage premiums tend to be (significantly) positive at the lowquantiles and negative at the high quantiles. The less experienced groups seem to makeexceptions. Here, there is no evidence of a change in the coefficient across the conditionalwage distribution.

Figure 7: Public sector wage premium stratified by levels of experience for males


If we compare the level of the public sector wage premium across experience groups formen, it seems that the premium does not vary with the experience of the workers. Thecoefficient of the public sector dummy is negative for all categories but it is significant onlyfor the employees with 11 to 20 or 21 to 30 years of experience. On the contrary, the level ofthe public sector wage premium depends significantly on experience for women. The publicsector wage premium estimated at the median is negative for the least experienced groups andincreases monotonically to a strongly significant 24% for the most experienced groups. Thisdifference between both sectors could be explained by the rigid hierarchical pay structure inthe public sector. Salaries increase more or less mechanically with seniority. Wage decreasesare difficult if not impossible. Moreover, A part of the discrimination of women takes theform of slower promotion rates. The centralized pay system in the public sector could reduce

10 or less years of experience

-0.35

-0.15

0.05

0.25


Quantile

From 11 to 20 years of experience

-0.35

-0.15

0.05

0.25

OLS***

10th 25th**

50th***

75th***

90th***

Quantile


-0.35

-0.15

0.05

0.25

OLS***

10th 25th 50th***

75th***

90th***

Quantile

More than 30 years of experience

-0.35

-0.15

0.05

0.25

OLS 10th***

25th*

50th*

75th***

90th***

Quantile

14

this form of discrimination. On the other hand, it may be easier to retain the same job after amaternity break or to change from full-time employment to part-time employment in thepublic sector. Therefore, females in the public sector could have more human capital thanfemales in the private sector with the same age and the same education level (Dustmann andVan Soest 1997). It should be noted, however, that the experience pattern reflects acombination of age, experience and cohort effect.

Figure 8: Public sector wage premium stratified by levels of experience for females


4.4 Public and private sector wages in specific occupations

Our analysis so far has compared individuals with similar human capital attributes but wehave not considered the possibility that the pay differential between both sectors may differacross occupations. To address such differences, we estimate now a wage equation similar to(7) for several occupational categories with substantial employment in both sectors.

Figure 9 gives a picture of the estimates with OLS and quantile regressions. Thecompression of the wage distributions by the public sector is again evident but not alwayssignificant, probably because of the reduced number of observations. Concerning thedifferences between occupations, the results confirm the earlier evidence that the public sectorpay premium is higher in low-skill occupations. For both genders, the professionals are less

10.5 or less years of experience

-0.15

0

0.15

0.3


Quantile


-0.15

0

0.15

0.3

OLS 10th**

25th*

50th 75th 90th

Quantile


-0.15

0

0.15

0.3

OLS***

10th**

25th**

50th**

75th 90th

Quantile

More than 31 years of experience

-0.15

0

0.15

0.3

OLS***

10th**

25th***

50th***

75th**

90th

Quantile

15

paid in the public than in the private sector. The pay differential is strongly significant for themales, with OLS as with quantile regressions alike. Clerks in the public sector are relativelybetter paid than professionals. Their public sector pay premiums are around as high as theaverage premium across all occupations. For male blue-collar workers, a slightly negative butnot significant public sector pay premium has been found. Finally, the occupational groupwhich profits most from being employed in the public sector are the women in sales andservices occupations. Their public sector premium of about 25% is strongly significant.

Figure 9: Occupation-specific estimates of public sector wage premium

Note: Premia are based on ordinary least sqares and quantile regressions. Variables controlled for in the regres-sions are education, experience, marital status, tenure and part-time status. One (two, three) star(s) under the barindicates that the estimated coefficient is statistically significant at the 10 (5, 1) per cent level.

Professiona ls, m en

-0.35

-0.25

-0.15

-0.05

0.05

0.15

OLS***

10th 25th***

50th***

75th***

90th***

Quantile

Clerks, m en

-0.35

-0.25

-0.15

-0.05

0.05

0.15

OLS 10th*

25th 50th*

75th***

90th*

Quantile

Blue -colla r, m en

-0.35

-0.25

-0.15

-0.05

0.05

0.15

OLS*

10th 25th 50th 75th***

90th**

Quantile

Professionals, women

-0.1

0

0.1

0.2

0.3


Quantile

Clerks, w om en

-0.1

0

0.1

0.2

0.3

OLS 10th***

25th**

50th 75th 90th

Quantile

Sa les and service w orkers, w om en

-0.1

0

0.1

0.2

0.3

OLS***

10th**

25th***

50th***

75th***

90th**

Quantile

16

5. Decomposition method

5.1 Methodology

One problem with the dummy variable approach is that the returns to productivity-relatedcharacteristics and job attributes are constrained to be equal across sectors. The effect of aworker’s sector of employment (public or private) is limited to be an intercept effect. Analternative approach involves estimating separate earnings functions for individuals in thepublic and private sectors. Following Blinder (1973) and Oaxaca (1973), the difference inaverage earnings between workers in each sector can be decomposed into differences inpersonal characteristics and job attributes and differences in coefficients. The first term isreferred to as the justifiable earnings or characteristics differential. The second term is calledthe surplus or rent payment and is interpreted as providing a measure of whether an identicalemployee working in the same job in the public and private sector would receive the samewage.

Formally, the first step is to estimate

β= + =j ji i iln ghearn x ’ u j public, private (8)

where ix is the same as in equation (7). The second step is to calculate

( ) ( )pub priv pub priv priv pub pub privˆ ˆ ˆln ghearn ln ghearn X X X ,β β β− = − + − (9)

where and j jln ghearn X are average log hourly wages and characteristics of employees in

sector j, jβ̂ is the estimated vector of returns to worker characteristics in sector j. The first

term on the right-hand side is the component of the log wage differential due to differences inendowments between public and private sector employees. The second term shows thecomponent due to differences in returns to these endowments.

A disadvantage of this approach is that it only focuses on differences at the means of thetwo earnings distributions. However, the decomposition technique may be combined withquantile regressions to determine the rent component at various points in the wage distribution(Mueller 1998 and Garcia et al. 2001). The difference in the conditional quantile of the logwage between the public and private sector evaluated at the level of covariates correspondingto the mean for each sector is decomposed as follows

( ) ( )pub pub priv privQuant lnghearn X Quant lnghearn Xθ θ− =

( ) ( )pub priv priv pub pub privˆ ˆ ˆX X X .θ θ θβ β β− + − (10)

Note that, contrary to (9), where jj jˆX ln ghearnβ = , j jˆX θβ is not equal to the θth sample

quantile of jlnearn . That is, the conditional quantile evaluated at the mean of the covariates isnot equal to the unconditional quantile.

17

5.2 Empirical analysis

Separate earnings equations for the public and the private sector and for both sexes have beenestimated2. It is interesting to note the differential rate of return found between sectors on anumber of characteristics. The returns to education are clearly higher in the private than in thepublic sector for both sexes. This explains why the public sector reduces the betweeneducational group inequality. Concerning the occupational variables, the results confirm thefindings of section 4.4. Employees in professional occupations earn more in the private sectorwhile sales and service workers earn more in the public sector. An important differencebetween both sectors exists concerning the part-time dummy. The wage penalty associatedwith the part-time status of the employee is higher in the public sector than in the privatesector for men: -18% vs. -11% but smaller for women: -4% vs. -15%. Another, maybe moreanodectical difference between both sectors is that married men earn 14% more than single inthe public sector but only 7% more in the private sector. For women, the coefficient of themarrital status dummy is not significant in both sectors.

The results of section 4 suggest that the gender gap in wage could be smaller in publicemployment than in private employment. To confirm or reject this hypothese, the gender paydifferential has been estimated separately for each sector, once using a gender dummyvariable and once by decomposing the wage differential (using the male coefficients asnondiscriminatory wage structure). Table 3 shown the estimated gender gaps along with theirstandard errors obtained by bootstraping. Whatever method we use, the magnitude of paydiscrimination by gender is clearly (more than 10 %) smaller in the public than in the privatesector. By decomposing the wage differential, the gender gap is even not significant differentfrom zero in the public sector.

Table 3: Estimates of the male female earnings differential with OLS and quantile regressions

Dummy variable Decomposition

Public sector Private sector Public sector Private sectorCoef. Std. err. Coef. Std. err. Coef. Std. err. Coef. Std. err.

OLS -0.078 0.026 -0.193 0.024 -0.033 0.043 -0.220 0.038

θ = 0.10 -0.105 0.054 -0.219 0.046 -0.107 0.080 -0.245 0.068

θ = 0.25 -0.066 0.026 -0.181 0.030 -0.040 0.061 -0.220 0.049

θ = 0.50 -0.089 0.026 -0.138 0.025 -0.000 0.071 -0.128 0.048

θ = 0.75 -0.116 0.034 -0.170 0.023 -0.086 0.063 -0.203 0.047

θ = 0.90 -0.114 0.042 -0.170 0.036 -0.177 0.066 -0.229 0.073Note: Other variables controlled for in the regressions are education, experience, occupation, marital status,tenure and part-time status. Standard errors are obtained by bootstrapping (1000 repetitions).

Interestingly, these results contradict standard economic theory. Unlike the private sector,the public sector is not subject to profit constraints. Becker (1971), for instance, suggests thatprofit-maximizing behavior is incompatible with the existence of discrimination. Followingthis argument, we might expect the adjusted male-female earnings differential to be smaller in 2 The detailed results are available upon request from the author.

18

the private sector. Why should nevertheless the male-female earnings differential be smallerfor public sector than private sector employees? First, the pay system for all public sectoremployees is uniform and centralized. It is regulated in the Federal Act on the Remunerationof Civil Servants (Bundesbesoldungsgesetz), which requires equal pay for all individuals withthe same seniority and qualifications who are employed in a given job. Equal opportunity andanti-discrimination policies are therefore more effectively implemented in public sector thanprivate sector labor markets. Second, we have found that the distribution of wages is morecompressed in the public than in the private sector. Blau and Kahn (1992) have shown that themore compressed the pay distribution, the lower the level of wage inequality. Their evidencerely on cross-national comparisons of the impact of different wage distribution on the genderwage inequality. Here, there are two different pay systems within one land.

A consequence of this large wage advantage of females in the public sector is theoverrepresentation of women in the public sector. They represent 49% of the public sectoremployees but only 39% of the private sector employees. Whether this wage policy isefficient or not depends on the interpretation of the gender earnings differentials. If it isbelieved that wage discrimination against females in private sector labor markets is causinginefficient resource allocation decisions, the presence of the public sector wage premium forwomen will reduce the degree of wage discrimination and improve the efficiency of the labormarket.

Using equation (9) and (10), total log wage differentials are broken into rent andcharacteristics differentials. Tables 5 and 6 show the decomposition of public/private sectorearnings differential for males and females respectively. The upper part of the tablesdecomposes the sectorial differential in the predicted log wage into rent payment (coefficientscomponent) and justifiable differential (characteristics component). The lower part of thetables shows how much of the total difference in characteristics is due to specific sets ofvariables.

Table 4: Decomposition of public/private sector earnings differential for men

OLS θ = 0.10 θ = 0.25 θ = 0.50 θ = 0.75 θ = 0.90Differences in estimatedln(wage)

0.067 0.201 0.128 0.052 0.008 -0.051

Rent payment -0.078 0.031 -0.016 -0.084 -0.128 -0.172

Characteristics differential 0.145 0.170 0.144 0.136 0.136 0.121

Differences due tocharacteristics:Experience 0.020 0.009 0.017 0.022 0.022 0.032

Tenure 0.045 0.064 0.055 0.041 0.038 0.012

Education 0.074 0.077 0.079 0.079 0.070 0.084

Occupation 0.005 0.017 -0.004 -0.003 0.006 -0.010

Married 0.004 0.004 0.004 0.002 0.002 0.003

Part-time -0.003 -0.003 -0.006 -0.005 -0.002 -0.001

Note: Totals may not sum exactly due to rounding.

19

Dealing with male employees first, the estimates obtained by decomposing the quantileregression results show a similar pattern to those obtained in section 4 by estimating thecoefficient of a dummy variable. The public sector wage premium declines from 3% at the 1st

decile to –17% at the 9th decile. The characteristics component shows that public sectoremployees should earn more than private sector employees at all points of the wagedistribution. Compared with private sector employees, public sector workers are bettereducated and have more years of job tenure and more experience. We do not observesubstantial differences in occupations or part-time employment. The characteristicsdifferential decreases as we move up the wage distribution, in particular because of thedeclining importance of job tenure. That shows that the more compressed wage distribution inthe public sector is partly due to differences in characteristics.

Table 6 shows the results for female employees. Again, the rent payment is very similarwith that obtained in section 4 with the dummy variable approach. The breakdown of thecharacteristics component is very different from that of males. Differences in experience andthe part-time status are of less importance for women than for men. Occupations, which wereinsignificant for men, are now the characteristics that explain the most part of the justifiabledifferential. Thus, the decomposition of the wage differential shows that there are nooccupational differences between the private and the public sector for men but there areimportant differences for women. Interpreting the occpuational differences of men andwomen as indicative of discrimination, these results suggest that women are not only betterpaid for the same job in the public sector but they have also better possibility to find a well-paid occupation in that sector. Interestingly, occupations seem to be the only characteristicsdifferential which varies across the quantiles.

Table 5: Decomposition of public/private sector earnings differential for women

OLS θ = 0.10 θ = 0.25 θ = 0.50 θ = 0.75 θ = 0.90Differences in estimatedln(wage)

0.204 0.207 0.294 0.170 0.140 0.081

Rent payment 0.078 0.133 0.145 0.052 0.007 -0.058

Characteristics differential 0.126 0.174 0.149 0.118 0.133 0.139

Differences due tocharacteristicsExperience -0.006 -0.007 -0.007 -0.007 -0.003 -0.002

Tenure 0.020 0.028 0.033 0.027 0.028 0.025

Education 0.050 0.043 0.042 0.026 0.056 0.068

Occupation 0.070 0.123 0.091 0.078 0.056 0.054

Married -0.002 -0.005 -0.003 -0.000 -0.000 -0.001

Part-time -0.007 -0.009 -0.008 -0.006 -0.004 -0.004

Note: Totals may not sum exactly due to rounding.

20

8. Conclusions and policy implications

The wage structure in the public and private sectors in West Germany has been examinedusing the 2000 GSOEP. OLS results suggest that wages are about 8 per cent lower in thepublic sector for males but substantially higher for females (about 9 per cent). The results ofquantile regressions indicate that the conditional pay distribution is more compressed in thepublic sector. The public sector wage premium is highest at the lower end of the wagedistribution and then decreases monotonically as we move up the wage distribution. Thus, thepublic sector reduces within-group inequality.

Allowing the premium to be different for four categories of education, we find that thepublic sector reduces also the between-group inequality. Those with basic schooling obtainthe highest public sector wage premium. The stratification of the premium by levels ofexperience suggests that the premium increases with the years of experience for women.

Upon decomposing these wage differences, we find similar results to those obtained byusing a public sector dummy variable. The breakdown of the characteristics component showsthat, for men, differences in education, tenure and experience explain most of theunconditional wage differences. For women, however, differences in occupation play a majorrole.

Are these differences in wages between the public and private sector “true” premiums, orare there other factors at work? The differences may reflect unobserved individualcharacteristics. The results may also be biased as a result of omitted explanatory variables.There may be “compensating differentials”, such as working conditions and fringe benefits.All these issues as well as the problems of selectivity and endogeneity have not been exploredin this study. Therefore, the results do not necessarily have a causal interpretation. Rather theyprovide a descriptive comparison of earnings distribution for public and private sectoremployees.

Quantile regression estimates show that the distribution of earnings is less dispersed forpublic than for private sector employees. This reduces the inequality of the wage structure butthis may also have implications for how workers with different abilities and productivities sortbetween those sectors. Employees at the bottom of the wage distribution, especially amongthe less skilled, may decide to queue for relatively high-paying jobs in the public sector ratherthan take low-paid jobs in the private sector (Blackaby et al. 1996). This can lead to “wait”unemployment. Katz and Krueger (1991) have found empirical relevance for this prediction.On the other hand, the wage penalty at the upper end of the public sector pay scale is useful inexplaining the exodus of senior managers to the private sector. Many observers have voicedconcern that the government will be increasingly unable to recruit highly skilled employeessuch as scientists, engineers and judges. Reforms allowing for more vertical flexibility in thepublic sector wage structure seem to be especially important in order to link more closelywage rates to individual performance and underlying labor market conditions.

21

Appendix Data

Table A.1: Explanation of variables

Variable Description

Wage Gross hourly earnings from employment. Gross hourly wage are derived bydividing gross monthly earnings by monthly actual hours worked.

Ln(wage) The natural logarithm of wage.Expr Number of years of potential work experience the individual has

accumulated. It is measured by min(age-schooling-6, age –18).Gender Dummy; 1 if women.Tenure Number of years with current employerPart-time Dummy; 1 if the individual is part-time or marginally employed.Married Dummy; 1 if married.Ed level Ordered variable on education:+Ed level 1 Dummy; 1 if basic or intermediate schooling with no training or no degree.Ed level 2 Dummy; 1 if basic schooling with apprenticeship.Ed level 3 Dummy; 1 if intermediate schooling with apprenticeship.Ed level 4 Dummy; 1 if high school (Abitur or Fachabitur) with no training or with

apprenticeship.Ed level 5 Dummy; 1 if high school with technical school or polytechnic.Ed level 6 Dummy; 1 if university.Management Dummy; 1 if occupation in management.Professional Dummy; 1 if professional.Clerk Dummy; 1 if clerk.Salesperson Dummy; 1 if salesperson.Service worker Dummy; 1 if service worker.Agriculture Dummy; 1 if occupation in agriculture.Blue collar Dummy; 1 if blue collar.Others Dummy; 1 if occupation in others categories.Psect Dummy; 1 if employed in the public sector.

22

Table A.2: Descriptive statistics, means

Variable All observations Men WomenAll Public

SectorPrivateSector

All PublicSector

PrivateSector

All PublicSector

PrivateSector

Ln(wage) 3.15 3.22 3.12 3.28 3.33 3.27 2.97 3.11 2.91Experience 20.50 21.28 20.23 20.71 22.36 20.22 20.22 20.18 20.24

Gender 0.44 0.50 0.41

Tenure 10.34 12.92 9.44 11.73 15.41 10.64 8.54 10.42 7.74Married 0.62 0.65 0.61 0.64 0.68 0.63 0.59 0.61 0.58

Part-time 0.22 0.27 0.20 0.05 0.07 0.04 0.45 0.48 0.44Education:

Ed level 1 0.08 0.05 0.09 0.07 0.02 0.08 0.11 0.09 0.11Ed level 2 0.36 0.26 0.39 0.40 0.30 0.43 0.31 0.22 0.34Ed level 3 0.26 0.30 0.25 0.21 0.26 0.19 0.34 0.33 0.34Ed level 4 0.08 0.07 0.08 0.08 0.06 0.08 0.08 0.08 0.08

Ed level 5 0.10 0.12 0.09 0.12 0.13 0.11 0.08 0.12 0.06Ed level 6 0.12 0.19 0.09 0.14 0.22 0.11 0.09 0.16 0.06

Occupation:

Professional 0.23 0.36 0.18 0.23 0.32 0.21 0.22 0.40 0.14Management 0.04 0.02 0.04 0.06 0.03 0.06 0.01 0.02 0.01

Clerk 0.28 0.32 0.27 0.18 0.26 0.16 0.40 0.37 0.42

Salesperson 0.08 0.01 0.11 0.05 0.02 0.07 0.12 0.00 0.17Service Worker 0.09 0.18 0.07 0.06 0.18 0.03 0.14 0.17 0.12

Agriculture 0.01 0.01 0.02 0.02 0.01 0.02 0.01 0.01 0.02Blue collar 0.25 0.09 0.30 0.38 0.16 0.44 0.08 0.02 0.10

Others 0.02 0.03 0.02 0.02 0.03 0.02 0.02 0.03 0.01

Number ofobservations 2972 770 2202 1677 386 1291 1295 384 911

23

Table A.3: Regression results for men

OLS θ = 0.10 θ = 0.25 θ = 0.50 θ = 0.75 θ = 0.90

Experience 0.163(9.48)

0.25(5.44)

0.196(7.65)

0.159(7.02)

0.15(7.87)

0.117(3.05)

Experience2 -0.01(-6.96)

-0.016(-4.69)

-0.013(-6.17)

-0.009(-5.44)

-0.009(-5.93)

-0.007(-2.18)

Experience3 2.61E-4(5.72)

4.56E-4(4.20)

3.42E-4(5.16)

2.35E-4(4.50)

2.46E-4(5.03)

1.18E-4(1.86)

Experience4 -2.58E-6(-5.17)

-4.62E-6(4.96)

-3.38E-6(-4.52)

-2.19E-6(-3.91)

-2.41E-6(-4.57)

-1.79E-4(-1.74)

Ed level 2 0.349(6.40)

0.325(1.90)

0.315(3.14)

0.296(3.90)

0.261(6.42)

0.248(3.57)

Ed level 3 0.422(7.44)

0.389(2.30)

0.41(4.13)

0.373(4.86)

0.352(7.16)

0.33(4.47)

Ed level 4 0.393(5.68)

0.119(0.55)

0.34(2.96)

0.446(4.94)

0.401(8.67)

0.295(3.25)

Ed level 5 0.653(10.65)

0.666(3.92)

0.621(5.79)

0.605(7.29)

0.524(9.40)

0.534(5.80)

Ed level 6 0.703(11.25)

0.622(3.57)

0.667(5.99)

0.691(8.16)

0.617(12.20)

0.57(5.77)

Management 0.085(1.66)

0.069(0.42)

0.051(0.80)

0.098(1.61)

0.109(2.76)

0.162(1.43)

Clerk -0.044(-1.34)

-0.019(-0.37)

-0.051(-1.47)

-0.069(-2.25)

-0.065(-1.75)

0.001(0.02)

Salesperson -0.149(-3.05)

-0.181(-2.22)

-0.151(-2.64)

-0.199(-3.27)

-0.178(-3.80)

-0.092(-1.18)

Serv. Worker -0.212(-5.83)

-0.21(-3.20)

-0.192(-4.18)

-0.153(-3.64)

-0.191(-5.17)

-0.229(-4.47)

Agriculture -0.499(-3.79)

-0.591(-1.13)

-0.585(-3.60)

-0.483(-3.84)

-0.203(-1.51)

-0.269(-3.02)

Blue collar -0.142(-4.51)

-0.083(-1.58)

-0.115(-2.72)

-0.132(-4.26)

-0.173(-5.04)

-0.16(-3.43)

Others 0.03(0.40

-0.059(-0.67)

-0.072(-0.71)

0.005(0.07)

0.004(0.05)

0.082(0.34)

Tenure 0.009(7.17)

0.011(5.46)

0.011(9.17)

0.009(6.95)

0.006(4.15)

0.003(1.86)

Married 0.089(4.22)

0.109(3.19)

0.086(3.46)

0.057(2.79)

0.07(3.21)

0.095(2.64)

Part-time -0.148(-2.70)

-0.218(-2.38)

-0.244(-3.76)

-0.247(-3.78)

-0.075(-0.94)

-0.04(-0.41)

Public sector -0.079(-3.89)

0.034(0.92)

-0.023(-0.98)

-0.091(-4.44)

-0.161(-7.89)

-0.15(-4.02)

Constant 1.885(29.02)

1.097(6.27)

1.619(19.72)

1.958(23.00)

2.275(25.29)

2.599(15.14)

2 1R resp. R 0.5269 0.4166 0.3549 0.3202 0.3006 0.2624

Note: T-statistics are given in parenthesis. Standard errors are obtained by bootstrapping (1000 repetitions) forquantile regression and using the Huber-White estimator for OLS.

24

Table A.4: Regression results for women

OLS θ = 0.10 θ = 0.25 θ = 0.50 θ = 0.75 θ = 0.90

Experience 0.213(10.44)

0.255(6.35)

0.254(10.29)

0.221(9.07)

0.152(4.88)

0.100(3.72)

Experience2 -0.015(-8.12)

-0.017(-4.71)

-0.018(-7.84)

-0.016(-7.58)

-0.010(-4.03)

-0.006(-2.68)

Experience3 4.18E-04(6.82)

4.62E-04(3.86)

5.31E-04(6.49)

4.49E-04(6.59)

2.81E-04(3.61)

1.64E-04(2.30)

Experience4 -4.21E-06(-6.05)

-4.63E-06(-3.44)

-5.44E-06(-5.66)

-4.52E-06(-5.90)

-2.84E-06(-3.41)

-1.71E-06(-2.21)

Ed level 2 0.147(2.85)

0.123(0.94)

0.089(1.29)

0.158(3.89)

0.142(3.29)

0.132(2.73)

Ed level 3 0.180(3.25)

0.134(1.02)

0.180(2.68)

0.180(3.71)

0.210(4.55)

0.201(4.05)

Ed level 4 0.252(3.82)

0.132(0.98)

0.181(2.23)

0.223(3.13)

0.357(5.25)

0.351(4.52)

Ed level 5 0.355(5.17)

0.344(2.33)

0.288(3.80)

0.290(5.23)

0.339(5.04)

0.418(3.22)

Ed level 6 0.425(6.06)

0.416(2.90)

0.323(3.92)

0.394(6.08)

0.503(7.15)

0.506(6.81)

Management 0.334(3.17)

0.349(3.61)

0.304(3.52)

0.213(2.28)

0.149(0.60)

0.909(2.40)

Clerk -0.100(-3.04)

-0.041(-0.64)

-0.119(-2.88)

-0.110(-3.54)

-0.116(-3.49)

-0.117(-2.94)

Salesperson -0.262(-5.27)

-0.261(-2.22)

-0.264(-3.35)

-0.268(-4.87)

-0.238(-4.99)

-0.206(-3.23)

Serv. Worker -0.339(-6.26)

-0.278(-2.66)

-0.299(-4.59)

-0.353(-9.29)

-0.288(-5.63)

-0.273(-4.73)

Agriculture -1.004(-4.54)

-2.221(-3.60)

-1.327(-2.25)

-0.707(-2.51)

-0.617(-3.36)

-0.471(-3.78)

Blue collar -0.300(-4.86)

-0.397(-3.17)

-0.321(-2.59)

-0.288(-5.53)

-0.243(-4.50)

-0.209(-2.89)

Others -0.340(-4.36)

-0.257(-1.39)

-0.369(-2.88)

-0.350(-3.26)

-0.249(-2.43)

-0.300(-3.92)

Tenure 0.008(4.13)

0.012(3.34)

0.012(5.23)

0.009(5.03)

0.009(5.96)

0.007(4.40)

Married -0.041(-1.58)

-0.114(-2.33)

-0.047(-1.45)

-0.014(-0.56)

-0.005(-0.18)

-0.002(-0.05)

Part-time -0.133(-4.93)

-0.236(-3.97)

-0.178(-4.50)

-0.100(-3.95)

-0.058(-2.21)

-0.061(-1.95)

Public sector 0.088(3.36)

0.166(3.78)

0.120(4.10)

0.070(2.66)

0.005(0.20)

0.009(0.27)

Constant 1.945(25.24)

1.308(7.33)

1.631(17.67)

1.950(22.95)

2.332(17.03)

2.681(22.46)

2 1R resp. R 0.3988 0.3116 0.2700 0.2464 0.2436 0.2512

Note: T-statistics are given in parenthesis. Standard errors are obtained by bootstrapping (1000 repetitions) forquantile regression and using the Huber-White estimator for OLS.

25

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