1

G ender pay gap in the W estern Balkan countries: Evidence from Serbia, Montenegro and M acedonia Marko Vladisavljevi , **, *** **** * Foundation for the Advancement of Economics (FREN), Belgrade, Serbia. E-mail: marko.vladisavljevic@gmail.com ** UN Women, Serbia. E-mail: nevenai@gmail.com *** London School of Economics, UK and FREN, Serbia. E-mail: sonjaavlijas@gmail.com *** Department of Economics, University of Bath, UK and FREN, Serbia. E-mail: s.vujic@bath.ac.uk Abstract Using the Labour Force Survey data in the period 2008-2011 and the Blinder-Oaxaca wage decomposition, this paper examines the scope and the characteristics of wage disparities between women and men in Serbia, Montenegro, and Macedonia. Estimation results show that in Serbia and Macedonia employed women have better labour market characteristics than employed men and, once this is taken into account, the wage gap increases in comparison to the simple difference in wages between an average working woman and an average working man. On the other hand, in Montenegro better female characteristics in terms of education are cancelled out by the fact that women work in the less paid sectors and occupations. Therefore, when adjusted for all labour market characteristics, the wage gap in Montenegro remains the same as the average gender difference in wages. The adjusted wage gap, usually ascribed to the effect of labour market discrimination, is the largest in Macedonia (17.9 percent), closely followed by Montenegro (16.1 percent), while the gap in Serbia is substantially lower (11 percent). Furthermore, striking differences in wage gap patterns between the private and the public sector are observed in all three countries, confirming the pronounced duality of labour markets in the Western Balkans. JEL Classification: J16, J31, J71. Keywords: Gender pay gap, Western Balkans, Blinder-Oaxaca decomposition.                                                                                                                  Acknowledgments:  

the Belgrade-based Foundation for the Advancement of Economics (FREN) and the Skopje-based University American College Skopje (UACS), within the framework of the Regional Research Promotion Programme in the Western Balkans (RRPP), run by the University of Fribourg upon a mandate of the Swiss Agency for Development and Cooperation, SDC, Federal Department of Foreign Affairs. The views expressed in the paper are those of the authors and do not necessarily represent opinions of the SDC and the University of Fribourg.  

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1. Introduction

opportunities. In spite of many policy actions of the EU to reduce the gender pay gap, it

persists as the global labour market phenomenon. The unadjusted gender pay gap, which

is a difference between the average wages of men and women is 16 percent in the

European Union (EU, 2013),1 while even higher in the United States 23 percent.2

Evidence on the gender pay gap in Serbia, Macedonia and Montenegro is scarce and in

recent years mainly policy-oriented (mainly the World Bank reports). Gender pay gap is

substantially more researched in Serbia than in Macedonia and Montenegro, while only

one paper (Blunch, 2010) compares the wage gaps between Serbia, Macedonia and four

other countries in the Eastern Europe and Central Asia, but not Montenegro. This paper

aims to contribute to the understanding of the gender pay gap in Serbia, Macedonia and

Montenegro, by providing systematic, country-specific and comparative analysis of the

gender pay gap in the three Western Balkan countries.

The Blinder-Oaxaca wage decomposition methodology enables us to go beyond the

simple differences in average earnings of men and women.3 It decomposes the unadjusted

gender pay gap into the two components: the explained part, which is due to differences

in earnings-relevant labour market characteristics of men and women; and the

unexplained part which is usually attributed to discrimination. The unexplained part is

also referred to as the adjusted pay gap. When analysing the gender pay gap we take into

account different labour market characteristic such as level of education, working

experience (tenure), industry sector and occupation of the workers, and the type of

                                                                                                               1 According to the recent publication of the European Commission, the EU-27 gender pay gap was 17.3 percent in 2008 and 16.2 percent in 2010, the year with last available data at the EU level. 2 http://www.iwpr.org/initiatives/pay-equity-and-discrimination. 3 Methodology was developed in the wider area of research on discrimination and may be applied to discrimination based on the race, ethnic, national or other differences. We discuss this methodology in a separate part of this paper.

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ownership. Special attention is also paid to the differences in the wage gap between

public and private sectors.

The structure of the paper is organised as follows. In Section 2 we introduce a theoretical

framework and present results of the existing empirical studies on the gender pay gap,

focussing also on differences in the results between the Western economies (that is,

Western Europe and the US) and the transition countries, considering Serbia, Macedonia

and Montenegro also as transition and emerging market economies. Section 3 explains

the data and the methodology used in this paper. Section 4 presents the comparative

estimation results on the gender pay gap in all three countries. Finally, Section 5 provides

discussion and conclusions.

2. Theoretical Perspectives on the G ender Pay Gap and Empirical F indings for the

W estern Economies

Western economies and the explained and the unexplained part of the unadjusted pay gap

As mentioned before, the research on gender pay gap is mainly based on the

methodological concepts of wage decomposition, which splits the unadjusted pay gap

into the explained and the unexplained parts. The unadjusted pay gap is a simple

difference in the average wages earned by women and by men. In other words it is a

difference between wages of an an employed

man in a given population. In all the countries average employed women and average

employed men differ in characteristics that determine the wages (such as the level of

education, working experience, etc). In the Western economies men usually have an

advantage in these characteristics which, as a labour group, makes them more attractive

to employers.

to

employers due to their advantage in the wage-relevant labour market characteristics. This

part of the gap is referred to as the explained part of the gap. Echrenberg and Smith

(2003) summarise the most important differences between men and women that are the

sources of the explained part of the gender wage gap. Most typically, in the Western

  4

economies women have lower levels of educational attainment and working experience.4

Additionally, women, on average, have worse occupational choices. Occupations and

sectors which are traditionally female in the sense that women work more frequently in

them, are characterised with lower wages than the traditionally male occupations and

sectors (Echrenberg and Smith, 2003). Occupational segregation is a part of the allocative

discrimination (see below) labelled as valuative discrimination (Petersen and Saporta,

2004). This phenomenon refers to the fact that higher paying male occupations do not

necessarily require higher skills or other wage-relevant factors.

Adjusted pay gap differences in returns and in unobservable characteristics

When the unadjusted gap is adjusted for male advantage in the differences in the labour

market characteristics, unexplained and in the literature is most often

interpreted as the discrimination of women. This unexplained part of the gap is also

referred to as the adjusted pay gap. The value of the adjusted gap is calculated using the

Blinder-Oaxaca decomposition, which is explained later in the paper. Since the

regression estimation of the wage equation holds all wage-relevant characteristics

statistically equal, the adjusted pay gap represents a measure of true magnitude of the

gender wage gap, cleaned from the differences in wage-relevant characteristics between

employed men and women. These unexplained differences persist due to the different

returns to characteristics for men and women or some unobserved heterogeneity between

the genders which affects their earnings.

Differences in returns to the labour market characteristics stem from a discriminatory

treatment of women on the labour market. Namely, if women are being rewarded less for

the same labour market characteristics then men, than it can be said that they are

discriminated. In the discrimination literature there is a distinction between the two types

of discrimination: taste-based and statistical discrimination (Altonji and Blank, 1999).

Whether taste-based or statistical, the discrimination in wages can work through two

                                                                                                               4 In recent years, there is evidence that gender differences in education levels are closing up (see, for example, Goldin et al., 2006).

  5

main mechanisms: direct or allocative discrimination.5 Direct or within-jobs

discrimination, refers to the situation where women are paid less when working at the

exactly the same position as men and within the same company. Although this type of

discrimination is directly tackled with equal pay for work of equal value policies,

within-job discrimination persists in a number of countries (see, for example for

Germany, Wolf and Heinze, 2010; Gartner and Hinz, 2009, in Ludsteck, 2010). On the

other hand, allocative discrimination occurs at the point of hire, or promotion. Namely,

although equally qualified as men, women, often face worse position at the start of their

carrier and lower opportunities for job promotion (Petersen and Saporta, 2004). Manning

and Swaffield (2005) show that the gender pay gap is small on entry in the labour market

but then widens quite rapidly due to the relatively lower labour market attachment of

women (that is, due to the family-induced career breaks) and because women are much

less likely to become managers. The latter represents the so-called glass ceiling effect, i.e.

the fact that women (although equally qualified) are not promoted to the top managerial

positions, which yield higher wages (Albrecht et al., 2003; Arulampalam et al., 2007).

Higher gender wage gap at the bottom of the wage distribution (the sticky floor effect)

also occurs in some countries and is also a part of allocative discrimination.

After controlling for both differences in characteristics and differences in returns, what

remains of the unadjusted pay gap is attributed to the unobserved heterogeneity between

men and women.6 For example, recent research shows the differences in job acceptance

due the differences in family responsibilities. While men show greater flexibility and

mobility, women prefer jobs with larger number of days off or proximity of work to

home, even if the wages are lower (Felfe, 2012). Women are also less likely than men to

negotiate wages and are more likely to accept the first wage on offer (Babcock and

Laschever, 2003), although evidence in this field is inconclusive (Leibbrandt and List,

2012). Additionally, unobserved heterogeneity is often attributed to the heterogeneity in

attitudes and psychological traits, such as less interest in competition and risk averseness

(see Bertrand, 2011, for overview).

                                                                                                               5 Distinguishing between these two types of discrimination requires firm level data, which for Serbia, Macedonia and Montenegro are not available. 6 Since these are the unobserved characteristics it is not possible to distinguish between differences in characteristics and returns to these characteristics.

  6

The data set that we use for this research Labour Force Survey, enables us to analyze

the levels of the unadjusted and the adjusted gap in each of the three countries, and to

separate the effects of the differences in returns and the unobserved heterogeneity in the

adjusted. However, the data does not include the information that would enable to

separate the effects of the taste-based and statistical discrimination, nor the effects of the

direct and allocative discrimination. While the former requires especially designed

surveys on attitudes, the latter requires firm level data, both of which are not available for

Serbia, Macedonia and Montenegro.

Gender pay gap in the transition economies and in the Western Balkans

Full employment and equal treatment of men and women in the labour market, were the

flagships of the economies in Eastern Europe and Former Soviet Union during the

Communist era. These proclaimed goals led to high levels of female labour market

participation. However, women were often viewed as secondary workers who are less

ambitious, with family responsibilities as their priorities.

One of the most important changes that occurred during the transition of these

economies, that affected the gender pay gap, was disproportionally high exit of women

with low skills from the labour market (Grajek, 2003; Hunt, 2002; Orazem and

Vodopivec, 2000; Olivetti and Petrongolo, 2008). Since women with low skills also had

low levels of earnings, their exit from the labour market led to a decrease in the

unadjusted gender pay gap, since the average wages of women were pushed upwards.

Thus, low unadjusted gender pay gap can be a result of low participation of women with

low wages. Second potentially off-setting change during the transition was an increase in

wage dispersion, which was expected to worsen the relative wage position of women,

who are predominantly located in the lower part of the wage distribution (Jurajda, 2005).

Literature on the size of the wage gap before and after the transition is therefore

inconclusive. Jurajda (2005) reports that some of the transition studies find the female-male

wage gap to be stable over time (for example, Newell and Reilly, 2001), some find it increasing

in countries with a dramatic rise in wage inequality (for example, Brainerd, 2000), and some find

  7

a decrease in the gap in countries with large outflows of low-earners from employment (for

example, Orazem and Vodopivec, 2000).

Current evidence for Serbia, Macedonia and Montenegro

Findings for Serbia almost unanimously conclude that while the unadjusted pay gap is

lower than in the EU countries, the adjusted pay gap is, contrary to the situation in the

Western economies, higher than the unadjusted pay gap

; Blunch and Sulla, 2010; Reva, 2012). In other words,

although the unadjusted gap is lower than in the EU countries, the wage discrimination is

far from extinct. This result is mainly explained, by the fact that employed women, on

average, have better labour market characteristics than men, due to the low labour market

participation of women with low skills. Evidence for Macedonia (Angel-Urdinola, 2008;

Angel-Urdinola and Macias-Essedin, 2008) suggests that there exists a large wage gap

between men and women and contributes it mainly to wage discrimination. Evidence

from Montenegro is largely descriptive and suggests that women more frequently occupy

low-paid jobs with few opportunities for career progression.

Blunch (2010) compares the gender earnings gap between Serbia, Macedonia and four

other countries in the Eastern Europe and Central Asia (Kazakhstan, Moldova, Tajikistan

and Ukraine). In all the countries there is a substantial unadjusted wage gap ranging from

12.4 percent in Serbia to 27.2 percent in Ukraine. In Macedonia the unadjusted wage gap

is 17.5 percent. Similarly to the above mentioned research for Serbia, women, on

average, have better labour market characteristics than men, in all the countries except for

Moldova and Tajikistan. Thus the adjusted wage gap is higher than the unadjusted and it

stands at 20 percent in Serbia and at 22.7 percent in Macedonia.

3. M ethodology and Data Description

Blinder Oaxaca decomposition

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We estimate the unadjusted and the adjusted gender pay gaps using the Blinder-Oaxaca

(BO) decomposition (Blinder, 1973; Oaxaca, 1973). The BO decomposition is essentially

a decomposition of mean differences in wages based on the linear regression models. The

BO procedure divides the wage differential between men and women into (a) the

explained part differences in labour market characteristics (education, work

experience, etc), and (b) the unexplained part, which is often used as a measure of

discrimination, but it also includes the effects of the unobserved heterogeneity between

genders (Jann, 2008). Starting point of the BO decomposition are separate wage

equations for men (M) and women (F):

(1a)

(1b)

where and are log hourly wages of men and women, and denote k-1 male

and female labour market characteristics (education, work experience, industry sector and

occupation, and the type of ownership) and respective intercept terms, and contain

the slope parameters, while and are the respective error terms. After separate OLS

estimations of the slope coefficients for men and women in (1a) and (1b), the difference

in average log hourly wages can be expressed as the difference in the linear prediction

evaluated at the group-specific averages of k-1 labour market characteristics:

(2)

where and are the observed averages of log hourly wages for men and women;

and are the averages of the relevant labour market characteristics and and are

estimated slope coefficients of the two earnings equations, including the estimated

intercepts. In order to separate the contribution of group differences in the labour market

characteristics from the overall difference in log hourly wages, (2) can be rearranged as:

. (3)

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Let us further assume that ,7 so we get:

. (4)

Equation (4) represents the Blinder-Oaxaca twofold8 decomposition in an aggregate

form, whereby the mean difference in log hourly wages ( ) is decomposed into

two parts.

The first part on

is a vector of differences in average labour market characteristics (level of education,

work experience, industry sector and occupation, and the type of ownership) of men and

women ( ), weighted by the vector of male slope coefficients . This part of

the gap is an estimation of the impact of the gender differences in the labour market

characteristics on the gender pay gap. In the Western economies this part of the gap is

positive since on average male labour market characteristics are better than female.

Current empirical evidence from the Western Balkan countries suggests that this part of

the gap is negative, since on average, employed women have better labour market

characteristics than employed men.

The second part on the

gap, is the vector of differences in estimated slope coefficients in male and female

earnings equations , weighted by the vector of female average

characteristics . The unexplained part of the gap is also called the adjusted pay gap

and is often contributed to discrimination. Since the explained part of the gap in the

Western economies is positive, in these countries the adjusted gap is lower than the

unadjusted. On the other hand, in the Western Balkan countries, due to a negative sign of

the explained part, the adjusted gap is higher than the unadjusted.

                                                                                                               7 See the discussion on the choice of the bellow. 8 The twofold BO decomposition is not the only decomposition of the wage gap in the literature. Besides this decomposition, there is also a three-fold BO decomposition, as well as other decompositions such as the Nopo decomposition (Nopo, 2008) and the Juhn-Murphy-Pierce decomposition (Juhn et al., 1993). In this paper we use the twofold BO decomposition since it is most appropriate with regards to the aims of the paper.

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Both the total explained and unexplained parts of the gap can further be decomposed into

detailed contributions of each predictor. Identifying the contributions of the individual

predictors to the explained or the unexplained part of the differential is straightforward

because the total component is a simple sum over the individual contributions. Starting

from the twofold decomposition in (4), we can write:

,

,

where equals 1.

While the explained part of the gap consists of k-1 impacts of the weighted differences in

labour market characteristics on the wage gap,9 the unexplained part of the gap consists

of k-1 differences in slope coefficients (returns to k-1 characteristics) and a difference in

intercepts ( ). The difference in estimated intercepts is used to estimate the effect

of unobserved heterogeneity on the gender pay gap (Reimer and Schröder, 2006).

We use the STATA function oaxaca developed by Jann (Jann, 2008) to estimate the

coefficients of the explained and the unexplained parts of the gap (both aggregate and

detailed). The estimation is based on the OLS estimates of the earnings equations for men

ing coefficients from the

pooled model10 of the wage equation as estimates of the non-discriminatory vector of

coefficients in (2), with gender dummy as a covariate. We further follow Jann in

applying the deviation contrast transformation11 to estimate the coefficients of the

unexplained part of the gap.

                                                                                                               9 Since the difference in the X X equals 1). 10 Pooled model refers to joint estimate of the coefficients for men and women. 11 The transformation is based on the series of estimations in which categories (for example, primary, secondary and tertiary level of education) are used one after another as the base (omitted) category, under the restriction that the sum of the coefficients must be equal zero. Using this estimation approach, the results of the Blinder Oaxaca decomposition are independent of the choice of the omitted category. This transformation is applied to all the dummy variables representing categorical variables with three or more categories. More details on the transformation can be found in Yun (2005) and Jann (2008).

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Data Description

The sample for the regression analysis consists of 25,580 individuals (11,271 women and

14,309 men) for Serbia, 19,737 individuals (8,107 women and 11,630 men) for

Macedonia and 8,969 individuals (4,973 men and 3,996 women) for Montenegro. Sample

is based on the employed individuals for whom we observe wages in the Labour Force

Survey (LFS) in the period from the 4th quarter of 2008 to the 4th quarter of 2011 (bi-

quarterly data).12 The base sample includes individuals in the age group 15-64, who work

full-time either in the public or in the private sector,13 are not in training or formal

education, and are not self-employed and/or unpaid family members.

In all regression specifications, the dependent variable is log hourly real wage,14 deflated

a

baseline).

Vectors of independent variables ( and ) in (1a) and (1b) include: dummy

variables for completed education, categorized into three levels (primary, secondary and

tertiary education); work experience,15 dummy variables for occupation, divided into

eight categories according to the International Standard Classification of Occupations

(ISCO);16 dummy variables for industry sector, divided into 5 categories from original 21

                                                                                                               12 Since the Labour Force Survey (LFS) in Serbia is conducted twice a year, in October and April, we observe wages from October 2008 to October of 2011 for Serbia for each of the waves (except April 2010). To have a comparative sample for the other two countries, we take only the 2nd and the 4th quarter from Montenegro and Macedonia, starting with the 4th quarter of 2008 and ending with the 4th quarter of 2011 (7 waves in total). For Serbia, we do not have data for April 2010. 13 We exclude those who are working in the socially owned enterprises and those who answer the

14 In Serbia, between 2008 and 2011, the definition of the wage variable changed twice. The first change occurred between 2009 and 2010, when the question on the exact wage amounts was replaced by the 14 wage brackets question. The second change occurred between April 2011 and October 2011, when the 14 wage brackets were replaced by the 10 wage brackets. In Macedonia wages are collected using the question with 10 wage brackets. Brackets changed between 2010 and 2011 to include 11 instead of 10 categories. In Montenegro, wage information is registered via the question on the exact wage amounts. In order to unify the variables for all the waves and the countries, the values of the wage brackets were replaced by the means of wage bracket intervals. In all the countries wage is registered at the monthly level and was divided by usual weekly working hours*23/5 to obtain hourly wages. Working hours are reported at the weekly level. To obtain monthly comparable working hours, we multiply with 5 (days in a week) and 23 (= average number of working days in a month for those working full-time). 15 For Macedonia and Montenegro we include tenure instead of working experience since the information on working experience was not available. 16 Categories are: senior officials and managers; professionals; technicians and associate professionals; clerks; service and sales workers; craft and trade workers; plant and machine operators; and elementary

  12

categories of the NACE Statistical Classification of Economic Activities (revision 2)

classification;17 dummy variable for whether a person works in a public or a private

sector; dummy variable for a temporary or a permanent contract; region,18 and time fixed

effects.

The BO decomposition was applied separately for each country, and within each country

separately for the public and the private sectors.

4. Estimation Results

Blinder-Oaxaca decomposition results and analysis of the explained part

Separate Mincer wage equations for all three countries are reported in Table A1 in the

Appendix. In all three countries all the coefficents have expected signs (for example,

higher level of education and working experience  have positive effect on the wages, see

table A1 for details).

Separate BO decompositions for all three countries are reported in Table A2 in the

Appendix. Figure 1 shows estimated coeficents for the unadjusted and the adjusted

gender gaps in Serbia, Macedonia and Montenegro.19 All the coefficents are statistically

significant at 0.01 level. Similarly to the results of the previous research on the gender

pay gap in Serbia, the differences in labour market characteristics between men and

women cannot explain the gender wage gap in the Western Balkans. On the contrary, the

explained part of the BO decompostion is negative in Serbia and Macedonia ( 0.077 and

0.044 respectively), while zero and statistically insignificant in Montenegro.

                                                                                                                                                                                                                                                                                                                                         occupations. Due to a very small sample size, skilled agricultural workers are categorized as technicians and associate professionals, while armed forces are grouped together with professionals. 17 These five categories are: (1) agriculture; (2) manufacturing; (3) trade, HORECA and transport; (4) modern services such as communication, financial intermediation, and real estate; (5) public administration, education, health, social service activities and activities of extraterritorial (ET) organisations and bodies; 18 Information on region was not available for Macedonia. 19 If coefficients for the unadjusted and the adjusted gap are multiplied by 100, they would approximately represent the percentage difference between the wages of men and women.

  13

Figure 1: Unadjusted and adjusted gender wage gaps.

Source: Own calculations from LFS data, 2008-2011.

In other words, in Serbia and Macedonia differences in characteristics hide the true

magnitude of the gap, because employed women in the two countries on average have

better labour market characteristics than employed men. Detailed BO decomposition

(Table 2) suggests that, in these two countries, female advantage is mainly due to their

higher average education and higher frequency in the better paid occupations (for detailes

on differences in characteristics, see Table A3 in Appendix). Better educational

characteristics lower the unadjusted gap by 0.028 and 0.018, while better occupational

characteristics lower the gap by -0.022 and 0.027 in Serbia and Macedonia, respectively.

Table 2: Detailed BO decomposition in three countries

Serbia Macedonia Montenegro Education -0.028 -0.018 -0.024 Work experience 0.004 0.002 0.002 Occupation -0.024 -0.020 0.013 Sector of activity 0.002 -0.007 0.021 Ownership -0.014 0.002 -0.001 Type of contract -0.004 -0.004 0.000 Region -0.010 NA -0.012 Source: Own calculations from LFS data, 2008-2011. * Coefficents in bold are statistically significant at the 0.05 level. ** Time effects are not presented in the table, since for all the countires they are statistically insignificant.

  14

In Montenegro, a different trend is observed. Women have higher level of education on

average, which lowers the gap by -0.024, while men, on average, work in better paid

occupations and sectors, which widens the gap by 0.033. In other words, employed

women in Montenegro have, higher average education than men, while at the same time

choose lower paid occupations more frequently (see Table A3 in Appendix for

details on the differences in characteristics). Occupational characteristics in Montenegro,

thus work to explain a part of the gap as they do in the Western economies. We will

discuss this trend in further detail after a separate analysis of the public and private

sectors in Montenegro. These advantages and disadvatages of employed women (together

with other characteristics in Table 1) each other, such that the difference

between the adjusted and the unadjusted gap is zero.

In other words, while the high unadjusted wage gap in Montenegro partially exists due to

the greater diversification of women across occupations and sectors of activity, and

Macedonia, we cannot find evidence for this type of discrimination.

The size of the unadjusted wage gap accross countries

When we compare the size of the unadjusted gap across the three countries, we find

further support for the trade-off between gender employment and the unadjusted gender

pay gap. As shown in Figure 1, the unadjusted gender wage gap is the most pronounced

in Montenegro (0.161), compared to Macedonia (0.139) and Serbia (0.033). The reason

for this might lie in the fact that the employment gap in Montenegro is the lowest of the

tree countries. Namely, the employment gap for the same period is estimated to be 13.4

percent in Montenegro, compared to 15.0 percent in Serbia and 18.6 percent in

Macedonia (FREN, 2013). Furthermore, the employment gap in the EU-27 (12.4 percent)

is lower than in all three Western Balkan countries, while the unadjusted wage gap is

higher and amounts to 0.176 (Eurostat, 2013).

On the other hand, although the employment gap is the highest in Macedonia, the

unadjusted gap is not the lowest in Macedonia (0.139)

in the trend is due to the fact that the discrimination effect measured through the

  15

adjusted gap coefficient is significantly higher in Macedonia then in Serbia (0.179 versus

0.110).

The unexplained part of the gap: differences in returns vs unobserved heterogeneity

In all three countries the adjusted gap is better explained by the unobserved heterogeneity

between the genders, than by differences in returns to observable labour market

characteristics.

In Serbia, the entire adjusted gap exists due to unobservable heterogeinity between men

and women, while on average we do not observe differences in returns to labour market

characteristics. Similarly, the largest share of the adjusted wage gaps in Macedonia and

Montenegro stems from unobservable characteristics of workers, (69

percent=0.125/0.179 in Macedonia, and 75 percent=0.120/0.161 in Montenegro), while

differences in returns to labour market characteristics account for 31 percent (0.054 of

0.179) and 75 percent (0.12 of 0.161) of the adjusted wage gap (Table A2 in Appendix).

Gender pay gap in the public and the private sector

All the Western Balkan countries show strong labour market duality (see, for example,

Arandarenko, 2012), where the public and the private sectors operate under the different

Thus, we test the hypothesis that the differences in wages between genders and

the determinants of the wage differences are different across the two sectors.

Separate BO decompositions for the public and the private sectors are reported in the

Table A3 in the Appendix. The unadjusted and the adjusted gaps for each country and

sector are presented in Figure 3. The analysis yields several interesting conclusions.

Firstly, the unadjusted and the adjusted wage gaps are significantly higher in the private

than in the public sector in all three countries (Figure 3, blue bars). This difference is due

to the fact that on average, the female wages in the private sector of all three countries are

significantly lower than the male wages, and also lower than the female wages in the

public sector.

  16

Secondly, the order of the unadjusted gap sizes is not disturbed by splitting the sample

into the public and the private sectors. Namely, the unadjusted wage gap in both sectors

are the highest in Montenegro (0.237 in the private and 0.087 in the public sector),

followed by Macedonia (0.177 in the private and 0.040 in the public sector), while they

are the lowest in Serbia (0.094 in the private and 0.016 in the public sector).

Figure 3: The unadjusted and the adjusted wage gap, public (left) versus private sector

(right).

Source: Own calculations from LFS data, 2008-2011.

Thirdly, the trends in the adjusted gaps show significant difference in the unadjusted gap

decompositions between the sectors (Figure 3, blue and red bars). While in the public

sector in all three countries adjusted gaps are significantly higher than the unadjusted

ones (at least by 0.03), in the private sector the adjusted gaps are only slightly above the

unadjusted gaps (in Serbia and Macedonia), while in Montenegro, the adjusted gap is

significantly lower than the unadjusted gap (by 0.06).

In other words, in the public sector, better labour market characteristics of women

substantially underestimate the unadjusted wage gap in all three countries. In Serbia and

Macedonia this advantage is, similarly to the overall analysis,

educational characteristics and occupational choices of women. Unlike the overall

analysis, in Montenegro, the better educational characteristics of women prevail over

  17

better choices of sector of activity of men, making the explained part of the gap negative

(Table A4 in Appendix).

On the other hand, in the private sector, female labour market characteristics are either

only slightly better than male, so they have no substantial impact on the gap (in Serbia

and Macedonia), or they are significantly worse, so they overestimate the gap (in

Montenegro). In Serbia and Macedonia these differences are mainly due to the higher

average level of education of women, although this advantage is significantly lower than

in the public sector. In Montenegro, men working in the private sector have signficantly

better characteristics than women due to their better occupational choices, while there are

no differences in education. Thus in this sector we find the only example of valuative

discrimination (Petersen and Saporta, 2004), that is, the fact that women tend to have

choices. Namely, while there is no advantage in the average level of

education between men and women, occupations which women choose more frequently

(mainly Service and sales workers, see Table A4 in Appendix) are partially responsible

for the highest unadjusted gap of all six gaps in Figure 3 (3 countries times 2 sectors).

This difference may be due to the strong tourism sector in Montenegro, which is highly

competitive, such that the higher level of valuative discrimination exists. This sector is

therefore closer to the Western trends where women have worse labour market

characteristics than men, which can partly explain the differences in earnings between the

two genders. However, even when we adjust for this difference we find that the adjusted

gap is very high in the public sector in Montenegro (0.174).

Lastly, due to these opposing trends in the labour market characteristics, the differences

in the adjusted gaps between the public and the private sectors in all three countries are

lower than the differences in the unadjusted gaps. In other words, after adjusting for the

differences in the labour characteristics, in all three countries the differences between the

wage gaps in the public and the private sector become smaller. The difference in the log

hourly wage differentials between the gaps in the public and the private sectors, fall from

0.078 to 0.035 in Serbia, from 0.137 to 0.071 in Macedonia, and from 0.150 to 0.056 in

Montenegro.

  18

The adjusted gender wage gap in the public and the private sectors

Similarly to the average observed trends in the overall analysis, in the public sectors in

Serbia and Macedonia the unexplained part of the gap is better explained in terms of the

unobserved heterogeneity than in terms of the returns of education. Namely, in both

countries the differences in returns are not significant, and the unobserved heterogeneity

accounts for the whole gap.20 In the public sector in Montenegro, returns are higher for

men, i.e. they explain 68 percent of the adjusted gap (0.07 out of 0.118), while the

unobserved heterogeinity comprises 32 percent of the gap (0.03 of 0.118).

In the private sector, in all three countries, men have higher returns than women. These

higher returns explain 42 percent, 20 percent and 36 percent of the adjusted gap in Serbia,

Macedonia and Montenegro, respectively. Although the differences in returns are

significant, the unobserved heterogeneity is still the main sourse of the adjusted gap and

explains 58 percent, 80 percent and 64 percent of the gap in Serbia, Macedonia and

Montenegro, respectively.

5. Conclusions

This paper shows that although the unadjusted gender pay gap in Serbia, Macedonia and

Montenegro is lower than the EU-27 average, the wage inequality between the genders is

far from non-existing. Namely, besides having lower wages on average, women in Serbia

and Macedonia have better and in Montenegro (on average) the same wage-relevant

labour market characteristics as men. Thus, unlike in the Western economies, where a

part of the wage differences can be attributed to the better labour market characteristics of

men, in the Western Balkans, this is not the case. In fact, after adjusting for labour market

characteristics, the results show that in Serbia and Macedonia the wage inequality is

higher than the simple difference in average wages between genders would suggest. In

Montenegro, controlling for labour market differences does not change the level of the

gap.

                                                                                                               20 Women actually have higher returns to the same labour market characteristics when working in the public sector than men (by 4pp on average), although these differences are not statistically significant.

  19

The advantage of women in terms of labour market characteristics in Serbia and

Macedonia is split between their higher education and better choices of occupations (that

is, occupations with higher returns). In Montenegro, women also have higher education

such that, on average, these characteristics

to disproportionally low participation of the low skilled women. Thus the main reason

behind the relatively low unadjusted gender pay gap in the analysed countries is high

gender employment gap among those with low education. Since women with low skills

would earn low wages, their absence from the labour market pushes the average wages of

women upwards, and the unadjusted gap downwards.

A very important factor in analysing gender pay gap is the sector of ownership. In the

public sector, in all three countries we find that the gaps are relatively lower, but at the

same time advantage in the labour market characteristics is more prominent. In

the private sector, the unadjusted pay gap is higher, but women have either insignificantly

better labour market characteristics than men (Serbia and Macedonia) or worse

characteristics than men (Montenegro). Thus, although the adjusted gaps are higher in the

private than in the public sector, the differences in the adjusted gaps between the sectors

are lower than the differences in the unadjusted gaps.

Our analysis suggests that the adjusted pay gap is better explained in all three countries in

terms of the unobserved heterogeneity between the genders, rather than by differences in

returns to wage-relevant labour market characteristics. As the literature suggests, the

unobservable heterogeneity stems from the differences in family responsibilities, and

heterogeneity in attitudes and psychological traits between the genders. This goes in line

with the strong traditional division of male and female gender roles which are

characteristic for the Western Balkan countries. Thus, in order to gain further

understanding of the gender difference in wages, future research of the gender wage

inequality in the Western Balkan should explicitly model these differences between

genders.

  20

Bibliography

Albrecht, J., Björklund, A., and Vroman, S. (2003). Is There a Glass Ceiling in Journal of Labour Economics, 21, pp. 145-177.

Altonji, J. G. and Blank, R. M. Race and Gender in the Labour Market, in Ashenfelter, O. and Card, D. (ed.), Handbook of Labour Economics, Vol. 3, Elsevier Service B.V., Chapter 48, pp. 3143-3259.

Angel-Urdinola, D. F. (2008). Can the Introduction of a Minimum Wage in FYR Macedonia Decrease the Gender Wage Gap? The World Bank Social Policy Discussion Paper No. 0837, Washington D.C., December.

Angel-Urdinola, D. F. and Macias, V. FYR Macedonia Labour Market Profile 2004-2007: A Policy Note , A Document of the World Bank, Human Development Sector Unit, Europe and Central Asia.

Arulampalam, W., Booth, A. L., and Bryan, M. L. Is There a Glass Ceiling over Europe? Exploring the Gender Pay G Industrial and Labor Relations Review, 60, pp. 163-186.

Babcock, L., and Laschever, S. (2003). Women Don't Ask: Negotiation and the Gender Divide, Princeton: Princeton University Press.

Bertrand, M. (2011). in Ashenfelter, O. and Card, D. (ed.), Handbook of Labor Economics, Vol. 4B, Elsevier B.V., pp. 1543-1590.

Black, S. E. and Brainerd, E. (2004). Importing Equality? The Effects of Increased Competition on the Gender Wage Gap Industrial and Labor Relations Review, 57, pp. 540-559.

Blinder, A. S. (1973). Wage Discrimination: Reduced Form and Structural Estimates The Journal of Human Resources, 8(4), pp. 436-455.

Blunch, N. H. The Gender Earnings Gap Revisited: A Comparative Study for Serbia and Five Countries in Eastern Europe and Central Asia Background paper

s Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department.

Blunch, N. H. and Sulla, V. (2010). The Financial Crisis, Labor Market Transitions and Earnings: A Gendered Paper commissioned by the

  21

Asia Region Department.

Boraas, S. and Rodgers, W. M. III (2003). How does gender play a role in the earnings Monthly Labor Review, pp. 9-15.

Brainerd, E Women in Transition: Changes in Gender Wage Differentials in Eastern Euro Industrial and Labor Relations Review, 54(1), pp. 138-162.

Busch, A. and Holst, E. (2009). Glass Ceiling E ffect and Earnings The Gender Pay Gap in Managerial Positions in Germany, Deutsches Institut für Wirtschaftsforschung, Berlin.

Echrenberg, R. G. and Smith, R. S. (2003). Modern Labor Economics: Theory and Public Policy, 8th edition, Pearson Education, Inc., Chapter 12, pp. 377-422.

European Union Tackling the Gender Pay Gap in the European Union Luxembourg: Publications Office of the European Union.

Felfe, C The Willingness to Pay for Job A Industrial and Labor Relations Review, 65(2), pp. 427-454.

Floro, M Financial Crisis, Gender, and Power: An Analytical Framework World Development, 28(7), pp. 1269 1283.

Goldin, C., Katz, L. F. and Kuziemko, I. (2006). The Homecoming of American College Women: The Reversal of the College Gender Gap Journal of Economic Perspective, 20(4), pp. 133 156.

Grajek, M. (2003). Gender Pay Gap in Poland Economics of Planning, 36, pp. 23 44.

Hunt, J. (2002). The transition in East Germany: when is a ten point-fall in the gender Journal of Labor Economics, 20, pp. 148 169.

Jann, B. (2008). The Blinder-Oaxaca Decomposition for Linear Regression Models The Stata Journal, 8(4), pp. 453-479.

Juhn, C., Murphy, K. M. and Pierce, B. (1993). Wage Inequality and the Rise in Returns to Skill Journal of Political Economy, 101(3), pp. 410-442.

  22

Jurajda, . (2005). Gender Segregation and Wage Gap: An East-West Comparison Journal of the European Economic Association, 3(2/3), pp. 598-607.

. and Barrett, G. (2011). Transition Comparative Economic Studies, 53, pp. 695-720.

. and Reilly, B. (2000). The gender pay gap in the FRY Ekonomska misao, 33 (3-4), pp. 191-213.

Leibbrandt, A. and List, J. A. Do Women Avoid Salary Negotiations? Evidence from a Large Scale Natural Field Experiment , NBER Working Paper No. 18511.

Ludsteck, J. (2010). Occupational Segregation within Establishments and the Gender Wage Gap Preliminary draft, IAB Nuremberg.

Manning, A. (2011). Imperfect Competition in the Labor Market , in Ashenfelter, O. and Card, D. (ed.), Handbook of Labor Economics, Elsevier B.V., Vol. 4B, pp. 973-1041.

Manning, A. and Swaffield, J. (2005). The Gender Gap in Early Career Wage Growth , CEP Discussion Paper No. 700, http://cep.lse.ac.uk/pubs/download/dp0700.pdf.

Newell, A. and Reilly, B. (2001). The Gender Wage Gap in the Transition from Communism: Some Empirical Evidence Economic Systems, 25(4), pp. 287-304.

Nopo, H. (2008). Matching as a Tool to Decompose Wage Gaps , Review of Economics and Statistics, 90(2), pp. 290-299.

Oaxaca, R. L. (1973). Male-Female Wage Differentials in Urban Labor Markets International Economic Review, 14(3), pp. 693-709.

Olivetti, C. and Petrongolo, B. (2008). Unequal Pay or Unequal Employment? A Cross Country Analysis of Gender Gaps Journal of Labor Economics, 26(4), pp. 621-654.

Orazem, P. F. and Vodopivec, M. (2000). Male female differences in labour market outcomes during the early transition to market: The cases of Estonia and Slovenia Journal of Population Economics, 13, pp. 283 303.

Petersen, T. and Saporta, I. (2004). The Opportunity Structure for Discrimination American Journal of Sociology, 109(4), pp. 852-901.

  23

Reva, A. (2012). Gender Inequality in the Labor Market in Serbia World Bank Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department Policy Research Working Paper No. 6008, Washington, DC: World Bank.

Yun, M. S. (2007). Wage Differentials, Discrimination and Inequality: A Cautionary Note on the Juhn, Murphy and Pierce Decomposition Method IZA Discussion Paper No. 2937.

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Appendix

Table A1: Mincer Equation Estimation Results (Full Specification; Age 15-64) VARIABLES Serbia Macedonia Montenegro Female -0.110*** -0.179*** -0.161*** (0.006) (0.006) (0.010) Secondary ed. 0.121*** 0.075*** 0.127*** (0.009) (0.008) (0.031) Tertiary ed. 0.367*** 0.261*** 0.414*** (0.012) (0.012) (0.034) Work experience 0.009*** 0.013*** 0.007*** (0.001) (0.001) (0.002) Work experience (square) -0.000*** -0.000*** -0.000* (0.000) (0.000) (0.000) Q2 2009 -0.027*** 0.097*** 0.011 (0.008) (0.010) (0.018) Q4 2009 -0.000 0.163*** 0.046** (0.008) (0.010) (0.018) Q2 2010 - 0.152*** 0.028 (0.010) (0.018) Q4 2010 -0.064*** 0.202*** 0.053*** (0.009) (0.010) (0.019) Q2 2011 -0.130*** 0.154*** 0.031 (0.009) (0.010) (0.020) Q4 2011 0.068*** 0.171*** 0.040** (0.008) (0.010) (0.019) Professionals(a) -0.109*** -0.058*** -0.116***     (0.019) (0.022) (0.031) Technicians and associate professionals (b) -0.276*** -0.208*** -0.163***     (0.019) (0.022) (0.031) Clerks -0.384*** -0.218*** -0.282***     (0.020) (0.023) (0.032) Service and sales workers -0.535*** -0.426*** -0.369***     (0.020) (0.023) (0.032) Craft and trades workers -0.471*** -0.377*** -0.262***     (0.020) (0.023) (0.037) Plant and machine operators -0.423*** -0.450*** -0.198***   (0.020) (0.023) (0.038) Elementary occupations -0.594*** -0.506*** -0.460***   (0.020) (0.023) (0.039) Industry 0.191*** 0.143*** 0.097*   (0.019) (0.017) (0.055) Traditional services(c) 0.150*** 0.143*** 0.021   (0.020) (0.017) (0.056) Modern services(d) 0.244*** 0.259*** 0.125**   (0.022) (0.021) (0.059) Public Services(e) 0.161*** 0.155*** -0.028   (0.020) (0.017) (0.053) Public Sector 0.190*** 0.157*** 0.017   (0.007) (0.009) (0.016) Permanent contract 0.181*** 0.086*** 0.021   (0.018) (0.009) (0.025) Formal contract 0.152*** - -   (0.011)

  25

Table A1: Mincer Equation Estimation Results (Full Specification; Age 15-64) continued from previous page VARIABLES Serbia Macedonia Montenegro Regional dummy 2 (f) -0.155*** - 0.216***   (0.007) (0.012) Regional dummy 3 (g) -0.202*** - 0.259***   (0.007) (0.018) Regional dummy 4 (h) -0.272*** - - (0.008) Constant 4.563*** 5.493*** 1.938*** (0.033) (0.029) (0.070) Observations 25,580 19,738 8,969 R-squared 0.437 0.411 0.233 Rmse 0.41 0.37 0.47 F 669 631 146 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. Results are robust to: (1) Changes of the sample age restriction (19-59; 19-64); (2) inclusion of part-time employees; and (3) Elimination of the extreme values from the dependent variable (wages). Robustness checks are available at request. (a) including armed forces, (b) including skilled agriculture workers, (c) Trade, HORECA, Transport (d) Communication, financial intermediation, Real Estate, Experts (e) Public Administration, Education, Health, Social Service Activities, ET Organisations; (f) Serbia: Vojvodina; Montenegro: Central region; (g)

Montenegro: Coastal region (h) Serbia: Eastern and Southern Serbia. Table A2: Detailed BO decomposition

Serbia Macedonia Montenegro VARIABLES Explained Unexplained Explained Unexplained Explained Unexplained Primary ed. -0.004*** 0.001 -0.006*** -0.005*** -0.004*** -0.004 (0.001) (0.002) (0.001) (0.002) (0.001) (0.002) Secondary ed. -0.003*** 0.002 -0.001*** 0.003 -0.003*** 0.016 (0.000) (0.005) (0.000) (0.005) (0.001) (0.015) Tertiary ed. -0.021*** -0.002 -0.011*** 0.008** -0.017*** 0.011 (0.001) (0.003) (0.001) (0.003) (0.002) (0.008) Work experience 0.020*** -0.013 0.010*** 0.022 0.004** 0.100** (0.002) (0.032) (0.002) (0.019) (0.002) (0.044) Work experience (square)

-0.016*** -0.005 -0.008*** -0.018 -0.002 -0.053** (0.003) (0.020) (0.002) (0.012) (0.001) (0.026)

Q4 2008 0.000 0.006*** -0.000 0.004** -0.000 0.001 (0.000) (0.002) (0.001) (0.002) (0.000) (0.004) Q2 2009 0.000 0.001 -0.000 0.002 0.000 -0.000 (0.000) (0.002) (0.000) (0.002) (0.000) (0.004) Q4 2009 -0.000 -0.000 -0.000 -0.008*** 0.000 0.000 (0.000) (0.002) (0.000) (0.002) (0.000) (0.003) Q2 2010 - - 0.000 0.001 0.000 -0.003 (0.000) (0.002) (0.000) (0.004) Q4 2010 0.000 -0.004** -0.000 0.002 -0.000 0.002 (0.000) (0.002) (0.000) (0.002) (0.000) (0.003) Q2 2011 0.000 -0.003 -0.000 -0.001 -0.000 0.001 (0.001) (0.002) (0.000) (0.002) (0.000) (0.003) Q4 2011 0.000 -0.001 0.000 -0.000 -0.000 -0.002 (0.000) (0.002) (0.000) (0.002) (0.000) (0.003) Managers 0.004*** -0.001 0.003*** -0.002** 0.003*** -0.002 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Professionals(a) -0.014*** -0.008*** -0.009*** -0.016*** -0.007*** -0.010** (0.001) (0.003) (0.001) (0.003) (0.001) (0.005)

  26

Table A2: Detailed BO decomposition continued from the previous page

Serbia Macedonia Montenegro VARIABLES Explained Unexplained Explained Unexplained Explained Unexplained Technicians and associate professionals (b)

-0.008*** -0.006** -0.003*** -0.007*** -0.004*** -0.013** (0.001) (0.003) (0.001) (0.002) (0.001) (0.006)

Clerks 0.001 -0.009*** -0.002*** -0.014*** 0.003*** -0.014*** (0.000) (0.001) (0.000) (0.002) (0.001) (0.004) Service and sales workers 0.018*** 0.010*** 0.002** 0.013*** 0.011*** 0.011

(0.001) (0.003) (0.001) (0.003) (0.002) (0.008) Craft and trades workers -0.021*** 0.012*** -0.016*** 0.005*** -0.004 0.002

(0.001) (0.002) (0.001) (0.001) (0.002) (0.001) Plant and machine operators

-0.011*** 0.002* 0.011*** 0.036*** 0.005 0.001 (0.001) (0.001) (0.001) (0.003) (0.003) (0.002)

Elementary occupations 0.007*** 0.003 -0.006*** 0.002 0.005*** 0.008** (0.001) (0.002) (0.001) (0.002) (0.001) (0.003)

Agriculture -0.003*** -0.000 -0.004*** -0.001 -0.000 -0.002** (0.000) (0.001) (0.000) (0.001) (0.000) (0.001) Industry 0.010*** 0.013*** 0.000 0.015*** 0.011*** 0.018*** (0.001) (0.004) (0.000) (0.006) (0.003) (0.005) Traditional services(c) -0.000 0.001 0.000 0.009** 0.001 0.002 (0.000) (0.004) (0.000) (0.003) (0.001) (0.010) Modern services(d) -0.002*** -0.001 -0.002*** -0.004*** -0.001 -0.001 (0.000) (0.001) (0.000) (0.001) (0.000) (0.003) Public Services(e) -0.002* -0.005 -0.001** 0.011** 0.010*** 0.045*** (0.001) (0.004) (0.001) (0.005) (0.002) (0.011) Private Sector -0.014*** -0.029*** 0.002 0.012* -0.001 -0.035** (0.001) (0.004) (0.001) (0.006) (0.001) (0.015) Permanent contract -0.004*** 0.040*** -0.004*** -0.015 0.000 -0.050 (0.000) (0.010) (0.001) (0.016) (0.000) (0.043) Formal contract -0.004*** -0.012 - - -0.008*** -0.003 (0.000) (0.017) (0.001) (0.004) Regional dummy 1 (f) -0.006*** 0.005** - - -0.002** 0.015 (0.001) (0.002) (0.001) (0.011) Regional dummy 2 (g) -0.000 0.004* - - -0.002*** -0.001   (0.000) (0.002) (0.001) (0.003) Regional dummy 3 (h)

-0.000 -0.004* - - -0.008*** -0.003 (0.000) (0.002) (0.001) (0.004)

Regional dummy 4 (i) -0.004*** -0.005** - - - - (0.001) (0.002) Constant 0.100*** 0.125*** 0.120** (0.023) (0.020) (0.055) Observations 25,580 25,580 19,738 19,738 8,969 8,969

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 a) including armed forces, (b) including skilled agriculture workers, (c) Trade, HORECA, Transport (d) Communication, financial intermediation, Real Estate, Experts (e) Public Administration, Education, Health, Social Service Activities, ET Organisations; (f) Serbia: Belgrade; Montenegro: Central region; (g) Serbia: Vojvodina; Montenegro: Central region; (h)

region (i) Serbia: Eastern and Southern Serbia.

  27

Table A3: Difference of Means for Variables Used in the Regression Analysis

Means Diff. in

means+ Means Diff. in

means Means Diff. in

means. Men Women Men Women Men Women

Primary ed. 0.130 0.106 0.024*** 0.177 0.127 0.05*** 0.072 0.048 0.024***

Secondary ed. 0.685 0.609 0.076*** 0.618 0.593 0.025*** 0.694 0.645 0.049***

Tertiary ed. 0.185 0.285 -0.1*** 0.206 0.281 -0.075*** 0.234 0.307 -0.073***

Work experience 18.1 15.9 2.183*** 11.1 10.3 0.756*** 12.2 11.6 0.546**

Managers 0.035 0.024 0.011*** 0.031 0.019 0.012*** 0.032 0.018 0.014***

Professionals(a) 0.107 0.163 -0.056*** 0.118 0.157 -0.039*** 0.134 0.197 -0.063***

Technicians (b) 0.144 0.256 -0.112*** 0.114 0.162 -0.048*** 0.170 0.227 -0.057***

Clerks 0.068 0.106 -0.038*** 0.081 0.118 -0.037*** 0.112 0.161 -0.049***

Service and sales workers 0.140 0.234 -0.094*** 0.167 0.178 -0.011** 0.198 0.280 -0.082***

Craft and trades workers 0.245 0.069 0.176*** 0.201 0.038 0.163*** 0.137 0.019 0.118***

Plant and machine operators 0.172 0.028 0.144*** 0.148 0.214 -0.066*** 0.155 0.011 0.144***

Elementary occupations 0.090 0.120 -0.03*** 0.139 0.113 0.026*** 0.062 0.085 -0.023***

Agriculture 0.038 0.018 0.02*** 0.047 0.021 0.026*** 0.017 0.007 0.01***

Industry 0.452 0.224 0.228*** 0.415 0.356 0.059*** 0.307 0.109 0.198***

Traditional services(c) 0.248 0.285 -0.037*** 0.245 0.213 0.032*** 0.305 0.359 -0.054***

Modern services(d) 0.062 0.080 -0.018*** 0.048 0.066 -0.018*** 0.055 0.063 -0.008

Public Services(e) 0.200 0.392 -0.192*** 0.245 0.344 -0.099*** 0.316 0.462 -0.146***

Private Sector 0.392 0.464 -0.072*** 0.349 0.338 0.011 0.461 0.501 -0.04***

Permanent contract 0.877 0.906 -0.029*** 0.843 0.886 -0.043*** 0.878 0.873 0.005

Formal contract 0.944 0.968 -0.024*** - - - - - -

Regional dummy 1 (f) 0.212 0.251 -0.039*** - - - 0.242 0.191 0.051***

Regional dummy 2 (g) 0.249 0.253 -0.004 - - - 0.641 0.668 -0.027***

Regional dummy 3 (h) 0.282 0.273 0.009 - - - 0.117 0.141 -0.024***

Regional dummy 4 (i) 0.257 0.223 0.034*** - - - - - -

N 14,309 11,271 11,631 8,107 4,987 4,012 + Statistical significance of the differences in means (t-test): *** p<0.01, ** p<0.05, * p<0.1 a)   including   armed   forces,   (b)   and   associate   professionals,   including   skilled   agriculture  workers,   (c)  Trade,  HORECA,  Transport  (d)  Communication,  financial  intermediation,  Real  Estate,  Experts  (e)  Public  Administration,   Education,   Health,   Social   Service   Activities,   ET   Organisations;   (f)   Serbia:   Belgrade;  Montenegro:   Central   region;   (g)   Serbia:   Vojvodina;  Montenegro:   Central   region;   (h)  and  Western  Serbia;  Montenegro:  North  region  (i)  Serbia:  Eastern  and  Southern  Serbia.  

   

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Table A3: Detailed BO Decomposition for Public and Private Sector Serbia Macedonia Montenegro Public Private Public Private Public Private VARIABLES Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Primary ed. 0.000 -0.003* -0.005*** 0.002 -0.008*** -0.005** -0.005*** -0.004* -0.003** -0.002 -0.004*** -0.004 (0.001) (0.002) (0.001) (0.002) (0.001) (0.002) (0.001) (0.002) (0.002) (0.002) (0.001) (0.004) Secondary ed. -0.005*** 0.009 -0.000* 0.001 -0.002*** 0.014** 0.000 -0.002 -0.003*** 0.011 0.002* 0.018 (0.001) (0.006) (0.000) (0.009) (0.001) (0.007) (0.000) (0.007) (0.001) (0.012) (0.001) (0.029) Tertiary ed. -0.032*** 0.007 -0.009*** -0.002 -0.027*** 0.015* -0.005*** 0.004* -0.033*** 0.009 -0.001 0.008 (0.002) (0.006) (0.001) (0.003) (0.003) (0.009) (0.001) (0.003) (0.004) (0.012) (0.002) (0.007) Work experience 0.023*** -0.046 0.034*** 0.032 -0.003 0.017 0.014*** -0.001 0.000 0.053 0.008* 0.030 (0.004) (0.056) (0.004) (0.039) (0.003) (0.045) (0.002) (0.019) (0.003) (0.067) (0.005) (0.051) Work experience (square) -0.021*** 0.035 -0.026*** -0.029 0.001 -0.032 -0.009*** 0.000 0.000 -0.050 -0.001 -0.002 (0.004) (0.034) (0.005) (0.025) (0.002) (0.029) (0.002) (0.011) (0.002) (0.041) (0.005) (0.029) October 2008 0.000 0.002 0.000 0.010*** -0.002 0.009*** 0.000 0.001 -0.000 0.000 -0.000 0.002 (0.000) (0.002) (0.000) (0.003) (0.002) (0.003) (0.001) (0.002) (0.001) (0.004) (0.000) (0.006) April 2009 -0.000 0.000 0.000 0.003 -0.000 0.003 0.000 0.002 -0.000 -0.003 0.000 0.000 (0.000) (0.002) (0.000) (0.002) (0.001) (0.003) (0.000) (0.002) (0.000) (0.005) (0.000) (0.006) October 2009 -0.000 -0.001 0.000 0.001 -0.000 -0.002 0.000 -0.012*** -0.000 -0.005 0.001 0.004 (0.000) (0.002) (0.000) (0.003) (0.000) (0.003) (0.000) (0.002) (0.000) (0.004) (0.001) (0.005) October 2010 0.000 -0.003 0.000 -0.005* -0.000 -0.004 0.000 0.004* -0.000 0.001 0.001 -0.008 (0.000) (0.003) (0.000) (0.003) (0.000) (0.003) (0.000) (0.002) (0.000) (0.005) (0.000) (0.006) - - - - -0.000 0.002 -0.000 0.002 0.000 0.003 0.000 0.003 (0.001) (0.003) (0.000) (0.002) (0.000) (0.004) (0.000) (0.005) April 2010 -0.000 0.001 0.000 -0.006** 0.000 -0.006** -0.000 0.002 -0.000 0.003 0.000 -0.000 (0.001) (0.003) (0.001) (0.003) (0.000) (0.003) (0.000) (0.002) (0.000) (0.004) (0.000) (0.005) October 2011 0.000 0.000 0.001 -0.003 -0.000 -0.002 0.000 0.001 -0.000 -0.000 -0.000 -0.002 (0.000) (0.003) (0.001) (0.002) (0.001) (0.003) (0.000) (0.002) (0.000) (0.004) (0.000) (0.005) Managers 0.006*** -0.003** 0.002* -0.000 0.007*** -0.002 0.002** -0.002 0.005*** -0.000 0.002** -0.003* (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) (0.001) (0.002) Professionals(a) -0.017*** -0.024*** -0.004*** 0.001 -0.020*** -0.034*** -0.002* -0.003 -0.010*** -0.012 -0.002 -0.004 (0.002) (0.006) (0.001) (0.002) (0.002) (0.009) (0.001) (0.002) (0.002) (0.010) (0.001) (0.004) Technicians and associate professionals (b)

-0.012*** -0.025*** -0.003*** 0.002 -0.006*** -0.023*** -0.001** -0.001 -0.005*** -0.020** -0.000 -0.006 (0.001) (0.006) (0.001) (0.003) (0.001) (0.006) (0.000) (0.002) (0.002) (0.009) (0.001) (0.006)

Clerks 0.002* -0.009*** 0.001 -0.011*** -0.001 -0.020*** -0.002*** -0.012*** 0.003*** -0.020*** 0.002** -0.009 (0.000) (0.003) (0.000) (0.002) (0.001) (0.004) (0.001) (0.002) (0.001) (0.005) (0.001) (0.005) Service and sales workers -0.005*** 0.010*** 0.054*** -0.004 -0.004** 0.006** 0.015*** 0.006 -0.003 0.013*** 0.051*** -0.015 (0.001) (0.002) (0.003) (0.006) (0.002) (0.002) (0.002) (0.004) (0.002) (0.004) (0.005) (0.016)

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Table A3: Detailed BO Decomposition for Public and Private Sector continued from previous page Serbia Macedonia Montenegro Public Private Public Private Public Private VARIABLES Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Explained Unexplained Craft and trades workers -0.015*** 0.004*** -0.025*** 0.017*** -0.012*** -0.001 -0.020*** 0.008*** -0.002 -0.000 -0.006 0.005* (0.002) (0.001) (0.002) (0.003) (0.002) (0.001) (0.002) (0.002) (0.003) (0.001) (0.004) (0.003) Plant and machine operators -0.005** 0.001 -0.015*** 0.002 -0.005*** 0.008*** 0.025*** 0.044*** -0.005** 0.001 0.009* 0.002

(0.002) (0.001) (0.002) (0.002) (0.002) (0.001) (0.002) (0.005) (0.003) (0.001) (0.005) (0.004) Elementary occupations 0.017*** 0.001 0.001 -0.001 0.001 0.005 -0.008*** -0.000 0.012*** 0.002 0.001 0.011** (0.002) (0.003) (0.001) (0.002) (0.002) (0.004) (0.001) (0.003) (0.003) (0.003) (0.001) (0.004) Agriculture -0.002* 0.000 -0.004*** -0.001 -0.003*** -0.001 -0.005*** -0.001 -0.001 -0.002** 0.000 -0.001 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Industry 0.011*** 0.012*** 0.006*** 0.013*** 0.002 0.013*** -0.000 0.011 0.011*** 0.015*** 0.000 0.012 (0.002) (0.003) (0.002) (0.001) (0.002) (0.004) (0.000) (0.009) (0.003) (0.005) (0.010) (0.014) Traditional services(c) 0.000 -0.004 -0.002 0.012*** 0.003*** 0.002 0.000 0.009 -0.002 0.002 0.005 0.006 (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) (0.000) (0.006) (0.001) (0.004) (0.007) (0.038) Modern services(d) 0.000 -0.003** -0.003*** 0.001 -0.001** -0.004*** -0.002*** -0.004** -0.000 -0.002 -0.001 -0.001 (0.000) (0.001) (0.001) (0.002) (0.000) (0.001) (0.001) (0.002) (0.001) (0.003) (0.001) (0.006) Public Services(e) 0.004 0.000 -0.002*** -0.002 0.005** 0.032** -0.002*** 0.004** 0.016*** 0.070*** 0.005** 0.008 (0.003) (0.011) (0.000) (0.001) (0.002) (0.015) (0.001) (0.002) (0.004) (0.025) (0.002) (0.007) Temporary contract - - -0.010*** 0.017 0.001 0.017 -0.004*** -0.018 - - - - (0.001) (0.015) (0.001) (0.034) (0.001) (0.018) Regional dummy 1 (f) -0.004*** 0.005 -0.007*** 0.003 - - - - -0.007*** 0.003 -0.005* -0.005 (0.001) (0.003) (0.001) (0.003) (0.001) (0.005) (0.003) (0.004) Regional dummy 2 (g) -0.000 0.009*** -0.000 -0.001 - - - - -0.001** 0.016 0.001 0.012   (0.000) (0.002) (0.000) (0.003) (0.001) (0.012) (0.002) (0.016) Regional dummy 3 (h)

-0.001* -0.005 -0.000 -0.003 - - - - -0.001** -0.005 -0.004** 0.002 (0.000) (0.003) (0.000) (0.003) (0.001) (0.003) (0.002) (0.005)

Regional dummy 4 (i) -0.005*** -0.012*** -0.002** 0.000 - - - - - - - - (0.001) (0.003) (0.001) (0.003) Constant 0.115*** 0.064** 0.106*** 0.147*** 0.038 0.112 0.038 (0.032) (0.026) (0.041) (0.024) (0.053) (0.074) (0.053) Observations 10,844 10,844 14,736 14,736 6,801 6,801 12,937 12,937 4,291 4,291 4,678 4,678

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 a) including armed forces, (b) including skilled agriculture workers, (c) Trade, HORECA, Transport (d) Communication, financial intermediation, Real Estate, Experts (e) Public Administration, Education, Health, Social Service Activities, ET Organisations; (f) Serbia: Belgrade; Montenegro: Central region; (g) Serbia: Vojvodina; Montenegro: Central region; (h) (i) Serbia: Eastern and Southern Serbia.