Gender and Motherhood Wage Gaps in Macedonia

Ivan Vchkov Marjan Petreski

Gender and Motherhood Wage Gaps in Macedonia Manual for Calculation with Applications in Stata

Contributors to particular phases Blagica Petreski Despina Tumanoska

Supervisors Sandra Leitner Sebastian Leitner Hermine Vidovic Simona Jokubauskaite

Involved institutions Finance Think – Economic Research & Policy Institute - Skopje, Macedonia (know-how recipient) The Vienna Institute for International Economic Studies (wiiw), Austria (know-how provider)

Publisher: Finance Think - Economic, Research and Policy Institute, Skopje Authors Ivan Vchkov Marjan Petreski Contributors to particular phases Blagica Petreski Despina Tumanoska Supervisors Sandra Leitner Sebastian Leitner Hermine Vidovic Simona Jokubauskaite Proofreader Daniela Parat Jovanovska Design Milosh Gjuroski Printing Kontura Photo pexels.com; pixbay.com, freepick.com Involved institutions Finance Think – Economic Research & Policy Institute - Skopje, Macedonia (know-how recipient) The Vienna Institute for International Economic Studies (wiiw), Austria (know-how provider) Printed Copies 50 Free Copy

CIP - Каталогизација во публикација Национална и универзитетска библиотека “Св. Климент Охридски”, Скопје 331.2:305(497.7)(035) VCHKOV, Ivan Gender and motherhood wage gaps in Macedonia : manual for calculation with applications in Stata / Ivan Vchkov, Marjan Petreski. - Skopje : Finance think, 2017. - VII, 88 стр. : илустр. ; 30 см Библиографија: стр. 77-88 ISBN 978-608-65704-2-2 1. Petreski, Marjan [автор] а) Плати - Родови аспекти - Македонија - Прирачници COBISS.MK-ID 102425354

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Contents 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Economics of the wage gaps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1..Economics of the gender wage gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1. Education, training, experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2. Occupation, industry, hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3. Marital status, fertility and childbearing . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2..Economics of the motherhood wage gap. . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3..Gender and motherhood wage discrimination. . . . . . . . . . . . . . . . . . . . . 12 2.4. .Considerations about the gender and motherhood wage gap in transition economies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3. Review of the empirical literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1..Review of cross-country studies on developed countries. . . . . . . . . . . . 17 3.2..Review of findings for the Western Balkan countries. . . . . . . . . . . . . . . 20 4..Methods used for estimation of the gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1. OLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2..Heckman sample selection method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3. Repeated imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5. M . ethods for decomposition of the gaps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1. Blinder-Oaxaca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2. Quantile regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.3. Weighting approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.4. Recentered interest function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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6..Applications in STATA with Macedonian data. . . . . . . . . . . . . . . . . . . . . . . . 41 6.1..Calculation of the gender wage gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.2..Calculation of the motherhood wage gap. . . . . . . . . . . . . . . . . . . . . . . . . 45 6.3..Decomposition of the gender wage gap. . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.4..Decomposition of the motherhood wage gap. . . . . . . . . . . . . . . . . . . . . . 60 7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

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Contents of Tables Table 1: Unadjusted Gender Wage Gap across Countries. . . . . . . . . . . . . . . . 18 Table 2: Results between Gender Wage Gap estimation models. . . . . . . . . . 43 Table 3: Results between Motherhood Wage Gap estimation models. . . . . 47 Table 4: Blinder-Oaxaca Decomposition on the Unadjusted Gender Wage Gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Table 5: Blinder-Oaxaca Decomposition with selection correction . . . . . . . 51 Table 6: Quantile Regression Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Table 7: Juhn-Murphy-Pierce Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Table 8: RIF decomposition of the Gender Wage Gap . . . . . . . . . . . . . . . . . . . 59 Table 9: Blinder-Oaxaca Decomposition of the Unadjusted Motherhood Wage Gap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Table 10: Blinder-Oaxaca Decomposition (Heckman Adjusted Motherhood Wage Gap). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Table 11: Quantile Decomposition of the Adjusted Motherhood Wage Gap.65 Table 12: JMP decomposition of motherhood wage gap. . . . . . . . . . . . . . . . . 69 Table 13: Motherhood Wage Gap - RIF approach . . . . . . . . . . . . . . . . . . . . . . . 71

Contents of Figures Figure 1: Densities of wages by gender. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Figure 2: Density of wages by motherhood. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

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1. Introduction Context of this study Even though the difference between male and female wages has been on the decline, women still have lower earnings than men. That women earn less than men has been a widely observed occurrence, albeit many countries implemented various anti-discrimination and employment laws directed to women, as well as gender equality policies, hence baffling many economists and sociologists. To see how pervasive the gender wage gap really is, Hayes (2013) claims that even though it has been improving - in terms of narrowing the wage differences between the sexes -such a trend is happening at such a slow pace that gender wage equality can be achieved in 2058.

Although the gender wage gap has been addressed in large number of studies and attracted a broad scholarly interest, the gap has also been viewed as a tendency of inefficient resource allocation due to discriminatory wage practices (Polachek and Xiang, 2014). Although the gender wage gap has been examined for the last several decades by means of various estimation methods, scholars still debate about its fundamental causes. Misra and Murray-Close (2014) claim that the gender wage gap is explored in the view that it is a result of differences in education, experience and skills, occupational segregation, employer discrimination and other human capital and individual characteristics. Waldfogel (1997) argues that even though women have increased their employment numbers, their tendency to have less labor market experience than men with otherwise comparable characteristics has been and still is an important factor for explaining the gender wage gap.

Another source of concern is that there is wide evidence that mothers, in particular, suffer a greater wage penalty in comparison to non-mothers and men. This not only concerns gender equality, but also raises worries for the capability of societies to accomplish a sustainable balance between their economic aims of active female participation in paid work and social aims of providing a fair distribution of income to support reproduction and the rearing of children (Grimshaw and Rubery, 2015). Similarly to the gender wage gap, motherhood wage gap has become increasingly important, mainly because contrary to the narrowing gender wage gap, the motherhood wage gap has increased (Waldfogel, 1997). The motherhood wage gap has also been examined through various estimation methods in order to explore whether it is a consequence of the different human capital and individual characteristics, as well as discrimination.

While the economic literature concerning the gender and motherhood wage gaps has been widely available and increasing in industrialized and developed countries, the literature for the gaps in developing and transition countries has been relatively scarce. The concern in the few studies on wage gaps in developing and transition economies is that the trend and consequences of the wage gaps have been opposite to the results in developed or industrialized countries.

Avlijas (2013) claims that in developed countries, one part of the gaps can be explained by the differences in personal labor market characteristics between the two genders (such as differences in the level of education, work experience, occupation and others), and the unexplained part, often regarded as effects of discrimination or other unobservable productivity differences.

When examining the gaps in developing and transition economies, such personal labor market characteristics fail, to a great extent, to explain parts of the wage gaps. Furthermore, while in developed countries the gaps tend to decrease in size once human capital and individual characteristics are considered, the opposite is true for developing and transition economies.

Because of this, these wage gaps should not be viewed as simple wage difference between the genders or difference between mothers and non-mothers and men, but should be analyzed through econometric models corrected for selection which include various characteristics that might explain the wage difference between the genders.

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The gender and motherhood wage gaps often not only differ in their size in various countries but there is sort of opposing trend between developed and developing countries when modelling the gaps. While the unadjusted wage gaps in developed and industrialized countries are much larger than those of developing and transition economies, the opposite is true when comparing the adjusted wage gaps. Simón (2012) claims that the wage gaps in Lithuania, Latvia, Italy, Norway, Portugal, Spain, the Netherlands, Czech Republic and Slovakia, when adjusted for characteristics, reduce by between 1.7 and 10.2 percentage points relative to their unadjusted sizes. On the other hand, Avlijas et al. (2013) and Nestic (2007) argue that the adjusted wage gaps increased by 7.7, 4.5 and 6.5 percentage points, relative to the unadjusted wage gaps for Serbia, Macedonia and Croatia, respectively. The economic literature mainly argues that such squeezing in the adjusted wage gap in developed countries is because women usually have disadvantage in comparison to their male counterparts, in terms of work experience, occupation, motherhood, children and other various human capital and individual characteristics that make them less competitive in the labor market compared to men or single women. What is perplexing is that the trend in developing and transition economies is usually for women to have a competitive advantage in the labor market to their male equivalents in skills and education, but yet receive lower wages in comparison. This often is regarded as the reason why raw wage gaps in these countries increase instead of decrease when adjusted.

Basic vocabulary In what follows, we offer a few definitions of terms that the reader would encounter in the course of this study: - Gender Wage Gap is the percentage wage difference between men and women, expressed in age of male’s wage. It could be unadjusted or raw; and adjusted or explained (see below); - Unadjusted Gender Wage Gap is defined as the percentage difference between gross monthly earnings of women and men working full- or part-time expressed as a percentage of men’s earnings. The unadjusted gap does not take into account individual and human capital characteristics of employees; - Adjusted Gender Wage Gap is calculated once the econometric models take into account any relevant productivity-related, human capital and individual characteristics, as well as any other variables considered important in the analysis. Hence, the adjusted wage gap refers to the percentage difference between male and female wage, for the same characteristics and job positions; - Motherhood Wage Gap/Motherhood Penalty is the percentage wage difference between mothers and non-mothers. The focus of this wage gap is only on women in their childbearing age, and mostly on mothers with children up to a certain age – usually three or six, but not more than 18 years of the children’s age.

- Family Wage Gap is the percentage wage difference between mothers and fathers. Not rarely, though, this difference is also regarded as a motherhood wage gap; - Horizontal Segregation is the tendency of the workforce in a specific industry or sector to be predominantly made up from one gender; e.g. textile industry is predominantly female industry, while construction or mining is predominantly male sector; - Vertical Segregation is the tendency of opportunities for a career progression in certain industries or sectors for a specific gender to be limited; e.g. females dominate lower-paid jobs (textile workers or elementary occupations), while males dominate managerial positions; -

Glass Ceiling effect is the unseen and unbreakable barrier that prevents women from 2

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career progression to higher and better-paid jobs, regardless of their qualifications, capabilities or achievements; - Sticky Floor effect is the tendency to keep women at the bottom of the occupational ladder. Women often hold low-paying and low-mobility positions that often do not allow for career progression.

ILO conventions One of the biggest advocate for gender equality at the global level is the International Labor Organization (ILO), a UN agency, whose goal is to bring together governments, employers and worker representatives of 187 member states in order to set labor standards, develop policies and devise programs that promote decent work for all women and men (ILO, 2016). Furthermore, ILO as an organization sets fundamental conventions which are obligatory for every member country, in order to improve labor standards, work rights, safety, social security, collective labor rights, and collective bargaining rights for people around the world. ILO conventions focused on gender equality include: - Equal Remuneration Convention, 1951 (No. 100) that focuses on the right to equal pay, without any discrimination on the basis of gender. The convention tackles employment discrimination where women are paid less than men for the same work or for work of equal value.

The convention prohibits distinctions, segregation or preferences made on the basis of gender that hinder the equality of opportunity, treatment in employment or occupation for all people.

- Workers with Family Responsibilities Convention, 1981 (No.156) promotes non-discrimination, work-family balance and the access to vocational training of mothers and fathers. The objective of the convention is to promote equality of opportunity and treatment of male and female workers that have family responsibilities. Thus, it aims to enable workers with family responsibilities to engage in employment, without discrimination, and to diminish their work-family conflict. It ensures that family responsibilities cannot be a valid reason for discrimination in employment or occupation for all.

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- Maternity Protection Convention, 2000 (No. 183) sets minimum standards for maternity protection and includes benefits such as: leave duration, entitlement to maternity pay set at suitable level, access to health benefits and the right to return to the same or an equivalent position.

Objectives of the study The objective of the study is to provide a manual for proper econometric calculation of the gaps through the use of the STATA statistical software. For that purpose, the study provides description of methods and their STATA coding to serve for capacity building for future endeavors in gaps calculations, and applies these methods with Macedonian data.

Structure of the study The study is structured as follows: Chapter 2 provides the theoretical background and previous economic literature on the gender and motherhood wage gaps. Chapter 3 presents empirical results and findings from previous studies that focus on the wage gaps. Chapter 4 discusses the various statistical mainstream methods used for estimation of the wage gaps. Chapter 5 presents the various decomposition methods of the wage gaps. Chapter 6 provides the manual for econometric calculation of the gaps through the use of STATA and applies these methods with Macedonian data. Finally, the conclusion is presented in the final chapter.

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2.

2.1.

Economics of the wage gaps Economics of the gender wage gap

The gender wage gap has been a widely researched phenomenon that defines and shows the wage differentials among male and female participants in the labor market. It has been broadly analyzed because as Weichselbaumer and Winter-Ebmer (2005) put it, the gap has led to discriminatory wage practices that could lead to an inefficient resource allocation. The gender wage gap appears because women tend to earn less than men, and it tends to be an indicator of gender inequality in a country. Gilbert (1994) states women earning less than men is a well-recognized fact that is evidenced by women having limited opportunities in the labor market, a prevailing bias toward women’s employment, bias towards childbirth and job obligations, and that women continue to have a higher share of household and nurturing duties than men. Furthermore, as Polachek and Blau (2003) claim, societal discrimination tends to be the main reason for the persistence in the wage gap. They even note that societal discrimination is much more important than the existent corporate discrimination.

Economists studying the gender wage gap have usually focused on differences in the human capital endowment, i.e. the wage difference among genders caused by differences in components/segments/elements, such as education, experience, productivity and also on differences in individual characteristics such as marital status, occupation, industry, fertility and childrearing (Cukrowska and Lovasz, 2014;Lips, 2012). Even though the above mentioned characteristics help to explain a large part of the gender wage gap, there is still an unobserved part that remains unexplained (Echernberg and Smith, 2003; Neuman and Oaxaca, 2004; Oaxaca, 1973; Wagner 2015) which is thought to represent discrimination. Wagner (2015) asserts that this discrimination in the labor market is often not evident since it is characterized by subtle stereotypes due to historic and cultural beliefs about the difference of responsibilities among the genders that cause an unconscious, i.e. indirect, gender bias. This gender bias in the labor market leads to wage gaps across industries, occupations and education levels and is not only attributed to male but female managers as well, where they often contribute to the bias in the hiring and pay of employees.

Though the literature about this gap has been growing, many authors have given relatively little attention to comparative studies across countries (Xiang and Polachek, 2014). From this literature it is evident that the reasons of the gap’s existence differ between countries (Blau and Kahn, 1992, 1996, 2003). Others such as Altonji and Blank (1999) conduct an overall overview of the labor market differences between genders by focusing on productivity differences in relation to the human capital theory and discrimination as main driving sources of wage differentials, while Blau and Kahn (1992, 1996) conduct international comparison of the wage differentials among genders. Other studies include Beblo et al. (2003), Albrecht et al. (2004); Neal (2004); Fortin (2005);Azmat et al. (2006); Machado (2012). Nevertheless, as Petreski et al. (2014) remark, the variation between the gender wage gap and gender employment gap in both quantities and prices in the labor market, particularly related to skills, has been rarely examined.

In what follows, we divide the economics of the gender wage gap into three important sets of human capital and individual characteristics in order to explore the logical reasoning of why the wage gap exists: personal attainment (education, training and work experience), employment (industry, occupation and work hours) and household status (marital status, fertility and childbearing).

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2.1.1. Education, training, experience Blau and Kahn (2007, 2006) state that education is a very important human capital characteristic in explaining the gender wage gap where education has provided an evident progress in narrowing it. They claim that in the U.S. less–educated women and highly educated women have narrowed the gaps with their respective male counterparts. Weichselbaumer and Winter-Ebmer (2005) also agree that in industrialized countries, the gender wage gap decreased significantly. The decreasing trend is due to women’s increased level of education and work experience (Blau and Kahn, 2006; Kolesnikova and Liu, 2011) although true, Blau and Kahn (2007) still remind that women continue to earn considerably less on average than men. Additionally, Kassenboehmer and Sinning (2014) argue that in the U.S., the growing education of women considerably explains the narrowing of the gender wage gap for the highest decile of the wage distribution in favor of women, while a sizable part of the narrowing of the gap in the lower tail of the wage distribution is attributable to work experience and tenure changes.

Even though education has been a key enforcer of the increased participation of married women in the workforce due to the increased number of jobs that demanded advanced education (Katz, Stem and Fader 2005), and though education tends to indicate that the more educated a woman is it is more likely that she would participate in the work force (Langdon and Klomegah, 2013), women still encounter gender wage gap even though they tend to have better college education than men (Lips, 2011).

Part of the gender wage gap is due to the fact that women pursue different fields of study and thus the gender segregation in education might contribute to the wage gap (Charles and Bradley 2002; Bradley 2000; Davies and Guppy 1997; Gerber and Schäfer 2004).This segregation by field of study is caused and preserved by strong social belief systems about the gender differences that might be a factor for the still persistent gender inequality. Glover, Fielding and Smeaton (1996) indicated that while there is an evident progress since 1979, women still actively choose not to pursue science, engineering and technology careers. Similarly, Langdon and Klomegah (2013) note that the traditional gender ideology influences the education system which tends to lead women into traditional lower paying female careers. Even though women can cope with the work responsibilities in such fields, they knowingly choose not to focus in such careers, since it can be difficult to participate in fields with values and cultures that tend to be dominated by males. A far more disappointing fact is what Furger (1998) concludes that women tend to be discouraged by teachers and families from entering into technology, science and math fields. Giapponi and McEvoy (2005) note that in some countries this tendency exists even in vocational training. They found that women are directed into lower paying fields that tend to be predominantly female such as cosmetology, day care and medical transcription rather than into fields such as electrical, plumbing or heating and air that tend to pay twice as much. In addition, Langdon and Klomegah (2013) conclude that the educational system indirectly or subtly directs women into careers that pay less, which tend to be characterized or dominated by females.

With respect to training and experience, Polachek (2004) argues that the human capital theory connects the participation in the labor market to an individual’s incentive to acquire marketable training. Langdon and Klomegah (2013) state that the training acquired determines the potential to earn. Additionally, Polachek and Blau (2003) observe that although formal education is a general form of attaining skills, jobs are also a factor in training workers in needed skills. Similarly, Taniguchi (1997) argues that workers accumulate human capital by working the same jobs continuously, which indirectly may give them more opportunities for on-thejob training and classroom training. Polachek and Blau (2004) claim that since training takes a substantial amount of effort, if gains are not high enough, if the woman is not in the labor force long enough, or if she is going to take time out of the work force, then the investment in 6

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her job training would be lower thus contributing to higher gender wage gap. Polachek (2004) concludes that due to these facts, women have a smaller incentive to invest in training. This is further supported by the fact that if a woman is anticipated to have intermittent participation in the labor market, she would initially have small investment in training until she is ready to enter the workforce permanently, which is different from a typical man’s training cycle.

Blau and Kahn (2007) state that even though education has been a major variable in determining the gender wage gap, in recent years, as women have increased their educational attainment and participation in traditional male professional fields, work experience has become very important as well. Since women have traditionally moved in and out of the labor market due to family duties, and subsequently in the recent years, even though married women or women with children have increasingly become active participants in the labor market, women still tend to be less experienced, on average, compared to men. This suggests that the difference in experience is quantitatively important in explaining (part of) the gender pay gap (Blau and Kahn, 2007). Mincer and Polachek (1974) claim that because of the traditional division of labor between genders, women tend to have less market experience than men. Additionally, Blau and Kahn (2007) argue, since women expect shorter or more discontinuous work lives, they tend to have lower motivation to invest in market–oriented formal education and on–the– job training, thus lowering their earnings relative to men. Wagner (2015) summarizes this by stating that even over time, fewer work hours cause women to have lower experience levels relative to men, which leads to lower pay.

2.1.2. Occupation, industry, hours According to Blau and Kahn (2000, 2003), gender differences in occupation and industry can explain a sizable portion of the wage gap. Occupation tends to be a very important variable in explaining the gender wage gap since women tend to work in different occupations and industries than men. Orloff (2009) argues that the differences in the participation in the labor force between men and women contribute to the wage gap. She states that since women value flexibility in the workplace, so that they could care for their children and manage family responsibilities, they tend to participate in part–time jobs. Even when mother’s participation is the same as the father’s, employment patterns differ, since women take more parental leaves and work reduced hours.

Gazso (2004) concludes that due to women’s choices, preference is given to the economic value of men in the workplace. This preference reinforces the dominant view in society about gender roles where women are caregivers and men are providers. In respect to the societal influence, Farrel (2005) notes that women are directed to choose care-giving careers, while men to provide for their families and thus to choose higher paying jobs. By women opting for specific jobs, they might experience lower risks but often lose many significant financial rewards. Additionally, Lips (2011) argues that society has an influential role in women’s occupational choices; in addition, occupations that are associated with women or require feminine skills are rated less prestigious and thus deserve less pay than those that are often associated as masculine jobs.

Blau and Kahn (2007) and Hill and Corbett (2012) relate the occupational segregation as being likely affected by individual choices and discrimination. Thomson (2006) claims that occupational segregation leads to a horizontal or vertical segregation where horizontal segregation indicates a tendency of a workforce to be predominantly made up of men in a specific industry or sector that often is higher-paying, while vertical segregation means opportunities for a career progression, in such industries or sectors, for women are limited. These two often cause a substantial difference of wages among the two genders where men tend to work in higher–paying “male” jobs while women into lower paying “female” jobs (Hill and Corbett 2012). In addition, such an occupational segregation is probably due to cultural gender norms which likely are not Manual for Calculation with Applications in Stata

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only imposed by the employers, but by the society as a whole. Hill and Corbett (2012), as well as Lipps (2011) argue that even if occupational choices are factors in the gender wage gap, wage differences exist in all occupations. This is additionally supported by Giapponi and McEvory (2005) and Thompson (2006) who observe that men are even paid higher wages in occupations that are traditionally seen as being predominantly female. These observations lead to a belief that society has structured the labor force to the advantage of males(Chafetz, 1978).

Related to the human capital theory, Taniguchi (1997) states that wives and mothers choose jobs that give them sufficient time to take care of their families but tend to be lower-paying.

Likewise, Broaas and Rodgers (2003) note that women tend to choose occupations, which are characterized as jobs that require skills that are not weakening with disuse and jobs that need smaller investments in human capital. The reason behind such occupations is that they better fit the schedules and long-term labor-force participation plans of women. Interestingly, Polachek and Blau (2003) found that the human capital model captures the effects of occupation.

They note that the increased labor-force participation of women, the decrease in the number of children, and the increase in divorce rates are caused by women having a longer commitment to staying in the labor force and thus causing the wage gap to decrease, while the opposite is true for women committed more to taking care of their families. Taniguchi (1997) summarizes this by stating that in order for women to advance in their jobs and wages, they must minimize the burden on their household responsibilities. Similarly, Blau and Kahn (2007) claim that firms might be reluctant to hire women for jobs which require large investments in skills that are unique for special job positions. They argue that since women may tend to avoid jobs that require more involvement in the firm or to remain with that employer for a longer period of time, firms might fear that they will not get full return on the investment if they hire a woman for a job position that requires the firm to bear some of the cost of that firm–specific training.

Kolesnikova and Liu (2011) claim that since women are likely to prefer and to work fewer hours than men, the best measure of the gender wage gap in terms of earning, is to consider the hourly wages instead of weekly earning, because if the gap is analyzed through weekly earnings, even if hourly wages are the same, the gap would be large. Additionally, as Becker (1985) argues, the traditional division of labor may put women at a disadvantage in respect of the hours spent in household activities and hours worked, which would mean that women tend to have decreased productivity and wages. With respect to working hours, Blau and Kahn (1997) note that working hours are good in explaining part of the gender wage gap. As previously mentioned, many authors would state that the fewer working hours for women are due to the fact that women prefer firms with family–friendly work practices that offer lower wages. Weiler (1986) points out that the working hours might be more connected with the possibility of child rearing than the firm’s fear that women will exit the labor market when they have children.

This notion could be supported by the fact that women tend to be paid lower wages due to the possibility that they will be less attentive at work or would not be able to work as many hours as men. As Kim and Polachek (1994) and Hecker (1998) refer, hours worked could be a measure which intends to measure the dedication and productivity of employees.

Similarly, Cha and Weeden (2014) state that the trend of working longer hours has become a widespread feature where employers tend to see the “ideal worker” as a worker who would be available to clients and supervisors at any hour of the day or week. They explain that due to this change in how employees are valued, a gender wage gap could arise since women would be expected to be less interested in working overtime. Coser (1974) claims that working longer hours tends to exist in professional and high–level managerial occupations, which he defines as “greedy”, since employers tend to want exclusive and complete loyalty from their employees.

Additionally, as Biggart and O’Brien (2010) and Cha and Weeden (2014) argue, in such occupations, overwork becomes part of the working culture and indirectly, not only becomes a value of productivity, but also a visible proxy for workers’ commitment and competence, where such 8

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workers tend to be rewarded with better work assignment and more frequent promotions.

Epstein et al. (1999) links this tendency to the gender wage gap, where the underrepresentation of women in the attitude to work longer hours is due to their household duties, hence indirectly causing a vertical segregation by limiting the entry of women in such occupations. They also state that in firms which evaluate workers by the amount of hours they spend at work, women would be more likely evaluated poorly therefore they would less likely earn promotions or experience wage growth. Cha and Weeden (2014) explain that due to these, the expectations of working hours would be in conflict with the expectations about women’s primary care-giving role which creates an orthogonality between the image of an ideal worker and the image of ideal mother. Goldin (2014) supports these notions by noting that the occupations which have the highest gender wage gap are those that link higher returns with working overtime. The cause of the gender wage gap could be due to the way jobs are organized and because many occupations tend to disproportionately reward individuals who work overtime. Cortes and Pan (2016) conclude that the effects of increasing returns to overtime work might actually influence women’s occupational choice and whether to drop out of the labor force. Cha and Weeden (2014) assert that the growing wage premium for overtime work, which causes a persistent gender wage gap, offsets the wage–equalizing effects of the women’s educational attainment. Given the fact that many studies have documented the stereotype towards women as less productive due to their household duties, child care, and the like, Selmi (2000) argues that such a stereotype could prevent women’s progression in the workplace, thus adding to the persistence of the gender wage gap.

2.1.3. Marital status, fertility and childbearing Korenman and Sanders (1992) state that the wage effects of marriage or children can be biased by unmeasured heterogeneity, such as women deliberately choosing different marital or fertility states on the basis of unmeasured variables that are connected with wages, such as career orientation. The effect of marital status of women on their wages has been hypothesized by Becker (1985) where he states that wage differentials between men and women may be a consequence of the household roles that are specialized by men and women. Generally, single women or unmarried women should have higher hourly earnings in comparison to married women for the same working hours and job positions because married women are expected to have higher household responsibilities which would lead these women to prefer more convenience and less energy-intensive work obligations. Becker (1985) connects this with the specialization theory, where married women tend to specialize more in household duties while married men tend to specialize more in labor-market activities. In relation to the specialization theory, Dolton and Makepeace (1986) note that single and married women have different selection specifications i.e. needs and obligations, and childless and childrearing women have different selection as well as earning equations.

Korenman and Sanders (1992) argue that the lower experience and tenure of women which are associated with marriage and motherhood could implicitly affect wages. Together with Becker (1985), Budig and England (2001) note that marital status and the number of children could affect married women’s wage on the basis of productivity, where wage depends on how time and energy is allocated between household duties along with childrearing, and jobs. Additionally, Waldfogel (1998) as well as Cukrowska and Lovasz (2014) claim that a wage penalty might arise when married women and mothers choose to spend more time at home in fulfilling their household duties or choose more mother–friendly jobs, if there is another adult’s income for support, in order to spend more time and energy at home.

The following section provides a more detailed discussion in regard to the effects of childrearing, childbearing, fertility and motherhood on women’s wages.

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2.2.

Economics of the motherhood wage gap

Grimshaw and Rubery (2015) define the motherhood wage gap as the wage differences between mothers and non–mothers (women without dependent children), as well as the wage differences between mothers and fathers. There is heterogeneity in the literature on what defines a mother, where most studies consider mothers to be women with children up to 3 or 6 years of age, while some studies consider children up to 18 years. The difference between the gender and the motherhood wage gap mainly lies in that the gender gap compares all women and men in the labor force, while the motherhood gap usually focuses only on females in childbearing age. Duvivier and Narcy (2015) claim that the motherhood wage gap could also be termed as the family wage gap or as motherhood penalty. The motherhood wage gap is becoming increasingly important; as Waldfogel (1997) remarks, even though in recent years the gender wage gap has decreased, the motherhood wage gap has been on the rise. Lips and Lawson (2009) explain that working fewer hours, taking time off for childrearing, stepping out of employment and preference for part-time or mother-friendly jobs has a negative economic toll on mothers, which then likely causes lower earnings.

Many of the studies note that the penalty due to childbearing and childrearing occurs because it disrupts the labor-market participation of mothers, which then causes a loss of work experience, training and specialization (Taniguchi 1999; Budig and England 2001; Gangl and Ziefle 2009). The number of children a mother has may be a cause of the gap, as it affects the perception of her productivity and on-the-job commitment. This also relates to the specialization theory according to which, married women and mothers tend to specialize more in the household and childrearing duties (Harkness and Waldfogel 2003; Dolton and Makepeace 1986; Caugh and Killewald 2013), thus either reducing their inclination to search for a job, or reducing their devotion at the workplace hence leading to a motherhood penalty.

Waldfogel (1998), Budig and England (2001) and Gangle and Ziefle (2009) all note that since work experience has been found to be a great determinant of wages, career interruptions could lead to a gap since such interruptions can actually reduce the work experience of a woman.

Additionally, Mincer and Polachek (1974) state that such interruptions can actually lower the knowledge. Budig and England (2001) and Budig and Hodges (2010) argue that the motherhood penalty is not only a consequence of women losing work experience, but also having reduced work hours after the birth of a child. Waldfogel (1998) claims that while the motherhood penalty exists once mothers reenter the labor market after childbirth, women who remain with the same employer after birth usually tend to have a lower motherhood penalty than others.

Nonetheless, Buding and England (2001) assert that even if women remain with the same employer after the birth of a child, they still experience a reduction in work experience and job seniority which directly influences their wages. Duvivier and Narcy (2015) hypothesize that such a gap between mothers who choose to remain with the employer and those who tend to change companies after birth, is due to the fact that mothers who change employer lose their job tenure and their firm–specific human capital. Winslow (2009) finds that the motherhood impact on earnings is mostly a result of the lower labor supply of mothers in contrast to their childless counterparts.

There exists a parenthood wage gap within a household where mothers incur a motherhood wage penalty while fathers, on the other hand, receive a fatherhood premium (Waldfogel 1997, 1998; Budig and England 2001). Becker’s theory of household specialization has been regarded to explain the penalty and premium through the human capital theory (Budig and England 2001; Hodges and Budig 2010; Gupta 1999, 2007; Anderson et al. 2002). This family wage gap or parenthood wage gap within a household arises due to the woman’s increased household and childrearing responsibilities (Hakrness and Waldfogel 2003; Polachek 1975a; Polachek and Xiang 2013; Caugh and Killewald 2013). Becker and Moen (1999) and Moen (2003) suggest that 10

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in couples where both spouses are employed, after the arrival of children, the success of the partner at work or at home comes at a cost for the other, which usually is borne by the mother.

Likewise, Gupta (1999; 2007) and West and Zimmerman (1987) claim that couples are likely to engage in household specialization after marriage which is intensified after children are born.

Such a trade-off between household labor and labor market would cause a family wage gap between spouses where married men would often get a fatherhood premium while wives would often receive a motherhood penalty. Moreover, married women will tend to specialize in familial obligations, but married men, on the other hand, will tend to increase their work hours and their commitment to work which consequently will increase their wage bonuses contrast to childfree husbands or single men (Becker 1985).

Caugh and Killewald (2013), Taniguchi (1997) and Broaas and Rodgers (2003) assert that the specialization in the household will cause women to adjust their working conditions after childbirth. In relation to mother’s working conditions, Cough and Killewald (2013), Waldfogel (1997) and Felfe (2012) argue that such specialization causes a within–couple division of labor where women will be willing to trade-off higher wages and other job benefits for jobs with more flexible schedules. These lower paying mother-friendly or part-time jobs allow mothers to have more time for childcare and household duties but cause a family wage gap within a household.

Moen (2003) and Abraham (2010) suggest that the family wage gap in terms of working condition is caused by typically prioritizing the husband’s career over the wife’s.

Cha (2010) notes that due to the specialization theory, fathers will tend to work overtime which would cause a wage increase, while, at the same time, it increases the probability of the wife to reduce or drop out of employment. Additionally, as mentioned in the previous section, women and especially mothers will tend to avoid working overtime due to their familial obligations, which indirectly induces a wage difference since those who work overtime will tend to be paid more. In that context, in work settings where working overtime is valued, mothers will be viewed as less productive due to these familial obligations by the employers. This not only causes a wage penalty due to limited job progress, but results in entry difficulties in such occupations (Epstein et al. 1999; Cha and Weeden 2014; Goldin 2014).

More recently, Belbo et al. (2013) and Cho (2006) suggested that the influence of fertility on women’s wages is the quality–quantity trade-off, where fertility might have negative effects on women’s wage rates, labor market participation and education. Knowing that having children could have a negative effect on women’s wage, fertility rate is expected to have an inverse relationship with women’s labor-market participation and wage (Polachek and Xiang 2014). Fertility could be the most influencing variable for both genders; as the number of children increases in the family, the division of labor tends to increase as well. Mincer and Polachek (1975) assert that in high fertility, women are expected to drop out of the labor force more frequently, which will lower their work experience, as well as human capital investment. Becker (1985) states that in high fertility, women are less likely to put higher efforts at work which could lead to a larger wage gap.

The tendency of women to stay out of the labor force could also be due to self-selection where childbearing is emphasized more than labor-market participation (Budig and England 2001; Sigle – Rushton and Waldfogel 2007). Women with children may have different characteristics from childless women, which might be the cause of their lower earnings or earning potential should they decide to find a job. Women who are less successful in the labor market may be expected to decide to have children, to have a higher number of children and to withdraw from the labor market indefinitely. There is likely a type of trade–off for women where due to poor employment outcomes they choose to invest more time in motherhood as opposed to human capital.

Additionally, Sigle – Rushton and Waldfogel (2007) and Williams and Cooper (2004) indicate Manual for Calculation with Applications in Stata

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that the motherhood gap could also be the cause of the inadequate leave policies and lack of childcare resources. They argue that these policy deficiencies could lead women to choose between work and family. The lack, insufficiency or underdevelopment of “family-friendly” policies such as parental leave with a stronger focus on fathers, job–protected maternity leave, work conditions (availability of part-time and mother-friendly jobs along with flexible working hours) and publicly subsidized childcare, accompanied by the low availability of childcare facilities could give rise to the motherhood wage gap (Sigle-Rushton and Waldfogel 2007; Williams and Cooper, 2004; Misra and Strader, 2013; Petreski and Mojsoska-Blazevski, 2015). Lips and Lawson (2009) argue that according to the expectancy-value theory, if women observe that there is a considerable societal support (well-structured policies) for combining motherhood and employment, then they would be expected to increase their commitment to employment which would lead to greater success in their work lives and potentially contribute to the narrowing of the motherhood wage gap. Additionally, changing the structure of the current situation through improving these policies could change the way individuals think about and make choice with respect to work and family. This helps to address the work-family conflict or tradeoff between specific duties that families make which, if improved, is expected to decrease the motherhood wage gap (Misra and Strader, 2013).

While women who have lower human capital characteristics will tend to choose to invest more time in motherhood, Hofferth (1984) argues that women who tend to become higher earners might be incentivized to avoid or postpone motherhood and stay in the labor force longer because of the correlation between motherhood and wages. There are five possible channels through which birth timing could affect wages related to women’s human capital accumulation or depreciation: the working hours, job exits, the longitude of labor-market inactivity, the schooling years of women; and women’s productivity in relation to job changes (Herr 2008).

Putz and Engelhardt (2014) note that birth timing and spacing are important since they compromise key issues women face in terms of balancing work and family obligations. Miller (2009) and Herr (2008) imply that the connection between women’s human capital depreciation with motherhood shows a causal effect of motherhood delay where women tend to time their births and minimize their time-off work in order to protect their human capital investment. Delaying motherhood during their twenties and early thirties is a way for women to achieve greater wage growth in their careers since wages increase with age due to increased productivity through on–the–job training and investment in human capital. Karimi (2014) claims that timing decisions of fertility could also be due to related costs of children such as education, child care costs, spousal earnings and female wage. When the rate of human capital depreciation and pre–birth human capital levels are high, there is a high likelihood that women would delay childbearing. Herr (2016) notes that effects of childbirth on wages could not be causal, but actually due to unobserved characteristics, women might anticipate the moment in their careers when their wages start to decline and thus they decide to become mothers.

2.3.

Gender and motherhood wage discrimination

Despite the fact the gender and motherhood wage gaps could be in part explained trough some individual and human capital characteristics, there is still a substantial part of the gap that remains unexplained (Budig and England 2001; Blau and Kahn 2000, 2007; Echernberg and Smith 2003; Janssen et al. 2016; Hirsch et al. 2009). The unexplained part of the gender or motherhood wage gaps is often thought to represent discrimination, but also it is an indicator that more individual and human capital characteristics must be taken into consideration, or that a set of unobservables drive distinct wages.

The role of unobservables is mostly discussed in terms of the unobserved individual or human capital characteristics of women. Budig and England (2001), Weichselbaumer and Winter-Eb12

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mer (2005), Blau and Kahn (2000, 2007) and Cardoso et al. (2016) argue that other than discrimination, unobservable productivity differences between the genders could give rise to the unexplained part of the gender or motherhood wage gaps. Moreover, less investment in on-the-job training, less work experience, greater time in housework and lower occupational attainments could be voluntary choices of women and not consequences of labor-market frictions. Additionally, Cardoso et al. (2016) claim that the persistence of the unexplained part could be partly due to some unobserved worker skills that could not be adequately captured by the econometric models that analyze the gender and motherhood wage gaps, like ability, ambition, devotion, motivation and the like. Nicodemo (2009) remarks that these unmeasured characteristics could be important since they can affect working women’s decision (labor market participation and working conditions) and the wage.

Regarding discrimination, Janssen et al. (2016), Budig and England (2001), Correl, Benard and Paik (2007) and Altonji and Blank (1999) consider four types of wage discrimination on the basis of gender as well as motherhood: stereotyping based on individual preferences, taste-based, statistical and normative discrimination.

Janssen et al. (2016) argue that social prejudice and stereotyping could directly influence individual gender-specific preferences for human capital investments and job choices. If there is a tendency for some jobs to be considered stereotypically male jobs and more people oppose gender equality rights, then there will be fewer women who might apply for those jobs. The discriminatory social attitudes could influence the behavior and relative productivity of women by the tendency of firms to assign women and men to different types of jobs and, as such, these preferences could often indicate that firms have large gender wage gaps. Additionally, social attitudes may deter women from undertaking large human capital investments which are needed for good performance in high-paying jobs thus negatively influencing their wage.

Fortin (2005) and Fernandez et al. (2004) assert that the societal expectations about the duties of a woman can also influence women’s decision whether to withdraw entirely from the labor market. Finally, these discriminatory social attitudes could also influence the behavior of how women negotiate their salaries which might affect their wages (Janssen et al. 2016). Women’s bargaining power for higher wages draws from the assumptions that women feel more inferior in negotiations since the cultural upbringing of women’s expected abilities has led women to think that they have less sense of entitlement to higher wages, hence their hesitancy to request higher wages and their tendency to undervalue their worth (Fortin 2008, Babcock and Laschever, 2003).

The studies involving taste–based discrimination often draw ideas from Becker’s (1971) concept of discriminatory social attitudes and pay–setting behavior of firms, where employers as well as customers and employees have a distaste towards women (Altonji and Blank 1999; Budig and England 2001; Janssen et al. 2016; Hirsch et al. 2009). In such a type of discrimination, employers do not make assumptions about women’s and mothers’ lower productivity due to various human capital and individual characteristics, but they simply find it distasteful to employ them. This draws from the idea that the employer dislikes employing women due to some prejudicial norms. The consequence of such a distaste is that the discriminatory employers will only be willing to hire mothers and women for a wage below their productivity level which leads to a gender wage gap. Additionally, sometimes co-workers or customers have this type of social prejudice or distaste towards women and mothers. If male co-workers refuse to work with female co-workers and if customers are not willing to be serviced by female employees, then employers find it expensive to employ women or mothers simply because they find it costly to offend customers or male employees. Janssen et al. (2016) argue that while employer discrimination influences the wage setting, employee and customer discrimination affects the productive output of women. Even so, firms’ profitability should not be affected by this lower productive output as long as these firms pay lower wages to women than to men. Interestingly, Manual for Calculation with Applications in Stata

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taste–based discrimination often is regarded to be prominent in social prejudicial and discriminatory regions where labor market competition is inadequate, firms have monopsony power and in the cases where search frictions and barriers to entry are larger for women than for men (Altonji and Blank 1999; Janssen et al. 2016; Hirsch et al. 2009).

Statistical discrimination is another type that influences the wages of mothers and women.

Budig and England (2001) and Altonji and Blank (1999) discuss that the basic assumption of this type of discrimination is that employers have limited information about the skills and productivity of potential employees and thus they often concentrate on easily, less-costly observable characteristics (such as gender, education, experience) to predict the individual productivity among applicants if these characteristics are connected with performance. Correl, Benard and Paik (2007) and Altonji and Blank (1999) claim that while statistical discrimination might often be considered rational where employers simply apply a seemingly unbiased standard to evaluate worker productivity, this discrimination might hide two systematically biased evaluations, where the first is biased towards stereotypical cultural beliefs that distort cognition, and the second is the precision of information that employers have in evaluating individual productivity. On the basis of this discrimination, employers will likely evaluate women and mothers less favorably than men or single women on productivity, which not only might influence the gender wage gap but also the motherhood penalty. Gangle and Ziefle (2009) remark that while motherhood and taking time off for childcare could have, in fact, little effect on the productivity of mothers, this type of discrimination could still significantly influence their wages because of the employer’s belief that more familial obligations correlate with lower job productivity which as a consequence could stigmatize working mothers.

Correl, Benard, and Paik (2007) and Correl and Benard (2010) introduce normative discrimination, as part of the status–based discrimination, as contributing to the motherhood penalty or motherhood wage gap. Unlike the status-based discrimination, where employers’ performance evaluation is biased in favor of the high–status group such as single women and men, the normative discrimination is compromised by the cultural belief of evaluators that the duty of the mothers is to remain at home with their children even though they recognize their competence in paid work. The idea behind is that employers, maybe unconsciously, discriminate towards mothers because of their beliefs that success in the paid labor market, especially for those jobs that are considered masculine, signals stereotypical masculine qualities such as assertiveness or dominance. This draws existence from the prescriptive stereotypes of cultural norms where motherhood affects perceptions of competence and commitment towards work and family.

The normative expectation is that mothers should engage in prioritizing the needs of their dependent children above all other activities. In such a setting, mothers are not only influenced by the employers but also by their spouses where the cultural norm determines that the woman is the one who must stay at home and take care of the children. The motherhood penalty or the motherhood wage gap arises in hiring and in salary because of the normative belief in the society that the “ideal worker” is the one who is unencumbered by competing demands (household duties) and is “always there” for his or her employer. Benard and Correl (2010) claim that when mothers break these norms they are held at stricter standards and penalized on recommendations for promotion, hire, and salary since they are viewed as interpersonally deficient in the work setting.

2.4. Considerations about the gender and motherhood wage gap in transition economies The research about the gender and motherhood wage gap has been vast and ever increasing in numbers. Although true, such research has been relatively scarce for transition economies, including the Western Balkan countries. Brainerd (2000) notes that in the socialist era of the 14

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transition economies, the gender wage difference was low because the system promoted wage equality on the basis of meritocracy. On the other hand, Jurajda (2001) remarks that although communism supported gender equality, such system was more authoritative than rights– based. Nestic (2007) argues that in the beginning of the transitional period, the large structural changes on the economies also influenced wage structures. The wage setting mechanism was liberalized which induced higher wage inequality. In particular, the collapse of the communism led to abolishing of the wage regulations, which encouraged the skill-related wage differences to keep rising, while also leaving room for wage discrimination in the private sector (Jurajda 2001).

Even though Ogloblin (1999) states that under the communist system most women worked and had full access to education and health care, Ham et al. (1995) assert that gender wage gap still existed and it was mainly due to discriminatory practices and occupational segregation of women into low–wage occupations. Ogloblin (1999, 2005) argues that the occupational segregation was a very important factor in explaining the gender wage gap in post-Soviet Russia, because of the employers’ discretion in wage setting which led to more discrimination.

During Russia’s transition, decentralization of the wage system, employers’ behavior, and the lesser restriction by the government, induced more patriarchal stereotypes and perceptions of women as less productive labor force. Interestingly, Ogloblin (2005) claims retirement could have been a factor in the gender wage gap during transition, since women had lower legal retirement age and since the state pensions were still received by most workers in proportion to their wages, which could have been viewed as deferred labor earnings. Jurajda (2001) applies the same notions of occupational segregation in analyzing the gap in the transition period of Slovakia and Czech Republic. While occupational segregation could be a factor for the gender wage gap in transition economies, women’s productivity and experience are also important especially in Czech Republic and Slovakia since these two countries rely on extensive public system of child–care, generous family allowances and guaranteed maternity leaves.

Brainerd (2000) notes that the structural changes of the economies and wage systems in Central and Eastern European countries in their first phase of transition have not contributed to widening of the gender wage gap. In contrast, Nestic (2007) argues that such notion could be misleading since women’s status in the transitional period of many countries in Europe could be affected by their relatively low employment rate, female employees’ educational advantage over their male counterparts and the perceived woman’s role in family responsibilities. The low employment rate of women is due to barriers in the labor market participation, while the level of education could suggest that the productive characteristics of women may be underappreciated. Additionally, the lengthy absence from work due to childbearing can limit women’s earnings. Finally, the gender wage gap might arise since as a transition country, Croatia, has a very generous maternity and paternal leave entitlements along with low labor mobility, weak job creation, a poor child care system and a relatively strong role of traditional lifestyle.

Petreski et al. (2014) and Arandarenko et al. (2013) argue that contrary to popular tendencies in the developed countries where the gender wage gap is explained through taking account of the human capital and individual characteristics, such tendency is not true for transition economies and especially in Western Balkans. Working women in these economies usually have better educational and human capital characteristics than men and usually work in better paid jobs. Thus, these labor-market characteristics not only fail to explain the gender wage gap but they amplify it. This observed phenomenon is mostly because of women’s low labor-market participation, labor-market frictions and other skill related characteristics that could not be easily observed.

Petreski et al. (2014) examine the gender wage gap in light of a factor prevalent in transition economies: women’s large inactivity or low labor market participation. The high rate of women’s labor market inactivity in Macedonia is related to several factors, part of which are pertiManual for Calculation with Applications in Stata

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nent to transition economies, especially of Southeast Europe: exiting the market in the early transition due to skill deficiency, the tendency of rural women to be unpaid family workers, the dependency on remittances (women’s trade–off between remittances sent by the male migrant and labor market participation), and women’s human capital depreciation due to long– term unemployment. Additionally, many of the above mentioned groups of women could also have a high reservation wage that causes them to stay outside the labor market. The absence of the above mentioned groups can provide a false gender wage gap due to their low labor market participation. Female’s inactivity may further impinge on the calculation of and policies related to the motherhood penalty. Petreski and Petreski (2015) and Petreski and Mojsoska – Blazevski (2015) are the two studies that analyze the motherhood wage gap and its effects on the gender wage gap. In addition to the above discussion, they argue that the large inactivity of mothers in Macedonia as well as of women in childbearing age could be because women have increased their investment in postgraduate education due to low supply of higher–paying jobs in the labor market and they might have a high reservation wage.

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3.

Review of the empirical literature

Despite the increase in employment and participation rates of women, the gender and motherhood wage gaps have remained persistent throughout the years across the world. As previously mentioned, the prevalence of the gender and motherhood wage gaps has been considered to be due to various human capital and individual characteristics, as well as discriminatory attitudes. Additionally, many of the countries around the world have differing sizes of the gender and motherhood wage gaps because of their cultural, structural and historic backgrounds. Table 1 shows the unadjusted gender wage gaps of developing and developed countries around the world where the gender wage differentials vary substantially across Europe and beyond.

These results indicate that the difference in cultural norms, labor markets, markets and policy structures of the countries, might directly or indirectly influence the severity of the gender wage gap.

In the next two sections, we review some studies on developed and developing (including transition) economies. Neither of the sections aims at exhaustiveness, as the empirical literature on the gender and motherhood wage gaps has been indeed extensive. Hence, the section on developed economies largely dwells on cross-country studies, leaving aside studies focused on individual countries. The section on transition economies, on the other hand, focuses on the Western Balkan countries.

3.1.

Review of cross-country studies on developed countries

Sigle-Rushton and Waldfogel (2007) conduct an international analysis in eight countries: Germany, the Netherlands, Canada, US, UK, Norway, Sweden and Finland. The paper compares wage differentials between women with children, women without children and men in Anglo-American, Continental European and Nordic countries. The analysis applies an OLS regression to estimate future average long-term earnings and compare those of women with children to women without children and men. Mothers in the Nordic countries with medium level of education earned by the age of 45, between 80 and 91% of what single women have earned. In the Netherlands, UK and Germany, mothers earned 58 to 67% of what women without children earned. Mothers in Canada earned 21 to 24% less than women without children, while in the US they earned only 11 to 19% less, in the long term. Furthermore, selection plays important role in the earnings, since if mothers are negatively selected, then the earnings forgone as a consequence would be even lower, while if they are positively selected then the earnings foregone could be higher. Considering the earnings of mothers relative to men, mothers in Germany and the Netherlands earn between 39 to 60% of men’s earnings; in the Anglo-American countries, they earned between 41 to 57%, while the Nordic countries were estimated to have the lowest wage gap between mothers and men, 56 to 68%. The study argues that labor market policies play important role in diminishing the wage gaps between genders and mothers to women without children.

Polachek and Xiang (2014) note that in Australia, Belgium, Italy and Sweden women earn 80% as much as males while in Austria, Canada and Japan this is around 60%. The study conducts multivariate regression model to estimate how the international differences in institutional variables are related to adjusted gender wage gap. The gender wage gap is positively associated with the fertility rate, husband and wife age gap at first marriage and to the marginal tax rate. These factors actually negatively affect women’s labor force participation, while collective bargaining is negatively associated with the gender wage gap. The study shows that marginal tax rates increase the gender wage gap, which tends to discourage women from labor market activity as secondary earners, while women’s educational attainment tends to decrease the gender wage gap.

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Gender and Motherhood Wage Gaps in Macedonia Table 1: Unadjusted Gender Wage Gap across Countries

Country/Region

Unadjusted Wage Gap 2008 – 2014 (%)

Central and Eastern Europe and Central Asia 21.8 Albania 11.5 Belarus 25.5 Bulgaria 20.6 Croatia 10 Cyprus 18.2 Czech Republic 21.5 Estonia 25.6 Hungary 21.6 Latvia 16.8 Lithuania 14.3 Poland 15 Moldova 11.6 Romania 7.8 Russia 25.8 Serbia 11.7 Slovakia 22.5 Slovenia 4.6 Macedonia 6.3 Turkey 7.1 Ukraine 22.8 Developed Regions 22.9 Australia 35.4 Austria 38.1 Belgium 22.7 Canada 24.4 Denmark 15 Finland 21.9 France 18.6 Germany 19.3 Greece 23.3 Iceland 23.1 Ireland 27.3 Italy 20 Japan 28.6 The Netherlands 42.4 New Zealand 30.8 Norway 12.1 Portugal 17.8 Spain 23 Sweden 11 Switzerland 38.3 United Kingdom 36.2 United States 17.4 World 23.5 *Progress of the World’s Women Report 2015 – 2016; U.N. Women 18

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Arulampalam et al. (2007) conduct analysis of the gender wage gap using the OLS and quantile regression methods in 11 countries in the European Union. The magnitude of the gender wage gap varied substantially across the countries and across the public and private sectors. The wage gap typically widened towards the top of the wage distribution indicating glass ceiling effect in 10 of the eleven countries – Austria, Belgium, Britain, Denmark, Finland, France, Germany, Italy, Ireland and the Netherlands, while Spain had a fixed gap of about 20–21% along the wage distribution. Additionally, France had an evidence of sticky floor effect where the gap at the lowest wage distribution was higher than the next wage quantile. On average, public and private sectors of the 11 countries had about 13 and 15% adjusted gender wage difference, respectively. The study suggests that the differences in childcare provision and wage setting institutions across the EU countries may partly be responsible for the variation in the gender wage gap patterns by country and sector.

Olivetti and Petrongolo (2008) analyze gender wage gaps corrected for sample selection due to non-employment in US, UK, Finland, Denmark, Germany, the Netherlands, Belgium, Austria, Ireland, France, Italy, Spain, Portugal and Greece. This cross-country analysis of gender wage gaps is done through the imputation technique in order to assign possible wages to the non-employed with respect to the median wage. The paper indicates that higher median wage gaps are obtained on imputed rather than actual wage distributions for most countries, which indicates that women tend on average to be more positively selected into work than men. The selection correction explains 45% of the observed negative correlation between wage and employment gaps. In the US and UK, the gender wage gap varied from 25-30%, while in continental and northern Europe this gap varied from 10-20%.The average wage gap in Italy, Spain, Portugal and Greece was estimated to be 10%.

Matteazzi el al. (2014) analyse the full-time/part-time wage gap for women in primetime (2559) age. The paper analyses the gap in Austria, Italy, Poland and the UK, which are countries representative of different welfare state regimes, labour market regulations and various forms of part-time employment. The method used is order probit model for individual choice with respect to employment status; secondly, an OLS regression is used to estimate log wages for fulltime and part-time women according to human capital and individual demographic characteristics along with a Blinder-Oaxaca decomposition with Heckman corrected sample selection.

The explanatory power of individual and household characteristics to the full-time/part-time wage gap is rather small, while job-related variables explain in great part this wage gap. The adjusted wage gap is mostly driven by job segregation where horizontal segregation explains between 24 to 38% of the explained wage gap, while vertical segregation explains 38 to 59% of it. Part-time women tend to hold temporary job and work in small companies where wages are less collectively negotiated and usually are less likely to be promoted to higher positions. Vertical segregation is mostly prominent in the UK and Poland, where part-time women are concentrated in low-skilled employment, especially in the UK where mothers who want to work in part-time can be downgraded due to lack of part-time opportunities in their current jobs.

In general, the full-time/part-time wage gap is higher in the two market-oriented economies (Poland and UK) where the bargaining coverage is low, wage setting is highly decentralized and income inequality is quite large. Conversely, in the two coordinated market economies, Austria and Italy, the lower wage gap can be explained due to higher bargaining coverage and more centralized wage setting.

SimĂłn (2012) examines the gender wage gap and its cross-country heterogeneity through harmonized international matched employer-employee data for nine European countries: Lithuania, Latvia, Italy, Norway, Portugal, Spain, the Netherlands, Czech Republic and Slovakia. The estimation relies on pooled wage structures of all workers, OLS estimation of the mean hourly wage and similar decomposition as Blinder-Oaxaca for analysis of the gender wage differentials. The estimated adjusted gender wage gaps of the analysed countries vary between 6.7 and Manual for Calculation with Applications in Stata

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31.3%. The findings generally show that differences in human capital characteristics and individual productivity-related characteristics play a minor role in the explanation of the adjusted gender wage gap. Female segregation into low-wage structures is considered to be the main contributor to the gender wage gap.

Dupuy and Fernandez-Kranz (2011) analyse the adjusted family wage gap in 35 OECD countries divided by Liberal (Australia, UK, US, New Zealand and Ireland), Southern European, Continental European, Eastern European, Nordic countries and Others (Chile, Cyprus, Brazil, Israel, Mexico, Philippines, Taiwan and Japan). The method regresses log earnings of mothers and non-mothers on working hours, part-time employment, education, work experience and number of children. Additionally, the family wage gap is estimated for each country through OLS estimation which includes various labour market institutions. The results show that the average adjusted family wage gap varied from the lowest of 10.6% in Other countries to the highest of 35.6% in Southern Europe. Furthermore, the adjusted gender wage gap was between 26% in Nordic countries to 41.7% in Eastern European countries. The motherhood wage penalty varies across countries, where mothers in Southern Europe suffer a penalty up to two times as large as mothers in Nordic countries, mainly because of bad combination of labour market policies.

Labour market institutions have opposite effects on the gender and motherhood wage gaps where different policies affect differently these two wage inequalities. Protecting mothers against contract termination through parental leave or job protection usually tend to be the most effective ways in reducing the two wage gaps. Policies that are traditionally associated with wage compression usually lower the gender wage gap but might lead to increase of the wage gap between mothers and women without children. Loss of market experience due to job transitions around childbirth usually is regarded as the main factor of why mothers lag in wages behind single women.

Alaez-Aller et al. (2014) apply kernel density functions of male and female hourly wages for each country and according to various human capital and individual characteristics. The European countries analysed in the paper are Austria, Belgium, Germany, Denmark, Finland, France, Greece, Ireland, Italy, the Netherlands, Portugal, Spain and UK. The objective of the paper is to analyse and explain the narrower wage gap in the Mediterranean countries. The average female hourly wage was equivalent to 90% of the male wage in Portugal and Italy, 88% in Belgium, 86% in Spain, 85% in Denmark, 84% in Ireland, 82% in Greece and the Netherlands, 81% in Austria and France, 80% in Finland, 76% in Germany and 78% in UK. The adjusted gender wage gap is widest at the bottom of the distribution in Mediterranean countries, while in central and northern EU countries it is widest at the top. The paper argues that the main reason of the gender wage gap across these countries is the job segregation in the private sector. While women usually find it difficult to access high-paying jobs across all countries, men, on the other hand, enjoy opportunities to access higher share of high-paying jobs in the central and northern EU countries. Conversely, the lower gender wage gap in the Mediterranean countries is regarded to be a consequence of the lower share of high-paying jobs and job segregation. The base of this notion is the tendency of private sector companies based in EU to fragment their value chains based on functional criteria that could reinforce job segregation by offering higher share of low-paying occupations in the Mediterranean region, while the high-paying jobs are concentrated in the central and northern EU countries.

3.2.

Review of findings for the Western Balkan countries

The literature about the gender and motherhood wage gaps in the Western Balkans has been scarce. Contrary to developed countries, studies involving econometric modeling to estimate the gender and motherhood wage gaps for the Western Balkan countries have yet to become popular in the region. Few studies that attempt to estimate the wage gaps for the Western 20

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Balkan countries are Avljas et al. (2013), Nestic (2007), Miluka (2012), Angel – Urdinola (2008), Petreski et al. (2014) and Petreski and Petreski (2015).

Avlijas et al. (2013) conduct a comprehensive analysis of the gender wage gap in Serbia, Macedonia and Montenegro. They apply the Mincerian earnings function alongside the Blinder-Oaxaca decomposition method, adjusted with the Heckman selection method to account for self-selection into employment. Additionally, the study examines the wage distribution according to quantile regression to identify any glass ceiling and/or sticky floor effects. The paper takes into account individual labor market characteristics and examines the gender wage gap across sectors, occupations, private and public ownership, as well as employment status. In all three countries women have better labor market characteristics than men, and are better qualified as a group than men who work. In the three countries women face high barriers in the labor market and thus this induces the need for them to be better qualified than men. While both low- and high-skilled men work, more high-skilled women work while low-skilled women are often inactive. In Serbia and Macedonia, high-skilled women are able to access better-paid occupations and sectors. Once the gender wage gap is adjusted for individual labor market characteristics, the gaps in Macedonia and Serbia rise relative to the unadjusted wage gaps; this trend is opposite from the Western economies, where working women are on average less qualified than working men which means that the gender wage gap is partially explained by women having worse labor market characteristics. The adjusted wage gaps in Macedonia and Serbia are 17.9 and 11%, respectively. In Montenegro the adjusted wage gap is estimated to be 16% which is the same as its unadjusted wage gap. Serbia contains a glass ceiling effect in the private sector and Macedonia in the public sector while Montenegro has such effect in both sectors. The raw unadjusted gender wage gap is the most pronounced in Montenegro, suggesting that women are often segregated into low-paying occupations. Macedonia has the largest adjusted gender wage gap suggesting that it could be due to labor market discrimination where women are segregated within low-paying occupations and sectors, although they have better labor market characteristics than men.

In the case of Croatia, Nestic (2007) examines the gender wage gap and the wage gap for women with children. The paper applies quantile and OLS regression techniques along with Blinder-Oaxaca decomposition method. While the unadjusted gender wage gap is low, once accounting for different control variables the adjusted gender wage gap is far greater. Women in Croatia received lower market rewards for their human capital characteristics than men even Manual for Calculation with Applications in Stata

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though women have a considerable educational advantage and a moderately low and narrowing deficit in work experience than men. In terms of education and experience, the adjusted gender wage gap was around 19% while adding more control variables reduced the gap to 17%.

There is evidence of occupational segregation and glass–ceiling effect in the gender wage gap in Croatia. After accounting for education, experience, employer size, sector and other control variables the motherhood wage gap was 19.2%, while single women received a gap of 17.4%.

This suggest that employers in the private sector provide lower wages to mothers with young children compared to other women with the same labor characteristics. This difference has been significant at the top of the wage distribution, while true the study finds that in the public sector there is no clear evidence of different wage treatment between mothers and single women.

Miluka (2012) examines different sources or determinants that influence the gender wage gap in Albania. The study applies Blinder-Oaxaca method of decomposition and Juhn-Murphy-Pierce decomposition with weighting in order to account for changes in the covariate distribution. According to the results, the determinants of the gender wage gap in Albania are education, work experience, occupational segregation and number of children. The study shows that women on average have more education than men in the labor market but are offered lower rewards for the same skills than their male counterparts. Female workers in Albania earn approximately 36% less than males according to the mean log wage difference. The tendency is women in Albania to have less experience on average than men which is associated with the fact that women take time off for childbearing and rearing responsibilities. Additionally, although insignificant, the impact of children is negative which could be a consequence of the lack of social support and childcare possibilities. Moreover, women in Albania have been found to be segregated in certain sectors with low-paying occupations. In regard to women that hold university degrees, work experience and occupational segregation are still regarded as important sources of the gender wage gap but the effect of children is not significant, and marriage has been found to have a positive effect. In respect of less-educated women, it was found that they display the largest gender wage gap, where low-skilled male workers substantially receive higher wages than their female counterparts. Occupational segregation and number of children have the largest negative impact on low-skilled female workers. Additionally, these women are most likely expected to stay out of the labor market for longer periods of time. The distance index has been found to matter for the low-skilled women workers thus indicating that wages for this group also depend on mobility. The study argues that highly-educated women are less vulnerable to taking time-off from labor market than their low-educated female counterparts because they might have larger means of support and the market seems to be less discriminatory towards them.

Angel – Urdinola (2008) analyzes whether introduction of a minimum wage could reduce the gap in Macedonia. The study applies simple regression that decomposes the gender wage gap to three key variables: wage factor (gender wage differences among low-skilled workers), segmentation factor (gender share differences in high-skilled workers) and returns to education factor (gender educational differences). The results indicate that low-skilled women earn 20% less than average. Furthermore, the share of high-skill workers is larger in the public sector than in the private sector and the share of high-skilled employees in both sectors is higher among women. The education differences among high- and low-skilled workers by gender indicate that female employees enjoy higher returns due to their education level than male employees, especially in the public sector. In general, the study shows that women tend to be 25% less paid than their male counterparts. Such a large gender wage gap usually is considered as a consequence due to the higher wage advantage in the private sector that low-skilled men enjoy over their female counterparts which reduces the positive effects women enjoy due to their education and higher skills. In this regard, the evidence of imperfect competition in the country 22

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because of the high levels of discrimination and low mobility could be the cause why firms are paying workers, in particular low–skilled women, below their marginal product of labor which leads to a decrease in the labor supply. Moreover, the study notes that raising the minimum wage will allow a possibility to increase the earnings of workers with lower wages especially for low–skilled workers and to increase the employment levels. Raising the wage between 40 to 50 denars per hour will decrease the gender wage gap from 15 to 23% which will have more positive effect on low–skilled women than on their male counterparts.

Petreski et al. (2014) examine the gender wage gap in Macedonia by accounting for the high inactivity in the labor market by women. The study applies Heckman sample-selection model and repeated imputation technique. The results show that selection explains 75% and 55% of the gender wage gap in the primary and secondary education group, respectively. In contrast, once selection is considered, the gap disappears in the college education group which as a result suggests that non–working women are not those with the worst labor market characteristics. The results suggest that the adjusted gender wage gap for characteristics and selectivity in primary, secondary and tertiary educated women is 5.4, 9.8 and 4.5%, respectively. The lower gender wage gaps in terms of repeated imputations show that women in Macedonia are more negatively selected into work than men. Interestingly, while the gap in highly-educated women is insignificant, the women who tend to individually decide not to participate in the labor market are those with low skills who belong to the primary education group as well as women with secondary education thus indicating that on average these women are not with the worst labor-market characteristics.

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Petreski and Petreski (2015) analyze whether motherhood has a depreciative effect on women’s wages in Macedonia. The paper applies repeated imputations technique and recentered influence function to analyze and decompose the wage and motherhood gap. After considering worker’s characteristics and selectivity, wages of females in childbearing age are penalized about 7 to 8% compared to men. Selection explains 60% of the gender wage gap and there is an existence of negative selection towards females into employment. Interestingly their study also found that the motherhood wage gap does not exist in Macedonia which means that it cannot be considered to have an explanatory power for the gender wage gap. The wage differentials found between mothers and childless women can completely be explained by observable characteristics, so there is no selection bias towards motherhood in Macedonia.

In the next two sections, we review some studies on developed and developing (including transition) economies. Neither of the sections aims at exhaustiveness, as the empirical literature on the gender and motherhood wage gaps has been indeed extensive. Hence, the section on developed economies largely dwells on cross-country studies, leaving aside studies focused on individual countries. The section on transition economies, on the other hand, focuses on the Western Balkan countries.

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4.

Methods used for estimation of the gaps

In 1974, Jacob Mincer introduced a novel way of analyzing earnings of individuals in his book Schooling, Experience and Earnings. According to Lemieux (2006), the Mincerian earnings function is regarded as having a profound and permanent influence on empirical work in the field of labour economics. This approach has allowed researchers to investigate earnings where according to the human capital theory, an individual’s wage rate reflects the productivity potential i.e. the wage of an individual depends on that individual’s human capital characteristics which influence the productivity potential (Budig and England 2001; Zajkowska 2013; Lemieux 2006; Rosen 1992; Polachek 2007). The Mincerian earnings function models the natural logarithm of earnings as a function of years of education and years of potential labour market experience (age minus years of schooling minus six (since school begins at age of six)). Rosen (1992) claimed that the function captured the empirical variation in earnings over the life cycle, which although increasing, indicated a concave shape of the path of earnings with age called “age-earnings profile”. This change puts emphasis from age to labor market experience by interpreting it as on-the-job training, in order to include school as well as participation in traineeships, job investment and other firm-specific training that were better in capturing labour market exposure than just age.

In that sense, the wage is linear to education and quadratic in work experience:

Log Yi,t = α0 + rSi,t + β1Xi,t + β2X2 i,t + ei,t Where Y is the logarithm of gross hourly wage of individual i at time t, α0 represents initial earnings capacity, S represents years of schooling, X is identified as work experience, X2is experience squared (capturing the concavity of the age-earnings profile) and e represents the unexplained part of the wage equation. Polachek (2007) explains that r captures the rate of return to education, β1 and β2 relate to both the amount and the financial return to work experience (on-the-job training and other firm-specific training).

Furthermore, Lemieux (2006) regards this equation as the fundamental part of empirical research on earnings determination. The tendency of this equation to be used and estimated on thousands of data sets for large number of countries and time periods, has made this equation the most widely used model in empirical analysis.

Concerning gender and motherhood wage gaps, the equation has been widely used as a fundamental approach in analyzing the determinants of the existing wage gaps, as well as examining the reasons of wage disparity between genders.

Thus, the equation can be reorganized as follows:

Log Yi,t = α0 + β1genderi,t + β2motheri,t + Σϒj*X’i,t + ei,t Where genderi,t is a dummy variable which takes the value of 1 for females, and 0 otherwise; motheri,t is a dummy variable taking 1 for mothers (of children less than certain age, usually 3 or 6), and 0 otherwise; X’ represents the vector of individual and labor-market characteristics such as: education, age and its square, experience, occupation, sector and others. β1measures the gender wage gap, while β2 the motherhood wage gap. Furthermore, one could choose whether to drop or keep any of the two dummy variables, depending on whether one wants to analyze either wage gaps or only one of the two. Finally, if there are no other variables in the model that account for individual and/or labor-market characteristics, the coefficients on the Manual for Calculation with Applications in Stata

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dummy variables would reveal the unadjusted gender and motherhood wage gaps; otherwise the adjusted for characteristics gender and motherhood wage gaps will be estimated. If gender wage gap exists, in the econometric models, the female gender dummy would have a negative coefficient. The same applies for the motherhood wage gap.

Rosen (1992) argues that the equation has allowed for an essential distinction in studying job turnover and wage differences between men and women. In that respect, the function has led to an empirical partitioning of experience between firm specific and general components in order to reflect Becker’s human capital theoretical explanation of gender wage differences.

Nowadays, most studies tend to expand the Mincerian earnings function by further regressing the logarithm of gross hourly wage on an additional set of variables, other than the three key variables, which represent individual and human capital characteristics. These newly added variables control for additional potential effects of jobs and individual characteristics on the wages, or in our context for the gender wage gaps. Moreover, in recent years, there has been a tendency to further develop various statistical regression models that could improve and provide better analysis of the wage gaps. Nonetheless, the present understanding of how wage is affected by the various individual and human capital characteristics still draws inference from the theoretical background of the Mincerian earnings function developed in 1974.

In what follows, we will provide a description of the currently various widely used statistical methods for the estimation and decomposition of the wage gaps.

4.1. OLS Beblo et al. (2003) argue that many of the studies use OLS for the estimation of wages between the genders. The rationale for using OLS model is that only individuals that work are considered. Blau and Kahn (2000) claim that such wage regressions to analyze the gender wage gap are helpful since they specify the relationship between wages and the productivity or human capital characteristics of men and women. These models divide the gap between two components: one due to gender differences in the observable characteristic and the other is the unobservable part seen as potentially due to discrimination. The models are consistent with the human capital differences and the perceived market discrimination. The unexplained part of the model also includes difference of unobservable factors that influence productivity and a difference due to the differential reward for equal characteristics (Nicodemo, 2009; Beblo et al.

2003). The approach assumes that men and women have the same expected productivity, and the only variable of interest is the gender variable in order to find possible gender differences.

Thus, the approach offers an estimate of the wage discrimination faced by the average woman, which indicates the overall extent of the wage gap. A problem in the OLS is noted by Beblo et al. (2003): if the sample used to analyze the gender wage gap has unemployed individuals then a problem of endogenous selection appears. The problem of selection is that the unobservable part in the wage and the participation equations are correlated. Nicodemo (2009) relates this selection problem to women, where, normally, a great part of the sample of women does not work, so it can be a non–random sample of all married women if there are variables that affect participation in the labor force. If such problem exists, OLS would be biased and inconsistent towards working women. The sample selection bias is determined by the gap between workers and non–workers since some parts of the work decisions are relevant in determining the wage process. Additionally, the unobservable characteristics affect the work decision and the wage.

Beblo et al. (2003) and Mysikova (2012) remind that since the participation rate in the labor market for men is usually high, the results should not be affected by selectivity problems, thus an OLS model should not be problematic towards males in the sample.

In terms of children and motherhood, Cukrowska and Lovasz (2014) claim that selection plays an important role in analyzing the gender and family inequalities. The female’s family gap in 26

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OLS estimates is underestimated, so if selection is considered, this selection leads to an increase of the magnitude of the gap. Furthermore, when analyzing fatherhood premium, motherhood gap and the family gap, one should account for selection since OLS fails to capture the influential reasons of the gap’s existence.

Arukampalam et al. (2007) argue that when comparing the raw gender gaps with estimates that control for men’s and women’s attributes, OLS fails to allow for the possibility that characteristics have different returns at different points of the distribution. Buchinsky (1998a) and Guarini (2013) remark that OLS does not efficiently identify the effect of the independent variables on the dependent variable throughout the distribution. OLS is inefficient when the errors are not normally distributed and it fails to make robust estimates of the coefficient vector that would make them insensitive to outliers in the independent variable.

When analyzing the gender wage gap in terms of the bargaining power of wages between employees and employers, Card et al. (2013) claim that the estimation problem in OLS models is that the residual components of wages can be correlated with specific patterns of mobility which would lead to biases in the estimated worker- or firm effects. There are three components that could be potentially correlated with the sequence of worker’s wage premiums which would lead to biases in the estimated firm effects. The first component is the deviation of an individual’s alternative wage in certain period from its average value in the sample period. The concern is that there is a presence of a component that is slowly evolving in the alternative wage for a person due to health shocks, learning about unobserved abilities or other random shocks. The second component is the employee share of any transitory fluctuation in the average surplus available at the firm. This slowly evolving component is related to firm’s profitability and it can be correlated with the direction of mobility of workers who move to higher or lower paying firms. The last component is the employee share of the idiosyncratic match effect for individual at a certain firm. This component is a concern in terms of mobility related to matching the worker with the specific job.

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Interestingly, Sigle – Rushton and Waldfogel (2006) claim, in their cross–sectional analysis of the motherhood and women’s earnings, that OLS is the second best approach when non–participation in the labor market, hours of work and hourly wages samples are too small. Their paper uses OLS regression for annual earnings for women regardless of their employment status to predict the expected value of earnings for women who follow a stylized life course. In this case, the OLS model is the best estimate to mean earnings of different groups in each country if the sample sizes are limited and do not allow averages to be calculated outright.

The regression parameters of the OLS analysis allow calculating the cross–sectional average earnings over the life course where the outcomes of older women with similar characteristics are supposed to represent a good estimate of the future of the younger women with the similar characteristics.

Ellwood et al. (2010) as well as Putz and Engelhard (2014) analyze the effects of first birth timing on the wages of women. They argue that OLS model on instrumental variables, as the ones used in Miller’s (2011) study, such as miscarriage, unexpected or unwanted pregnancy and lag in years from first attempt to conceive to first birth, fails because of the complexity of the mechanisms underlying the relationship of interest and the quality of instrumental variables is not a testable principle (Putz and Engelhard, 2014). Moreover, Ellwood et al. (2010) claim that endogeneity is a problem in analyzing the impact of childbearing on wages. The reason behind is that skilled women are expected to delay childbearing more than less skilled women which in theory might be related to costs of childbearing. Another reason for the failure of the OLS is the unobserved heterogeneity, meaning that characteristics of men and women might make them less appealing to potential partners which reduced their likelihood of becoming parents thus making them less successful in the labor market.

4.2.

Heckman sample selection method

As previously stated, the OLS method fails to account for the selection bias. Heckman (1979) argues that such bias arises due to using non–randomly selected samples that have a missing wage data problem. He proposed a method to use estimated values of the omitted or missing variables as regressors in order to estimate behavioral functions of interest and eliminate the specification error in the case of censored samples.

The Heckman sample selection method is a widely used method in studies that analyze the gender and motherhood wage gaps. Beblo et al. (2003) as well as Nicodemo (2009) and Mysikova (2012) and many other studies assert that the rate of participation of women is lower than that of men. Heckman (1979) argues that the selection bias could be due to individuals’ self-selecting not to participate and due to sample selection decisions. Thus, by not counting selection, the unobservable part in the wage and the participation equations are correlated. If unobservables in the participation and wage equations are correlated, then OLS coefficients will be biased.

Heckman selection method is a two-staged method: the first stage is a maximum likelihood estimation of a selection assuming bivariate normality of the error terms in the wage and participation equations, and the second stage is a twofold estimation of the participation equation and calculation of the correction term lambda or Mill’s ratio through OLS or GLS (Heckman, 1979; Beblo et al. 2003; Mysikova, 2012). The lambda term is often named normal hazard which is estimated in order for the correction terms to be added as independent variables. Nicodemo (2009, p. 12) remarks that in order to apply the Heckman method, three assumptions must be fulfilled: joint normality of the distribution of the error terms in the participation and wage equations; both terms are independent of both sets of observable variables; and there should be standard normalization for the probit selection equation.

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In recent years, most studies analyzing the gender gap tend to apply the Heckman selection method on their Mincerian models. Mysikova (2012) notes that if there is positive selection effect (negative correlation between unobservable in female wages and participation), then the selection corrected gender wage gap would be lower than the observed one if people who are currently not working had the same observed characteristics as those who are currently working. Since there are different observable characteristics of participating and non–participating women, this positive sign does not mean that if all women worked their average wage will be higher; on the contrary, the positive effect occurs when non–participating women possess better unobserved characteristics than working women in terms of wage reward. If a negative selection effect occurs, thus positive selection on unobservables, then actual wages of working women are higher than the assumed wages of the random female population sample with similar set of observed characteristics. The negative selection effect arises when non–participating women have worse unobserved characteristics than working women, i.e. low skills and abilities lower their probability of participation and potential wage.

Though most studies used this method extensively when analyzing the gender or motherhood wage gaps, the method itself does not come without criticism. Beblo et al. (2003) remark that since both procedures require variables that determine the propensity to work and are not related to wages, in practice such exclusion restrictions are hard to find and that problems with collinearity arise. There is a problem with the twofold method since it hinges on the correct specification of the lambda regressor. Many of the studies also argue that the two–staged parametric Heckman selection method is too parametric (the control variable is known), thus making it very restrictive (Vella, 1998; Beblo et al. 2003; Machado, 2012; Buchinsky, 1998b). Manski (1989) claims that the Heckman method lacks distributional assumptions and robustness. Vella (1998) proposed various non–parametric and semi–parametric ways to estimate the control variable, when unknown, to alleviate the restrictiveness of the Heckman method. He claims Manual for Calculation with Applications in Stata

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that in order to implement identification of unobservables under the control function, one must apply exclusion restriction which is unrelated to wages. The exclusion restriction must be applied since identification without an instrument is weak and since the correction term is usually a linear function of the variables used in the equation. Buchinsky (1998b) argues that the two staged non–parametric methods can approximate the selection bias by expanding the propensity scores from the selection equation.

Finally, Machado (2012) asserts that selection models, such as Heckman’s, do not incorporate unobserved heterogeneity in the treatment models. In terms of women and the labor market, heterogeneity must be taken into account since selection could be different in different parts of the labor market. If such a difference exists, then the selection correction methods would not recover true parameters. Therefore, if there is a presence of unobserved heterogeneity, then using a proxy (observed uncorrected gap) for the local gender gap could be less distorting than usual selection corrections.

4.3.

Repeated imputation

The multiple imputation is an empirical approach that was firstly introduced by Rubin (1987).

This method, as Allison (2000) claims, is one of the most attractive methods in handling missing data in multivariate analysis. Rubin’s (1987) technique is based on the median regression where the missing values are imputed by incorporating random variation. The method usually suggests imputing the missing values 3 to 5 times, producing M complete datasets. Additionally, once the data sets are produced, one should perform the desired analysis on each data set using standard complete–data methods (as is OLS). After the desired analysis, the values of the parameter estimates across the M samples should be averaged to produce a single estimate.

Finally, the standard errors are calculated by averaging the standard errors of the M estimates, calculation of the variance of the M parameter estimates across the samples, and combining the two quantities.

Rubin (1987) remarks that in order to use the multiple imputation method, few requirements must be fulfilled: first, the data must be missing at random (probability of missing data on a particular variable can depend on other observables but not on the variable itself; second, the model used to generate imputed values must be correct; and third, the model used for analysis must match, to a certain extent, with the model used in the imputation. The certain features that are good when implementing such method are: the introduction of appropriate random error into the imputation process leads to approximate unbiased estimates of all parameters, the use of non–deterministic imputation can do this in general setting, the use of repeated imputation leads to good estimates of the standard errors, the single imputation does not allow for additional error when using imputation and the multiple imputation can be used with any kind of data and any kind of analysis.

In terms of studying wage differentials of population by use of certain explanatory variables, the studies of Johnston et al. (2000) and Neal (2004) are the most commonly known for applying imputation methods. In the absence of wage observations in the sample, the imputation method is applied in order for every missing variable to be randomly imputed m times. This approach requires assumptions on the position of the imputed wage observations with respect to the median. The feature of the imputation method is that it does not require assumptions on the actual level of missing wages which is typically required in the matching approach and it does not need arbitrary exclusion restrictions and lack of robustness found in the Heckman selection correction models (Manski, 1989; Petreski and Petreski, 2015; Olivetti and Petrongolo, 2008).

Petreski and Petreski (2015) and Olivetti and Petrongolo (2008) claim that the imputation tech30

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nique allows for the missing wage observations to fall completely on one or other side of the median regression line, so the results can be only affected by the position of wage observations with respect to the median and not by specific values of imputed wages. This allows a researcher to make assumptions related to the economic theory on whether an individual who is unemployed should have a wage observation below or above the median wages for their gender and for the fact that she is a mother. Both studies estimate raw median wage gaps on the sample of employed workers and wage imputations for the unemployed which means that selection problems are alleviated. Olivetti and Petrongolo (2008) also remind that one disadvantage for a researcher in analyzing wage distributions is if the individuals have never worked in the sample period, then one should apply educated guess concerning their potential wage in respect to the median, based on observable characteristics such as age, experience, education, etc. This approach allows for the assumption that those who are unemployed would earn wages similar to those employed if they have similar matching observable characteristics. Finally, through the application of probability models, the individuals in the sample who have missing wage observations can have assigned variables on either side of the median of the wage distribution depending on their observable characteristics.

Olivetti and Petrongolo (2008) use the imputation technique in analyzing the gender wage gap in cross–sectional study. The study applies three methods of imputation in order to provide more robust analysis: single imputation, repeated imputation, and both on an expanded sample that is augmented with wage observations from adjacent wave. Single imputation will tend to overestimate the median gender wage gap on the imputed sample if there is large number of non–employed women. Secondly, repeated imputation is based on probit model which means random draws under the chosen model for non–employment thus accounting for the additional variability underlying the presence of missing values. Though repeated imputation is better, again, selection could be a problem since the median could be different than that of the potential wage distribution (the median observed if all were employed). In order to alleviate such a problem, the paper applies simple and repeated imputation on expanded sample

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(imputation on observables and unobservables) that is augmented with the wage observations from adjacent waves. In this way, the estimate will be much closer to the “true” median. The problem with such a method is that different imputed sample would have different impact on the estimated wage gap. Since women are more likely to be non–employed than men, and the non–employed individuals will tend to receive lower wage offers than their employed counterparts, the larger the imputation sample, the better is the estimated selection correction.

The extent of the selection correction from the various imputation methods depends on the wages of the non–employed who were previously or latter employed and by their observable characteristics.

Petreski and Petreski (2015) apply repeated imputations in analyzing the potential explanatory power of motherhood in the wages of women in Macedonia. They offer some lines of criticism of the technique, related to the work of unobservables in determining participation (absence of wages from the wage distribution) and wages simultaneously. The apparent problem arises when an individual values family: a person may be family–oriented and has a partner but no children and is thus unwilling to work or does not want to work long hours. Such inclination of devoting more time to familial obligation can affect nonparticipation, hence the motherhood wage gap. The apparent main weakness in the selection correction in the repeated imputation technique is its before-mentioned strength: not requiring arbitrary exclusion restriction. This weakness implies that individual unobservable variables such as cultural beliefs, stereotypes, individual beliefs and values could not be detected by the repeated imputation technique. Another problem could arise from the employer perspective, in a country such as Macedonia in which there is low labor mobility, low labor market participation especially for women, and the private sector is often regarded as lethargic and monopsonistic, the assigned wages could very much be irrational, that is, the wage could not be a correctly offered wage in respect to the worker’s human capital characteristics. This is also claimed by Angel – Urdinola (2008) that Macedonia has low market wages, high reservation wages and harsh employment conditions that could be influencing women’s labor-market decisions. In such a country, the use of repeated imputations method could not be the best model since the method depends on assigning wages to people who are not employed but have “similar” human capital characteristics with the employed, thus the method assumes that if those people were employed then the wages would have been similar with their employed counterparts. As noted before, such a method could create a bias, since in Macedonia often the offered and assigned wages in reality do not conform to the human capital assumption but are mostly based on the employer’s belief.

Allison (2000) claims that requirements for imputations methods could be violated in practice.

Often data observations could not be missing at random. Moreover, even though there are estimation methods for data that are not missing at random, such models tend to be complex, untestable and require specialized software. This problem causes any application of general– purpose method to assume that data are missing at random. Additionally, even if missing at random is satisfied, production of unbiased estimates of the desired parameters through imputations is neither easy nor straightforward.

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5.

Methods for decomposition of the gaps

5.1.

Blinder-Oaxaca

Most literature concerning the analysis of gender and motherhood wage gap uses the Blinder-Oaxaca decomposition method. This method was introduced by Oaxaca (1973) and Blinder (1973) who presented a more flexible approach in investigating the earnings gap or the wage differentials based on the human capital theory (Beblo et al. 2003; Weichselbaumer and Winter–Ebmer, 2005). Beblo et al. (2005) argue that such a method allows for the individual wage rate to reflect the potential of productivity on the basis of the established human capital characteristics.

The Blinder-Oaxaca method analyses the wage differentials between two groups of people.

In the model, the gap in the average expressed logarithmic earnings is segmented into two parts: the endowment effects and the discrimination or remuneration effects. The endowment effects represent the observable human capital characteristics, while the remuneration effects represent the unexplained component often assumed as wage discrimination. The unexplained component in the method should often be named remuneration instead of discrimination or unexplained, because of the complexity of the statistical calculation. There is a wide debate on the interpretation of the gender wage gap in terms of discrimination, also because the difference in endowments in the model between women and men can be already the result of discrimination due to the feedback effects. The second reason for naming the residual as unexplained leads to ambiguities since the unobserved component can still consist of some unobserved differences in the human capital characteristics.

Most of the literature uses this decomposition to estimate the adjusted wage gap that controls for the human capital characteristics and thus accounts for the observed differences in personal and job characteristics of women and men (Beblo et al. 2005). The method estimates the wages separately for individuals of different groups (males and females) by using log wages and controls for individual observable characteristics. After this, the total wage differential between the two genders is analyzed through the difference in the mean wages decomposed in the two components that were previously mentioned (Weichselbaumer and Winter–Ebmer, 2005). Plasman and Sissoko (2004) claim that this wide use of the model is due to the fact that it is based on the Mincerian earnings function and that it combines the two schools of thought that were introduced by Mincer and Polachek (1974), according to whom the gender gap depends on the endowment effect, and Becker’s (1971) idea that economic agents belonging to a specific group might have discriminatory preferences against members of another group. If hiring a person of a discriminated group implies an additional psychological cost for the employer, then the employer will offer lower wage to that worker and therefore the discriminated worker would accept the lower wage in order to be employed. Firpo et al. (2007) stress that this method provides a straightforward way of dividing up the contribution of each covariate to both composition and wage structure effects due to the linearity assumption. Through this assumption, the Blinder-Oaxaca decomposition can be estimated by replacing the parameter vectors by their OLS estimates and the expected value of the covariates by the sample averages.

Though a very popular method and much used in the literature, the Blinder-Oaxaca has been a subject of much scrutiny and criticism. Beblo et al. (2005) point out two problems: first, the endowment effect is based on one of the sexes (the male in most applications), thus a problem of potential dissymmetry in the effects arises depending on which gender is considered.

Though true, Oaxaca and Ransom (1994) apply a matrix of combinations of both male and female prices in decomposing the wages. However , Olsen and Walby (2004) claim that this twoterm approach is incoherent and does not contribute to sensible findings in the analysis of the gender wage gap since it only partially solves the problem but still has deep difficulties with the Manual for Calculation with Applications in Stata

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unexplainable part of the gender wage gap. Gender segregation in their analysis shows a false appearance of women having a higher slope and therefore more positive labor market experiences indicating that the slope differential is intimately tied up with the differential intercepts of women’s and men’s wage equations. The second problem with the Blinder-Oaxaca method is that the method considers only the wage decomposition at the mean, meaning that it does not catch potential variations of the different effects over the wage distribution. Conversely, Juhn–Murphy-Pierce (1993) decomposition method is far more reliable in this respect. The decomposition of the gender wage gap must be an estimation process of the wage equation that should offer better, more efficient, factor-based wage determination process.

Yun and Lin (2015) offer a solution for the two technical difficulties of the Blinder-Oaxaca method. The first problem is that results may vary depending on the choice of the base group (non– discriminatory wage structure) or referred to as the index problem. The second is an identification problem or the coefficient effect of a set of dummy variables, i.e. the existence of wage gap due to the coefficient difference of the set of dummy variables which is not invariant to the choice of the reference group. Both problems can be resolved by using the normalized regression equation. It fixes the index problem because it does not rely on the unobserved non–discriminatory wage structure.

Weichselbaumer and Winter–Ebmer (2005) also question the Blinder-Oaxaca method by questioning whether the included characteristics are affected by discrimination itself (if yes then the estimated discrimination is understated), and whether the included variables measure productivity comprehensively. If not, then the estimate is biased upwards or downwards. Interestingly, the unexplained part is put into question whether to be rightfully called discrimination since the employer is assumed to have the exact knowledge of all relevant employee’s productive characteristics; the researcher, on the other hand, possesses a restricted number of productivity indicators in the data. The problem from this arises when the omitted variables are correlated with the gender, which implies that the unexplained part not only captures discrimination but unobserved group differences in the productivity as well. Finally, in terms of this, individual characteristics such as less investment in on–the–job training, less experience, greater time in housework and lower occupational attainments of women might be voluntary choices by women which would not be sufficiently captured in the data and could be included and be responsible for the unexplained residual.

Heinze (2010) and Albrecht et al. (2003) claim that the method is based on the assumption that the explanatory factors do not vary with the wage structure which is questioned by Albrecht et al. (2003) who find an increasing impact of education on the wage differences across the wage distribution. Heinze (2010) remarks that innovative human resources practices, collective bargaining and co–determination along with firm’s profit could also have a substantial effect on wage differentials across the wage distribution.

Plasman and Sissoko (2004) argue that the wage structure is an integral part in analyzing the gender wage disparities; additionally, the authors note that collective bargaining structure, occupational segregation and legislations that force gender equality must be taken into account when applying a decomposition method. Additionally, Firpo et al. (2007) and Barsky et al. (2002) remark that the decomposition method provides consistent estimates of the wage structure and composition effect only if the conditional expectation is assumed to be linear.

Firpo et al. (2007) propose to estimate the conditional expectation by using non–parametric methods in order to fix this problem, while Barsky et al. (2002) propose a non–parametric reweighting approach. The problems of these two solutions as Firpo et al. (2007) argue are that they do not provide direct ways to divide the contribution of each covariate further in the wage structure and composition effects. Firpo et al. (2007) propose a recentered interest function as a solution of the problems in the Oaxaca–Blinder method.

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In turn, we discuss decomposition methods that overcome the problems of the Blinder-Oaxaca method.

5.2.

Quantile regression

This method for analyzing the gender wage differentials has been adopted by Buchinsky (1998a). The method is a semi–parametric technique which is used to estimate conditional quantile functions. Quantile regression is becoming widely popular in analyzing wage data since it takes into account the wage structure and the wage distribution in general. It estimates conditional median, which gives a more complete and comprehensive causal analysis than a simple OLS. As the name itself states, the model estimates a variable in quantiles, in the case of analyzing the gender and motherhood wage gaps this variable is the log wage conditional on a number of variables that are regarded of having explanatory power (human capital and individual characteristics). Furthermore, it allows investigating the cause of change in wages at different quantiles of the wage distribution. Heinze (2010) notes that quantile regressions are used more recently in order to decompose the gender wage gap at different points of the wage distribution. While OLS and other methods of estimation and decomposition investigate the mean effects, this method allows to study the marginal effects of covariates on the dependent variable at various points in the distribution, not only the mean.

Machado and Mata (2005) propose this alternative decomposition method by combining the quantile regression and a bootstrap approach to estimate counterfactual density functions. Using such a method, Albrecht et al. (2003) found that the Swedish gender gap increases throughout the wage distribution and rises in the upper tail indicating a glass ceiling effect. Additionally, Hübler (2005) and Fitzenberger and Kunze (2005) claim that by using linear local matching and quantile regressions, the gender wage gap is the highest in the lower part, and the lowest in the upper part of the wage distribution which indicates an occupational segregation and lower occupational mobility among females.

Beblo et al. (2003) use another decomposition method based on quantile regression named Juhn – Muphy – Pierce introduced by Juhn et al. (1993). From their finding, Beblo et al. (2003) argue that according to the results of the private and public sector there is a U–shaped wage gap. Additionally, one should consider the confidence bands for the adjusted wage gap and the different effects in the wage decomposition when using quantile regression methods such as Juhn–Murphy–Pierce or Machado–Mata. As Firpo et al. (2007) argue, running quantile regression with such a method would describe the whole conditional wage distribution. Using an estimated parameter for one group, one could create a counterfactual distribution for the other group and then such counterfactual distribution can be used to compute the overall composition- and wage-structure effect. Moreover, relating to a single covariate, it is possible to estimate the contribution of the covariate to the wage structure effect like in the Oaxaca–Blinder decomposition.

Albrecht et al. (2003) and Machado and Mata (2005) use the conditional quantile regressions to create a counterfactual unconditional wage distribution. Machado and Mata (2005) propose quantile regressions to extend the traditional Oaxaca–Blinder decomposition. The suggestion is for estimating the counterfactual wage distribution by creating random sample of size m, from a uniform distribution, and then calculating the conditional quantile regression for each group.

Furthermore, the method simulates the wage distribution of the second group on the basis of the wage distribution and characteristics of the first group and repeating the steps m times (Machado and Mata, 2005; Firpo et al. 2007; Nicodemo, 2009). Albrecht et al. (2003) apply this procedure by choosing percentile 1 through 99, and by generating 100 draws for each quantile.

In such a procedure the difference of the unconditional quantile between the two groups is decomposed in two segments: explanatory characteristics of both groups and the counterfactual Manual for Calculation with Applications in Stata

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unconditional wage distribution. The unconditional reweighting procedure should be used to compute the contribution of each covariate to the composition effect. Mysikova (2012) claims that the quantile regression can be more informative than the Heckman selection model due to the fact that the quantile regression method analyses the wage differences by quantiles rather than just at the mean.

As in the case of all other methods of decomposition, the quantile regression method also faces some shortcomings. Nicodemo (2009) argues that although it is easy to estimate the conditional distribution function by inverting the conditional quantile function, the estimated conditional quantile function is not necessarily monotonic and problems could arise in trying to invert it.

In her study, she uses the Melly (2006) method by integrating the conditional distributions over the range of covariates in order to obtain an estimate of the unconditional distribution. The Melly (2006) procedure is able to correct the standard error with a bootstrap estimation for 100 times.

Firpo et al. (2007) remark that the Machado and Mata (2005) procedure does not provide a way to divide the composition effect into the contribution of each single covariate. Additionally, there is a certain difficulty to computationally implement it, because it involves a large number of estimated quantile regressions thus conducting large scale simulations. The third shortfall of the Machado and Mata (2005) technique is with respect to the mean: the decomposition is only consistent if the right functional form is used for quantile, and since the functional form must be chosen for each and every quantile making certain that the specification is correct, it is a difficult approach. Another problem could arise if the correct functional form is not linear, thus making it difficult to calculate the contribution of each covariate to the wage structure effect, because there is no longer a single coefficient associated to a given covariate. These problems are also faced in the Juhn- Murphy- Pierce method, since all these quantile regression approaches do not provide a way to divide the composition effect into the contribution of each individual covariate. Though true, these methods at least could be used to compute the overall wage structure and composition effects for various distributional statistics.

5.3.

Weighting approach

The weighting decomposition approach was initially introduced by Barsky et al. (2002) as an alternative decomposition method in terms of analyzing wealth to the Oaxaca–Blinder method.

In their study, the Oaxaca–Blinder method is regarded to offer misleading results as it requires parametric assumption for the form of the conditional expectation function. Misspecification of the conditional expectation function could arise because of the nontrivial errors in inference, in terms of the portion attributable to differences in the distribution of the explanatory variables.

This distributional problem is due to compounding of the conditional expectations outside the observed range of the conditioning variables.

Due to the problems mentioned in the Blinder-Oaxaca traditional decomposition method, Barsky et al. (2002) provide an alternative nonparametric approach that reweights the empirical distribution of the outcome variable by using weights that would equalize the empirical distributions of the explanatory variable between genders. Black et al. (2008) apply this nonparametric method in the analysis of the gender wage differentials of the highly-educated people. Firpo et al. (2007) note that such reweighting nonparametric method allows to be applied in more general distributional statistics. FrĂślich (2007) argues that such a nonparametric approach differs from the parametric one in two ways: firstly, the regression function is not specified as linear; and secondly, the adjusted mean wage is simulated only for the common support subpopulation.

The propensity score matching method is an alternative weighting method to analyze or esti36

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mate the average effects of the treatment when selection is on observables. The method estimates the effect of a treatment by matching it with the control group. In terms of the gender and motherhood wage gaps, the technique allows for matching comparisons through probability weights to find matched samples with similar observable features (human capital characteristics of both genders), except for the treatment which is used to group observations into two sets, the treated and the control group (Ñopo, 2008; Goraus and Tyrowicz, 2014). By controlling the differences in the observed characteristics, the treatment of the impact could be measured.

Frölich (2007) claims that the method allows for estimating the average treatment effects when selection is on observables. Moreover, it allows using one-dimensional non-parametric regression to estimate the effects, even with many confounding variables. In this method, matching on one-dimensional probability is sufficient instead of matching on all covariates. Ñopo (2008) considered the gender variable as a treatment and used matching to select sub–samples of males and females by finding complete match (no differences) between the observable characteristics of the matched males and females. In such a way, a method was developed to measure four components of the overall wage differences: wages of men identical to women in the sample, wages of women identical to men in the sample, wages of men for whom there are no identical women and wages of women for whom there are no identical men in the sample (Ñopo, 2008; Goraus and Tyrowicz, 2014). Goraus and Tyrowicz (2014) assert that the two components could be considered similar to the Oaxaca–Blinder, while the other two tackle explicitly the problem of overlapping and measure the quantitative effect of overlap on the overall wage differential.

Petreski and Petreski (2015) apply the weighting technique by employing a model that estimates the probability of being a male/childless woman rather than female/mother, using a set of explanatory variables. The method uses a probit model to predict male/childless woman and use the predictions to calculate and apply weights by the ratio of the probability of being male/ childless woman and female/mother. The weighted statistics are the counterfactual mean and deciles of log wages that would make the females/mothers to have the same characteristics as males/childless women. Ñopo (2008) applies the matching by matching individuals based on characteristics (matching on covariates) rather than propensity score due to the “ignorability of treatment” problem which is required by the propensity score matching and is not satisfied Manual for Calculation with Applications in Stata

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if gender is used as treatment. Frölich (2007) remarks that the propensity score matching allows using one–dimensional nonparametric regression technique to estimate treatment effect even with many confounding variables. Conversely, to the “ignorability of treatment” problem, Frölich (2007) notes that one–dimensional probability is sufficient instead of matching all covariates. There are several instances of the applicability of the propensity score matching: the procedure is not based on any properties of potential outcomes and it does not require additional assumptions other than those that are required for justifying matching on confounding variables, the procedure could be applied for estimating the whole distribution, can be used to simulate changes in the wage distribution if the distribution of certain characteristics in the population were changed, and finally the procedure can be used to account for survey non– response or attrition in longitudinal studies where non–random attrition has to be taken into consideration.

Though the benefits of applying such a procedure are abundant, Firpo et al. (2007) remark that the disadvantage of this approach is that in general it does not provide a direct way for further dividing the contribution of each covariate in the wage structure and composition effects. Ñopo (2008) lists several shortfalls of the matching approach; firstly, one should recognize gender differences since not all males are comparable to all females, so the failure to recognize this problem leads to overestimation of the unexplained component of the wage gap. Secondly, there is an important share of the wage gap where great number of males have a set of characteristics which are highly rewarded in the labor market, but those characteristics are absent in the female population. Thirdly, two issues for using this method are the need to use only discrete variable and the dimensionality problem, where the inclusion of many explanatory variables (use of many matching characteristics) or a high number of possible values for a single matching (discrete) variable may decrease the chances to obtain adequate number of matched observations, thus limiting the the usefulness of the method (Ñopo, 2008, p. 299). In line with this, Goraus and Tyrowicz (2014) explain that this dimensionality problem in regard to the extent to which the gender wage gap can be explained, depends on the number of explanatory variables, having in mind that the increase in explanatory variables decreases the potential of matching characteristics.

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Zanutto (2006) also criticizes the propensity score method. The propensity score method estimates the gender gap averaged over the population which could obscure important interactions. While linear regression can analyze interactions and effects of and with other covariates, the propensity score method only estimates the overall gender effect. Additionally, the propensity score matching can have problems with omitted variables named “tainted” or variables like job rank which could be affected by gender discrimination. Additionally, even if one controls for job rank, the method might conceal gender differences in salary due to discrimination in promotion such as in the case where male and female supervisors might have similar wage, but women might be rarely promoted to supervisors. Finally, the propensity score matching can be affected by complex survey designs, where they must be incorporated into estimates from the method in order to obtain unbiased estimates of population–level effects.

5.4.

Recentered interest function

This method was firstly introduced by Firpo et al. (2007) as an alternative method of decomposition in order to tackle and alleviate the issues that are faced by other methods of decomposition. The method is closely related to the Oaxaca–Blinder decomposition method since it only extends the decomposition to any distributional measure, not only the mean, and allows for a much more flexible non-parametric wage setting model. The idea of the method is to replace the dependent variable by the corresponding recentered influence function for the interested distributional statistics. The influence function is a widely used concept in robust statistics which is easily computable and could be estimated as easy as an OLS regression.

The method firstly divides the distributional changes into a wage structure effect and a composition effect through the reweighting method. This reweighting method will allow estimating the two segments of the components of the decomposition without having to estimate the structural wage-setting model. Secondly, the two segments are further divided into the contribution of each explanatory variable through the use of the recentered influence function regressions. These regressions would estimate the impact of the explanatory variables directly on the distributional statistic of interest. Antencio and Posadas (2015) remark that this methodology has an advantage over other methods since it can compute several statistics, not only the mean, without losing the ability to identify the contribution of each covariate to the wage structure and the composition effects. This reweighted recentered influence function decomposition allows for the estimation of each covariate on the wage structure and composition effects across the whole earnings distribution (Antencio and Posadas, 2015).

The benefit of this method is that it assumes ignorability, which implies that the unobservables are equally distributed between the two analyzed groups; in terms of the gender wage gap, this means that there is no random selection of women into the labor force (Antencio and Posadas, 2015). The other benefit is that it assumes common support over the observables and unobservables, which means that the method assumes no combination of individual characteristics specific to males or females. The method uses unconditional quintile regressions based on the recentered influence function, which means running regression of a transformation of the outcome variable - its recentered influence function, on the explanatory variables. Thus allows for the evaluation of the marginal impact of changes in the distribution of the explanatory variables on the quintiles of the marginal distribution of the dependent variable (Firpo et al. 2007; Antencio and Posadas, 2015). This means that the estimated coefficients show the effect of increasing the mean value of the certain human capital characteristic on the unconditional quantile.

Firpo et al. (2009) and Antencio and Posadas (2015) claim that the counterfactual distribution of earnings must be produced in order to decompose the difference of earnings between the genders into composition and wage structure segments. The counterfactual distribution would represent what women would have earned if they had received the same return to their labor Manual for Calculation with Applications in Stata

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market skills as men. Firpo et al. (2009) argue that once the counterfactual distribution and the recentered influence functions are estimated, the remaining steps are similar to the Oaxaca– Blinder method since the coefficients of the influence function can be estimated by simple OLS regressions. Petreski and Petreski (2015) assert that the counterfactual estimation is still based on linearity assumption which allows for the possibility to do out of the sample predictions. The approach allows decomposing the mean (or quintiles) wage gap into an explained part which is attributable to differences in characteristics between the two genders, and unexplained part, alongside contribution decomposition of the specific characteristics to the wage gap. Kassenboehmer and Sinning (2014) and Rothe (2012) argue that the linear recentered influence function assumption which is used to define the decomposition is not without any problems. The linearity assumption suggests that the respective feature of the outcome distribution depends on the marginal distribution of the covariates only through their mean.

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6.

Applications in STATA with Macedonian data

Chapter 6 provides a description of the methods and their STATA codes in order to offer capacity building for any interested researchers in their future endeavors in gaps calculation. The chapter is a manual for econometric calculation of the wage gaps through the use of STATA and applies these methods with Macedonian data. The data used is based on the Survey of Income and Living Conditions (SILC) from 2010 which is performed in accordance with the Eurostat SILC. The survey has a representative sample of about 13,800 Macedonian individuals and their households, thus offering a sufficiently rich set for analysis. The dataset consists of individuals between 15 to 64 years of age who are able to work. Out of these 13,800 individuals, 3,000 are women that belong to the childbearing age cohort of between 24 and 45 years of age, which is the cohort we use for the calculation of the motherhood wage gap. Furthermore, in order this chapter to be more understandable, we have applied the usual and most commonly used human capital characteristics as explanatory variables in our econometric models when calculating and analyzing the gender and motherhood wage gaps. This means that these variables are not the only ones that should be used in further research of the economics of the wage gaps, and that other human capital characteristics with the combination of these should be applied in further econometric modelling. The reason being that the application of other human capital characteristics might show different understanding of the wage gaps and these variables might have an important explanatory power and could provide different interpretation about the existence of the wage gaps.

Before starting with the analysis in STATA, it is recommendable that one performs an update of it, done through ado update

and also install a user-written missing command, through: ssc install command-name

Secondly, write the command that will specify which data set STATA should upload by writing use “C:\destination of the dataset\the dataset�.

In what follows, we will present the commands and the associated results in the following manner: first, of the calculation of the gender and motherhood wage gap; then, of the decompositions.

6.1.

Calculation of the gender wage gap

In its simplest unadjusted form, the gender wage gap could be calculated by using OLS, with the following command: reg lny dgn, vce(robust)

This measures the unadjusted wage gap by regressing lny on gender. lny is the logarithmic hourly wage and dgn represents gender. The vce(robust) is a command to correct for errors’ heteroscedasticity.

Then, to avoid rewriting the list of variables all the way through, we will create a list. global creates a matrix of few individual and/or labor-market characteristics whose name is zlist. Variables are as follows: deduc32 is the secondary education, deduc33 is the tertiary education, dag is age, dag2 is age squared, exper represents work experience. Primary education is dropped from analysis due to perfect collinearity (i.e. represents reference category). global zlist dgn deduc32 deduc33 dag dag2 exper

Then, we regress the logarithmic wage on the vector of individual characteristics, so as to obtain the adjusted-for-characteristics gender wage gap.

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reg lny $zlist, vce (robust)

*** The Heckman sample-selection method could be coded in the following manner: heckman lny $zlist, select(dgn deduc32 deduc33 dag dag2 exper mstatus nmb_child) robust

heckman is the STATA coding term for applying the Heckman sample correction, select refers to the variables on which heckman should adjust the sample correction, mstatus represents marital status and nmb_child represents number of children. The latter two variables in the select command are often used as exclusion restrictions, since women might self-select not to participate in the labor market due to education, gender, experience, marriage and the number of children in the household. *** The repeated imputations method could be coded in the following manner: Firstly, we generate gender specific median, as the 50th percentile of the variable (in this case the log wage lny). bysort sorts the variable by gender (first the wages of males, then of females); egen is generating a new variable (median_gender) which will be equal to a constant that contains the 50th percentile, if p(#) is not specified, 50 is assumed which means median. bysort dgn: egen median_gender = pctile(lny), p(50)

The following code generates another variable named d_median which is, at the outset, generated as a missing variable (.). The following statement replaces all missing cells with 1 if the logarithmic wage is greater than the median, for all individuals with positive wage. Similarly, the third statement replaces the missing cells with 0 if the logarithmic wage is lower than the median. Finally, the last statement drops from the analysis all observations that are missing. gen d_median=. replace d_median=1 if lny>median_gender replace d_median=0 if lny<median_gender replace d_median=. if lny==.

The following two lines run a probit model to predict whether a person belongs below or above the median wage depending on age, age squared, secondary education, tertiary education and experience (i.e. the same variables as in the basic OLS specification). The yhat is the generated variable that shows the prediction according to the probability. probit d_median dag dag2 deduc32 deduc33 exper predict yhat

As yhat is continuous, in the next lines, we will reduce it to a dummy variable. The first line generates a new variable named d_yhat that is set missing at the outset. The second replaces this missing variable with 0 if yhat is less or equal to 0.5, and the third line of command replaces it with 1 if yhat is greater than 0.5. gen d_yhat=. replace d_yhat=0 if yhat<=0.5 replace d_yhat=1 if yhat>0.5

Once this is done, mi set mlong declares a multiple imputation data. The mlong is a marginal long style dataset where it first marks incomplete observations, then it omits assigned observations that are zeros, and last it records arbitrarily coded observation-identification variable.

The mi register imputed registers that lny is the variable needed for analysis and that should be imputed. The mi describe describes the multiple imputation data where it shows how many are imputed, how many are complete and incomplete.

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Table 2: Results between Gender Wage Gap estimation models

Gender Secondary Ed Tertiary Ed Age Age squared Experience Constant Lambda (Mill's ratio)

Unadjusted Gen- Adjusted Gender Adjusted Gender der Wage Gap Wage Gap (OLS) Wage Gap (Heckman) (1) (2) (3) -0.141*** -0.192*** -0.191*** (0.0302) (0.0269) (0.0268) 0.168*** 0.162*** (0.0365) (0.0379) 0.710*** 0.701*** (0.0424) (0.0452) 0.0324 0.0301 (0.0284) (0.0286) -0.000607 -0.000568 (0.000408) (0.000411) 0.0226*** 0.0216*** (0.00308) (0.00345) 4.220*** 3.375*** 3.426*** (0.0178) (0.492) (0.498) -0.0139 (0.033) 3,018

Adjusted Gender Wage Gap (RI 100 imputations) (4) -0.0795*** (0.0248) 0.0107 (0.0311) 0.497*** (0.0383) 0.0204 (0.024) -0.000317 (0.000346) 0.01*** (0.00239) 3.676*** (0.410)

Observations 1,488 1,488 3,018 R-squared 0.015 0.228 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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mi set mlong mi register imputed lny mi describe

The next command sets initial value of random-number seed. The option is used to reproduce results anytime. set seed 29390

The mi impute mvn uses multivariate normal data augmentation to impute missing values of continuous imputation lny where it is equal to d_yhat (above or below the median we created earlier). The add (100) force means that this should be imputed 100 times. mi impute mvn lny = d_yhat, rseed(29390) add(100) force

Then, the mi estimate computes multiple imputation estimates of coefficients by fitting estimation command to the multiple imputation data. The following displays the replication dots or the imputed observations where one dot is displayed for each successful replication. Then lny is regressed on dgn, and then on all variables (which we previously put in zlist). mi estimate, dots post: regress lny dgn, vce(robust) mi estimate, dots post: regress lny $zlist, vce(robust)

*** After the usage of the STATA commands, in the next table we provide the estimated results.

According to the results in Table 2, the unadjusted gender wage gap (column 1) indicates that women in Macedonia are paid 14.1% less than male workers. Once adjusted for generally-accepted workersâ&#x20AC;&#x2122; characteristics (column 2), the OLS model increases the gap to 19.2%. This is the phenomenon according to which, the adjusted gap in the transition economies is larger than the unadjusted one, as females in employment have better labor market characteristics than males in employment. The statistical significance of education and experience not only shows that these are good explanatory variables, but it also indicates that usually women have better labor-market characteristics than men.

Once adjusted for selectivity (column 3), the Heckman model shows that selection does not explain the gender wage gap in Macedonia since the adjusted wage gap does not shrink in comparison to the OLS model. The model implies that selection into the labor force does not play a role in the gender wage gap in Macedonia which could be surprising and misleading.

Hence, we apply the alternative method of repeated imputations that accounts for selection other than Heckman (column 4). Results suggest that the adjusted wage gap according to repeated imputation decreases to 7.95%, which is a sizeable decline of about 11.25 percentage points in comparison to the adjusted gender wage gaps according to OLS and Heckman. Additionally, the results indicate that selection is able to explain around 60% of the gender wage gap. Furthermore, tertiary education and experience are the only variables that are statistically significant, which reveals that there is a negative selection of females into employment and these women that are outside the labor market are not those with the worst human capital characteristics.

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6.2.

Calculation of the motherhood wage gap

In what follows, we will use almost the same statistical coding as we did when we estimated the gender wage gap (Section 6.1). Before we start estimating the motherhood wage gap, we write the following command: keep if dgn==1

This tells STATA that only women should be selected for analysis from the dataset. The unadjusted motherhood wage gap could be calculated by using OLS, with the following command: reg lny mother, vce(robust)

The unadjusted motherhood wage gap is calculated by regressing lny on mother, where mother represents a dummy variable that indicates if a woman is a mother or not (with a child up to 6 years of age).

As in the OLS estimation of the gender wage gap, we analyze the motherhood wage gap through creating mlist matrix of the same observable characteristics. global mlist mother deduc32 deduc33 dag dag2 exper reg lny $mlist, vce(robust)

*** The Heckman sample-selection method will be coded in the same manner as in section 6.1: heckman lny $mlist, select(mother deduc32 deduc33 dag dag2 exper mstatus nmb_child) robust

The only difference is that instead of gender, we now consider the mother dummy in order to analyze the motherhood wage gap.

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*** The repeated imputations method will be coded in the same manner as in section 6.1, where the only difference is that now we consider the mother dummy instead of gender, implying that we also work with the motherhood-specific median. bysort mother: egen median_mother = pctile(lny), p(50) gen d_median=. replace d_median=1 if lny>median_mother &lny>0 replace d_median=0 if lny<median_mother & lny>0 replace d_median=. if lny==. probit d_median dag dag2 deduc32 deduc33 exper predict yhat gen d_yhat=. replace d_yhat=0 if yhat<=0.5 replace d_yhat=1 if yhat>0.5 mi set mlong mi register imputed lny mi describe set seed 29390 mi impute mvn lny = d_yhat, rseed(29390) add(100) force global ilist deduc32 deduc33 dag dag2 exper mi estimate, dots post: regress lny mother, vce(robust) mi estimate, dots post: regress lny mother $ilist, vce(robust)

*** In the next Table 3 we provide the results of the motherhood wage gap according to the different econometric models. The unadjusted motherhood wage gap, which is statistically signif-

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icant, indicates that mothers in Macedonia are paid 8.7% less than childless women. Though true, once the wage gap is adjusted for general accepted workersâ&#x20AC;&#x2122; characteristics, the OLS model decreases the gap to 3.7% and it becomes insignificant. It suggests that any observed motherhood wage gap is actually due to observable characteristics. The statistical significance of education and experience indicates that mothers usually have worse labor-market characteristics than employed childless women.

Once adjusted for selectivity (column 3), the Heckman model shows that selection does not explain the motherhood wage gap in Macedonia since the adjusted wage gap remains the same as in the OLS model. The model suggests that selection does not play an important role in the wage differences of mothers and childless women in Macedonia.

The repeated imputation technique (column 4) decreases the motherhood wage gap to 0.57%, but remains insignificant. Though it decreases by about 3.1 p.p., it still indicates that selection does not play an important role. Moreover, the results suggest that education and experience are very strong predictors of the motherhood wage gap. Furthermore, selection and the statistical insignificance of the motherhood wage gap indicate that the gap can be solely explained by individual and human capital characteristics. This interpretation is supported from all three statistical estimation models. Finally, all three models, not only show that motherhood is irrelevant for wage differentials, but they also show that mothers have, on average, worse labor-market characteristics than employed childless women.

Table 3: Results between Motherhood Wage Gap estimation models Unadjusted Gender Wage Gap

Mother

(1) -0.0870* (-0.0504)

Secondary Ed Tertiary Ed

Adjusted Gender Adjusted Gender Adjusted Gender Wage Gap (OLS) Wage Gap (Heck- Wage Gap (RI man) 100 imputations) (2) (3) (4) -0.0373 -0.0375 -0.00578 (0.0484) (0.0481) (0.0421) 0.173*** 0.169*** -0.00453 (0.0650) (0.0622) (0.0445) 0.776*** 0.770*** 0.571*** (0.0692) (0.0662) (0.0563)

Age Age squared Experience Constant

4.139*** (-0.0401)

0.0563 (0.0506) -0.000966 (0.000721) 0.0231*** (0.00467) 2.800*** (-0.866)

0.0554 (0.0505) -0.000951 (0.000723) 0.0226*** (0.00546) 2.822*** (-0.867) -0.0079278

Lambda (Mill's ratio)

(0.0511) 1,579

0.0224 (0.0362) -0.000348 (0.000518) 0.00941*** (0.00331) 3.497*** (0.615)

Observations 634 634 1,579 R-squared 0.004 0.26 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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6.3.

Decomposition of the gender wage gap

The most commonly used decomposition of the gender wage gap is the Blinder-Oaxaca decomposition. The unadjusted gender wage gap can be decomposed with the following commands: global dlist deduc32 deduc33 dag dag2 exper oaxaca lny $dlist, by(dgn) vce(robust)

As we did before, we first assign the general accepted human capital characteristics in a matrix.

The oaxaca command tells STATA to use the Oaxaca model to estimate the logarithmic wage on the human capital characteristics; additionally, the by() command tells STATA to analyze the logarithmic wages on the human capital characteristics by gender.

The statistical significance of the endowments (observable characteristics) and the coefficients (unobservables) in Table 4 demonstrates that the unadjusted wage gap highly depends on both. While secondary education increases the wage gap and tertiary education decreases it by 1.6 and 9% respectively, the insignificance of experience shows that the endowments do not depend on the difference of work experience between males and females. The effect of unobservables is estimated to give rise of the unexplained wage gap to 18.6%.

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Table 4: Blinder-Oaxaca Decomposition on the Unadjusted Gender Wage Gap Blinder - Oaxaca Decomposition (Unadjusted Gender Wage Gap)p) Overall Endowments Coefficients Interactions Men (mean 4.220*** log-hourly wage) (0.0179) Women (mean 4.079*** log-hourly wage) (0.0245) Gender Wage 0.141*** Gap (0.0303) Endowments -0.0570*** (0.0174) Coefficients/Un0.186*** explained Part (0.0281) Interactions 0.0118 (0.0111) Secondary Ed 0.0161** -0.00572 -0.000916 (0.00745) (0.0453) (0.00726) Tertiary Ed -0.0960*** -0.0454 0.0173 (0.0199) (0.0286) (0.0113) Age -0.0243 -0.842 0.0126 (0.0291) (2.058) (0.0318) Age squared 0.0343 0.5 -0.0162 (0.0338) (1.076) (0.036) Experience 0.013 -0.0152 -0.000934 (0.00841) (0.057) (0.00355) Constant 0.595 (1.012) Observations 1,488 1,488 1,488 1,488 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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*** Another very common approach used in the decomposition of the gender wage gap is the Blinder-Oaxaca approach adjusted by Heckman sample-selection. This method can be coded as follows: oaxaca lny $dlist, by(dgn) model1(heckman, select(deduc32 deduc33 dag dag2 exper mstatus nmb_child)) robust

The command is the same as the original one, the only difference being that we assign a command model1 that specifies which model should be used with the oaxaca approach. The heckman command is the same as in the previous sections.

Results are presented in Table 5. While the effect of endowments in Table 5 is the same as in Table 4, the effect of unobservables shows a statistical significance at the tertiary education where unobservable characteristics decrease the wage gap by almost 7%. The biggest difference in the Blinder-Oaxaca approach adjusted for selection is in the overall effect of unobservables. Although, as we have seen previously, the adjusted gender wage gap rises to 19%, the effect of unobservables rises to 24%. This suggests that even after adjusting for selection, the role of unobservables is highly significant in explaining the rise and existence of the gender wage gap.

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Table 5: Blinder-Oaxaca Decomposition with selection correction Blinder - Oaxaca Decomposition (Heckman Adjusted Gender Wage Gap) Overall Endowments Coefficients Interactions Men (mean 4.272*** log-hourly wage) (0.0324) Women (mean 4.079*** log-hourly wage) (0.0245) Gender Wage 0.193*** Gap (0.0406) Endowments -0.0570*** (0.0174) Coefficients/Un0.242*** explained Part (0.0405) Interactions 0.00794 (0.0117) Secondary Ed 0.0161** -0.0301 -0.00482 (0.00745) (0.0482) (0.00784) Tertiary Ed -0.0960*** -0.0698** 0.0266** (0.0199) (0.0326) (0.0133) Age -0.0243 -1.772 0.0265 (0.0291) (2.14) (0.0361) Age squared 0.0343 1.067 -0.0346 (0.0338) (1.131) (0.0415) Experience 0.013 -0.0935 -0.00575 (0.00841) (0.0698) (0.00556) Constant 1.14 (1.068) Observations 1,488 1,488 1,488 1,488 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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*** The quantile decomposition approach is another very commonly used approach in decomposing the wage structure. The approach can be coded in the following way: global qlist dgn deduc32 deduc33 dag dag2 exper qreg lny $qlist, quantile(0.1) qreg lny $qlist, quantile(0.2) qreg lny $qlist, quantile(0.3) qreg lny $qlist, quantile(0.4) qreg lny $qlist, quantile(0.5) qreg lny $qlist, quantile(0.6) qreg lny $qlist, quantile(0.7) qreg lny $qlist, quantile(0.8) qreg lny $qlist, quantile(0.9) qreg lny $qlist, quantile(0.99)

The command qreg denotes quantile regression, whereby the logarithmic wage is regressed on the matrix of human capital characteristics. The quantile() command assigns which quantile the regression should analyze. In our analysis, we have divided the wage structure in deciles. If needed or if one wants, one could divide the wage structure in 2, 3, 4, 5 percentile parts by using the quantile() command.

Table 6 shows that the gender wage gap is statistically significant at all deciles of the wage distribution. Additionally, secondary education has no significant effect at the 10th and 99thpercentile, while tertiary education has no significance at the 99thpercentile. There is an indication of a glass ceiling effect at the highest decile. Interestingly, on a general note, while both levels of education and experience are statistically significant in explaining the wage gap across the wage distribution, at the highest decile only work experience is found to have an effect on the gap.

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53

1,488

(0.547)

(1.153)

1,488

2.929***

(0.00377)

(0.00836)

1.874

0.0250***

(0.000458)

(0.000971)

0.0335***

-0.000815*

(0.0318)

(0.0671)

-0.00155

0.0439

(0.0554)

(0.113)

0.0908

0.681***

(0.0501)

(0.103)

0.648***

0.140***

(0.0325)

(0.0663)

0.124

-0.230***

-0.162**

20

1,488

(0.514)

2.737***

(0.00352)

0.0226***

(0.000432)

-0.000976**

(0.03)

0.0584*

(0.0535)

0.744***

(0.0482)

0.149***

(0.0311)

-0.186***

30

(0.0238)

0.0545**

(0.0431)

0.745***

(0.0385)

0.129***

(0.0247)

-0.180***

50

1,488

(0.422)

2.955***

(0.00279)

0.0214***

(0.000353)

1,488

(0.408)

3.035***

(0.00266)

0.0229***

(0.000342)

-0.000969*** -0.000931***

(0.0246)

0.0557**

(0.0442)

0.769***

(0.03960)

0.152***

(0.0256)

-0.203***

40

1,488

(0.418)

3.497***

(0.00269)

0.0203***

(0.000349)

-0.000568

(0.0243)

0.0307

(0.0437)

0.759***

(0.0389)

0.173***

(0.025)

-0.163***

60

Adjusted Gender Wage Gap (OLS) by Deciles Deciles

1,488

(0.558)

4.049***

(0.00358)

0.0205***

(0.000466)

-0.000239

(0.0324)

0.00588

(0.0586)

0.751***

(0.0519)

0.177***

(0.0331)

-0.158***

70

1,488

(0.559)

4.169***

(0.00359)

0.0153***

(0.000465)

-0.000143

(0.0323)

0.00344

(0.0588)

0.759***

(0.0516)

0.254***

(0.0333)

-0.169***

80

1,488

(0.789)

4.790***

(0.00527)

0.0115**

(0.000662)

0.000326

(0.0458)

-0.0269

(0.0812)

0.862***

(0.0713)

0.340***

(0.0461)

-0.177***

90

1,488

(2.223)

5.963***

(0.0111)

-0.0218**

(0.00192)

0.000719

(0.132)

-0.0321

(0.225)

0.286

(0.181)

-0.134

(0.127)

-0.295**

99

Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

Observations

Constant

Experience

Age squared

Age

Tertiary Ed

Secondary Ed

Gender Wage Gap

10

Table 6: Quantile Regression Decomposition

Gender and Motherhood Wage Gaps in Macedonia

Gender and Motherhood Wage Gaps in Macedonia

*** The weighting decomposition approach can be coded in the following manner.

Once we have created the matrix of observable human capital characteristics, we generate a new dummy variable named male that is set to 0. Additionally, third command replaces the dummy with 1 if the gender variable is 0 (male). global wlist deduc32 deduc33 dag dag2 exper gen male=0 replace male=1 if dgn==0

We save a new (temporary) dataset named temp01, where it keeps the observations if the gender dummy is 1 and drops the zeros. The following command replaces the dataset with temp02 with a gender dummy set on 2. Then, the second temporary file is appended on the first one. save temp01,replace keep if dgn==1 replace dgn=2 save temp2, replace use temp01, clear append using temp2

The following lines run a probit model to predict the wage of a man according to the matrix of human capital characteristics between those that belong to the gender dummy if it is 0 or 1 according to the two datasets. This is predicted depending on age, age squared, secondary education, tertiary education and experience (i.e. the same variables as before). The newly generated variable pmale that shows the predicted probability is then summed up if the male dummy is equal or close to 1. probit male $wlist if dgn==0 | dgn==1 predict pmale summ pmale if male~=1, detail

Then, the pmale variable is replaced with 0.99 if the variable contains data that is greater than 0.99 and close or equal to 1. We next sum the male dummy if it is less than 2 by using the quietly command which indicates that STATA should not provide the output of the results of this summation. Once done, we generate a pbar variable which denotes the mean which is restored from the list stored from the r() command. replace pmale=0.99 if pmale>0.99 & male~=1 quietly summ male if male<2 gen pbar=r(mean)

Once we create this pbar variable, we generate a new variable phix which is calculated from the equation below if the gender dummy is 2. The last command line details the summarized results. gen phix=(pmale)/(1-pmale)*((1-pbar)/pbar) if dgn==2 sum phix, detail

We estimate a univariate kernel density estimation through the kdensity command if the gender dummy is 0, 1 and 2, respectively. Since the kernel density produces graphs, we write the nograph command as to not provide a graph in the output at this time. The variables generated are evalm1/evalf1 which represent the log wages of males and females, respectively. The densm1/densf1accordingly represent the densities of the wage distribution. The width() sets the width of bins which specifies how the data should be aggregated. For the gender dummy that equals 2, the weights estimated from the variable phix are estimated in the kernel density estimation.

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kdensity lny if dgn==0, gen(evalm1 densm1) width(0.10) nograph kdensity lny if dgn==1, gen(evalf1 densf1) width(0.10) nograph kdensity lny if dgn==2 [aweight=phix], gen(evalfm densfm) width(0.10) nograph

The next long command line creates a graph that depicts all three kernel densities. The graph twoway command creates graphs, allowing options for the appearance of the graph. First, the kernel density function of women is represented in a red dashed line; secondly, the kernel density of men is represented as blue longer dashed line; and the last is the kernel density estimation when females have been assigned the characteristics of males, represented in purple long dashed line. ytitle command tells that the y-axis should be named density and the ylabel denotes the values of the density. The same goes for the xtitle and xlabel. The command legend() creates a legend in the graph representing what data is represented. The order command shows which keys appear and their consecutive order. The graph has not style and symxsize assigns the width of the key’s symbol. Additionally, keygap and textwidth assign the gap between the symbols or text and the text’s width. Finally, the graph is saved as den_weight. graph twoway (connected densf1 evalf1, m(i) lp(dash) lw(medium) lc(red) ) (connected densm1 evalm1, m(i) lp(longdash) lw(medium) lc(blue) ) (connected densfm evalfm, m(i) lp(longdash) lw(medium) lc(purple) ), ytitle(“Density”) ylabel(0.0 0.2 0.4 0.6 0.8 1.0) xlabel(1.5 2 2.5 3 3.5 4.0 4.5 5.0 5.5 6.0 6.5) xtitle(“Log(wage)”) legend(ring(0) pos(2) col(1) lab(1 “Women”) lab(2 “Men”) lab(3 “Women as Men”) order(1 3 2 4) region(lstyle(none)) symxsize(8) keygap(1) textwidth(25) ) saving(den_weight,replace)

Figure 1: Densities of wages by gender

Source: Author’s calculation.

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Figure 1 shows the density function once weights are assigned in analyzing the gender wage gap. The figure presents the wage distribution of men and women, as well as the wage distribution of women if they had the observable characteristics of men (purple dashed line). As it can be seen, the gender wage gap is persistent for the entire wage distribution with a declining trend towards the upper half. The graph suggests that once weighting is imposed, the difference between the wages of men and women decreases. Though true for the highest wages, there is again a jump in the wage gap, likely suggesting a glass ceiling effect. *** The Juhn-Murphy-Pierce decomposition method can be coded in the following manner.

We regress the general observable human capital characteristics by gender. Estimates from both regressions are accordingly stored. global jlist deduc32 deduc33 dag dag2 exper regress lny $jlist if dgn==0 estimates store male regress lny $jlist if dgn==1 estimates store female

The jmpierce command decomposes the estimates of the gender wage differentials. reference(1) decomposes the gender wage gap using the residuals/prices of the male model estimates. Additionally, the statistics command line specifies the summary statistics for which the decomposition is displayed. In our case the summary statistics are on the mean, median and by a couple of percentiles. jmpierce male female, reference(1) statistics(mean median p5 p10 p25 p50 p75 p90 p95)

Table 7 provides the wage decomposition according to the Juhn-Murphy-Pierce method . As in the other methods, both levels of education and experience have statistical significance indicating that these human capital characteristics have explanatory power in explaining the gender wage gap. It is important to note that this approach analyses the earnings inequality based on quantities (the assigned human capital characteristics) and prices of skills (the coefficients or the returns from the human capital characteristics) in the labor market.

Changes in observed skill quantities account for -34% (i.e. -0.0481/0.141) of the wage inequality at the mean, indicating that women have better human capital characteristics than men (what we have already documented). Additionally, changes in observed skill prices have affected the wage inequality at the mean by 131%, indicating that menâ&#x20AC;&#x2122;s returns on these characteristics are much higher than womenâ&#x20AC;&#x2122;s. The effect of unobservables is only 2% of the wage inequality.

When considering the wage gap at the median, the effect in the changes in observed skill quantities, skill prices and unobservables is -17%, 115%, and 2%, respectively.

The decomposition at the percentile levels shows that the wage gap has been decreasing except at the 95th percentile indicating a glass ceiling effect (also documented before). The changes of the wage gap at the 25th percentiles in skill quantities, skill prices and unobservables account for -13%, 107% and 6% below the mean, respectively. In that respect, the decrease of the wage gap In the Juhn-Murphy-Pierce method, the wage gap is calculated as male wage minus female wage, thus the positive values indicate a male wage advantage. Moreover, the same formula is implemented in analysing differences in skill quantities (observable human capital characteristics), skill prices (returns on the human capital characteristics i.e. the estimating coefficients) and unobservables (unobservables in skill quantities and prices).

1

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Table 7: Juhn-Murphy-Pierce Decomposition

Secondary Ed Tertiary Ed Age Age squared Experience Constant Mean Median 5th percentile 10th percentile 25th percentile 50th percentile 75th percentile 90th percentile 95th percentile

Juhn-Murphy-Pierce decomposition Contribu- ContribuTotal tion of dif- tion of difDifference Overall ferences in ferences in (male-feobservable observable male) quantities prices 0.174** (0.0736) 0.780*** (0.0779) 0.0462 (0.0408) -0.000824 (0.000588) 0.0231*** (0.00457) 2.946*** (0.698) 0.1410 -0.0481 0.1861 0.1671 -0.0289 0.1928 0.2559 -0.0360 0.2451 0.2311 -0.0037 0.1974 0.2007 -0.0254 0.2142 0.1671 -0.0289 0.1928 0.0750 -0.0070 0.1537 0.0426 -0.0393 0.1175 0.0834 -0.0373 0.1495

Contribution of difference in unobservable quantities and prices

0.0029 0.0032 0.0469 0.0374 0.0119 0.0032 -0.0092 -0.0357 -0.0288

Observations 634 R-squared 0.26 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient. at the 75th percentile is accounted for by -9%, 205%, and -12% from the changes in quantities, prices and unobservables, respectively.

Overall, the method shows that women manage to decline the wage gap at the wage distribution mainly due to their advantage in their observed human capital characteristics and from the effect of unobservables. While these two tend to increase for women across the wage distribution, women still are disadvantaged in comparison to men when considering the returns from these human capital characteristics (the coefficients). This can be seen when comparing the 5th and 90th percentiles. The changes in the observable skill quantities, prices and unobservables at the 5th percentile are -14%, 96% and -18%, while for the 90th, -92%, 276% and -84%, respectively.

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Gender and Motherhood Wage Gaps in Macedonia

*** The RIF decomposition method can be coded in the following manner.

The STATA coding is similar as in the quantile regression approach; the only exception is the rifreg command which tells STATA to apply unconditional quantile regression (UQR) with fixed effects. global rlist dgn deduc32 deduc33 dag dag2 exper rifreg lny $rlist, q(10) rifreg lny $rlist, q(20) rifreg lny $rlist, q(40) rifreg lny $rlist, q(50) rifreg lny $rlist, q(60) rifreg lny $rlist, q(70) rifreg lny $rlist, q(80) rifreg lny $rlist, q(90) rifreg lny $rlist, q(99)

Table 8 presents the quantile wage decomposition according to the recentered influence function (RIF). Not surprising, as seen from the results according to the other methods, both levels of education and experience are statistically significant.

The most evident differences are in terms of the magnitude of the gender wage gaps, as well as, the tendency of the wage gaps in the highest percentiles in the wage distribution. The RIF approach indicates that the gender wage gap is much higher than previously observed in the other methods, such as the quantile regression, across the wage distribution. While other methods indicate a glass ceiling effect in the highest percentiles, the RIF approach presents results that show that the gender wage gap dissipates in the right half and vanishes for the highest-paid jobs. The most evident is the highest percentile where the gender wage gap loses its statistical significance and magnitude thus revealing that not only there is no glass ceiling effect, but also that women do not suffer a wage discrimination for the highest-paid jobs. Furthermore, while the quantile regression showed that experience was the only human capital characteristic that had explanatory power in the wage setting at the highest percentile, the RIF approach shows that none of the considered human capital characteristics have statistical significance in explaining the wage gap. Interestingly, at the 90th percentile, age and age squared are also statistically significant demonstrating that womanâ&#x20AC;&#x2122;s age in these jobs has some sort of importance in explaining the wage.

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0.097

0.138

1,488

(0.54)

3.803***

(0.00360)

0.0221***

(0.000449)

-0.000194

(0.0313)

-0.000115

(0.0552)

0.568***

(0.0546)

0.163***

(0.0322)

-0.255***

30

0.146

1,488

(0.538)

3.808***

(0.00354)

0.0223***

(0.000446)

-0.000300

(0.0311)

0.00759

(0.0540)

0.614***

(0.0534)

0.167***

(0.0316)

-0.222***

40

0.182

1,488

(0.589)

3.768***

(0.00377)

0.0260***

(0.000489)

-0.000370

(0.0340)

0.0103

(0.0543)

0.837***

(0.0518)

0.249***

(0.0338)

-0.199***

50

0.227

1,488

(0.614)

3.754***

(0.00381)

0.0228***

(0.000508)

-0.000212

(0.0354)

0.00573

(0.0503)

1.042***

(0.0447)

0.301***

(0.0348)

-0.145***

60

0.231

1,488

(0.596)

3.739***

(0.00355)

0.0167***

(0.000493)

-0.000327

(0.0345)

0.0183

(0.0496)

1.017***

(0.0378)

0.241***

(0.0340)

-0.136***

70

0.207

1,488

(0.58)

3.558***

(0.00345)

0.0102***

(0.000484)

-0.000497

(0.0337)

0.0375

(0.0503)

0.907***

(0.0272)

0.193***

(0.0332)

-0.114***

80

0.104

1,488

(0.839)

2.970***

(0.00493)

0.0106**

(0.000711)

-0.00118*

(0.0493)

0.0868*

(0.0843)

0.848***

(0.0422)

0.143***

(0.0489)

-0.117**

90

0.004

1,488

(2.082)

6.536***

(0.00956)

-0.00623

(0.00169)

0.00114

(0.119)

-0.0679

(0.216)

0.148

(0.169)

-0.0878

(0.0998)

-0.00861

99

Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

0.051

R-squared

1,488

(0.569)

(0.956)

1,488

3.762***

(0.00384)

(0.00698)

3.809***

0.0223***

(0.000469)

(0.000808)

0.0386***

-9.82e-05

(0.0328)

(0.0559)

-5.43e-05

-0.00522

(0.0606)

(0.105)

-0.0194

0.455***

(0.0598)

(0.102)

0.403***

0.154**

(0.0343)

(0.0574)

0.147

-0.224***

20

-0.212***

Observations

Constant

Experience

Age squared

Age

Tertiary Ed

Secondary Ed

Gender Wage Gap

10

Gender Wage Gap according to the RIF approach Deciles

Table 8: RIF decomposition of the Gender Wage Gap

Gender and Motherhood Wage Gaps in Macedonia

Gender and Motherhood Wage Gaps in Macedonia

6.4.

Decomposition of the motherhood wage gap

In what follows, we will use almost the same statistical coding for the motherhood wage gap as we did when we were applying the decomposition method for the gender wage gap. keep if dgn==1

The statistical coding for the Blinder-Oaxaca wage decomposition of the unadjusted motherhood wage gap is: global mlist deduc32 deduc33 dag dag2 exper oaxaca lny $mlist, by(mother) vce(robust)

The statistical insignificance of the endowments (observable characteristics) and the coefficients (unobservables) in Table 9 indicates that the unadjusted motherhood wage gap does not depend on either of them. Only tertiary education has a statistical significance, which increases the gap by 8.8% thus indicating that childless women are with better human-capital characteristics than mothers.

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Table 9: Blinder-Oaxaca Decomposition of the Unadjusted Motherhood Wage Gap Blinder - Oaxaca Decomposition (Unadjusted Motherhood Wage Gap) Overall Endowments Coefficients Interactions Childless women (mean log-hourly 4.139*** wage) (0.0406) Mothers (mean 4.052*** log-hourly wage) (0.0305) Motherhood Wage 0.0870* Gap (0.0508) Endowments 0.0567 (0.0383) Coefficients/Unex0.0724 plained Part (0.0476) Interactions -0.0421 (0.0402) Secondary Ed -0.0104 0.0561 -0.00707 (0.00850) (0.0727) (0.00997) Tertiary Ed 0.0885** -0.0446 -0.0170 (0.0344) (0.0402) (0.0165) Age -0.0535 3.181 -0.116 (0.0925) (3.607) (0.143) Age squared 0.0479 -1.779 0.0914 (0.0719) (1.893) (0.113) Experience -0.0157 -0.107 0.00690 (0.0158) (0.0924) (0.00905) Constant -1.234 (1.740) Observations 634 634 634 634 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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Gender and Motherhood Wage Gaps in Macedonia

*** Furthermore, we present the adjusted motherhood wage gap with the Blinder-Oaxaca approach adjusted by Heckman sample-selection. This method can be coded as follows: oaxaca lny $mlist, by(mother) model1(heckman, select(deduc32 deduc33 dag dag2 exper mstatus nmb_child)) robust

The results from the endowments effect in Table 10 are the same as in Table 9, the effect of unobservables increases to 24% which indicates that unobservable characteristics are highly influential for the motherhood wage gap, especially experience which shows that unobservable characteristics decrease the gap by -30%. Additionally, the motherhood wage gap increases to 25.5% in comparison to the 8.7% in Table 9. This increase suggests that if selection was random, the gap would have been positive hence a penalty for non-mothers. As such, the smaller wage gap in Table 9 means that there is a negative selection of mothers since those with worse characteristics work. Additionally, mothers outside the labor market are maybe with better endowments such as the husband works or are from wealthier families. The statistical significance of tertiary education in the observable characteristics indicates that still childless women are better educated than mothers.

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Table 10: Blinder-Oaxaca Decomposition (Heckman Adjusted Motherhood Wage Gap) Blinder - Oaxaca Decomposition (Heckman Adjusted Motherhood Wage Gap) Overall Endowments Coefficients Interactions Childless women (mean log-hourly 4.306*** wage) (0.113) Mothers (mean 4.052*** log-hourly wage) (0.0305) Motherhood Wage 0.255** Gap (0.117) Endowments 0.0567 (0.0383) Coefficients/Unex0.242** plained Part (0.122) Interactions -0.0437 (0.0440) Secondary Ed -0.0104 -0.00894 0.00113 (0.00850) (0.0915) (0.0115) Tertiary Ed 0.0885** -0.103 -0.0393 (0.0344) (0.0648) (0.0286) -0.0535 Age 2.454 -0.0898 Age squared Experience

(0.0925)

(3.652)

(0.140)

0.0479

-1.256

0.0645

(0.0719)

(1.935)

(0.108)

-0.0157

-0.307*

0.0197

(0.0158)

(0.166)

(0.0222)

Constant

-0.538 (1.794)

Observations 634 634 634 634 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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Gender and Motherhood Wage Gaps in Macedonia

*** The quantile decomposition approach for the motherhood wage gap can be coded in the following way: global mhlist mother deduc32 deduc33 dag dag2 exper qreg lny $mhlist, quantile(0.1) qreg lny $mhlist, quantile(0.2) qreg lny $mhlist, quantile(0.3) qreg lny $mhlist, quantile(0.4) qreg lny $mhlist, quantile(0.5) qreg lny $mhlist, quantile(0.6) qreg lny $mhlist, quantile(0.7) qreg lny $mhlist, quantile(0.8) qreg lny $mhlist, quantile(0.9) qreg lny $mhlist, quantile(0.99)

Table 11 shows that the motherhood wage gap is statistically insignificant at all deciles of the wage distribution. This means that wage penalty on the basis of motherhood does not exist in Macedonia. The most interesting part is, while mothers receive less than childless women, at the 80th and 90th percentiles, mothers are actually at a wage advantage in respect to the childless women. Experience and both levels of education are significant across the wage distribution, while on the 10th and 20th percentiles secondary education proves not to be important for explaining the wage gap. Furthermore, the wage gap tends to decrease from left to right, reaching its peak at the 80th and 90th deciles, where mothers are at a wage advantage. This again immediately changes to a gap of 12% at the highest percentile indicating a glass ceiling effect where mothers have a high disadvantage at the highest paid jobs in the market. However, as said, all these coefficients are actually not statistically different than zero.

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634

(1.133)

(2.343)

634

1.876*

(0.00738)

(0.0149)

-1.843

0.0268***

(0.000951)

(0.00196)

0.0389***

-0.00156

(0.0665)

(0.137)

-0.00458**

0.0955

(0.116)

(0.201)

0.301**

0.709***

(0.111)

(0.191)

0.727***

0.118

(0.0720)

(0.137)

0.113

-0.0699

-0.112

20

634

(0.875)

1.977**

(0.00587)

0.0258***

(0.000747)

-0.00151**

(0.0519)

0.0924*

(0.0920)

0.840***

(0.0878)

0.173**

(0.0573)

-0.0378

30

634

(0.697)

2.988***

(0.00450)

0.0213***

(0.000592)

-0.000750

(0.0412)

0.0409

(0.0758)

0.844***

(0.0717)

0.146**

(0.0466)

-0.0110

40

634

(0.713)

3.242***

(0.00452)

0.0246***

(0.000607)

-0.000581

(0.0422)

0.0285

(0.0772)

0.869***

(0.0727)

0.177**

(0.0476)

-0.00424

50

634

(0.583)

3.345***

(0.00360)

0.0230***

(0.000498)

-0.000677

(0.0346)

0.0321

(0.0617)

0.873***

(0.0578)

0.192***

(0.0388)

-0.00367

60

634

(0.803)

4.316***

(0.00484)

0.0190***

(0.000689)

0.000190

(0.0479)

-0.0238

(0.0832)

0.876***

(0.0773)

0.256***

(0.0537)

-0.00838

70

634

(1.043)

4.560***

(0.00614)

0.0142**

(0.000897)

0.000204

(0.0622)

-0.0261

(0.105)

0.821***

(0.0970)

0.277***

(0.0691)

0.0446

80

634

(0.992)

5.347***

(0.00575)

0.0103*

(0.000858)

0.000611

(0.0591)

-0.0592

(0.106)

0.906***

(0.0973)

0.320***

(0.0635)

0.0844

90

634

(2.137)

5.029**

(0.00871)

-0.0271***

(0.00182)

-3.19e-07

(0.128)

0.0206

(0.0514)

0.146***

(0.0878)

-0.273***

(0.141)

-0.123

99

Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

Observations

Constant

Experience

Age squared

Age

Tertiary Ed

Secondary Ed

Motherhood Wage Gap

10

Gender Wage Gap according to the RIF approach Deciles

Table 11: Quantile Decomposition of the Adjusted Motherhood Wage Gap

Gender and Motherhood Wage Gaps in Macedonia

Gender and Motherhood Wage Gaps in Macedonia

*** The weighting decomposition of the motherhood wage gap can be coded in the following manner: global wmlist deduc32 deduc33 dag dag2 exper gen female=0 replace female=1 if mother==0 save temp01,replace keep if mother==1 replace mother=2 save temp2, replace use temp01, clear append using temp2 probit female $wmlist if mother==0 | mother==1 predict pfemale summ pfemale if female~=1, detail replace pfemale=0.99 if pfemale>0.99 & female~=1 quietly summ female if female<2 gen pbar=r(mean) gen phix=(pfemale)/(1-pfemale)*((1-pbar)/pbar) if mother==2 sum phix, detail kdensity lny if mother==0, gen(evalm1 densm1) width(0.10) nograph kdensity lny if mother==1, gen(evalf1 densf1) width(0.10) nograph kdensity lny if mother==2 [aweight=phix], gen(evalfm densfm) width(0.10) nograph graph twoway (connected densf1 evalf1, m(i) lp(dash) lw(medium) lc(red) ) (connected densm1 evalm1, m(i) lp(longdash) lw(medium) lc(blue) ) (connected densfm evalfm, m(i) lp(longdash) lw(medium) lc(purple) ), ytitle(“Density”) ylabel(0.0 0.2 0.4 0.6 0.8 1.0) xlabel(1.5 2 2.5 3 3.5 4.0 4.5 5.0 5.5 6.0 6.5) xtitle(“Log(wage)”) legend(ring(0) pos(2) col(1) lab(1 “Mother”) lab(2 “Childless woman”) lab(3 “Mother as Childless woman”) order(1 3 2 4) region(lstyle(none)) symxsize(8) keygap(1) textwidth(40) ) saving(moth_ weight,replace)

*** Figure 2 shows the density function once weights are assigned to analyzing the motherhood wage gap. The figure illustrates the wage distribution of childless women and mothers, as well as the wage distribution of mothers if they had the same observable characteristics as childless women. As it can be seen, mothers tend to be paid higher wages for the lowest jobs than childless women. There is also likelihood that if mothers had the same observable characteristics as childless women, they will be paid more in jobs around the upper middle of the wage distribution. Not surprising, at the highest paid jobs, mothers likely face glass ceiling effect even if they had the same observable characteristics as childless women indicating where mothers are paid less simply because they are mothers.

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Gender and Motherhood Wage Gaps in Macedonia

Figure 2: Density of wages by motherhood

Source: Authorâ&#x20AC;&#x2122;s calculation. *** The Juhn-Murphy-Pierce decomposition method for the motherhood wage gap can be coded in the following manner: global jmlist deduc32 deduc33 dag dag2 exper regress lny $jmlist if mother==0 estimates store female regress lny $jmlist if mother==1 estimates store mother jmpierce female mother, reference(1) statistics(mean median p5 p10 p25 p50 p75 p90 p95)

Table 12 indicates that tertiary education and experience have statistical significance implying that these human capital characteristics have explanatory power in explaining the motherhood wage gap. Changes in observed skill quantities account for 12% of the wage inequality at the mean, indicating that childless women have better human capital characteristics than mothers. Additionally, what is interesting is that changes in observed skill prices have affected the wage inequality at the mean for 83% thus indicating childless womenâ&#x20AC;&#x2122;s returns on these characteristics are much higher than those of mothers. The effect of unobservables is only 0.4% of the wage inequality at the mean.

When considering the wage gap at the median, the effect in changes in observed skill quantities, prices and unobservables is -111%, 184%, and 28%, respectively. This means that at the median mothers tend to have better observable human capital characteristics but the returns from these are much lower than those of childless women. Furthermore, the decomposition at the percentile levels shows that the wage gap has been decreasing except at the 90th and 95th Manual for Calculation with Applications in Stata

67

Gender and Motherhood Wage Gaps in Macedonia

percentiles. Interestingly, mothers tend to have better observable characteristics at the 10th, 25th, 50th and 90thpercentiles, showing an advantage in skill quantities of -8, -7, -111 and -23%, respectively. Though true at these percentiles, the returns from these characteristics are 86, 107, 183 and 122%, respectively, which shows that they are much higher for childless women than for mothers. Furthermore, at the 95th percentile, even though childless women are at advantage in regard to observable human capital characteristics, the returns from those are only 10%, thus indicating that the returns of childless women and mothers are quite similar regardless of their huge differences in observed human capital characteristics. Additionally, at the 90th percentiles, mothers are at a wage advantage and have better skill quantities, but irrelevant to this the returns from the observable characteristics still benefit childless women.

Moreover, changes in unobservables tend to be 48, 22, -0.01, 28, -152, 0.62 and -30% for the 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles, respectively. This indicates that the highest influence of unobservables in the wage gap can be found in the lowest and highest percentiles.

Overall, childless women have a much higher advantage in human capital characteristics and on returns due to these characteristics, which is not surprising according to the human capital theory. What is surprising is that in some parts mothers tend to have better observable characteristics, but still are disadvantaged in terms of returns relative to childless women. Additionally, at the highest percentile, though childless women tend to have better human capital characteristics, changes in returns between mothers and childless women for this percentile are the lowest in comparison to all others, which means that being a mother might not be an influential factor in the highest paid jobs.

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Gender and Motherhood Wage Gaps in Macedonia

Table 12: JMP decomposition of motherhood wage gap Juhn-Murphy-Pierce decomposition Contribu- ContribuContribution Total tion of dif- tion of dif- of difference in Difference Overall ferences in ferences in unobservable (male-feobservable observable quantities and male) quantities prices prices Secondary Ed 0.138 (0.0856) Tertiary Ed 0.804*** (0.0918) Age 0.0411 (0.0543) Age squared -0.000714 (0.000780) Experience 0.0261*** (0.00549) Constant 2.962*** (0.937) Mean 0.0870 0.0106 0.0724 0.0040 Median 0.0809 -0.0905 0.1486 0.0227 5th percentile 0.5108 0.1055 0.1561 0.2492 10th percentile 0.1823 -0.0153 0.1572 0.0404 25th percentile 0.0975 -0.0071 0.1046 0.0000 50th percentile 0.0809 -0.0905 0.1486 0.0227 75th percentile 0.0198 0.0238 0.0261 -0.0301 90th percentile -0.0800 0.0187 -0.0982 -0.0005 95th percentile 0.1054 0.1255 0.0115 -0.0316 Observations 436 R-squared 0.291 Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

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Gender and Motherhood Wage Gaps in Macedonia

*** The RIF decomposition method can be coded in the following manner: global rmlist mother deduc32 deduc33 dag dag2 exper rifreg lny $rmlist, q(10) rifreg lny $rmlist, q(20) rifreg lny $rmlist, q(30) rifreg lny $rmlist, q(40) rifreg lny $rmlist, q(50) rifreg lny $rmlist, q(60) rifreg lny $rmlist, q(70) rifreg lny $rmlist, q(80) rifreg lny $rmlist, q(90) rifreg lny $rmlist, q(99)

*** Table 13 presents the results of the quantile wage decomposition according to the recentered influence function (RIF). The motherhood wage gap is only statistically significant at the 10th and 70thpercentiles. Additionally, mothers are found to have a wage advantage over childless women from the 50th to 90thpercentile, although it lacks significance. The magnitude of the motherhood wage gaps is highest at the lowest and highest percentiles of the wage distribution and although statistically insignificant there is still an indication of a certain glass ceiling effect where mothers are paid less than childless women at the highest percentile. Secondary education is only important for the 20th to 80th percentile jobs, while tertiary is statistically significant across the wage distribution except for the highest percentile. Moreover, experience loses its explanatory power for the highest paid jobs, thus meaning that other human capital characteristics that are not taken into account for this model are more important for these high-paying jobs.

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0.098

0.149

634

(0.877)

3.971***

(0.00574)

0.0241***

(0.000740)

0.000131

(0.0517)

-0.0245

(0.0988)

0.637***

(0.102)

0.172*

(0.0562)

-0.0244

30

0.214

634

(0.886)

3.160***

(0.00591)

0.0236***

(0.000753)

-0.000648

(0.0525)

0.0281

(0.0927)

0.873***

(0.0949)

0.261***

(0.0583)

-0.0501

40

0.252

634

(0.955)

4.013***

(0.00625)

0.0277***

(0.000810)

0.000149

(0.0564)

-0.0297

(0.0821)

1.092***

(0.0812)

0.368***

(0.0626)

0.0811

50

0.285

634

(1.095)

4.519***

(0.00657)

0.0256***

(0.000927)

0.000499

(0.0646)

-0.0515

(0.0853)

1.267***

(0.0787)

0.330***

(0.0703)

0.103

60

0.302

634

(1.048)

4.200***

(0.00596)

0.0205***

(0.000880)

9.70e-05

(0.0615)

-0.0185

(0.0787)

1.210***

(0.0653)

0.250***

(0.0655)

0.125*

70

0.246

634

(0.985)

4.778***

(0.00482)

0.00635

(0.000836)

0.000582

(0.0583)

-0.0397

(0.0776)

0.916***

(0.0443)

0.121***

(0.0631)

0.0677

80

0.123

634

(1.216)

4.454***

(0.00605)

0.00271

(0.00107)

0.000197

(0.0739)

-0.00725

(0.107)

0.725***

(0.0605)

0.0770

(0.0814)

0.111

90

0.011

634

(3.132)

5.243*

(0.0183)

-0.0148

(0.00267)

2.98e-05

(0.185)

0.0154

(0.448)

0.218

(0.380)

-0.254

(0.211)

-0.103

99

Source: Authorsâ&#x20AC;&#x2122; calculations *, ** and *** denote statistical significance at the 10, 5 and 1% level, respectively. The number in the brackets represents the standard error of the representative coefficient.

0.062

R-squared

634

(0.971)

(1.473)

634

3.185***

(0.00651)

(0.00993)

1.004

0.0230***

(0.000807)

(0.00124)

0.0425***

-0.000354

(0.0567)

(0.0862)

-0.00246**

0.0123

(0.116)

(0.152)

0.147*

0.584***

(0.119)

(0.156)

0.283*

0.251**

(0.0591)

(0.0831)

0.0220

-0.0917

20

-0.160*

Observations

Constant

Experience

Age squared

Age

Tertiary Ed

Secondary Ed

Motherhood Wage Gap

10

Motherhood Wage Gap according to the RIF approach Deciles

Table 13: Motherhood Wage Gap - RIF approach

Gender and Motherhood Wage Gaps in Macedonia

Gender and Motherhood Wage Gaps in Macedonia

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7. Conclusion The objective of this paper is to act as a manual that offers guidelines in researching, analyzing and calculating gender and motherhood wage gaps. Its intent is not to be strictly seen as a classic academic research paper, but to leave a legacy i.e. instruction to future and current economic researchers in Macedonia who are interested in labor economics and are willing to conduct an econometric analysis of the gender and motherhood wage gaps. Additionally, it tries to spur higher interest in advocacy and promotion of the ways these wage gaps can be diminished; it tries to encourage much more academic interest and concern from all relevant stakeholders.

The paper offers an extensive overview of the theoretical background literature of the gender and motherhood wage gaps which provides to any interested individual an understanding of the importance and the implications that these wage gaps have, not only to women and households, but also to the overall economy of a country. Moreover, it provides the mainstream econometric methods that calculate and decompose the gender and motherhood wage gaps, thus bestowing an econometric knowledge to any interested individual. For the sake of simplicity, we have used the generally accepted and regularly used human capital characteristics as explanatory variables in analyzing and calculating the gender and motherhood wage gaps.

We recognize that these are not the only ones that should be used when analyzing the wage gaps and thus promote and encourage that other human capital characteristics (such as type of sector, employment contract, industry, occupation etc.) should be also considered as these variables could have an important influential explanatory power over the analysis of the gender and motherhood wage gaps. Additionally, we also acknowledge that the coding procedure that we present is not the only way of coding in STATA, but we do refer to it as the most simplistic approach.

According to our analysis, women in Macedonia are paid 14.1% less than men before adjusting for human capital characteristics. Once we adjust for the generally-accepted workerâ&#x20AC;&#x2122;s characteristics, in the OLS model, the gap increases to 19.2%. This indicates that females in employment have better labor market characteristics than males in Macedonia. We found that the Heckman model fails with our data since it indicates that selection into the labor force does not play a role in the gender wage gap in Macedonia. Conversely, the repeated imputations model reduces the gender wage gap to approximately 8%, thus indicating that selection is able to explain around 60% of the gender wage gap. Generally, all three models show that the level of education and experience are important explanatory variables that have implications for the gender wage gap.

We find that the unadjusted motherhood wage gap in Macedonia is 8.7%. Once we adjust for workersâ&#x20AC;&#x2122; characteristics, it loses significance and drops to 3.7% in a simple OLS model. The selectivity-adjusted wage gap according to Heckman model remains the same as in the OLS, which indicates that the role of selection is inconsequential to the wage differences of mothers and childless women in Macedonia also. Once we apply the repeated imputation technique, that gap decreases to 0.57% and remains insignificant, thus confirming that selection is not important for the motherhood wage gap in Macedonia. Additionally, this decrease suggests that there is almost no wage difference between mothers and childless women. Generally, the models show that the motherhood wage gap can be fully explained by observable individual and human capital characteristics, thus implying that motherhood per se is irrelevant for the wage differentials between mothers and childless women. Moreover, the three models also indicate that mothers, on average, have worse labor-market characteristics than employed childless women thus providing a clear support of the human-capital theory.

Considering the decomposition of the gender wage gap, according to the Blinder-Oaxaca deManual for Calculation with Applications in Stata

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Gender and Motherhood Wage Gaps in Macedonia

composition, the statistical significance of the endowments and coefficients shows that the unadjusted wage gap highly depends on both. In particular, the unobservables are estimated to result in an increase of the unadjusted wage gap of 18.6% Moreover, when considering the Blinder-Oaxaca approach adjusted for Heckman selection, tertiary education is the only variable that influences the unobservables, where it reduces the gap by around 7%. Additionally, the Blinder-Oaxaca approach with Heckman selection shows that the adjusted wage gap not only rises to 19%, but also the effect of unobservables rises to 24%, thus indicating that other individual and human capital characteristics must be taken into account when analyzing the gender wage gap.

According to the quantile regression, the gender wage gap is statistically significant at all deciles of the wage distribution. The model shows a glass ceiling effect at the highest decile, while at the same time only work experience is found to have a significant effect on the wage gap. Conversely, the RIF approach indicates that the gender wage gap is much higher across the wage distribution than previously observed in the quantile regression. Additionally, the approach shows that there is no glass ceiling effect and the tendency of the gender wage gap is to dissipate towards the right half and vanishes for the highest-paid jobs. This suggests that women do not suffer from wage discrimination for the highest-paid jobs.

Following the weighting approach we find that although the gender wage gap is persistent at the entire wage distribution, it declines towards the upper half. Additionally, once weighting is imposed, the wage difference of men and women decreases, thus suggesting that women receive lower wages for the same human-capital characteristics than men. Additionally, again, the model shows that there is an indication of a glass ceiling effect even after considering that women would receive the same wage as men for the same human capital characteristics.

The Juhn-Murphy-Pierce decomposition generally supports the findings of the other methods.

The method only provides more insight on the difference between the human-capital characteristics and their returns. Overall, the method shows that the wage gap declines along the wage distribution mainly because of the womenâ&#x20AC;&#x2122;s observed human capital characteristics and because of the effect of unobservables. Moreover, it also supports the findings of other methods whereby it indicates that even though women have better human capital characteristics than men, they still have lower returns.

In relation to the decomposition of the motherhood wage gap, the Blinder-Oaxaca model shows that there is a statistical insignificance of the endowments and coefficients in the unadjusted motherhood wage gap, which primarily means that the gap is not under their influence.

The statistical significance of tertiary education indicates that childless women have better human-capital characteristics than mothers. Interestingly, once the model is adjusted for Heckman selection, the effect of unobservables increases to 24%, thus coefficients of the human capital characteristics have great effect over the motherhood wage gap where especially experience shows that it reduces the unobservable effect by 30%. Moreover, the motherhood wage gap increases to 25.5% in comparison to the unadjusted gap of 8.7% suggesting that there is a penalty for non-mothers, but it also shows that there is a negative selection of mothers since those who work have worse characteristics and thus get lower wage. Furthermore, mothers who do not participate in the labor market are maybe with better endowments such as the spouse works or are from wealthier families.

Considering quantile regression method, the motherhood wage gap is statistically insignificant at all deciles of the wage distribution. This indicates that there is no wage penalty for motherhood in Macedonia; alongside these lines, there is also an indication that mothers receive more than childless women at the 80th and 90th percentiles. Furthermore, both levels of education and work experience are found to be significant across the wage distribution and the wage gap tends to decrease from left to right. Even though the wage gap decreases from left to right, 74

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there is clear indication of a glass ceiling effect at the highest percentile and the statistical significance of work experience tends to indicate that work experience is an important factor for the highest job positions. The insignificance of the motherhood wage gap is furthermore supported by the RIF approach. Although lacking statistical significance, mothers are found to have wage advantage over childless women from the 50th to 90thpercentiles. This finding is contrary to the human capital theory which states that motherhood could be a decreasing factor for womanâ&#x20AC;&#x2122;s wage.

The wage distribution according to the weighting approach shows that mothers tend to be paid higher wages for the lowest jobs than childless women. Additionally, there is likelihood that if mothers had the same observable characteristics as their childless counterparts, they would be paid more in jobs around the upper middle of the wage distribution. As expected, mothers likely face a glass ceiling effect, even if they had the same observable characteristics.

The Juhn-Murphy-Pierce method supports the findings of the previous methods where in general at the mean childless women have better human capital characteristics and thus have much higher returns than mothers. On the contrary, once we consider the wage gap at the median, the method shows that mothers actually have better human-capital characteristics than their childless counterparts, but still receive lower returns. In general, childless women have a much higher advantage in human capital characteristics and on returns than mothers, which does not oppose the human-capital theory. In some cases mothers tend to have better observable characteristics, but still get lower returns than childless women. Furthermore, at the highest percentile, we find that although childless women have an advantage on observable characteristics, the returns between mothers and childless women for this percentile are the lowest, thus suggesting that motherhood might not be an important factor for the highest paid jobs.

We hope that this manual with applied work for Macedonia will be very helpful to any interested individuals, not only due to the extensive information it offers, but it also should be looked as a thought provoking document that should incentivize or motivate researchers and others to start analyzing, calculating and estimating the gender and motherhood wage gaps in Macedonia.

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WITH FUNDING FROM

This manual is part of the project “Heroes and she-roes”: Knowledge for analysis of and advocacy for equal pay for women and mothers in Macedonia (SHE-ROES), supported by the Central European Initiative - CEI, KEP AUSTRIA with funding provided by the Austrian Development Cooperation.