ABSTRACT
On average, women earn less than men. This is evident in countries with the largest economies of the world. The purpose of this research is to discover whether a woman’s name rather than that of a man contributes to this difference. In an experiment involving 281 employees from several companies, participants were asked to pretend that they were human resource managers. They were required to offer a salary to a female or male applicant for a sales representative or manager’s job, and to also suggest a salary for a niece or nephew for similar jobs. Participants received identical curriculum vitae (CV) for each job, but half who were randomly selected received a female name on the CV and the other half a male name. On five measures for both jobs, all female applicants and nieces “received” lower salaries than male applicants and nephews, irrespective of their gender, age, or position.
Key words: Gender, wage gap, salaries, experiment, differences.
In countries from all the habitable continents of the world, women on average earn less than men. These include all the major economic areas of the world, that is, the United States (Kim, 2013; Konstantopoulos and Constant, 2008), China (Chen et al., 2013), Japan (Miyoshi, 2008), India (Rendall, 2012), Russia (Semykina and Linz, 2007), and all member countries of the European Union (European Commission, 2014). This is also true for several other countries, such as Australia (Meagher, 2012), Brazil (tourism industry) (Guimarães and Silva, 2016), Iceland (Velferdarraduneytid, 2014), Indonesia (Hallward-Driemeier et al., 2015), Korea (Cho, 2007), Mexico (Popli, 2013), Norway (Barth and Dale-Olsen, 2009), Thailand (Nakavachara, 2010), Turkey (Akhmedjonov, 2012), as well as in the capital cities of seven West-African countries: Benin, Burkina Faso, Ivory Coast, Mali, Niger, Senegal, and Togo (Nordman et al., 2011). No reports were found of women receiving the same or a higher salary than men.
Most studies on the gender wage gap have focused on attributes related to employees and few have utilized the experimental method. Therefore, the present research focuses on those who offer salaries and those who advise others on what salaries to ask for and accept by employing an experimental method.
The difference in the earnings of women and men can, for example, be measured in terms of hourly wages or monthly salaries, and whether overtime and bonuses are included. Whereas the earnings of women are often shown as a percentage of what men earn (Harris, 2015; ILO, 2016; Velferdaraduneytid, 2014), account is sometimes not taken of the amount of work that is delivered, work experience, education, or the type of work. Generally, the gender wage gap is persistent to a varying degree, regardless of how it is calculated or controlled for. In a report by the International Labour Organization (ILO, 2016), Women at work: Trends 2016, in which the gender wage gap in about 50 countries worldwide is reviewed, it is stated that “gender wage gaps can be substantial but appear to be showing signs of a moderate reduction over time.” (p. 28). According to the report, the worldwide gap is estimated at 23% in 2016, women receiving on average 77% of what men earn, but without controlling for variables such as hours worked, work experience, education, or the type of work (ILO, 2016: 28).
The gender wage gap tends to widen proportionally in higher-paid jobs, which has been referred to as the “glass ceiling” effect. Research conducted in the Czech Republic (Jurajda and Paligorova, 2009) and the US (Konstantopoulos and Constant, 2008) supports this finding. In the latter country, the glass ceiling effect was evident for Black, Hispanic, and White Americans alike. Spanish research (Navarro-Gómez and Rueda-Narváez, 2014) also reveals the glass ceiling effect and the gender wage gap increasing with higher education levels.
According to a Harris (2015), which was conducted in the United States, Canada, United Kingdom, France, Germany, the Netherlands, and Switzerland, 89% of employed adults believed that women and men should be paid equally. In a report by the European Commission (2014), Tackling the gender pay gap in the European Union, it is stated that closing the gender wage gap would be good for business and the economy, and that employers would thereby avoid complaints and litigation, which would save time and money. Relatively higher salaries should in general also contribute to women becoming more independent. Evidence from the US shows that a relative increase in the salaries of women increased their bargaining power in the US and reduced domestic violence to which they were exposed (Aizer, 2010). Living conditions in turn being improved for children. Amongst the other examples of problems associated with the gender wage gap are increased levels of depression and anxiety experienced by women (Platt et al., 2016), including a higher mortality rate (Kawachi et al., 1999).
Most research on the reasons for the gender wage gap focus on employees and the employee environment, work experience (Cho, 2007; Miyoshi, 2008), education (Miki and Yuval, 2011; Miyoshi, 2008; Nakavachara, 2010), productivity (Chen et al., 2013; Petersen et al., 2007), type of occupation (Furnham and Wilson, 2011; Mandel, 2016; Petersen et al., 2007), percentage of women in the occupation (de Ruijter and Huffman, 2003; Grönlund and Magnusson, 2013), having children (Brown et al., 2011; Cukrowska-Torzewska and Lovasz, 2016; Erosa et al., 2002), and other family obligations (Petersen et al., 2007) being some examples.
Other less obvious explanations for the gender wage gap include the type of housework performed. The time spent on “female” tasks at home, such as preparing meals and cleaning, had a greater impact on the wage gap than time spent on other household duties (Noonan, 2004). Women often being younger than men in marriage limited their mobility and increased the wage gap (Elul et al., 2002). When minimum wages are prescribed as in Indonesia, the gender gap decreases (Hallward-Driemeier et al., 2015).
Part of the gender wage gap has been explained by Semykina and Linz (2007) in terms of personality traits-women being more likely to exhibit external locus of control in their work, while men exhibit internal locus of control and a need to be challenged. Other findings include that there was a lower gender wage gap in jobs where more injury risks are prevalent (Razzolini et al., 2014), women being less likely to use and benefit from negotiations than men (Roche, 2014), women that experienced childhood sexual abuse tend to earn lower wages and more likely than men to be abused (Robst, 2008), and woman as a boss decreasing the gender wage gap (Cohen and Huffman, 2007; Maume and Ruppanner, 2015).
A feature of almost all research on the gender wage gap is that it is non-experimental; it is often difficult to draw causal inferences about the results. Such an experiment could, for example, directly compare salaries offered to a woman and a man irrespective of other factors than gender like education, type of job, or experience. And, in that way can make causal inference about the wage gap.
Rationale for the present experiment
As previously mentioned, most of the research on the gender wage gap has focused on attributes related to employees and their environment. However, when wages are negotiated, the opinion or attitude of an employer is important in terms of how much a woman’s work is worth compared to that of a man. In the present experiment, this is tested by asking participants to offer a salary to an applicant for a job. There is also the fact that those starting out with their career may seek guidance from more experienced people they know, such as parents and relatives who know the job market better. Although we do not have data on this, we got this from our experience in Iceland and some other cultures as well as consulting with experienced people when negotiating salaries. This raises the question whether women in such cases would be advised to ask for lower salaries than men? Here, it is tested by asking participants to advise their niece or nephew about salaries of a job which they are applying for. Based on previous research and this rationale, two hypotheses were tested:
The first is a woman having same curriculum vitae (CV) with a man will be offered a lower salary.
The second hypothesis is that a niece will be advised to ask for a lower salary than a nephew, despite both having identical CVs.
An experimental design was chosen in this research in order to control for all factors that may affect the wage gap, other than gender. By this it can be determined whether women earn lower wages than men just because they are women.
Participants
Participants in the experiment were 281 employees from several companies in Iceland – 142 women (50.5%), 136 men (48.4%), and three who did not reveal their gender. Of these, 144 were managers (51.2%), 135 were non-managerial staff (48.0%), and two did not indicate their position. In the analysis, participants were divided into three age groups: younger than 35 years old (29.8%), 35 – 44 years old (34.8%), and 45 years or older (35.5%). About 400 employees were contacted by phone, 320 agreed to participate (80%), and of the 320 who obtained an email with the web survey link, 281 responded, which is about a 70% overall response rate.
The participants’ task
The participants were asked to pretend that they were a human resources manager of a company for one morning. In that role, they were supposed to meet a female or male applicant for a sales representative or manager’s job, offering her or him a salary, depending on their CV. They were also supposed to answer a call from a relative (niece or nephew) who was asking for advice about their salary in a new job (manager or sales representative). It was randomly determined for each participant whether the applicant for the job was female or male, a manager or sales representative, and whether a niece or nephew was calling to ask for advice. Where the participant met an applicant for the manager’s position, the niece or nephew who called would be asking for advice regarding a sales representative’s position, and vice versa.
Research materials
Two kinds of CVs were prepared – one for the sales representative and another for the manager, each with either a female or male name. These were common Icelandic names: Anna, Gudrun, Gudmundur, and Sigurdur. Basic information accompanied the name, that is, date of birth, education, work experience, competencies, and interests. Their CVs differed on all accounts for sales representative and manager, except for the type of information revealed. The applicants for the manager’s position were older (38 years old), had more extensive work experience and had a master’s degree; whereas the sales representatives were younger (27 years old), had less extensive work experience and a bachelor’s degree.
Experimental design
The participants’ task was to offer a salary to an applicant and advise their niece/nephew about salaries. It was randomly determined which came first in each case. Each participant received one of 16 possible versions of the experimental setup (2 x 2 x 2 x 2 = 16). The factors that varied, with 16 versions were:
1)Whether a participant reviewed an applicant for the manager or sales representative’s job (if the applicant was a manager, the participant advised their niece or nephew about the sales representative’s job or if the applicant was a sales representative, the participant advised their niece or nephew about the manager’s job),
2) Whether participants offered the applicant a salary before or after advising the niece or nephew,
3) Whether participants had a female or male applicant/niece or nephew, and
4) The two names for each gender: Anna or Gudrun as a female and Gudmundur or Sigurdur as a male. Factors 1 and 3 are of interest with respect to the hypotheses (factors 2 and 4 have controlling function); therefore, this is a 2 x 2 experimental design.
One version was, for example, that first an applicant (Anna) for the manager’s position was offered a salary, and then a nephew (Gudmundur) called for advice regarding the salary for a sales representative’s position. Another version was that a nephew (Sigurdur) called for advice on salary for a manager’s position, and then an applicant (Gudrun) for the sales representative’s position was offered a salary. Each participant could also receive two female CVs or two male CVs.
Data collection and independent variables
This is an experiment where the data were collected using a web survey method. The experimental factors were the gender of the applicant and whether a niece or a nephew called, and whether the job was a manager or a sales representative position. Non-random independent variables were participants’ gender, age, and whether they were in a managerial or non-managerial position. Participants’ self-reported salaries were used as a covariate.
Dependent measures
In a variety of situations during the survey – depending on the experimental version – the participants were asked five key questions. These were: how much salary they would offer the applicant; how much they thought the applicant would accept; how much salary they would advise their relative (niece or nephew) to ask for; how much salary they think their niece/nephew would be offered; and how much salary they would advise their niece/nephew to accept. As these questions were either asked in the context of a sales representative or manager’s job, the dependent measures of the experiment were ten.
Procedure
Each participant was first contacted by telephone. The interviewer introduced herself; said that she was calling from Reykjavik University and asked the interviewee to participate in a survey about human resource issues. Those who accepted provided their email address and then received a link to the survey through that email. The survey software (Outcome) presented the scenarios of “a morning in a human resources manager’s life”, asked the questions, and recorded and saved the responses. Respondents were informed that they were neither obliged to answer the survey as a whole nor specific questions, and that their answers would be anonymous. Those who did not respond were reminded up to three times in a follow-up email, that they had accepted to participate in a survey about human resource issues and referred them to the initial email with the link to the survey. The Data Protection Agency in Iceland was notified about the survey, in accordance with Icelandic laws.
Data analysis
Descriptive analyses were performed to show means and standard deviation for the five dependent measures, which were salaries in the Icelandic currency, króna (ISK), broken down by the experimental factors, that is whether the applicant was female or male (niece or nephew), for both the manager and sales representative’s job.
The data were analysed for significant differences between the salaries of female and male applicants, nieces and nephews, and for each type of job, with the participants’ self-reported salary as a covariate (analysis of covariance, ANCOVA). The two hypotheses being directional, significance tests were one-tailed. Thereafter, the effects of the non-experimental variables (participants’ gender, age, and position) were calculated, also using ANCOVA. Two indicators for effect size were calculated. These were per cent differences between what women and men were offered or suggested in terms of salaries and partial eta squared (η2).
Finally, in order to test the assumption of equality of error variances, the Levene’s test was conducted. It was not significant in any of the ten comparison cases, five measures for sales representative and five for manager, indicating that the error variance of the dependent measures was about the same in the experimental group
The main results of the experiment are presented in Table 1. In all cases, that is, in the five measures for sales representative and five for manager’s job, lower salaries were suggested when the CV had a female name opposed to a male name. This held true both when the participants were “dealing with” applicants’ salaries and when they were providing consultation on salaries for their niece or nephew. The difference was statistically significant in seven out of ten cases, and twice the p-value for the non-significant cases was marginal (.065 and .053). Only when salaries for the manager’s position were offered was the p-value well above the .05 significance level.
As it is customary to talk about the gender difference in salaries in terms of percentages, this is presented in the column to the far right in Table 1. There, it can be seen that in order to obtain the same salaries as men, women needed 3.8 to 17.9% increases in salaries. The average was 12% for sales representative’s measures and 8% for manager’s measures; therefore, 10% overall. Partial eta squared (η2) ranged from very low, .003 in the case of offering manager salaries, to .139 in the case of what the sales representative would accept. The gender of the name on the CV statistically explained 13.9% of the variability in the salaries in the latter case.
Table 1 also shows that when the CV had a female name, participants wanted to offer her higher salaries on average than they thought she would accept – both in the case of the sales representative and manager. The reverse holds true for the male CV, the man being offered lower salaries than the participants thought he would, on average, accept for both the sales representative and manager positions. In the case of the participants’ relative, niece or nephew, the highest salaries suggested were what the relative should ask for, then what they should accept and the lowest suggestion was what the participants thought the relative would be offered. The same pattern was found here for niece and nephew.
Table 2 shows that on all 10 measures, both female and male participants suggest lower salaries for women than they did for men. This was significant in more than half of the cases, despite considerably less power due to half the size of degrees of freedom, as the significance test was performed for female and male participants separately (not shown in the table). On average, female participants suggested slightly lower salaries for the sales representative’s measures than male participants did (11.5% and 12.4% respectively) and higher for the manager’s measures (9.5% and 7.5% respectively). In no case was the interaction between participants’ gender and the gender of the CV significant; that is there was no difference between female and male participants in terms of differentially suggesting or evaluating salaries for female and male applicants, or nieces and nephews.
Participants’ gender had significant effects on salary measures in three cases when employing ANCOVA (Table 2). Women suggested lower salaries than men in all cases when a relative (niece or nephew) asked for a consultation when applying for the manager’s job. That is, how much salary they would advise their niece or nephew to ask for, how much salary they thought their niece or nephew would be offered, and how much salary they would advise their niece or nephew to be satisfied with or accept. This significant difference between female and male participants was evident for both nieces and nephews.
As Table 3 shows, the applicants’ position did not have much effect on salaries offered. On all measures, both non-managerial staff and managers suggested lower salaries on average when the CV had a female name as opposed to a male name. The difference was significant in half of the 20 cases, that is, where the difference in percentages was over 10%. Also, as with participants’ gender, the interaction between participants’ position and the gender of the CV was never significant, that is, there was no difference between non-managerial staff and managers in terms of differentially suggesting or evaluating salaries for female and male applicants or nieces and nephews. In one measure, that is, how much salary participants thought the applicant would accept for the manager’s position, non-managerial staff suggested significantly lower salaries than managers for both female and male applicants.
As with participants’ gender and position, participants in all age groups suggested lower salaries for women than men on all 10 measures (results not shown in a table). In addition, there was no significant interaction between participants’ age and the gender of the CV, which means that there are not differential effects of age in terms of suggesting salaries to women or men – women “obtained” lower salaries on average from all age groups.
Participants’ age had significant effects on salary measures in three cases. These dealt with how much salary they would offer the applicant (p = 0.006) and how much they thought the applicant would accept (p = 0.002) for the sales representative’s job, and how much salary they thought their niece/nephew would be offered for the manager’s position (p = 0.045). In the first two cases, the oldest participants (45 years or older) suggested higher salaries than the younger ones, and in the last case, older participants suggested lower salaries than the youngest (younger than 35 years old).
Female applicants were both offered a lower salary than male applicants, and participants thought that female applicants would accept a lower salary than male applicants for both the sales representative and manager’s job, irrespective of participants’ gender, age, or position. Participants also suggested lower salaries for nieces than nephews on all three salary measures, that is, 1) what participants recommended nieces or nephews to ask for in salaries, 2) what they thought nieces or nephews would be offered, and 3) how much salary participants recommended nieces or nephews to accept – all three also irrespective of participants’ gender, age, or position. Therefore, both hypotheses were supported: that a woman will be offered a lower salary than a man, despite having identical CVs; and that a niece will be advised to ask for a lower salary than a nephew, again despite having identical CVs.
This difference in salaries offered and suggested, which was 10% on average, is only due to the fact that participants either saw a CV with a female or male name. Features of the names other than the gender, e.g. peculiarities of the names, strangeness, or specific associations are highly unlikely, as the names are very common in Iceland – Anna, Gudrun, Gudmundur, and Sigurdur. It is very likely that each participant knew at least a few people with these names or had seen characters with those names in movies, books, or TV series. Therefore, the names on the CVs should not have evoked specific positive or negative attitudes from the participants, only an indication of gender.
These results of the differential salaries of women and men in the present experiment are not surprising, as research shows that women earn less than men in every part of the world (e.g. European Commission, 2014; ILO, 2016). In prior research on the reasons for the gender wage gap, the focus has been on issues such as work experience (Cho, 2007; Miyoshi, 2008), education (Miki and Yuval, 2011; Miyoshi, 2008; Nakavachara, 2010), and family obligations (Petersen et al., 2007), to name only a few. There has also been research focus on less obvious reasons, such as personality traits (Semykina and Linz, 2007), negotiations (Roche, 2014), childhood sexual abuse (Robst, 2008), and having a woman as a boss (Cohen and Huffman, 2007; Maume and Ruppanner, 2015). All of these studies, except the two on having a woman as a boss, focused on reasons or attributes related to the employee or the working environment. The present research expands the focus to those who offer salaries or advice on them, yielding comparable results to the studies cited above. That is, women are offered lower salaries than men and are advised to ask and accept lower salaries than men.
As with the present experiment, the studies conducted by Cohen and Huffman (2007) and Maume and Ruppanner (2015) both focused on the boss, that is, the person who can affect the wages by, for example, negotiating the salaries. Both showed that having a woman as a boss tends to decrease the wage gap. That was, however, not supported in this experiment – female participants wanting to offer women lower salaries than men, just as did male participants. A possible reason for this discrepancy might be that in the present experiment, participants were not directly comparing women and men in terms of salaries – the difference being due to their unconscious attitudes regarding the worth of women’s and men’s work. On the other hand, a female manager dealing with both women and men for months or years in an organisation, might consciously try to correct the salary difference.
When taking their first steps into the job market, many will negotiate their salaries or accept or deny salaries that are offered when applying for a job. Consultation with more experienced relatives or other important figures can be important for young people in this regard, which has often been experienced by the authors in Iceland and in some other cultures as well. The present experiment gives indications that those important figures advise women to ask for and accept lower salaries than they would suggest to men. This might in turn contribute to maintaining the gender wage gap. The facts established by this experiment – all else being equal, women are offered less and advised to accept less – might partially explain the gender wage gap observed throughout the world. The related measures in this experiment, that is, that participants thought that female applicants would accept and be offered lower salaries than male applicants, support the observed difference in how highly women's and men's work are valued. This difference is also supported by the fact that the effect was found both in the case of the sales representative and manager’s positions. The latter does not, however, support the glass ceiling effect (that is, the gender wage gap is relatively greater at higher salaries) found in some of the previous research (Jurajda and Paligorova, 2009; Konstantopoulos and Constant, 2008; Navarro-Gómez and Rueda-Narváez, 2014; Popli, 2013).
Furthermore, present results show that the gender wage gap was independent of three major background variables of the participants: their gender, age, and position. This poses a problem in finding possible causes of why people will offer or recommend lower salaries for women than men. Presuming that a woman’s work is worth the same as a man’s work and that people are not born with the notion that a woman’s work is worth less, the possible explanation must be sought in the environment. Therefore, one of the important next steps in research in this area would be to investigate the age at which this difference starts to emerge in the life of adolescents or even children. Important research questions would be how adolescents or children value typical female and male work in addition to jobs where there is a gender balance, and what factors in the upbringing of adolescents and children affect their evaluation of the worth of a woman and man’s work. These are important questions that need to be answered in our long journey towards bridging the gender wage gap.
A limitation of the present experiment is that it was conducted in a simulated environment. The participants were supposed to pretend that they were a human resources manager for one morning. In that regard, it is worth pointing out that despite the simulation participants were put into, differences in salaries between women and men were consistently obtained on several measures for two positions, and these differences were found in all background groups related to the participants. This simulated situation was a strength at the same time, as it made the experimental design possible, allowing stronger causal inferences that the difference in terms of salaries must have happened because people were dealing either with salaries for a woman or man. Another strength is that people in different jobs in several companies of different sizes and in all major industries were contacted, the high response rate of about 70% adding to the generality of the results.
The results of this experiment reveal the fact that women seem to be looked upon as being worth less than men in the job market, employees with very different backgrounds on five measures for two different jobs suggesting lower salaries on average when a CV had a female name opposed to a male name. Future research needs to look for answers in the adolescent environment, children needing to answer the question of why a woman’s name is worth less than that of a man.
The authors have not declared any conflict of interests.
The authors are grateful to Dr. Leifur Geir Hafsteinsson for valuable advice on the experimental design and also appreciate Dr. Jack James, Dr. Kristjan Kristjansson, and Sarah Dearne for their insightful comments and suggestions on drafts of this paper.
This work was supported by a grant from The Equal Rights Research Foundation of The Prime Minister’s Office in Iceland. The Equal Rights Research Foundation had no involvement in the study.
REFERENCES
|
Aizer A (2010). The gender wage gap and domestic violence. The American Economic Review 100:1847-1859.
Crossref
|
|
|
|
Akhmedjonov A (2012). New evidence on pay gap between men and women in Turkey. Economics Letters 117:32-34.
Crossref
|
|
|
|
|
Barth E, Dale-Olsen H (2009). Monopsonistic discrimination, worker turnover, and the gender wage gap. Labour Economics 16:589-597.
Crossref
|
|
|
|
|
Brown S, Roberts J, Taylor K (2011). The gender reservation wage gap: Evidence from British panel data. Economics Letters 113:88-91.
Crossref
|
|
|
|
|
Chen Z, Ge Y, Lai H, Wan C (2013). Globalization and gender wage inequality in China. World Development 44:256-266.
Crossref
|
|
|
|
|
Cho D (2007). Why is the gender earnings gap greater in Korea than in the United States? Journal of the Japanese and International Economies 21:455-469.
Crossref
|
|
|
|
|
Cohen PN, Huffman ML (2007). Working for the woman? Female managers and the gender wage gap. American Sociological Review 72:681-704.
Crossref
|
|
|
|
|
Cukrowska-Torzewska E, Lovasz A (2016). Are children driving the gender wage gap? Economics of Transition 24:259-297.
Crossref
|
|
|
|
|
de Ruijter JMP, Huffman ML (2003). Gender composition effects in the Netherlands: A multilevel analysis of occupational wage inequality. Social Science Research 32:312-334.
Crossref
|
|
|
|
|
Elul R, Silva-Reus J, Volij O (2002). Will you marry me? A perspective on the gender gap. Journal of Economic Behavior and Organization 49:549-572.
Crossref
|
|
|
|
|
Erosa A, Fuster L, Restuccia D (2002). Fertility decisions and gender differences in labor turnover, employment, and wages. Review of Economic Dynamics 5:856-891.
Crossref
|
|
|
|
|
European Commission (2014). Tackling the gender pay gap in the European Union. Available at:
View
|
|
|
|
|
Furnham A, Wilson E (2011). Gender differences in estimated salaries: A UK study. The Journal of Socio-Economics 40:623-630.
Crossref
|
|
|
|
|
Grönlund A, Magnusson C (2013). Devaluation, crowding or skill specificity? Exploring the mechanisms behind the lower wages in female professions. Social Science Research 42:1006-1017.
Crossref
|
|
|
|
|
Guimarães CRFF, Silva JR (2016). Pay gap by gender in the tourism industry of Brazil. Tourism Management 52:440-450.
Crossref
|
|
|
|
|
Hallward-Driemeier M, Rijkers B, Waxman A (2015). Can minimum wages close the gender wage gap? Evidence from Indonesia. The Review of Income and Health 61:1-25.
Crossref
|
|
|
|
|
Harris P (2015). Global gender pay gap survey for Glassdoor.
View
|
|
|
|
|
International Labour Organization (ILO) (2016). Women at work: Trends 2016. Available at:
View
|
|
|
|
|
Jurajda Š, Paligorova T (2009). Czech female managers and their wages. Labour Economics 16:342-351.
Crossref
|
|
|
|
|
Kawachi I, Kennedy BP, Gupta V, Prothrow-Stith D (1999). Women's status and the health of women and men: A view from the States. Social Science and Medicine 48:21-32.
Crossref
|
|
|
|
|
Kim M (2013). Policies to end the gender wage gap in the United States. Review of Radical Political Economics 45:278-283.
Crossref
|
|
|
|
|
Konstantopoulos S, Constant A (2008). The gender gap reloaded: Are school characteristics linked to labor market performance? Social Science Research 37:374-385.
Crossref
|
|
|
|
|
Mandel H (2016). The role of occupational attributes in gender earnings inequality, 1970-2010. Social Science Research 55:122-138.
Crossref
|
|
|
|
|
Maume DJ, Ruppanner L (2015). State liberalism, female supervisors, and the gender wage gap. Social Science Research 50:126-138.
Crossref
|
|
|
|
|
Meagher G (2012). Recognition at last: Care work and the equal remuneration case. The Journal of Industry Relations 54:377-385.
Crossref
|
|
|
|
|
Miki M, Yuval F (2011). Using education to reduce the wage gap between men and women. The Journal of Socio-Economics 40:412-416.
Crossref
|
|
|
|
|
Miyoshi K (2008). Male-female wage differentials in Japan. Japan and the World Economy 20:479-496.
Crossref
|
|
|
|
|
Nakavachara V (2010). Superior female education: Explaining the gender earnings gap trend in Thailand. Journal of Asian Economics 21:198-218.
Crossref
|
|
|
|
|
Navarro-Gómez ML, Rueda-Narváez MF (2014). Sexual-based wage discrimination when educational outcomes are endogenous: The Spanish case. Procedia Social and Behavioral Sciences 116:1869-1874.
Crossref
|
|
|
|
|
Noonan MC (2004). The impact of domestic work on men's and women's wages. Journal of Marriage and Family 63:1134-1145.
Crossref
|
|
|
|
|
Nordman CJ, Robilliard A-S, Roubaud F (2011). Gender and ethnic earnings gaps in seven West African cities. Labour Economics 18:132-145 (Supplement: Labour markets in developing countries).
Crossref
|
|
|
|
|
Petersen T, Snartland V, Meyersson Milgrom EM (2007). Are female workers less productive than male workers? Research in Social Stratification and Mobility 25:13-37.
Crossref
|
|
|
|
|
Platt J, Prins S, Bates L, Keyes K (2016). Unequal depression for equal work? How the wage gap explains gendered disparities in mood disorders. Social Science and Medicine 149:1-8.
Crossref
|
|
|
|
|
Popli GK (2013). Gender wage differentials in Mexico: A distributional approach. Journal of the Royal Statistical Society: Series A (Statistics in Society) 176:295-319.
Crossref
|
|
|
|
|
Razzolini T, Leombruni R, Mastrobuoni G, Pagliero M (2014). Beneath the surface: The decline in gender injury gap. Labour Economics 30:282-288.
Crossref
|
|
|
|
|
Rendall M (2012). Structural change in developing countries: Has it decreased gender inequality? World Development 45:1-16.
Crossref
|
|
|
|
|
Robst J (2008). Childhood sexual abuse and the gender wage gap. Economics Letters 99:549-551.
Crossref
|
|
|
|
|
Roche K (2014). An active-learning exercise on learning negotiation as a way to mitigate the gender wage gap for introductory microeconomics. International Review of Economics Education 15:32-42.
Crossref
|
|
|
|
|
Semykina A, Linz SJ (2007). Gender differences in personality and earnings: Evidence from Russia. Journal of Economic Psychology 28:387-410.
Crossref
|
|
|
|
|
Velferdarraduneytid (Ministry of Welfare) (2014). Launarannsóknir og átaksverkefni um launajafnrétti á vinnumarkaði (Wage research and initiatives of equal pay in the labor market). Available at:
View
|
|
