University of Amsterdam Faculty of Economics and Business
The gender wage gap in the Netherlands
Research on the change in the gender wage gap in the Netherlands between
2010 and 2014
Mila Huisman Student number: 11061146
University of Amsterdam Bachelor thesis Economics and Business
Supervisor:
Abstract
Differences in pay between men and women exist in almost all countries. This thesis studies whether the gender wage gap in the Netherlands has declined between the years 2010 and 2014 on the basis of previous literature and an empirical research. It gives several possible explanations for the existence of pay differences based on two theories: the theory of human capital and the theory of pure wage discrimination. Subsequently, an empirical research using an ordinary least squares regression and data from the Dutch OSA Labor Supply Panel has been performed. The results of this study show that there is no significant decline in the gender wage gap in the Netherlands between 2010 and 2014.
Statement of Originality
This document is written by Mila Huisman who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Outline
1. Introduction ... 5
2. Literature review ... 6
2.1 The gender wage gap elucidated ... 6
2.2 Dutch research on the gender wage gap ... 9
3. Data and Method ... 10
3.1 Mincer Wage Equation ... 10
3.2 Data ... 12 3.3 Hypotheses ... 13 4. Results... 15 4.1 OLS regression ... 15 5. Conclusion ... 19 6. Discussion ... 20 References ... 21
1 Introduction
Equal pay day is a yearly celebrated, important day that unfortunately still might sound unfamiliar to most people. Nevertheless, this day, that evolves around the missing equality in earnings between men and women worldwide, celebrates its twenty-second anniversary this year (National Committee on Pay Equity, n.d.). Equal pay day is a symbolic day, dedicated to raising awareness to the gender wage gap. This symbolic day is created by the National Committee on Pay Equity: a coalition of women’s and civil rights organizations, labor unions, professional, legal and educational associations and individuals working to eliminate sex- and race-based wage discrimination and to achieve pay equity (National Committee on Pay Equity, n.d.).
But still, after all these years of striving for equality between men and women all over the world, it seems like the aimed change has not been reached yet: earnings of men and woman still are not equal and the world is still trying to cope with the existence of the gender wage gap. Equal pay day is a great example to measure the differences between salaries of men and women. The exact date of equal pay day differs both per year and country, since this day symbolizes how far into the current year women must work to earn what men earned in the previous year (National Committee on Pay Equity, n.d.). In Europe for example, equal pay day in 2018 has taken place on the 28th of February. This means that European women had to
work for a period of almost one year and two months to earn an equal wage to that of what European men earned in the previous year (Equal Pay Day, n.d.).
What exactly is meant by the gender wage gap is defined by the European
Commission as “the difference in average gross hourly earnings of male and female paid employees” (European Comission, 2017). A more precise definition of the gender wage gap can be found in an article by Oaxaca (1973), which states that “discrimination against females can be said to exist whenever the relative wage of males exceeds the relative wage that would have prevailed if males and females were paid according to the same criteria”.
Even in a developed country as the Netherlands, in which men and women have the same rights, there exists a gender wage gap. The differences in wage between men and women in the Netherlands are examined every two years by the Dutch Central Bureau of Statistics in their emancipation report. Results from their most recent research available regarding the gender wage gap, which took place in the year 2014, show that Dutch women in the labor market earn seven percent less than their male colleagues (Centraal Bureau voor de Statistiek, 2014).
This thesis will focus on the changes in the gender wage gap in the Netherlands between the years 2010 and 2014. The time period has been chosen based on the fact that the year 2010 is the year holding the oldest, useful available data, while 2014 is the year holding the newest available data regarding this subject. Since the awareness of wage inequality between men and women is increasing every year, this research will test whether the wage gap has decreased. This leads to the following research question: “Has there been a significant decline of the gender wage gap in the Netherlands between 2010 and 2014? “
This thesis is structured as follows. In the literature review, an overview of the economic theories on the gender wage gap will be presented, and previous literature will be discussed. Thereafter, in Chapter three: the data and method section, the hypotheses will be introduced and there will be a description of the methodology of the research as well as a brief description of the dataset. Subsequently, in Chapter four: the result section of this thesis, the results of the empirical research are presented and interpreted. The thesis will be ended by providing a conclusion of the research results and a discussion on any potential shortcomings of the research.
2 Literature review
In this chapter, background information about the gender wage gap and related theories will be discussed. Research on the gender wage gap can be decomposed into two views on the sources of earnings differentials, which will both be explained. The first focuses on gender specific factors, as in the human capital theory. The second presumes pure wage
discrimination. Eventually, the research about the gender wage gap in the Dutch labor market will be briefly discussed.
2.1 The gender wage gap elucidated
Although the gender wage gap has narrowed since the 1950s, differences in pay between genders still exist in all industrialized nations (Blau & Kahn, 1996). Most of the research that has been done on the gender wage gap, has focused on so-called gender-specific factors. These factors can be defined as “gender differences in either qualifications or labor market treatment of similarly qualified individuals” (Blau & Kahn, 2000). The gender differences in qualifications have been extensively studied within the context of the human capital theory.
The human capital theory is a theory on gender wage differences developed by, among others, Gary Becker (1962). The theory assumes that people with a higher investment in
human capital, will have a higher productivity on the labor market. As a higher productivity will by itself lead to a higher wage, differences in wage can therefore be explained by differences in productivity between men and women. Within his theory, Becker tries to describe the factors that drive people to invest or not invest in their own human capital.
Human capital refers to one’s joint skills, abilities, qualities and knowledge that are relevant to deliver labor. There are several ways to invest in human capital, but the most important ones include schooling, labor market experience, on-the-job training and taking care of health (Becker, 1962). By improving the physical and mental abilities of people, all of these investments increase the wage. However, they have different relative effects (Becker, 1962).
Mincer and Polachek (1974) found that periods of disruption on the labor market can affect the investments in human capital. Most women anticipate to have a period of labor market disruption, because they expect that they will have children at some point. According to Blau and Kahn (2000) this will ensure that women do not invest in themselves as much as men do. First of all, this will cause a gap in the education level between men and women. As women know they will not be active in the labor market for as long as men will be, it might result in lower incentives to invest in education and on-the-job training (Blau & Kahn, 2000). Second, during their pregnancy, women will be on a maternity leave for a certain period. Their absence from their job during their maternity leave leads to a gap in work experience. Furthermore, after the birth of their children, it is usual that women start working part-time, as they take a greater share in the education of their children. This means that they will spend even less time on the labor market, resulting in an even larger gap in work experience. This gap in work experience as well as in years of schooling, lowers the productivity of women, which in turn leads to a lower wage.
On the contrary, men expect to participate in the labor market for a long and continuous period of time, so they have more incentives to invest in labor market human capital than women (De Ruijter, Van Doorne-Huiskes, & Schippers, 2003). For the same reasons, employers generally tend to invest less in the human capital of their female employees which will lead to a gap in wage (De Ruijter et al., 2003).
Another factor that could explain differences in wage between men and women originates from the past. Back in the days, labor in the family was divided in a way in which the man performed the paid labor and the woman carried out the domestic work (De Ruijter et al., 2003). In most traditional households, women had been the ones who cleaned, did the laundry, cooked and, as mentioned above, took most care of the children. The extra energy
and time they had to put into the household, decreased the effort they put into their labor market jobs compared to men, and hence also reduced their productivity and wages (Blau & Kahn, 2000). The results of this division in the household are still visible in today’s wages, especially in traditional families.
In conjunction with the labor market experience, the education gap between men and women is the biggest source of the gender pay gap. It is a common fact that people with a higher degree in education almost always have a wage well above the average (Becker, 1994). Yet, a higher level of education in the sense of more knowledge is not necessarily an
assurance for a higher wage. What does lead to a higher wage, are possibly the extra years of education and the deferred reward that comes with these extra education years. Employees will be compensated for the deferred reward by their employers by a higher income.
Another cause of the wage gap which is not attributable to the human capital theory, but found by research on gender-specific factors as well, shows that men and women are employed in different fields of labor (Blau & Kahn, 1996). More specifically, in their research Blau and Kahn (2000) suggest that women tend to work in sectors that pay less on average. Historically, most women tend to have low paid jobs. They worked for example as teachers and nurses. But towards the end of the 1970s, women started to choose other jobs, which resulted, in most cases, in a successful transition. It sometimes appears that women have similar functions as men, but that generally, that women occupy these similar positions in sectors that pay less. A good example for this is a male, working as a director of a bank, and a female, working as a director of a school. Because of this it can be said that there also is a difference in pay between sectors, and not only between type of jobs. The sector in which one works can thus influence the size of the wage gap.
Although, differences in wage may arise from differences in human capital, several researchers find that they do not account for the total gender gap in wages (De Ruijter et al., 2003). Besides productivity, wage differences can be caused by pure discrimination. Labor market discrimination can take two forms. On one hand, it can be direct differences in pay, and on the other hand it can take the form of the lower possibility for women to work in better paid sectors and jobs, keeping all other factors equal. Direct pay differences arise when men and women have the same function and productivity, but do not receive the same wage (Schippers & Siegers, 1972). Though, Schippers (1972) and Oaxaca (1973) agree on the findings that an unequal wage for equal work is not the most important and significant source of labor market discrimination. The biggest part of the wage differentials can be explained by the concentration of women in lower paying jobs (Oaxaca, 1973). This is caused by the fact
that women are excluded from male-dominated jobs, which are mostly the better paying jobs. These male-dominated jobs include technical, electrical and construction occupations.
Besides, the exclusion leads to an excess supply of feminine labor in the female-dominated sectors (Blinder, 1972). From basic microeconomics, it is commonly known that a higher supply results in a lower price. In this case, that price is the hourly wage of women.
2.2 Dutch research on the gender wage gap
Despite it being a very popular topic among researchers, the Dutch research on the gender wage gap is limited. Researchers focused either on very specific situations within gender discrimination or talked about the gender wage gap in general, not specifically in the
Netherlands. De Ruijter et al. (2003) analyzed why employees in so called female-dominated occupations earn less than employees in dominated occupations. As said before, male-dominated occupations are typically technical, electrical or construction jobs. On the other hand, female-dominated occupations include nurses, teachers, secretaries and assistants. They found that women, as well as men, experience a wage penalty if they work in
female-dominated instead of male-female-dominated occupations (De Ruijter et al., 2003). However, their empirical results show a relatively small wage gap, which they find surprising since the Netherlands is often described as a very conservative welfare state (De Ruijter et al., 2003). A conservative welfare state is defined as a state in which economic dependence of women on their husbands and working part-time as a woman is favored (De Ruijter et al., 2003). This should thus imply a large wage gap, rather than a small gap.
According to the research of Schippers and Siegers (1972), the differences in human capital investments play a huge part in the creation of the wage inequality. Though, their findings suggest as well that pure wage discrimination also contributes to the gender wage gap, but to a limited extent. They state that the main reason of wage differences within this discrimination is the distribution of men and women across job levels.
Albrecht, van Vuuren and Vroman (2004) analyzed the gender wage gap among the full-time working population in the Netherlands, using data from the OSA Labor Supply Panel from 1992. They found a pay gap of twenty percent, which is significantly higher than the relatively low gap de Ruijter et al. found in 2003. However, the research of Albrecht et al. contained a selection bias. They simply focused on the full-time working population, whereas the number of part-time working women was relatively large and not taken into account.
3 Data and method
3.1 Mincer wage equation
To estimate whether the gender wage gap has declined between 2010 and 2014, the Mincer wage equation will be used. The Mincer wage equation is one of the most widely used models in empirical economics, and has been used for a wide range of datasets, a large number of countries and time periods (Lemieux, 2003). The most important version of the Mincer wage equation is probably the use of results from the human-capital theory to derive an estimating earnings equation (Björklund & Kjellström, 2002). The equation was founded by Jacob Mincer, who described the relationship between human capital and wage by letting the logarithm of the wage depend on the years of schooling and a quadratic equation of years of work experience. The formula is as follows:
ln y = ß0 + ß1schoolingi + ß2experiencei + ß3experience2 + ε
However, in this thesis, a number of extra variables will be added to the Mincer equation. As mentioned in the previous chapter, wage may be influenced by more factors than schooling and experience and therefore those other factors will be added as well. This leads to the following equation:
ln y = ß0 + ß1schoolingi + ß2experiencei + ß3experiencei2 + ß
4age + ß5age2 +
ß6male + ß72014 + ß8male*2014 + γX + ε
The adjusted wage equation will be used in an Ordinary Least Square regression. This is a statistical method to estimate the unknown parameters in a regression model by minimizing the squared residuals. From here onwards, uppercase letters will be used to refer to a variable in the text.
The dependent variable in this equation is the natural logarithm of the monthly wage. Logarithms are often used for an easier interpretation (Lemieux 2003). Besides, it lowers the impact of heteroscedasticity. Another advantage of using logarithms is that it makes the distribution of the transformed variable appear more symmetric, hence normally distributed. The influence of the outliers in the dependent variable is reduced this way.
ß0 is the constant variable in the equation and represents the wage of an individual
without any education and work experience, and when all other variables take the value of zero.
SCHOOLING is the total years of education until graduation. It is well known that people with a higher degree of education have a higher wage than the people who do not. Therefore, it is expected for this variable to have a positive relationship with wage. EXPERIENCE is the total years of work experience in the labor market. It is
estimated that the effect of a higher working experience on wage is not linear, but decreases with time. So, the relationship between wage and experience is concave. Therefore,
EXPERIENCE2 is added as a quadratic term in this formula. A small difference compared to
the actual Mincer wage equation is that in the actual Mincer wage equation, the labor market experience is calculated as age minus years of schooling minus six years, but in this case the actual years of labor market experience have been taken in consideration (Lemieux, 2003). The six years in the first version is a measure of the period of absence on the labor market between an individual’s graduation and retirement. However, these six years are simply an estimation; therefore, making the use of the actual number of years is more valid. Besides, taking the actual years of labor market experience has been done in most recent studies.
For the same reason as EXPERIENCE, AGE is given as a quadratic term. Experience increases with age, and therefore productivity as well. However, the increase in productivity is higher for younger people, than for someone who is about to retire. So, productivity is diminishing as age increases.
Creating a dummy variable involves converting nominal or categorical data into multiple levels to make it easier to compare different groups between a degree of interest and the other levels of the variable. Since this research is about the difference between earnings of men and women, the dummy variable MALE is added to the wage equation. This variable will have a value of 1 when the individual is a male, and 0 if it is a female. The dummy variable 2014 is added to the equation to represent the difference between the years 2010 and 2014. Finally, the most important variable is the interaction variable between MALE and 2014. This variable will show directly whether the gender wage gap has declined between 2010 and 2014.
The remaining variables that are explained below are the rest of the control variables. Control variables are added to an equation to assess the relative relationship between the dependent and the independent variable in an unbiased way. The vector of the control variables includes the following.
As mentioned in the previous chapter, whether someone works part-time or full-time also has an impact on earnings. People who work full-time earn relatively more than the ones
who work part-time. So, to prevent a selection bias, it has to be added as a dummy variable to the equation.
Other control variables are the sectors in which an individual works. As pointed out by Blau & Kahn (2000), there is a difference in pay between sectors. Consequently, the sectors need to be added to the equation as well. The following ten sectors were given in the data which was used for the regression: agriculture, industry, construction, trade, transport, business, health, other services, government and education. To prevent from an error, nine of these sectors were transformed into dummy variables. This way, the effect of the left-out sector is still measurable, namely when all other sector dummy variables take a value of zero.
3.2 Data
The data that has been used for the OLS regression was obtained by the OSA Labor Supply Panel. This is a longstanding survey among the Dutch population about their labor situation. It was designed to plot various aspects of the labor supply over time. Important themes within this panel are labor mobility, schooling, search behavior for a job, working time, opinions about labor in general and current job. The targeted population was the Dutch population from sixteen to sixty-six years old.
For this research, data from the years 2010 and 2014 has been used. After filtering for irrelevant information and un-useful answers of some variables, 2266 candidates were left for the year 2010. Un-useful answers were answers defined as ‘does not apply’ or ‘not shown in list’. The dataset of 2014 consisted of 2354 candidates after filtering the useful answers. This made a total of 4620 candidates for the regression. However, SPSS, the statistical computer program that was used to do the regression, neglected one candidate for unknown reasons. Since the sample population was quite large, this did not matter for the results of the regression.
Another adjustment to the data was for the variable schooling. The level of education was given in name, not in total years until graduation. So, this had to be transformed into years. Westenberg (2015) computed the average total years of schooling per education until graduation, using data from the Dutch Central Bureau of Statistics. The results of these computations are given in the table below.
Table 3.1: Years of schooling per type of education
Education Total years of schooling
VMBO 13.5 HAVO 14 VWO 14.5 MBO 20 HBO 22 WO 24 Source: Westenberg (2015) 3.3 Hypotheses
Recent trends on the emancipation of women in the Netherlands are provided by the Emancipatiemonitor. In its reports of 2014 and 2016, it is seen that more women started to follow a higher education. From Table 3.1 we see that a higher degree of education comes with more years of schooling. According to the Mincer wage equation, this will lead to a higher wage.
Furthermore, the reports show that the labor participation of women has increased over the years. This could be associated with a higher demand for labor. When the demand for a good increases, its price will increase as well and this means that the wage of women on the labor market should increase.
Other reasons for an increase of the availability of women in the labor market is the shifting from domestic tasks to either day-care facilities, leaving possibilities to rejoin the labor market, or the change in the division of domestic tasks and paid labor within
households. Figure 3.1 shows that the share of men taking care of the household has since 1985 increased. For that reason, not only women will have more time for their labor market jobs, they will also have more energy left to put into their jobs, since they do not have to spend that much energy on the household anymore. This results in an increase in productivity, and therefore their wages. Furthermore, these changes in day-care opportunities shorten the break women take from the labor market as well. Hence, women would both get longer
Figure 3.1: Participation of men in labor and household tasks
man in paid labor man in household man taking care of children
Source: Centraal Bureau voor de Statistiek & Sociaal en Cultureel Planbureau (2016)
These trends in combination with the theory provided in the previous chapter, have led to the following hypotheses: the gender wage gap in the Netherlands has significantly declined in the years between 2010 and 2014. In our model, this results in the following null and alternative hypotheses:
H0: gender wage gap 2014 = gender wage gap 2010
H1: gender wage gap 2014 < gender wage gap 2010
Rewriting these hypotheses in terms of the model parameters leads to the following hypotheses:
H0: ß8 = 0
H1: ß8 < 0
If H0 will be rejected, there won’t be enough evidence to conclude that the gender wage gap
4 Results
In this chapter, the results of the OLS regression will be discussed. It starts with a brief explanation on how the OLS regression has been done, followed by the presentation and the interpretation of the results.
4.1 OLS regression
Since the wage equation contains two quadratic variables, multiple regressions have been done. As described in the previous chapter, the use of a quadratic term for an independent variable implies that the relationship between that independent variable and the dependent variable is non-linear. The aim of doing multiple, in this case four, regressions, is to check whether these relationships are indeed non-linear. This is done by excluding and including the variables in different ways in each regression.
In the first regression, both of the quadratic variables were excluded. The following two regressions excluded either one of the two quadratic variables. Finally, the fourth regression contained all variables, and is seen as the main regression in this research.
The results and the summary statistics of the four OLS regressions are described in table 4.1. This table shows the value of the coefficients, their standard deviation and also illustrates whether the variables are significant. Because the dependent variable is a natural logarithm and the independent variables are measured in levels, we have to multiply the coefficients by hundred in order to see what a one unit change in the variables will mean in terms of a percentage change in the dependent variable.
Table 4.1 Regressor (1) (2) (3) (4) Constant 0.1557** (0.071) 0.897*** (0.088) 1.492*** (0.071) 0.877*** (0.101) Schooling 0.034*** (0.001) 0.034*** (0.001) 0.035*** (0.001) 0.034*** (0.001) Experience 0.001 (0.001) 0.002** (0.001) 0.017*** (0.002). 0.001 (0.003) Experience2 0.000** (0.000) -0.0000021 (0.000)
Regressor (1) (2) (3) (4) Age 0.008** (0.001) 0.043** (0.003) 0.006*** (0.001) 0.045*** (0.005) Age2 0.000* (0.000) 0.000** (0.000) Male 0.073*** (0.015) 0.073*** (0.015) 0.075*** (0.015) 0.073*** (0.015) Part-time 0.032*** (0.011) 0.022** (0.011) 0.025** (0.011) 0.022** (0.011) Industry -0.078 (0.063) -0.124** (0.062) -0.104*** (0.062) -0.124** (0.062) Construction -0.144** (0.066) -0.177*** (0.065) -0.165** (0.065) -0.177*** (0.065) Trade, catering, repair -0.177*** (0.063) -0.211*** (0.062) -0.201*** (0.063) -0.210*** (0.062) Transport -0.111* (0.065) -0.157** (0.064) -0.134** (0.064) -0.157** (0.064) Business -0.014 (0.062) -0.053 (0.061) -0.037 (0.062) -0.053 (0.061) Care -0.105 (0.063) -0.138** (0.062) -0.124** (0.062) -0.138** (0.062) Other services -0.047 (0.065) -0.071 (0.064) -0.058 (0.064) -0.071 (0.064) Government -0.012 (0.063) -0.042 (0.062) -0.031 (0.062) -0.042 (0.062) Education -0.061 (0.063) -0.085 (0.062) -0.080 (0.063) -0.085 (0.062) 2014 0.076*** (0.012) 0.083*** (0.012) 0.081*** (0.012) 0.083*** (0.012) Male_2014 -0.005 (0.018) -0.002 (0.017) -0.004 (0.018) -0.002 (0.017)
Summary Statistics (1) (2) (3) (4)
SER 0.300 0.295 0.298 0.295
R2 0.256 0.280 0.269 0.280
Adjusted R2 0.253 0.277 0.266 0.277
n 4619 4619 4619 4619
*, ** and *** mean that the variable is significant at the 10%, 5% and 1% significance level, respectively.
As mentioned before, according to Becker (1994), schooling and work experience are the two most important investments in an individual’s human capital. Therefore, one would expect for these variables to be relatively large, and significant as well. In the main
regression, the coefficient for the variable SCHOOLING has a value of 0.034, which implies an expected increase of 3.4% in the wage for one extra year of schooling. This is in
accordance with the human capital theory, which states that a higher education will lead to higher productivity, and therefore a higher wage (Becker 1962). The coefficients for this variable are quite similar within all four regressions. This might be interpreted as schooling to be indeed an important investment for a higher wage.
For the variable EXPERIENCE, we found a value of 0.001 which is low. Especially since, according to Becker, experience on the labor market is quite determinative for the wage of an individual. However, the value we found is not significant. The third regression, in which AGE2 is omitted, shows a relatively high value for EXPERIENCE, compared to its
values in the other regressions. A potential reason for such a difference could be that AGE2 is
highly significant and possibly high correlated with EXPERIENCE. So, omitting it might influence other variables, and thus could result in this high value for the coefficient on
EXPERIENCE. The founded value for EXPERIENCE2 is negative and remarkably small. The
negative value is in line with the model of Mincer, which states that productivity is not infinitely increasing with experience. To test its relationship with the dependent variable, EXPERIENCE2 was excluded from two of the four regressions. In order to indeed have a
non-linear relationship with wage, the coefficient on the nonlinear term has to be significant and the R2 should be higher in the regressions that include that term. The R2 is defined as the
proportion of variance explained by the regression model, and therefore is a measure of success for predicting the dependent variable from the independent variables (Nagelkerke, 1991). As showed in Table 4.1, the R2 is quite similar for all regressions. However, the value
to the regression and that the relationship it has with the dependent variable wage is rather linear than non-linear.
When considered individually, both EXPERIENCE and EXPERIENCE2 do not have
significant values in the regression. However, it is possible that the variables are jointly significant. In order to test the joint significance of these terms, an F-test has been executed using the following formula:
F = (RUR
2 − (R R 2)/q
(1 − RUR2 )/(n − k − 1) ∼ Fq, n-k-1
In this formula, q is the number of restrictions, k is the number of independent variables in the unrestricted model, and n equals the number of observations. The unrestricted model here is the main regression from before, and the restricted model is a fifth regression in which the variables EXPERIENCE and EXPERIENCE2 were omitted. The value of the F-statistic is
3.194, which is higher than the critical value of 2.9957 at a confidence interval of 5%. This means that the variables combined have predictive power on the gender gap. So, including these variables improves the model’s fit.
The regression shows a significant value of 0.045 for the variable AGE. So, as
expected, wage is indeed increasing with age. The value of the coefficient for AGE2 is almost
zero and is significant. Similar to EXPERIENCE2, the Mincer model assumes a diminishing
productivity for the variable AGE2 as well. Since the value for the variable is significant and
the R2 is higher for the regressions that include AGE2, we can say that the relationship it has
with wage is indeed non-linear.
The variable MALE shows the gender wage gap in 2010. In the regression, its coefficient has a significant value of 0.073. So, this implies that in 2010, male workers earned, on average, 7.3% more than female workers.
The control variable PART-TIME shows a positive relationship with wage. The estimated coefficient is 0.22 which means that the wage of a part-time worker is expected to be 2.2% higher than a full-time worker, keeping everything else constant. This is not in line with the theory provided in Chapter two, where it is said that full-time working people have higher hourly wages than part-time working people. An explanation for this positive
relationship could be found in the fact that part-time working people have higher-skilled jobs. Another reason could be that part-time working people work part-time, because their earnings are high enough to be able to work part-time. The estimated coefficient on the time variable
2014 is 0.083. It shows that the average wage in the Netherlands in 2014 is 8.3% higher than the wage in 2010.
In this research, the most important variable is the interaction dummy MALE_2014. The value of the coefficient of this variable directly shows whether the gender wage gap in the Netherlands has decreased between the years 2010 and 2014, and if so, how much. The regression shows a value of -0.002, which is a decrease of 0.2% in pay differences. However, this value is not significant. So, this result suggests that there was no change in the gender wage gap in 2010 and 2014.
5 Conclusion
In this thesis, the change in the gender wage gap in the Netherlands between 2010 and 2014 has been examined. The research question of this thesis stated whether there has been a significant decline in the gender wage gap in the Netherlands over the time period described above. To find an answer to this question, at first, a literature review has been conducted to find possible explanations for the existence of wage differences between men and women. Firstly, the human capital theory of Becker (1962) is mentioned: a theory that assumes that wage is dependent on the productivity of an individual. This productivity can be raised by investing in human capital. Thus, wage differences between men and women can, according to this theory, therefore be explained by differences in productivity. On the other hand, it was argued that a part of the wage gap could be assigned to pure discrimination. This can be divided into direct wage differences, and the missing access for women to obtain higher and better paid jobs.
Subsequently, an empirical research was done by means of an OLS regression. The results of this regression show a decline in the gender wage gap, however these results were not significant. The null hypothesis stated that there was no decline in the gender wage gap. Since the result is not significant, we fail to reject this hypothesis. So, there is not enough evidence to conclude that the gender wage gap has changed between 2010 and 2014.
6 Discussion
In contrast to the expectations in this study, no support has been found for the expected significant decrease in the gender wage gap in the Netherlands between 2010 and 2014. Several possible explanations can be given for this.
First, there may be impurities in the wage equation due to omitted relevant variables. For example, the variables health and on-the-job training are named as investments in human capital by the human capital theory. However, these variables were not included in the data for the regression. Including more variables might lead to a more significant regression output.
In the theory section of this thesis, it has been said that the number of children has an impact on the wage earned by women as well. However, in this research the number of children was not included as a variable. The results therefore may be biased. A suggestion for further research thus, is to include the number of children as a variable.
Another possible reason for the insignificance of the result, is that within sectors, men and women could still have different jobs with very different earnings. An example could be the health sector, whereas the majority of medical specialists are male, while the majority of nurses are female (Vrouwenstudies Medische Wetenschappen Nijmegen, 2011).
This study has been based upon statistics from 2010 to 2014. During this period, the Netherlands experienced one of the deepest recessions of all time. Even now, in 2018, we see an almost full recovery of the labor market, but wages are still lagging and not increasing enough to spur the economy to a next level. With these facts in mind, one can easily understand why the overall economic situation took its toll on the expected decrease in the gender wage gap.
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