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MOTHERHOOD AND INCOME

A study on how motherhood affects women’s income HAWRAZ KAMARI

JONATAN GROOP

School of Business, Society and Engineering

Course: Bachelor Thesis in Economics Course code: NAA305

Subject: Economics Credits: 15 hp

Program: Analytical Finance

Supervisor: Mats Ekman Date: 2020-06-01 E-mail:

Hki15003@student.mdh.se Jgp16001@student.mdh.se

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ABSTRACT

This paper explores the income effect which motherhood has on women, using (American) Census data. The hypothesis states that the income effect is negative. Previous studies have shown that indeed children lower the income of women and that it is decreased with every additional child. We test our hypothesis using data from the Census Bureau from the year 2018, consisting of over 800 000 answers, and running multiple regressions to measure the effect which the number of children have on a woman’s income. As predicted, the results indicate that our hypothesis is true with a 26% decline in income when a woman has one or more children. Marriage has a negative effect on income while completing higher levels of education raises it.

KEYWORDS

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CONTENTS

INTRODUCTION ... 1

RELATED LITERATURE ... 2

DATA ... 5

3.1 Ordinary Least Squares – OLS ... 6

3.2 Dummy Variables ... 6

3.2.1 Female ... 7

3.2.2 Marriage ... 7

3.2.3 Partner ... 7

3.2.4 Education (High School and College) ... 7

3.3 Age ... 8

3.4 Divorced ... 8

3.5 Number of Children ... 8

3.6 Household Income ... 8

3.7 Correlation between variables ... 9

RESULTS ... 9

DISCUSSION ... 11

BIBLIOGRAPHY ... 14

LIST OF TABLES

Table 1: Summary Statistics ... 5

Table 2: Correlation coefficients between independent variables ... 9

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INTRODUCTION

The debate surrounding income equality between men and women has been ongoing for many years across all corners of the world. There is a general belief that women in the labour market earn less than their male counterparts simply because of their gender, but is this really the case? Some believe the differences in income are due to men holding many of the managerial and executive positions in firms which leads to patriarchal power, others believe it is because women work less and therefore have less experience than men. However, since the feminist movement’s second wave began in the 1960s in the United States of America there have been numerous gains for women in the labour market; increased labour force participation, maternity leave rights and laws banning employment discrimination, just to name a few (Burkett, 2020). There are also industries where female skills are overrepresented, such as pharmacy because it is a family friendly profession where women can work flexible hours (Goldin and Katz, 2016). Women also complete higher levels of education compared to men ("Women outnumber men in higher education…”, 2019). These are all factors which can help raise the wages and incomes for women in the labour market; so why is it that we still see income inequality around the world? Since women have throughout history been given the responsibility to take care of the household and raise children, it is not unreasonable to believe that motherhood has a negative impact on a woman’s income.

This paper explores the relationship between income and motherhood to see what income effect children may have on women. Several other variables will be considered, such as the number of children, the woman’s partners income, her level of education, etc. Using data from the Census Bureau of the United States of America we find that the number of children a woman has lowers her income, but so does being married. These results are consistent with multiple studies done by economists where the woman’s income diminishes with every new-born child. The studies

compare incomes of women by examining data from large databases such as Census. However, as is discussed in section 5, the number of children is not the only factor which contributes negatively to a woman’s income. Age and level of education can contribute both positively and negatively, as can ones decision to get married or not.

The hypothesis tested and examined in this study is as follows: there is a negative relationship between the number of children a woman has and her income. This in turn widens the wage gap between men and women. The effects of a low income are many, a declined mental health (Platt, Prins, Bates & Keyes, 2016) being one of them. It is therefore important to address this issue which affects many women.

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The remainder of this paper proceeds as follows: Section 2 presents overview of the previous literature on the topic which will be studied. This section intends to give a review of what other researchers have concluded in their studies, as well as giving some relevance to the aim of this paper by identifying similar work done within the same topic, Section 3 presents information about the type of data and the regression models used to achieve the results, Section 4 presents the main findings from the regressions in a table, Section 5 ends the paper with a discussion of our findings together with the limitations of the study, finishing with suggestions for further research and improvements.

RELATED LITERATURE

Previous studies have concluded that mothers suffer a wage penalty for having a child; the level of penalty and the reasons behind it differs in the different studies. The entire pay gap between the genders cannot be explained by the penalty for motherhood; social norms play another part in the disparity of income.

Budig et al. (2001) examine the differences in income between married and unmarried females (with both groups having children) between the ages of 14 and 21. The study is built on a previous study by Waldfogel in 1997, using data from the National Longitudinal Survey of Youth from the United States Bureau of Statistics for the years 1982-1993 . The data samples include only women working part-time or full-time, calculating the percentage of females in each occupational group and industry in a fixed-effects regression model. It is shown that the wage penalty for motherhood is approximately 7 % per child. One third of the penalty cost is attributed to the loss of work experience and seniority that originates from some mothers’

decision to work part time or choosing to be out of the labour force for periods of time. The remaining two-thirds is alleged to come from lower productivity which the responsibility of motherhood leads to and discrimination from employers against mothers.

Anderson et al. (2003) estimate that the wage penalty of having a child is in the same region, with 2-10 % reduction of income from one child and 5-13 % for two or more. Their study shows that mothers only obtain on average 12,5 years of

schooling, while the corresponding number for non-mothers is 13,2 years. Additional reasons for the gap are credited to loss of firm-specific human capital that occurs when mothers choose not to return to the same employer after birth and

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the disparity is left unexplained. According to the study, the wage penalty is higher when mothers first return to work due to the task of trying to learn how to balance caring for their child and work responsibilities. The report suggest that educational attainment affects the size of the penalty; for those that are high-school dropouts and college graduates, the wage gap is small, but larger for high-school finishers. In this study log hourly wages are used in a regression on race, marital status and

motherhood status. Variables such as years of schooling, age and work experience are added to show the effect on the motherhood wage penalty.

A working paper by Grimshaw and Rubery (2015) shows similar results. The paper, which compares different studies on the pay gap between mothers and women without children, conclude that, in both developing and less developed countries, younger children have a larger negative effect on the mothers’ income. The largest pay penalty happens when the child is under three years old while children above age 13 have no significant effect.

Khan et al. (2014) have examined the long-term effects of motherhood on wage and employment. Their findings suggest that women with no children at age 25 earn higher compared to women who become mothers at an early age. It was also concluded that mothers with three or more children suffer a wage penalty of at least 4 % per child into the 40s and 50s. Having more children equals a lower wage in the findings of Grimshaw and Rubery (2015) as well. The difference in labour

participation between mothers and non-mother that were observed when they were in their 20s and 30s are, by the time they reach their 40s, almost gone. In terms of employment status there is no difference or, if anything, mothers are more likely than their childless counterparts to be employed when they approach their 50s (Khan et al. 2014).

One further reason of the wage gap is the social norm that husbands should earn more than their wife. High-earning women distort their labour supply by staying at home, working fewer hours, or taking a less demanding job with lower pay to make them seem less threatening to their husbands (Bertrand et al. 2015). The study uses data from both the Survey of Income and Program Participation and the U.S. Census Bureau, showing that there are less couples where the woman has a larger share of the household income than her partner than if she had earned less than him. Couples avoid getting married if the woman earns more than the husband and as women’s income(relative to men’s), the decline in marriages continue. Among those couples that do get married and have children, the discontinuity at equal income drops by 15,2%. However, there seems to be an underlying notion amongst people that the traditional gender roles should be upheld(where the husband is the breadwinner and the wife stays at home). To conform to this, women who have potential to out-earn their husbands are more willing to not participate in the labour force. This effect is particularly strong in among less educated couples (Bertrand et al. 2015). Despite the numerous achievements women have made in terms of employment and education,

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the study confirms that women’s income suffer from societal gender norms, with or without children.

Goldin (2014) claims that the productive differences between the genders have largely been eliminated by the increase in school attainment for women compared to men and the increase in labour market participation of women. The remaining pay-gap is due to hours worked being worth more when worked at particular times and continuously. Different occupations show different level of punishment for not being at work certain hours of the day and taking time off for caring for children. Three examples are made in the study with professions where MBA or JD degree are common displays a large punishment, while pharmacists receive a minor

disadvantage for working fewer hours. For MBA graduates the pay-gap is close to zero when beginning to work; 10-16 years after receiving their degree 23% of the women work part-time, 60% work full-time, and 17% are unemployed. Mothers make up 51% of the group working full-time. Children are the biggest reason for the

decrease in labour participation for women since mothers work 24 % fewer hours than women without children. Women with a high-earning spouse approximately work 19% hours fewer per week and have 18.5% lower labour partition rates than those who have husbands that do not belong to the high-earning segment. Income effect and the fact that mothers have to compensate for the absences of the high-earning spouse in the family life are understood to be the reasons (Goldin 2014).

There are also differences in women’s wages depending on their profession. The gender wage-gap for fulltime working men to female for pharmacists is, after adjusting for hours worked, 93-95%, while the unadjusted gap is 0.85. The gap is one of the lowest for a high-earning occupation. During the last decades pharmacist have gone from being employed by small independent retailers to vaster extent being employed by retail chains and hospitals. The changes in the type of employer, together with standardization of drug administration, and the use of computers systems have made pharmacist more of perfect substitutes for each other. There is no longer a need for patient-specific knowledge to be able to help patients in a sufficient way. The fact the pharmacist is perfect substitutes for each other and can take over with no or low handover cost have made the punishment for working part-time disappear. The fact that there is no punishment for working part-time has led women with children to remain in the labour force rather than to dropout. 40% of mothers chose to work part-time from their early thirties into their fifties (Goldin 2014).

Another profession women overrepresent is nursing but, unlike pharmacist, men still earn a higher wage (Muench et al. 2015). Ordinary least-squares regression was used in the study to determine how much of the annual salary differences could be explained by factors such as work hours, experience, etc. Despite the data

containing only 7% male registered nurses(RNs), women earn over $5 000 dollar less than their male colleagues. Half of this pay gap can be explained by “employment and other measured characteristics”, according to the authors. Similarly, women are more

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inclined to work part-time and take career breaks because of dependent children (Whittock, Edwards, McLaren & Robinson, 2002). Additionally, 85% of those who work part-time are married. This shows once more that women take more

responsibility when it comes to home- and childcare and thus, their income is affected negatively.

DATA

Since the study in this paper is of the empirical kind, it was of importance to use data from a reliable source with many variables available. The microdata for the

regressions is extracted from the United States Census Bureau’s ACS 1-year Estimates. The U.S. Census Bureau is a part of and administrated by the U.S.

department of Commerce and has been collecting data relating to the citizens of the United States of America since 1970 (Census.gov). Below is a simple summary of the data from the year 2018 which is used in the study.

Table 1: Summary Statistics

Sample size 762414 Average number of children 0,71

Female 375181 Male 431375

Median age 43 Average age 42,35

Median income 35000 Average income 52716

High school diploma 350606 College degree 333228 Less than high school diploma 78579 Married 410611

Living with unmarried partner 64238 Average number of divorces 0.34 Median household income 85700 Average household income 111736

Female: median income 30000 Male: median income Female: average income 41591 Male: average income

Female: median age 43 Male: median age

Female: average age 42,51 Male: average age Female: average number of children 0,63 Male: average number of children

Female: median number of children 0 Male: median number of children Female: high school diploma 163160 Male: high school diploma

Female: college degree 178449 Male: college degree Female: less than high school diploma 33572 Male: less than high school diploma

Female: married 196181 Male: married

Female: living with unmarried partner 31305 Male: living with unmarried partner Female: median household income 83700 Male: median household income Female: average household income 108780 Male: average household income Female: average number of divorces 0,38 Male: average number of divorces

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The empirical study in this paper will be completed by conducting multiple

regressions using the different variables stated below. Ordinary least squares (OLS) regressions will be used for this study where the woman’s income is the continuous dependant variable and the rest are independent (explanatory) variables.

3.1

Ordinary Least Squares – OLS

As this study involves modelling a relationship between a dependant variable and multiple independent variables, a multiple linear regression is used.

The model can be written as such:

𝑌 = 𝛽

0

+ 𝛽

1

𝜒

1

+ 𝛽

2

𝜒

2

+ ⋯ 𝛽

𝑛

𝜒

𝑛

+ 𝜖

( 1 )

Where the left-hand side of the equation is 𝑌, the dependant variable which we want to find. The right hand side consists of 𝑛 number of independent variables

𝜒1, 𝜒2, … , 𝜒𝑛 and regression weights β0+ β1+ ⋯ βn together with the error term ϵ

(also known as the residual).

The ordinary least squares (OLS) model is used to minimize the sum of

squared residuals (Freedman, 2009). Limitations which can arise with this method is that the dependent variable can have a non-linear relationship with the independent variables, as well as outliers which can have a significant effect on the slope of the regression line. Since this study is only on a small scale, we expect these limitations to have insignificant effect on the results.

3.2

Dummy Variables

Some variables used in the data have more than two subcategories which makes the regression more difficult to interpret, such as the variable Education which has 24 subcategories. To combat this issue, these “large” variables will take on the values of zero or one only, each value indicating the presence or absence of a specific condition (Studenmund, 2017).

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3.2.1 Female

Since the concept of gender in nature is simply quantitative and thus not expressed as a number, it can be quantified by setting this variable as a dummy variable taking on the values of zero or one; zero indicating male, one indicating female.

3.2.2 Marriage

Marital status can make a difference in how much time parent can spend at work and earn an income because their spouse can take care of the children in the meantime. The variable takes on the value of zero when a person is not married and one when they are married.

3.2.3 Partner

This variable is similar to Marriage because, as stated above, there might be a

positive relationship between taking care of children alone versus having a current or former partner who shares that responsibility. It takes on the value zero when a person does not have an unmarried partner in their household and the value one for having an unmarried partner in their household.

3.2.4 Education (High School and College)

The level of education a person has is of great importance as well because studies show that income rises with a higher level of completed education. The variable high school diploma will take value of one if the highest educational attainment is a high school diploma, and zero for a lower educational level or if the person has a college degree (associate’s degree or higher). The variable college degree takes the value of one for associate’s degree or higher, and zero for less educational attainment. Education is measured as an index of the highest level of education a person has completed.

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3.3

Age

Since this paper is focused on persons in the labour force, it only makes sense to use data where the age of the participants is between 15 and 64 since this is the timeframe where most people enter and leave the labour market. Women who are in the age group 60-64 should be considered as grandmothers if the data shows that they have one or more children.

3.4

Divorced

This variable tells us how many times a person is divorced, going from 0 to 3 where 3 indicates either three divorces or more.

3.5

Number of Children

The number of children a person has is of most importance because the aim of the paper is to see if this has any effect on a woman’s income. It is a discrete variable which takes on the numbers 0-19, representing how many children a person has

3.6

Household Income

Completing a higher level of education is also positively correlated with the total household income (Bronfenbrenner et al. 1996). This can be problematic to use in the same regression because these two variables are correlated. To show that there is a small but insignificant correlation between these two variables we conduct a matrix using the Pearson product-moment correlation coefficients.

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3.7

Correlation between variables

As this study uses several variables there is a chance that some of them are correlated on some level. Below is a table which shows the correlation coefficients between the independent variables.

Table 2: Correlation coefficients between independent variables

Almost all variables have a weak correlation between them just under ±0,4. As mentioned above, HINCP has a weak correlation with both High School and College; -0,20 and o,26 respectively. It confirms previous studies which show that the higher level of educational attainment one has, the higher income. However, since these coefficients are so small we are confident it does not interfere with the objectiveness of the results. An important note is that not all variables are used in each regression and therefor the correlations between them are not always relevant.

RESULTS

Below are the main results from numerous regressions compiled in two tables:

Variable Mother-hood Female No. of Children AGEP High school

College Married Partner Divorced Househ old income Female 0,46 1,00 No. of Children 0,51 -0,01 1,00 Age -0,18 0,01 -0,22 1,00 High school -0,07 -0,05 -0,08 0,00 1,00 College 0,05 0,08 0,02 0,06 -0,81 1,00 Married 0,08 -0,03 0,20 0,34 -0,09 0,16 1,00 Partner -0,01 0,00 -0,02 -0,12 0,04 -0,04 -0,32 1,00 Divorced -0,04 0,07 -0,10 0,37 0,09 -0,08 -0,13 0,04 1,00 Household income 0,03 -0,03 0,06 0,04 -0,20 0,26 0,20 -0,05 -0,10 1,00 LN(Income) -0.11 -0,16 -0,01 0,29 -0,16 0,33 0,27 -0,01 0,05 0,38

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Table 3: Regression Results (1) (2) (3) (4) (5) Female Constant 10,463295 (5092,768081) 10,463295 (5096,718099) 8,514497 (1309,419348) 8,003331 (1408,895491) 7,983872 (887,546218) Female -0,419711 (143,304926) -0,366884 (111,017637) -0,423050 (137,135092) Mother -0,150333 (34,411178) -0,510676 (31,974796) Age 0,018371 (150,052319) 0,019388 (171,446612) 0,018737 (108,057684) Number of children 0,073379 (41,533253) 0,147791 (92,677565) -0,117556 (38,992610) High school diploma 0,661541 (134,690508) 0,660579 (144,772723) 0,657505 (80,235345) College degree 1,343014 (269,166756) 1,128204 (239,551543) 1,220299 (146,625627) Marriage 0,419944 (122,533594) 0,294667 (89,063503) -0,073353 (13,792715) Unmarried partner 0,326730 (62,335109) 0,314030 (62,566605) 0,206650 (25,010555) Number of divorces 0,028186 (11,370395) 0,032643 (13,775804) 0,031601 (8,684558) Household income 0,000004 (290,307623) 0,000004 (153,748571) Age  Mother 0,013511 (35,995209) 0,001913 (6,866535) 0,002863 (9,076631) Number of children  Mother -0,181470 (53,966492) -0,267470 (85,097653) High school diploma  Mother 0,203098 (17,675936) 0,087855 (8,838353) 0,096420 (8,155142) College degree  Mother 0,178207 (15,005455) 0,182815 (16,985753) 0,094963 (7,489220) Marriage  Mother (19,262155) -0,169839 (8,593739) -0,072465 (30,395349) 0,294226 Unmarried partner  Mother -0,034290 (2,534147) -0,060744 (4,701377) 0,047994 (3,222476) Number of divorces  Mother 0,076638 (11,403284) 0,152966 (23,973858) 0,152705 (21,295482) Household income  -0,000002 (57,810469) -0,000001 (40,814912)

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𝑅2 0,026230 0,027740 0,271039 0,330170 0,265289

Table 3: Regression Results shows five variations of regressions where the fifth column includes only female, as well as the t-statistics in parentheses. The t-statistics under each coefficient are given to show how much the values differ from the null

hypothesis, meaning our results are in favour of the alternative hypothesis stated in section 1. If the t-statistic is greater than 1.96 it indicates statistical significance at a 5% level. The 𝑅2 values at the bottom of the table tell us how accurate our regression

models are to the actual responses; column (4) in table 3 has the highest R-squared value which indicates this model fits the data best.

From column 3 and 4 we see that a mothers income decreases when she has at least one child and that her income increases when attaining higher levels of

education. Column 4 also shows that total household income decreases slightly when a woman becomes a mother, so little that we can neglect this value. An interesting observation is that her income increases depending on the number of divorces she has gone through; this will be further discussed in the next session.

DISCUSSION

The goal of this small study was for the authors to prove their original hypnotises which stated that women who become mothers earn less than their childless counterparts. Data from the Census Bureau’s large database was used in several regressions to see the impact children have on their mothers income. The results above show that mother do in fact suffer an income loss of 26% percent, compared to women who do not have children.

The results show us that the motherhood penalty is smaller for those with a college degree, which is consistent with the findings of Andersson’s study in 2004. According to a 1997 study by Matthews and Ventura, birth rates are lower for women who either start or complete their college degree and thus indicate that delayed

childbearing becomes more and more common with higher levels of education. When girls attend school their lifetime earnings increase, giving them more power over their lives and lowering child marriage rates ("Girls' education", 2020). It is therefor important that policymakers and politicians implement the right policies for all girls and women to have the chance to attend school, upper secondary school in particular,

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so that those who do not want children early can have a chance of starting a career instead. In the United States of America the cost of going to and studying at a (private)college is very high compared to high school which can affect a persons’ decision between working and starting a family or get a bachelor’s degree. Just as Bertrand and Pan noted, social norms affect women greatly because the household work is most often distributed unevenly, most of it falls on the woman. This can further decrease a woman’s income, even more so when she has a child. Thus, school attainment is an important part of a woman’s life and should be made more

accessible all around the world.

The results do not show the impact of having children for the different age groups of women. We suspect, however, that older mothers earn more than younger mothers because they have gained more work experience and seniority at their

workplaces. This makes is easier for the older mothers to go back to work and earn as much as their did prior to childbirth because they have high acquired work

experience compared to younger women who do not and therefor do not have high wages to begin with. Matthew and Ventura’s 1997 study confirms this since women in their 30’s had a higher first birth rate than women in their 20’s, indicating a trend of delayed childbearing. Had we been given more time and knowledge about this subject, we would have performed regressions based on the different age groups of women to see how their incomes differ when having one or more children.

The dataset in this study includes only number of children who are between the ages zero and 18, living with the parent. An advancement would be to know the age of each child to see which level of income effect the child has in relation to its age, due to the fact younger children require more care and it is likely that they have a lager effect on the parent’s income compared to older children. This fact is supported by Grimshaw and Rubery’s 2015 study which reported that the most significant penalty occurs when the child is under three years old whereas those above 13 years old have no significant effect on the income. Had we known during which ages the child has the most significant effect on a woman’s’ income, we could implement policies which help women with their costs. This includes paying for medical expenses, clothing and food for the child.

The results presented in this article regarding the negative effect of marriage and living with an unmarried partner on income for women is consistent with what previous studies have shown; social norms and the substitution effect believed to be the reason. There could also be a difference in income if the child’s parents are of the same sex or not because same sex parents might divide their time more equally as opposed to a heterosexual marriage where the woman often puts more time into childcare than the man. It is also important to consider those who are not married but still live with a partner. An observation from the results is that mothers who are divorced increase their income. The previous studies we mentioned in section 2 pointed out that when married women have children, their working hours are

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lowered and their husband earn more than before to make up for the loss. The same phenomena applies in this case as well when women are divorced with children. The mother has to work more hours in order to provide not just for herself, but for her child as well because she cannot rely on a husband anymore.

The purpose of the study, which was stated in the first section, has been to identify what kind of effect children have on a woman’s income and this has been shown above. However, there is room for improvements even in a small-scale study of this kind. Firstly, it would be of value if the data had included children which did not live with the parent because the child can still have some type of effect on the parent's income such as: visiting the child if they live in another town or state which requires the parents to take time off from work. Secondly, as stated above, some explanation to the differences in income between the age and gender groups could be explained by a person's profession. Further extension of the study can involve a comparison between different states in the USA, particularly those that offer paid maternity leave for mother and those that do not. This can explain why incomes vary between not just ages but also profession and states. It could also be valuable in terms of how women determine to have children at all and if so, how many. Being on a paid maternity leave for a few weeks after giving birth can attract more women to have children rather than paying for that expense themselves.

As an effect from multiple factors, the wage gap being one of them, women are more likely to suffer from depression and anxiety than men (Platt et al. 2016). This further highlights why the wage gap between women and men needs to be reduced, as well as the wage gap for mothers since they suffer a greater loss of income. Women who earn less than their male counterparts might be more inclined to stay in abusive relationships where the partner can provide for her, which puts these women ate a greater risk.

The effect of children on the wage gap between the genders is complex and influenced by several factors. This study has strengthened what previous studies have concluded; there exists a wage-penalty for mothers. With limited variables, this study has shown that wage penalty to be 26%. In addition to income equality between men and women, it is important to reduce the motherhood gap because we as a society benefit from child rearing. Raising the new generation requires time, energy and money from those who rear children so it only makes sense that employers pay these mothers a fair wage, no matter which industry they work in.

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BIBLIOGRAPHY

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Bertrand, M. Kamenica E. & Pan, J. (2015). Gender Identity and Relative Income within Households. The Quarterly Journal of Economics, Vol. 130(2), May 2015,

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Bronfenbrenner, U. et al. (1997). The State of Americans: This Generation and the Next. Journal of Macromarketing. Vol.17(1), Spring 1997, pp.130.

Budig, M. & England, P. (2001). The wage penalty for motherhood, American Sociological Review. Vol. 66(2), 2001, pp. 204–225.

Burkett, E. (2020). Women´s rights movement. Encyclopaedia Britannica, accessed 3 May 2020, <https://www.britannica.com/event/womens-movement>.

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APPENDIX

Table A.1: Statistic summary for age interval 15-19

15-19

Individuals 35628 Average number of children 1,21 Female 17097 Less than high school diploma 20300

Male 18531 High school diploma 14672

Median income 3400 College degree 296

Average income 6280 Married 621

Median household income 88200 Living with unmarried partner 2141 Average household income 114439 Average number of divorces 0,002

Median income female 3000 Median income male 3600 Average income female 5782 Average income male 6749 Female average number of children 1,21 Male average of children 1,21

Female median number of children 1 Male median number of children 1 Female high school diploma 7164 Male high school diploma 7508

Female college degree 177 Male college degree 119

Female less than high school diploma 9756 Male less than high school diploma 10545

Female married 363 Male married 258

Female living with unmarried partner 1138 Male living with unmarried partner 1003 Female median household income 87000 Male median household income 89500 Female average household income 113731 Male average household income 115105

Female average number of divorces 0,003 Male average number of divorces 0,002

Table A.2: Summary Statistics for age interval 20-29

20-29

Individuals 138448 Average number of children 0,42

Female 67940 Less than high school diploma

Male 70509 High school diploma 74276

Median income 23000 College degree 55993

Average income 29276 Married 31548

Median household income 75000 Living with unmarried partner 20942 Average household income 95443 Average number of divorces 0,04

Median income female 20000 Median income male 25000 Average income female 26134 Average income male 32304 Female average number of children 0,47 Male average of children 0,38

Female median number of children 0 Male median number of children 0 Female high school diploma 33499 Male high school diploma 40777

Female college degree 31292 Male college degree 24701 Female less than high school diploma 3149 Male less than high school diploma 5031

Female married 16917 Male married 14631

Female living with unmarried partner 10962 Male living with unmarried partner 9980 Female median household income 72500 Male median household income 25000 Female average household income 92733 Male average household income 32304 Female average number of divorces 0,047 Male average number of divorces 0,027

(22)

Table A.3: Summary Statistics for age interval 30-39

30-39

Individuals 171222 Average number of children 1,18 Female 87854 Less than high school diploma 14243

Male 83368 High school diploma 69088

Median income 37000 College degree 87891

Average income 49098 Married 101784

Median household income 83750 Living with unmarried partner 18128 Average household income 105358 Average number of divorces 0,18

Median income female 29000 Median income male 45000 Average income female 38332 Average income male 60443 Female average number of children 1,33 Male average of children 1,03

Female median number of children 1 Male median number of children 0 Female high school diploma 32420 Male high school diploma 36668

Female college degree 48931 Male college degree 38960 Female less than high school diploma 6503 Male less than high school diploma 7740

Female married 53742 Male married 48042

Female living with unmarried partner 8620 Male living with unmarried partner 9508 Female median household income 82000 Male median household income 85000 Female average household income 104147 Male average household income 106635

Female average number of divorces 0,21 Male average number of divorces 0,15

Table A.4: Summary Statistics for age interval 40-49

40-49

Individuals 168695 Average number of children 1,06 Female 87033 Less than high school diploma 16257

Male 81662 High school diploma 70664

Median income 40000 College degree 81774

Average income 60126 Married 114146

Median household income 92900 Living with unmarried partner 11611 Average household income 121141 Average number of divorces 0,38

Median income female 30000 Median income male 53000 Average income female 44056 Average income male 77254 Female average number of children 1,04 Male average of children 1,09

Female median number of children 1 Male median number of children 1 Female high school diploma 33984 Male high school diploma 36680

Female college degree 45407 Male college degree 36367 Female less than high school diploma 7642 Male less than high school diploma 8615

Female married 58296 Male married 55850

Female living with unmarried partner 5579 Male living with unmarried partner 6032 Female median household income 90600 Male median household income 95000 Female average household income 119404 Male average household income 122992

(23)

Table A.5: Summary Statistics for age interval 50-59

50-59

Individuals 201692 Average number of children 0,26 Female 105242 Less than high school diploma 20406

Male 96450 High school diploma 97444

Median income 39000 College degree 83842

Average income 58867 Married 134849

Median household income 96450 Living with unmarried partner 10628 Average household income 105242 Average number of divorces 0,55

Median income female 28600 Median income male 50000 Average income female 42307 Average income male 76937 Female average number of children 0,21 Male average of children 0,32

Female median number of children 0 Male median number of children 0 Female high school diploma 49853 Male high school diploma 47591

Female college degree 45774 Male college degree 38068 Female less than high school diploma 9615 Male less than high school diploma 10791

Female married 68757 Male Married 66092

Female living with unmarried partner 5176 Male living with unmarried partner 5452 Female median household income 85000 Male median household income 91200 Female average household income 114004 Male average household income 121597

Female average number of divorces 0,59 Male average number of divorces 0,50

Table A.6: Summary Statistics for age interval 60-64

60-64

Individuals 105763 Average number of children 0,07 Female 55589 Less than high school diploma 10161

Male 50174 High school diploma 53261

Median income 32000 College degree 42309

Average income 51919 Married 70733

Median household income 74100 Living with unmarried partner 4037 Average household income 102442 Average number of divorces 0,64

Median income female 24000 Median income male 43900 Average income female 37307 Average income male 68108 Female average number of children 0,05 Male average of children 0,08

Female median number of children 0 Male median number of children 0 Female high school diploma 27912 Male high school diploma 25349

Female college degree 22633 Male college degree 19676 Female less than high school diploma 5044 Male less than high school diploma 5149

Female married 35266 Male Married 35467

Female living with unmarried partner 1914 Male living with unmarried partner 2123 Female median household income 70300 Male median household income 78400 Female average household income 97568 Male average household income 107841

(24)

Box 883, 721 23 Västerås Tfn: 021-10 13 00 Box 325, 631 05 Eskilstuna Tfn: 016-15 36 00

Figure

Table 1: Summary Statistics
Table 2: Correlation coefficients between independent variables
Table 3: Regression Results  (1)  (2)  (3)  (4)  (5)  Female  Constant  10,463295  ( 5092,768081 )  10,463295 ( 5096,718099 )  8,514497 ( 1309,419348 ) 8,003331 ( 1408,895491 ) 7,983872 ( 887,546218 )  Female  -0,419711  ( 143,304926 )  -0,366884 ( 111,017
Table A.2: Summary Statistics for age interval 20-29
+3

References

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