Gender Wage Gap Regression Analysis

Gender and Income Inequality Elizabeth Applegate and Jackie Reilly 1 Introduction While the gender-wage gap has already been well-established since women entered the workforce, policymakers have sought to level the playing field, beginning with the Equal Pay Act of 1963. This amendment to the Fair Labor Standards Act dictates that it is illegal to wage discriminate based on gender for equal work in a workplace (Equal Pay Act, 1963). While the gap has decreased in the past forty years, it still persists today. In 2018, the average woman earned 85 cents for every dollar a man earned (Graf, Brown, & Patten, 2020). This is the driving motivator for our study. What is contributing to the 15 cent gap?

The main hypothesis of our study is that the gender wage gap is influenced by various factors, making our broader goal to define what those variables are. Since there are many levers which contribute to the perpetuation of the wage gap between men and women, the relationships we are seeking to test are related to work, age, education, home life, race, and gender. In the subsequent section, we will discuss studies that reinforce our hypothesis that the gender wage gap is impacted by various elements. Owing to the fact that the gender wage gap’s existence is clear, we have the benefit of exploring numerous studies which have examined different, yet similar, combinations of variables.

2 Literature Review In “The Gender Pay Gap in the USA: A Matching Study,” two different cross-section datas from the Current Population Survey (CPS) are used to assess the gender wage gap that is experienced within the USA. To examine this study, matching estimators are used through an Oaxaca-Blinder recentered influence function (RIF) in order to model this pay gap. Two

characteristics that are important to include are union membership and part-time working. Part time working, for example, has a significantly lower hourly wage than full-time employees.

Additionally, a much larger fraction of females work part-time, as compared to males. As for unions, union workers experience a considerable increase in hourly rates as compared to nonunion workers. Females, unlike males, are a lot less likely to be unionized. Getting into the data itself, this study includes two cross-section samples from the monthly USA CPS. The first being October 2011 to March 2012 and the second being October 2017 to March 2018. Totaling to 907,775 and 877,776 observations with 77,097 and 76,308 individuals, respectively. The first table includes three characteristics for each gender across different age intervals aged 15 and over: Not in labor force, full-time labor force, and part-time labor force employment rates.

Overall, females not in the labor force were up 10% higher than men. In both samples, females working part-time were substantially higher than males working part-time, as mentioned previously. However, in the later sample, this proportion of females roughly doubled. Another independent variable is found in Table 2 which records gender segregation indexes by occupation and industry. Although there is a decline in gender segregation, there is still an elevated level of occupational segregation. As for the regression analyses, the first sample dating from October 2011 to March 2012 results in part-time work being a statistically significant independent variable at the 99% confidence level with a T-statistic of 33.6 and a standard error of .0064. This results in a 19.2% pay gap. The union membership, also statistically significant at the 99% confidence level, has a T-statistic of 19.12 with a standard error of .0067, resulting in a pay gap of 13.6%. As for the October 2017-March 2018 second sample, part-time working was also statistically significant at the 99% confidence level with a T-statistic of 34.8 and standard error of .0067. This resulted in a 20.9% pay gap. The union membership, also statistically significant at the 99% confidence level,

has a T-statistic of 17.51 with a standard error of .0070, resulting in a pay gap of 13.9%. When the first sample data is utilized, the impact of the regression with industry and occupation dummy variables is as follows: statistically significant at the 99% confidence level with a standard error of .0059, resulting in a 11% pay gap. When the second sample data is used, the impact of the regression with industry and occupation dummies is statistically significant at the 99% confidence level with a standard error of .0058, resulting in a 12.2% pay gap. From these results, we can conclude that a gender wage gap does exist, ranging anywhere from 11% to 20.9%. To interpret these results, this means that women make anywhere from 11% to 20.9% less than men under the circumstances of union membership status, being a part-time worker, and what industry the employee belongs to.

In “Gender Wage Disparities among the Highly Educated,” US college educated women’s earnings are observed and compared to their white male counterparts. Additionally, the wage disparity for the following four groups of women, white, black, Hispanic, and Asian are examined. This data is stemmed from the 1993 National Survey of College Graduates (NSCG) which is from the National Science Foundation (NSF). The sample is composed of 129,252 respondents. Women are typically under-represented in high-paying majors such as engineering while they are also over represented in low-paying majors such as the humanities, arts and education. In the United States, college-educated women earn approximately 30 percent less than their white male counterparts (Black et al. 2015). The methodology used in this study is presented in a nonparametric matching method which is simplistic and intuitive. The comparison comes from matching women to men on only pre-market characteristics. These include: age, highest degree, and major. This method differs from a familiar approach, the Blinder-Oaxaca. As for the results, there are 3 major regression equations worth noting that all fall under the category the gender wage gap with the use of pre-

market factors only. With the NSCG-reported highest degree and taking the natural logarithm of the wages, white women experience a .333 wage gap (.0056), black women experience a .258 wage gap (.0089), Hispanic women experience a .339 wage gap (.0134) and Asian women experience a .337 wage gap (.0120) all relative to white men. Regression 2 includes taking both the NSCG education measure and college major into consideration. This results in white white women experiencing a .339 wage gap (.0062), black women experiencing a .268 (.0104) wage gap, Hispanic women experiencing a .345 wage gap (.0149) and Asian women experiencing a .

340 wage gap (.0133). Lastly, the third regression includes three independent variables, which are the NSCG education measure, college major, and speaking English at home all relative to white men who speak English at home. The results are as follows: .342 wage gap experienced by white women (.0065), .265 wage gap experienced by black women (.0108), .299 wage gap experienced by Hispanic women (.0258), and a .260 wage gap experienced by Asian women (.0203). All of these regressions reveal an existence of a gender wage gap which consists of women from four different races as compared to a white male.

In “The Gender Earnings Gap in the Gig Economy: Evidence from Over a Million Rideshare Drivers,” the gender wage gap is explored outside of the traditional work setting. Gig work can be defined as more of a flexible work schedule, often part-time with independent contractors.

There has been hypothesizing how this would favor women and close the gender wage gap in the gig economy. However, this is not the case and this gap can all be explained by three variables: 1) learning through experience 2) location preferences (where to work) and 3) driving speed. The sample that is used within this study numbers 1.87 million Uber drivers from January 2015 to March 2017, about which 512,000 are female. With this sample, it is found that men still earn roughly 7% more per hour than women on average (Cook et al., 2018). The gender wage gap

in weekly earnings is .4142 (.002) with an R-squared of .125. After controlling conditions and city for a week, a significant gender pay gap still emerges. The paper then shifts focus to Chicago data, coming in at 120,223 drivers in which 36,391 are female. The gender weekly earnings wage gap experienced by women in Chicago is .4315 (.007) which is .0173 higher than all of the US. The R squared for this case is .038. After reviewing the data, it is evident that the gender gap continues in newer types of jobs.

Another study being considered is “ The Gender Wage Gap: Extent, Trends, and Explanations” which focuses on examining traditional and new variables to understand what is impacting the gap in wage. The methodology here was to use literature review to explain reasoning behind the variables being considered and then using data from the Michigan Panel Study of Income Dynamics (PSID) and the March Current Population Survey (CPS) to expand on current trends of the wage gap using a sample of full-time, nonfarm wage and salary workers, ages 25 to 64 with at least 26 weeks of employment . The data on earnings is lagged one years, so the data is 1981, 1990, 1999, and 2011. The traditional variables here are related to human capital (called Human Capital Specification), and are education, experience, region, and race. Education is measured to control for years of schooling and uses dummy variables for having a bachelor’s degree or higher. Experience is measured by both full-time and -part-time experience. Race is controlled for using four mutually exclusive categories: white non-Hispanic (excluded), black non Hispanic, other non-Hispanic, and Hispanic. The variable region controls for three of the four Census regions and uses a dummy variable for a residence in a metropolitan area (Blau and Kahn, working paper). The newer variables (included in the Full Specification) examine unionization, industry, and occupation.

The data from PSID compares how much human capital variables explain the effect in the gender gap from 1980 to 2010, as well as the full specification (all variables) from 1980 to

2010 and for the 10th, 50th, and 90th percentiles. Using the Blinder Oaxaca decomposition, the wage regression conducted separates male and female to account for some discrimination in the labor market. Over the period 1980-2010, women’s covariates improved relative to men in both human capital and full specifications, resulting in a decline of .09-.10 log points in the gender wage gap across the distribution (Blau and Kahn, working paper). The findings of the study were that traditional variables do explain the gap and changes to the gap from 1980 to 2010, but were not as significant as the differences in occupation and industry, which are the most significant factors in explaining the gender wage gap.

In “Women’s Quest for Economic Equality,” the main research question is why are women economically disadvantaged and why it is so difficult to improve their position. Fuchs examines occupational segregation, hours of paid work, wages’, and the demand for children’s impact on inequality. Marital status and having a young child can further contribute to women being economically disadvantaged, in the context that women who are married and have children often work part-time (Fuchs, 1986). Occupational segregation is measured using the Duncan index of dissimilarity. It is calculated by adding the absolute difference between the percent of employed men and employed women in different occupations and then divided by two. The index is from 0 to 100 (0 being that the occupational distributions are the same and 100 being that there is complete segregation). The data came from the Census data of Population and Housing from 1960 and 1980. The occupational segregation by gender is clear when compared with the index by race. The Duncan indexes depict a story that, despite the women’s liberation movement during the period of 1960-1980 did not significantly impact occupational segregation as it did in occupational segregation by race. Education and age were relevant variables in determining the level of segregation in occupations from 1960-1980, as women with a college degree went from an

index of 66 to 50 over that twenty year period, whereas women with a high school degree went from an index of 66 in 1960 to 62 in 1980 (Fuchs, 1989). Women ages 25-34 in 1960 had a Duncan index change from 67 to 55 in 1980, whereas women ages 45-54 had a change from 63 to 60 over that same period.

The impact of occupational segregation is even clearer upon examining the difference in earnings (Fuchs, 1989). By examining the wage ratios within occupations that employed at least 2500 workers, black men had a higher ratio of a white man’s earning (.90) and that 74% of white men and women were employed in jobs where the ratio was between .50 and .69. This data is not significant, however, on the aggregate scale., still leaving the gap unexplained.

Finally, in “Gender Wage Discrimination Bias? A Meta-Regression Analysis,” gender wage discrimination is explored across a total of 55 publications. The meta-regression is designed to estimate and account for any biases in the publications being studied (Stanley and Jarrell, 1998). The studies included in the meta-regression analysis are those that had enough information to calculate the estimate of the gender wage gap, consider gender wage discrimination in the U.S., based on national data, and the estimate must be from a regression analysis. Because many studies do not report the estimate, the sample of 41 estimates found that wage discrimination was statistically significant at the 99% confidence level at 11.4. When the sample included a total of 55 estimates of gender wage discrimination, the mean gap drops from 33.7% to 31.8%, indicating a downward trend in the gender wage discrimination.

Because this data is derived from multiple studies and estimates, there are many variables considered. They are broken down into broader categories of independent variables: economic conditions, alternative measures of wages, model specifications, data sets, worker characteristics, and researcher characteristics (to account for any biases associated with the estimates used in the

samples). The results find that all variables reported were statistically significant (Stanley and Jarrell, 1998). Other variables later included were not statistically significant at the 99% confidence level. These variables are year, salary (study used annual salary is measure of wages), week (study used weekly salary as measure), wage year (if hourly wages came from annual salary), select (if a study did not account for selection bias), new entrants (study only looked at wages of new entrants), dummy (if a dummy variable is used for sex), government (if a study omitted a government/private employment distinction), age (if study omitted worker’s age), experience (study omitted job experience), industry (if study omitted industry), and male (if study was authored by men) (Stanley and Jarrell, 1998). The adjusted R-squared is .80972 with a standard error of .071 and p = .0001. Furthermore, the conclusion of this meta-regression analysis was that the way in which the wages are calculated can impact on what the estimated gender wage gap is and there is also gender bias in the research.

As a result of these studies, we conclude that there are several areas topics that warrant further examination as they contribute to the gap. We will be examining these topics through cross sectional data. The sample we will be utilizing is from the 2018 New Jersey Census data where the person earned a wage greater than zero, amounting to 74,398 people. Owing to some limitations (which will be discussed in greater detail in our conclusion), we are unable to use occupation as an independent variable which can be found in our literature review. This is due to wanting as little bias as possible as well as the large amount of data and our ability to recode all of the occupations in the NJ Census data. Another point of interest would be to examine the relationship between unions and the gender wage gap, however, we do not have access to union data. Therefore, to explore the relationship between work and the wage gap we will examine age, hours worked per week, and educational attainment.

The two dependent variables we will be focusing on in our following study are wage and total person’s income, for these two dependent variables are heavily impacted by the gender wage gap. We have opted to separate the two because, upon examination of how these variables are defined in the Census Data, they measured two different things. The key difference in these variables is that wage measures a person’s wages or salary income over a year-long period and is a positive number. Income measures a person’s total income, accounting for losses, as well as a higher total dollar amount (to be discussed in further detail in the subsequent Data section).

Following prior studies, the independent variables that are statistically significant and should be included in this study are marital status, years of schooling, gender, hours worked per week, presence of children in the home, race (Asian, Black, and White), science and engineering degree, how well you speak English, age, and age squared.

Age is a relevant worker characteristic as it pertains to wage because of the quadratic relationship between the two variables, as we have examined ourselves prior to this study (see Figures 3A & 3B). How many hours an employee works per week allows us to determine whether they are a part-time or full-time employee, which will have an impact on overall income (Fuchs, 1989). In the study within “The Gender Pay Gap in the USA: A Matching Study,” part-time working proves to be statistically significant, meaning less hours worked per week impacts wage. Educational attainment is another relevant variable for us to study because as one attains a bachelor's degree (or seeks a higher degree) it has a positive effect on overall income, gender disparities still exist (Black et. al., 2015). The class of worker is another relevant characteristic we will test. This is because different types of work are valued differently (Stanley & Jarrell, 1998) and therefore, will have either a positive or negative impact on wages.

In “Gender Wage Disparities among the Highly Educated,” the wages of four different

races of women are observed where a gender wage gap is evident as compared to their white male counterparts. We picked three of those four races, being White, Black, and Asian to observe within our study. Within the same source, we can observe two more independent variables which include college major and English speaking. All three of the regressions ran in this study, which include college major, race, and speaking English at home, reveal an existence of a gender wage gap. More specifically, in our paper, we wanted to explore the impact of majoring in a science or engineering field on the gender wage gap.

Gender is relevant to each of our independent variables, which we have discussed in our review of the literature. To further expand upon its contribution to the wage gap, we have selected marital status and the presence of one’s own children in the home as our gender-related variables, in addition to gender itself. In “Women’s Quest for Equality” study, marital status can impact whether a woman is a part-time worker or a full-time worker because the need for income is less so than if she were not married (Fuchs, 1989). This study also relates the presence of children in the home and marital status to how many hours a woman might work - part-time or full-time (Fuchs, 1989).

3 Data The gender wage gap, as discussed in our review of the literature, is not entirely explainable owing to the fact the discrimination cannot simply be isolated and empirically measured.

However, we do know it exists and that many variables can account for the gap. In an effort to estimate what influences on the gender wage discrimination are the most impactful, we have chosen to use data from the US Census which include data points that will result in a lower or higher wage for women. The US Census data provides us with an original sample size (N) of 74,398. There is validity within our study for this sample size hones in on New Jersey’s

population which was estimated to be 8.886 million in 2018. Therefore, our T-statistics will not be too high and they will be statistically significant.

We examine the effects of work, race, schooling, home life, and age. Our justification for selecting the following independent variables can be found within our literature review.

The impact that these characteristics have on the gender wage gap is estimated by male and female wage earners and total male and female income.

Where our dependent variables are: WAGP is the wages or salary income in the past 12 months, greater than $0. PINCP is total person’s income, greater than $0.

Where our independent variables are: SEX means either male or female, which we recoded into a dummy variable: 0 for female, 1 for male.

SCHL is educational attainment.

AGESQ is age squared.

AGEP is age.

COW is the class of the worker. We recoded the following into dummy variables depending whether their class added to or lessened wage / total person’s income: 1. Employee of a private for-profit company or business, or of an individual, for wages, salary, or commissions 2. Employee of a private not-for-profit, tax-exempt, or charitable organization 3. Local government employee (city, county, etc.)

4. State government employee 5. Federal government employee 6. Self-employed in own not incorporated business, professional practice, or farm 7. Self-employed in own incorporated business, professional practice or farm 8. Working without pay in family business or farm 9 .Unemployed and last worked 5 years ago or earlier or never worked MAR is a dummy variable for marital status. We recoded the Census data into 1 for married and 0 for widowed, divorced, separated, and never married or under 15 years old.

PAOC is a dummy variable for the household presence and age of one's own children. We recoded this into 1 for females with no children and 0 for females with own children under 6 years only, females with own children 6 to 17 years only, females with own children under 6 years and 6 to 17 years, and females with no children.

WKPH is usual hours worked per week in the past 12 months.

RACASN is a dummy variable where 1 is Asian alone or in combination with one or more other races and 0 is not Asian.

RACBLK is is a dummy variable where 1 is Black or African American alone or in combination with one or more other races and 0 is not Black.

RACEWHT is a dummy variable where 1 is white alone or in combination with one or more other races and 0 is not White.

SCIENGP is a dummy variable where the field of degree in Science and Engineering is 1 and no degree in this field is 0.

ENG is the ability to speak English where we coded 1 for very well and well, and 0 for not well and not at all.

As far as bias, it is likely there is omitted variable bias. This can be attributed to the fact that there is no exact wage equation and every study uses different variables (Stanley & Jarrell, 1998). This study already encompasses a lot of data but lacks some specific relationships we were looking for. This data doesn’t always exist so we must acknowledge that there are omitted variables and therefore, bias. An example of this being union membership which is found in our literature review. It influences wages, yet there is no Census data for it. Further data limitations and biases will be discussed in our conclusion.

4 Descriptive Statistics WAGP

COW

MAR

SCHL

SEX

WKHP

PAOC

Mean

44,468.27

.86

.45

16.63

.49

37.88

.76

Median

16,000.00

1.00

.00

18

.00

40

1.00

1

1

23

1

98

1

Range

626,000

N

74,398

RACAS N

RACBL K

RACW HT

SCIENG P

ENG

AGEP

AGEPSQ

PINCP

.12

.89

.74

.39

.84

42.50

2,363.6386

58,713.27

.00

1.00

1

.00

1.00

44.00

1,936.0000

35,000.0

1

1

1

1

1

95

1,936.0000

1,561,600

Above lies our summary statistics where the first row is the mean, the second row is the median, and the third row is the range which is all stemming from a sample size of 74,398 where NJ residents earned more than $0 in wage and total income. First, it is important to note what this data tells us about our dependent variables. In the state of New Jersey, the average wage from 2018 was $44,468.27 while the average total person’s income was $58,713.27. The top numbers of these being $626,000 and $1,561,600 and the median $16,000.00 and $35,000.00 for wage and total person’s income, respectively. From these results, we can conclude that there is a large difference between what most people actually make, the median, as opposed to the average. For wage this difference is as large as $28,468.27 and for total person’s income it is $23,713.27.

As for our independent variables, there are a few which are important to discuss to get a deeper understanding of why the gender wage gap emerges. The average amount of schooling is over 16 years. This is the equivalent to graduating college with a bachelor’s degree, meaning it is common for a New Jersey resident to have a college education. The median is even higher than this at 18 years. This means that most NJ residents actually have two additional years of education on top of a bachelor’s degree. The top amount of schooling is 23 years. This reflects

New Jersey being ninth in the highest educated states in the US.

Figure 1A

Figure 1B

As seen in Figures 1A and 1B, there is a positive relationship between years of schooling and both wage and total person’s income. Meaning, as years of schooling increases, wage and total

person’s income increases as well.

The next independent variable is hours worked per week. The average hours worked per week is 37.88 while the median is 40 hours per week. This is expected because the typical NJ resident probably works a normal work week, which consists of forty hours. The top of this range being 98 hours worked per week.

Figure 2A

Figure 2B

Similar to years of schooling, hours worked per week results in a positive trend with wage and total person’s income. Referring to Figures 2A and 2B, as more hours are worked per week, a NJ resident’s wage and total person’s income rises.

Figure 3A

Figure 3B

The trends portrayed in Figures 3A and 3B are telling of the quadratic relationship between age and wage. From Figure 3A, we can observe that age has a positive impact on wage up until a certain point, this point being age 45. After age forty-five, age begins to have a negative impact on wage. This is reflected in the graph as it has a negative quadratic relationship, an inverted U-

shape. To account for our one non-linear independent variable, we introduced an additional independent variable, age-squared.

Figure 4A

Figure 4B

In Figure 4A, the relationship between age-squared and wage is portrayed. The distribution shows that wage increases then remains steady for a while as age progresses. Then after a certain point, as your age values become larger and larger, the wage begins to drop off. In Figure 4B, the relationship between age-squared and total person's income is graphed. A similar trend occurs, but less dramatic. At larger values of age, there is a gradual decrease in total person’s income.

In retrospect, we could have used log specification to shrink the problem of outliers. As you can find in almost all of our graphs, there are several data points which have a high range.

This impacts the mean, for the outliers cause the mean to increase. To improve this study in the future, we would take the log of the data to minimize this issue.

5 Results We chose a simple linear regression model to represent our data. For most of our independent variables, there is a positive impact on our dependent variables. This means that as our independent variables increase, so do wage and total person’s income. The quadratic form would not have been the most effective way to model the data. This is primarily due to the fact that the majority of our variables have linear relationships. It is for this reason that we selected the simple linear regression model. A possibility for other models would be to take the log of all of our data (interpreting them as percentages) in order to shrink the numbers and, therefore, allowing the graphs to depict the relationship more clearly. This would also minimize the outliers in our data.

Regressions - Wage WAGP SEX

24,395.702** (527.091)

SCHL

4667.468** (71.043)

WAGP

WAGP

4597.929** (121.313)

13,988.224** (1610.252)

AGESQ

-49.051** (.671)

-13.062** (1.837)

-39.310** (8.729)

AGEP

4750.625** (68.1770)

1644.625** (167.306)

4316.126** (784.678)

COW

15,042.555** (1048.618)

21,409.978** (3591.914)

MAR

4606.322** (849.668)

1330.780 (3126.941)

PAOC

-7172.271** (938.705)

-10,159.103** (3127.561)

WKHP

1683.992** (31.995)

2097.079** (114.198)

RACASN

13,092.088** (4838.406)

RACBLK

1316.236 (5967.275)

RACWHT

1759.613 (4715.918)

SCIENGP

7030.681** (2642.170)

ENG

15,339.259** (6019.215)

Adjusted R-squared

.170

Sample Size (N)

74,398

.233

.182

*p < 0.1, **p<0.05, ***p<0.01 Regression - Total Person’s Income PINCP SEX

32,503.259** (584.405)

PINCP

PINCP

SCHL

6008.010** (78.768)

5216.455** (130.990)

15,259.808** (1701.018)

AGESQ

-37.095** (.744)

2.731 (1.983)

-25.605** (9.221)

AGEP

4225.170** (75.590)

607.695** (180.650)

3384.271** (828.909)

COW

4615.954** (1132.258)

8194.509** (3794.380)

MAR

2841.684** (917.440)

2434.494 (3303.199)

PAOC

-10,103.810** (1013.578)

-12,271.747** (3303.854)

WKHP

1658.174** (34.547)

2157.112** (120.635)

RACASN

12,563.868** (5111.134)

RACBLK

3485.047 (6303.635)

RACWHT

1277.153 (4981.742)

SCIENGP

8304.245** (2791.103)

ENG

17,520.358** (6358.502)

Adjusted R-squared

.168

Sample Size (N)

74,398

.214

.173

Statistically significant *p < 0.1, **p<0.05, ***p<0.01 FIRST REGRESSION: 1.1) WAGEP = -149,090.353 + 24,395.702(SEX) + 4,667.468(SCHL) - 49.051(AGESQ) + 4,750.625(AGEP)

1.2) PINCP = -170,928.333 + 32,503.259(SEX) + 6,008.010(SCHL) - 37.095(AGESQ) + 4,225.170(AGEP) In the first wage regression model, we ran gender, educational attainment, age, and age squared. Each one of these is statistically significant at the 95% confidence level. When we set up the quadratic functional form, we essentially get an equation that looks like this: wage = b0 + b1(age) - b2(age-squared). The age-squared variable represents that at larger values of age, there will be a negative coefficient. This is found within our data as we have 4,225.170(age) 37.095(age-squared). There is a concave relationship between these two variables. The (age) component relays that age has a positive impact on wage while the (age-squared) describes the negative impact on wage as age increases.

The gender variable has the largest impact on both wage and total personal income in this regression, all else constant. That is, a $24,395.702 or $32,503.259 boost if the employee is male, or 0 if they are female. We are able to conclude from this that whether you are a male or female in the workplace has a significant impact on how much money you earn in New Jersey.

Thus, attributing to the gender wage gap. The adjusted R-square is low at .170 for wage and .

168 for total person’s income, indicating that this regression with only a few independent variables may not be the best fit for such a complex phenomenon.

SECOND REGRESSION: 2.1) WAGP = -149,344.000 + 4,597.929(SCHL) - 13.062(AGESQ) + 1,644.625(AGEP) + 15,042.555(COW) + 4,606.322(MAR) - 7,172.271(PAOC) + 1,683.992(WKHP) 2.2) PINCP = -131,033.625 + 5,216.455(SCHL) + 2.731(AGESQ) + 607.695(AGEP) +

4,615.954(COW) + 2,841.684(MAR) -10,103.810(PAOC) + 1,658.174(WKHP) In the second regression model, we include more variables in addition to schooling, age, and age-squared. These additional variables are class of worker, marital status, presence of children in the home, and hours worked per week. The presence and age of own children in the home results in a negative coefficient in the case of both wage and total person’s income. All else constant, there is a loss of $7,172.27 in earned wages when the NJ resident is female and there are children present in the home. Similarly, all else constant, there is a loss of $10,103.81 in total person’s income when the NJ resident is female and there are children present in the home. This is consistent with our findings in previous literature telling of gender discrimination in wages and the pay gap that follows this.

Furthermore, there is an unexpected sign in the 2.2 regression equation involving age squared and total person’s income. We would normally expect the sign of age-squared to be negative, but in regression 2.2, it’s positive causing a biased coefficient. This could be due to a missing variable. The variable that is included, age-squared, takes on the impact of a missing variable. As for the power behind this regression, the adjusted R-square increases as compared to Regression 1, being .233 and .214 for wage and total person’s income, respectively. This is indicative that the regression is a better fit with the addition of these variables.

THIRD REGRESSION: 3.1) WAGP = -451,744.996 + 13,988.224(SCHL) - 39.310(AGESQ) + 4,316.126(AGEP) + 21,409.878(COW) + 1,330.780(MAR) - 10,159.103(PAOC) + 2,097.079(WKHP) + 13,092.088(RACASN) + 1,316.236(RACBLK) + 1,759.613(RACWHT) + 7,030.681(SCIENGP) + 15,339.259(ENG)

3.2) PINCP = -456,044.985 + 15,259.808(SCHL) - 25.605(AGEAQ) + 3,384.271(AGEP) + 8,194.509(COW) + 2,434.494(MAR) - 12,271.747(PAOC) + 2,157.112(WKHP) + 12,563.868(RACASN) + 3,485.047(RACBLK) + 1,277.153(RACWHT) + 8,304.245(SCIENGP) + 17,520.358(ENG) Finally, we include all of our studied variables in Regression 3. The new independent variables pertain to race (black, white, and Asian), degree in science or engineering, and the ability to speak English. Being Asian, having a degree in science and engineering, and the ability to speak English are statistically significant, while being African-American or white is not. The ability to speak English has relatively high positive coefficients. All else constant, the ability to speak English very well increases wage by $15,339.26 while it increases total person’s income by $17,520.36. In the final regression analysis of the relationship between gender and wage, we find that class of worker has the highest coefficient of $21,409.878. This means that if a worker’s employer is the federal, state, or local government, or the employee works for a for-profit company, this increases the wage, all else being held constant. In the final total person’s income regression analysis, however, class of worker has less of an impact than in the relationship with wage, with the coefficient being $8,194.509, all else constant. This is a $13,215.37 gap and may be attributed to the way the Census data records a person’s total income as opposed to wage. The adjusted R-squared decreases from Regression 2 to Regression 3, but it is still greater than Regression 1. The values being .182 and .173 from the wage and total person’s income regressions, respectively. Meaning, not much of our dependent variables are explained by our independent variables, there is not a lot of strength in their relationship. As we increase the amount of independent variables, the adjusted R-squared decreases. This may be due to two things: 1) omitted variables or 2) irrelevant variables. Irrelevant variables have a lack of correlation with

other independent variables that are included while omitted variables will cause an unexpected relationship (often changes the sign) between two variables. The inclusion of too many irrelevant variables or the lack of important variables may be skewing our adjusted R-squared.

6 Conclusion Our findings, as expected, were consistent with previous studies. While specific to New Jersey, our findings may be indicative of wider trends in the United States as a result of gender wage discrimination still persisting in the U.S. on average (Graf, Brown, & Patten). In our final wage regression, we found educational attainment, age, class of worker, hours worked per week, being Asian, having a degree in science or engineering, and speaking English all had a positive and statistically significant impact on wage. In our final total person’s income regression, we also found the same to be true. This we had predicted based on our review of multiple studies, many of which addressed how each of these variables had impacted the gender wage gap.

Another variable we expected to have a negative statistically significant impact on wage and income was the presence of one’s own children in the home, which was consistent with Fuchs’ study on “Women’s Quest for Equality.” What was interesting was the fact that marital status was not statistically significant in Regression 3, indicating that, since Fuchs’ study in 1989, that women have seen a shift in how important marriage is relevant to their ability to make money as of 2018 NJ Census Data.

A compelling expansion on this relationship would be a study on how, as COVID-19 has allowed more families to work from home, if women are presented with a unique opportunity to have children and wage equality. For women with children, this could prove to be an opportunity for gaining more equality in the workplace, and perhaps, closer to an equal and fair wage for equal work. As far as policy, the implementation of a progressive idea to provide childcare for

families for free could free women (as well as men) to be less burdened at work, and with less fear of maintaining jobs that discriminate against them for having children. Two prime examples of this type of policy are the Healthy Families Act and the FAMILY Act, which advocate for the end of this type of discrimination.

Additionally, now that we have a basis (from previous studies) for the reasoning behind the gender wage gap, there is potential for this information to be applied elsewhere, perhaps newer jobs. The side job industry is booming as millennials and people need extra cash to make a living. Examples of these companies would be Uber, Lyft, Instacart, and Uber Eats. There are strong groundings in the data that we have already, and it would be interesting to see if these findings still carry over into newer jobs.

Throughout this study, there were some data limitations that surfaced which prevented us from further exploring the core causes of the gender wage gap. The first of these limitations being the coding of the Census data. As our literature review touches upon, occupation has an impact on wages and total person’s income. However, the Census data provides a number pages worth of codes for every occupation. Due to time constraints and us being beginning researchers, we were unable to incorporate occupation which plays a major role in the gender wage gap. As for another limitation, discrimination and other factors such as ability are not exactly measurable.

There are underlying causes of our independent variables that are omitted because they are simply not measurable. For example, education and ability are correlated. However, we can only include education (years of schooling) since you cannot measure ability, so it remains omitted. The same goes for discrimintation and race. There is discrimintation which is a factor in race relating to the gender wage gap, but it is not measurable. Therefore, there is omitted variable bias. For the third limitation, we ran into some difficulties in SPSS as it was declaring gender as

constant after the first regression. As a result, we could only interpret the gender variable in our first regression. Lastly, another limitation to our study was that we did not consider using log form in order to shrink our outliers. In hindsight, this method would have served us well to help condense our data as well as to minimize the outliers.

Works Cited Black, Dan A., et al. “Gender Wage Disparities among the Highly Educated,” PMC, 17 June 2015, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470569/. Accessed 15 November 2020.

Blau, Francine D., and Lawrence M. Kahn. “The Gender Wage Gap: Extent, Trends, and Explanations.” National Bureau of Economic Research, Working paper, https://www.nber.org/system/files/working_papers/w21913/w21913.pdf. Accessed 15 November 2020.

Cook, Cody, et al. “The Gender Earnings Gap in the Gig Economy: Evidence from Over a Million Rideshare Drivers.” National Bureau of Economic Research, June 2018, https://www.nber.org/system/files/working_papers/w24732/w24732.pdf. Accessed 15 November 2020.

Equal Pay Act, 29 U.S.C., Chapter 8 sec. 206(d) (1963). https://www.eeoc.gov/statutes/equalpay act-1963 Fuchs, Victor R. “Women's Quest for Economic Equality.” Journal of Economic Perspectives, vol. 3, no. 1, 1989, pp. 25-41. American Economic Association, https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.3.1.25.

Graf, N., Brown, A., & Patten, E. (2020, August 14). The narrowing, but persistent, gender gap in pay. Retrieved December 12, 2020, from https://www.pewresearch.org/fact-tank/2019/03/22/gender-pay-gap-facts/

Meara, Katie, et al. “The gender pay gap in the USA: a matching study.” Journal of Population Economics, 5 September 2019, https://link.springer.com/article/10.1007/s00148-019- 00743-8. Accessed 15 November 2020.

Stanley, T.D., and Stephen B. Jarrell. “Gender Wage Discrimination Bias? A Meta-Regression

Analysis.” The Journal of Human Resources, vol. 33, no. 4, 1998, pp. 947–973.

JSTOR, www.jstor.org/stable/146404. Accessed 16 Nov. 2020.